Joel, how have the unique challenges faced by small and medium-sized enterprises influenced their adoption of AI in identity management and security practices?

So we commission a biannual small to medium-sized enterprise (SME) IT Trends Report that looks specifically at the state of SME IT. This most recent version shows how quickly AI has impacted identity management and highlights that SMEs are kind of ambivalent as they look at AI. IT admins are excited and aggressively preparing for it—but they also have significant concerns about AI’s impact. For example, nearly 80% say that AI will be a net positive for their organization, 20% believe their organizations are moving too slowly concerning AI initiatives, and 62% already have AI policies in place, which is pretty remarkable considering all that IT teams at SMEs have to manage. But SMEs are also pretty wary about AI in other areas. Nearly six in ten (62%) agree that AI is outpacing their organization’s ability to protect against threats and nearly half (45%) agree they’re worried about AI’s impact on their job. I think this ambivalence reflects the challenges of SMEs evaluating and adopting AI initiatives – with smaller teams and smaller budgets, SMEs don’t have the budget, training, and staff their enterprise counterparts have. But I think it’s not unique to SMEs. Until AI matures a little bit, I think that AI can feel more like a distraction.

Considering your background in identity, what critical considerations should SMEs prioritize to protect identity in an era dominated by AI advancements?

I think caution is probably the key consideration. A couple of suggestions for getting started:

Data security and privacy should be the foundation of any initiative. Put in place robust data protection measures to safeguard against breaches like encryption, secure access controls, and regular security audits. Also, make sure you’re adhering to existing data protection regulations like GDPR and keep abreast of impending regulations in case new controls need to be implemented to avoid penalties and legal issues.

When integrating AI solutions, make sure they’re from reputable sources and are secure by design. Conduct thorough risk assessments and evaluate their data handling practices and security measures. And for firms working more actively with AI, research and use legal and technical measures to protect your innovations, like patents or trademarks.

With AI, it’s even more important to use advanced identity and authentication management (IAM) solutions so that only authorized individuals have access to sensitive data. Multi-factor authentication (MFA), biometric verification, and role-based access controls can significantly reduce that risk. Continuous monitoring systems can help identify and thwart AI-related risks in real time, and having an incident response plan in place can help mitigate any security breaches. 

Lastly, but perhaps most importantly, make sure that the AI technologies are used ethically, respecting privacy rights and avoiding bias. Developing an ethical AI framework can guide your decision-making process. Train employees on the importance of data privacy, recognizing phishing attacks, and secure handling of information. And be prepared to regularly update (and communicate!) security practices given the evolving nature of AI threats.

AI introduces both promises and risks for identity management and overall security. How do you see organizations effectively navigating this balance in the age of AI, particularly in the context of small to medium-sized enterprises?

First off, integrating AI has to involve more than just buzzwords – and I’d say that we still need to wait until AI accuracy is better before SMEs undertake too many AI initiatives. But at the core, teams should take a step back and ask, “Where can AI make a difference in our operations?” Maybe it’s enhancing customer service, automating compliance processes, or beefing up security. Before going all in, it’s wise to test the waters with pilot projects to get a real feel of any potential downstream impacts without overcommitting resources.

Building a security-first culture—this is huge. It’s not just the IT team’s job to keep things secure; it’s everybody’s business. From the C-suite to the newest hire, SMEs should seek to create an environment where everyone is aware of the importance of security, understands the potential threats, and knows how to handle them. And yes, this includes understanding the role of AI in security, because AI can be both a shield and a sword.

AI for security is promising as it’s on another level when it comes to spotting threats, analyzing behavior, and monitoring systems in real time. It can catch things humans might miss, but again, it’s VITAL to ensure the AI tools themselves are built and used ethically. AI for compliance also shows a lot of promise. It can help SMEs stay on top of regulations like GDPR or CCPA to avoid fines but also to build trust and reputation. 

Because there are a lot of known unknowns around AI, industry groups can be a good source for information sharing and collaboration. There’s wisdom and a strength in numbers and a real benefit in shared knowledge. It’s about being strategic, inclusive, ethical, and always on your toes. It’s a journey, but with the right approach, the rewards can far outweigh the risks.

Given the challenges in identity management across devices, networks, and applications, what practical advice can you offer for organizations looking to leverage AI’s strengths while addressing its limitations, especially in the context of password systems and biometric technologies?

It’s a surprise to exactly no one that passwords are often the weakest security link. We’ve talked about ridding ourselves of passwords for decades, yet they live on. In fact, our recent report just found that 83% of organizations use passwords for at least some of their IT resources. So I think admins in SMEs know well that despite industry hype around full passwordless authentication, the best we can do for now is to have a system to manage them as securely as possible. In this area, AI offers a lot. Adaptive authentication—powered by AI—can significantly improve an org’s security posture. AI can analyze things like login behavior patterns, geo-location data, and even the type of device being used. So, if there’s a login attempt that deviates from the norm, AI can flag it and trigger additional verification steps or step-up authentication. Adding dynamic layers of security that adapt based on context is far more robust than static passwords.

Biometric technologies offer a unique, nearly unforgeable means of identification, whether through fingerprints, facial recognition, or even voice patterns. Integrating AI with biometrics makes them much more precise because AI algorithms can process complex biometric data quickly, improve the accuracy of identity verification processes, and reduce the chances of both false rejections and false acceptances. Behavioral biometrics can analyze typing patterns, mouse or keypad movements, and navigation patterns within an app for better security. AI systems can be trained to detect pattern deviations and flag potential security threats in real time. The technical challenge here is to balance sensitivity and specificity—minimizing false alarms while ensuring genuine threats are promptly identified.

A best practice with biometrics is to employ end-to-end encryption for biometric data, both at rest and in transit. Implement privacy-preserving techniques like template protection methods, which convert biometric data into a secure format that protects against data breaches and ensures that the original biometric data cannot be reconstructed.

AI and biometric technologies are constantly evolving, so it’s necessary to keep your systems updated with the latest patches and software updates. 

How has the concept of “identity” evolved in today’s IT environment with the influence of AI, and what aspects of identity management have remained unchanged?

Traditionally, identity in the workplace was very much tied to physical locations and specific devices. You had workstations, and identity was about logging into a central network from these fixed points. It was a simpler time when the perimeter of security was the office itself. You knew exactly where data lived, who had access, and how that access was granted and monitored.

Now it’s a whole different ballgame. This is actually at the core of what JumpCloud does. Our open directory platform was created to securely connect users to whatever resources they need, no matter where they are. In 2024, identity is significantly more fluid and device-centered. Post-pandemic, and with the rise of mobile technology, cloud computing, and now the integration of AI, identities are no longer tethered to a single location or device. SMEs need for employees to be able to access corporate resources from anywhere, at any time, using a combination of different devices and operating systems—Windows, macOS, Linux, iOS, Android. This shift necessitates a move from a traditional, perimeter-based security model to what’s often referred to as a zero-trust model, where every access transaction needs to have its own perimeter drawn around it. 

In this new landscape, AI can vastly improve identity management in terms of data capture and analysis for contextual approaches to identity verification. As I mentioned, AI can consider the time of access, the location, the device, and even the behavior of the user to make real-time decisions about the legitimacy of an access request. This level of granularity and adaptiveness in managing access wasn’t possible in the past.

However, some parts of identity management have stayed the same. The core principles of authentication, authorization, and accountability still apply. We’re still asking the fundamental questions: “Are you who you say you are?” (authentication), “What are you allowed to do?” (authorization), and “Can we account for your actions?” (accountability). What has changed is how we answer these questions. We’re in the process of moving from static passwords and fixed access controls to more dynamic, context-aware systems enabled by AI.

In terms of identity processes and applications, what is the current role of AI for organizations, and how do you anticipate this evolving over the next 12 months?

We’re still a long away from the Skynet-type AI future that we’ve all associated with AI since the Terminator. For SMEs, AI accelerates a shift away from traditional IT management to an approach that’s more predictive and data-centric. At the core of this shift is AI’s ability to sift through vast, disparate data sets, identifying patterns, predicting trends, and, from an identity management standpoint, its power is in preempting security breaches and fraudulent activities. It’s tricky though, because you have to balance promise and risk, like legitimate concerns about data governance and the protection of personally identifiable information (PII). Tapping AI’s capabilities needs to ensure that we’re not overstepping ethical boundaries or compromising on data privacy. Go slow, and be intentional.

Robust data management frameworks that comply with evolving regulatory standards can protect the integrity and privacy of sensitive information. But keep in mind that no matter the benefit of AI automating processes, there’s a critical need for human oversight. The reality is that AI, at least in its current form, is best utilized to augment human decision-making, not replace it. As AI systems grow more sophisticated, organizations will require workers with  specialized skills and competencies in areas like machine learning, data science, and AI ethics.

Over the next 12 months, I anticipate we’ll see organizations doubling down on these efforts to balance automation with ethical consideration and human judgment. SMEs will likely focus on designing and implementing workflows that blend AI-driven efficiencies with human insight but they’ll have to be realistic based on available budget, hours, and talent. And I think we’ll see an increase in the push towards upskilling existing personnel and recruiting specialized talent. 

For IT teams, I think AI will get them closer to eliminating tool sprawl and help centralize identity management, which is something we consistently hear that they want. 

When developing AI initiatives, what critical ethical considerations should organizations be aware of, and how do you envision governing these considerations in the near future?

As AI systems process vast amounts of data, organizations must ensure these operations align with stringent privacy standards and don’t compromise data integrity. Organizations should foster a culture of AI literacy to help teams set realistic and measurable goals, and ensure everyone in the organization understands both the potential and the limitations of AI technologies.

Organizations will need to develop more integrated and comprehensive governance policies around AI ethics that address:

How will AI impact our data governance and privacy policies? 

What are the societal impacts of our AI deployments? 

What components should an effective AI policy include, and who should be responsible for managing oversight to ensure ethical and secure AI practices?

Though AI is evolving rapidly, there are solid efforts from regulatory bodies to establish frameworks, working toward regulations for the entire industry. The White House’s National AI Research and Development Strategic Plan is one such example, and businesses can glean quite a bit from that. Internally, I’d say it’s a shared responsibility. CIOs and CTOs can manage the organization’s policy and ethical standards, Data Protection Officers (DPOs) can oversee compliance with privacy laws, and ethics committees or councils can offer multidisciplinary oversight. I think we’ll also see a move toward involving more external auditors who bring transparency and objectivity.

In the scenario of data collection and processing, how should companies approach these aspects in the context of AI, and what safeguards do you recommend to ensure privacy and security?

The Open Worldwide Application Security Project (OWASP) has a pretty exhaustive list and guidelines. For a guiding principle, I’d say be smart and be cautious. Only gather data you really need, tell people what you’re collecting, why you’re collecting it, and make sure they’re okay with it. 

Keeping data safe is non-negotiable. Security audits are important to catch any issues early. If something does go wrong, have a plan ready to fix things fast. It’s about being prepared, transparent, and responsible. By sticking to these principles, companies can navigate the complex world of AI with confidence.

Joel Rennich

VP of Product Management at JumpCloud 

Joel Rennich is the VP of Product Strategy at JumpCloud residing in the greater Minneapolis, MN area. He focuses primarily on the intersection of identity, users and the devices that they use. While Joel has spent most of his professional career focused on Apple products, at JumpCloud he leads a team focused on device identity across all vendors. Prior to JumpCloud Joel was a director at Jamf helping to make Jamf Connect and other authentication products. In 2018 Jamf acquired Joel’s startup, Orchard & Grove, which is where Joel developed the widely-used open source software NoMAD. Installed on over one million Macs across the globe, NoMAD allows macOS users to get all the benefits of Active Directory without having to be bound to them. Joel also developed other open source software at Orchard & Grove such as DEPNotify and NoMAD Login. Over the years Joel has been a frequent speaker at a number of conferences including WWDC, MacSysAdmin, MacADUK, Penn State MacAdmins Conference, Objective by the Sea, FIDO Authenticate and others in addition to user groups everywhere. Joel spent over a decade working at Apple in Enterprise Sales and started the website afp548.com which was the mainstay of Apple system administrator education during the early years of macOS X.

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Bernard, kindly brief us about Generative AI and its impact on various industries such as retail, healthcare, finance, education, manufacturing, marketing, entertainment, sports, coding, and more?

Generative AI (GenAI) is revolutionizing multiple sectors by enabling the creation of new, original content and insights. In retail, it’s personalizing shopping experiences; in healthcare, it’s accelerating drug discovery and patient care customization. Finance is seeing more accurate predictive models, while education benefits from tailored learning materials. Manufacturing, marketing, entertainment, sports, and coding are all experiencing unprecedented innovation and efficiency improvements, showcasing GenAI’s versatility and transformative potential.

Your latest book, “Generative AI in Practice,” is set to release soon. Could you share some key insights from the book, including how readers can implement GenAI, its differences from traditional AI, and the generative AI tools highlighted in the appendix?

In “Generative AI in Practice,” I explore how GenAI differs fundamentally from traditional AI by its ability to generate novel content and solutions. The book offers practical guidance on implementing GenAI, highlighting various tools and platforms in the appendix that can kickstart innovation in any organization. It’s designed to demystify GenAI and make it accessible to a broader audience.

With your extensive experience advising organizations like Amazon, Google, Microsoft, and others, what role do you see GenAI playing in transforming business strategies and performance?

It’s clear that Generative AI (GenAI) is poised to become a pivotal element in reshaping business strategies and boosting performance across industries. By leveraging GenAI, companies can gain a significant competitive advantage through the acceleration of innovation, the automation of complex and creative tasks, and the generation of actionable insights. This transformative technology enables businesses to refine their decision-making processes and enhance customer engagement in ways previously unimaginable. As we move forward, the integration of GenAI into core business operations will not only optimize efficiency but also open up new avenues for growth and value creation, marking a new era in the corporate landscape.

Why is Generative AI considered the most powerful technology humans have ever had access to, and what makes it stand out compared to other advancements in the tech industry?

Generative AI not only stands out as perhaps the most potent technology available today due to its capacity for creativity and innovation, surpassing prior tech advancements by enabling machines to understand, innovate, and create alongside humans, but it also offers a pathway to artificial general intelligence (AGI). This potential to achieve AGI, where machines could perform any intellectual task that a human can, marks a significant leap forward. It represents not just an evolution in specific capabilities, but a foundational shift towards creating systems that can learn, adapt, and potentially think with the breadth and depth of human intelligence. This aspect of generative AI not only differentiates it from other technological advancements but also underscores its transformative potential for the future of humanity.

GenAI brings forth unique risks and challenges. Can you discuss how businesses and individuals can navigate these challenges, especially in areas such as misinformation, disinformation, and deepfakes, particularly in an election year?

The unique risks and challenges presented by Generative AI, particularly in the realm of misinformation, disinformation, and the creation of deepfakes, demand a proactive and informed approach, especially during critical times such as election years. Businesses and individuals can navigate these challenges by adopting a commitment to ethical AI use, which includes the development and implementation of policies that emphasize accuracy and integrity. Additionally, investing in and utilizing advanced detection tools that can identify AI-generated misinformation or deepfakes is crucial. Equally important is the cultivation of GenAI literacy, ensuring that users can critically assess the information they encounter and understand its origins. This multi-pronged strategy is essential for safeguarding the informational ecosystem and maintaining public trust in digital content.

The impact of GenAI on the job market is a critical topic. What types of work do you anticipate being replaced or significantly altered by this groundbreaking technology, and how can individuals prepare for these changes?

The advent of Generative AI is set to significantly reshape the job market, introducing efficiencies that automate routine tasks, which could lead to the displacement of jobs in areas such as data entry, content creation, and customer service. Despite these disruptions, GenAI also promises the emergence of new job categories focused on AI supervision, ethical governance, and the creative industries, reflecting the technology’s dual impact on the workforce. To navigate this evolving landscape, individuals must prioritize lifelong learning and skill development, focusing on areas that AI is unlikely to replicate easily, such as creative problem-solving, emotional intelligence, and ethical decision-making. By adapting to the changes brought about by GenAI, workers can prepare for and thrive in the new job market dynamics it creates.

In your forthcoming book, you touch on how GenAI interacts with other transformative technologies. How do you foresee GenAI collaborating with gene editing, immersive internet, conventional AI, blockchain, quantum computing, etc., to create a world of hyper-innovation?

I explore the transformative potential of Generative AI (GenAI) as it intersects with groundbreaking technologies such as gene editing, the immersive internet, conventional AI, blockchain, and quantum computing, heralding a future of hyper-innovation. GenAI’s capability to produce novel content and solutions enhances gene editing for personalized medicine, enriches the immersive internet with dynamic virtual experiences, and augments conventional AI’s problem-solving abilities. In combination with blockchain, it promises more secure and efficient transaction systems, while its integration with quantum computing could revolutionize our approach to complex challenges, from material science to cryptography. This synergy across technologies suggests a paradigm shift towards a future where the acceleration of breakthroughs across fields from medicine to environmental science could vastly expand the horizons of human capability and knowledge.

Ethical concerns surrounding GenAI, including misinformation and deepfakes, are important considerations. What measures do you believe should be taken to address these concerns and ensure responsible use of Generative AI?

To effectively address the ethical concerns surrounding Generative AI, a multi-faceted approach is essential. This includes establishing transparency in AI development and deployment processes, adhering to rigorous ethical standards that are continuously updated to reflect emerging challenges, and actively engaging the public and stakeholders in discussions about AI’s societal impacts. Furthermore, the development of robust guidelines and regulatory frameworks for responsible AI use is critical, not only to mitigate risks like misinformation and deepfakes but also to foster trust and understanding among users. Such measures should aim to balance innovation with ethical considerations, ensuring GenAI serves the public good while minimizing potential harms.

Everyday activities are expected to be impacted by GenAI. Could you provide examples of how GenAI will influence tasks like searching for information, cooking, and travel in the near future?

Generative AI is poised to revolutionize everyday activities by enhancing efficiency and personalization. In the realm of information search, GenAI can provide more accurate and context-aware results, effectively understanding and anticipating user needs. For cooking, it could offer recipe customization based on dietary preferences, available ingredients, or desired cuisine, making meal planning simpler and more enjoyable. When it comes to travel, GenAI can tailor recommendations for destinations, accommodations, and activities to individual tastes and requirements, simplifying the planning process and enhancing the travel experience. These examples illustrate just a few ways GenAI will make everyday tasks more intuitive, enjoyable, and aligned with personal preferences.

In tracing the evolutionary blueprint of GenAI, from the 1950s to today, what key milestones and developments have played a significant role in shaping its current capabilities and applications?

The journey of Generative AI from its nascent stages in the 1950s to its current state has been marked by several pivotal milestones. The invention of neural networks laid the foundational architecture for AI to process information in a manner akin to the human brain. Subsequent advancements in machine learning algorithms have dramatically improved AI’s ability to learn from data, leading to more sophisticated and capable AI systems. The launch of platforms capable of generating human-like text and understanding natural language has significantly broadened GenAI’s applications, enabling it to write articles, compose music, develop code, and more. These key developments have not only advanced the capabilities of GenAI but also expanded its potential applications, setting the stage for its continued evolution and growing impact on society.

Bernard Marr

CEO and Founder of Bernard Marr & Co.

Bernard Marr is a world-renowned futurist, influencer and thought leader in the fields of business and technology, with a passion for using technology for the good of humanity.

He is a multi-award-winning and internationally best-selling author of over 20 books, writes a regular column for Forbes and advises and works with many of the world’s best-known organisations.

He has a combined following of 4 million people across his social media channels and newsletters and was ranked by LinkedIn as one of the top 5 business influencers in the world.

The post AITech Interview with Bernard Marr, CEO and Founder of Bernard Marr & Co. first appeared on AI-Tech Park.

Background:

Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

Code optimization in AI is all about maximizing results while minimizing inefficiencies and operational costs. It’s a key factor in driving the success and sustainability of AI initiatives in the dynamic and competitive landscape of modern businesses.

Code Optimization:

What are some common challenges and issues businesses face with code optimization when implementing AI solutions?

Businesses implementing AI solutions often encounter several challenges with code optimization, mainly due to the dynamic and complex nature of AI systems compared to traditional software optimization. Achieving optimal AI performance requires a delicate balance between code, model, and data, making the process intricate and multifaceted. This complexity is compounded by the need for continuous adaptation of AI systems, as they require constant updating to stay relevant and effective in changing environments.

A significant challenge is the scarcity of skilled performance engineers, who are both rare and expensive. In cities like London, costs can reach up to £500k per year, making expertise a luxury for many smaller companies.

Furthermore, the optimization process is time- and effort-intensive, particularly in large codebases. It involves an iterative cycle of fine-tuning and analysis, demanding considerable time even for experienced engineers. Large codebases amplify this challenge, requiring significant manpower and extended time frames for new teams to contribute effectively.

These challenges highlight the necessity for better tools to make code optimization more accessible and manageable for a wider range of businesses.

Could you share some examples of the tangible benefits businesses can achieve through effective code optimization in AI applications?

AI applications are subject to change along three axes: model, code, and data. At TurinTech, our evoML platform enables users to generate and optimize efficient ML code. Meanwhile, our GenAI-powered code optimization platform, Artemis AI, can optimize more generic application code. Together, these two products help businesses significantly enhance cost-efficiency in AI applications.

At the model level, different frameworks or libraries can be used to improve model efficiency without sacrificing accuracy. However, transitioning an ML model to a different format is complex and typically requires manual conversion by developers who are experts in these frameworks.

At TurinTech AI, we provide advanced functionalities for converting existing ML models into more efficient frameworks or libraries, resulting in substantial cost savings when deploying AI pipelines.

One of our competitive advantages is our ability to optimize both the model code and the application code. Inefficient code execution, which consumes excess memory, energy, and time, can be a hidden cost in deploying AI systems. Code optimization, often overlooked, is crucial for creating high-quality, efficient codebases. Our automated code optimization features can identify and optimize the most resource-intensive lines of code, thereby reducing the costs of executing AI applications.

Our research at TurinTech has shown that code optimization can improve the execution time of specific ML codebases by up to around 20%. When this optimized code was tested in an Azure-based cloud environment, we observed cost savings of about 30% per hour for the virtual machine size used. This highlights the significant impact of optimizing both the model and code levels in AI applications.

Are there any best practices or strategies that you recommend for businesses to improve their code optimization processes in AI development?

Code optimization leads to more efficient, greener, and cost-effective AI. Without proper optimization, AI can become expensive and challenging to scale.

Before embarking on code optimization, it’s crucial to align the process with your business objectives. This alignment involves translating your main goals into tangible performance metrics, such as reduced inference time and lower carbon emissions.

Empowering AI developers with advanced tools can automate and streamline the code optimization process, transforming what can be a lengthy and complex task into a more manageable one. This enables developers to focus on more innovative tasks.

In AI development, staying updated with AI technologies and trends is crucial, particularly by adopting a modular tech stack. This approach not only ensures efficient code optimization but also prepares AI systems for future technological advancements.

Finally, adopting eco-friendly optimization practices is more than a cost-saving measure; it’s a commitment to sustainability. Efficient code not only reduces operational costs but also lessens the environmental impact. By focusing on greener AI, businesses can contribute to a more sustainable future while reaping the benefits of efficient code.

Generative AI and Its Impact:

Generative AI has been a hot topic in the industry. Could you explain what generative AI is and how it’s affecting businesses and technology development?

Generative AI, a branch of artificial intelligence, excels in creating new content, such as text, images, code, video, and music, by learning from existing datasets and recognizing patterns.

Its swift adoption is ushering in a transformative era for businesses and technology development. McKinsey’s research underscores the significant economic potential of Generative AI, estimating it could contribute up to $4.4 trillion annually to the global economy, primarily through productivity enhancements.

This impact is particularly pronounced in sectors like banking, technology, retail, and healthcare. The high-tech and banking sectors, in particular, stand to benefit significantly. Generative AI is poised to accelerate software development, revolutionizing these industries with increased efficiency and innovative capabilities. We have observed strong interest from these two sectors in leveraging our code optimization technology to develop high-performance applications, reduce costs, and cut carbon emissions.

Are there any notable applications of generative AI that you find particularly promising or revolutionary for businesses?

Generative AI presents significant opportunities for businesses across various domains, notably in marketing, sales, software engineering, and research and development. According to McKinsey, these areas account for approximately 75% of generative AI’s total annual value.

One of the standout areas of generative AI application is in data-driven decision-making, particularly through the use of Large Language Models (LLMs). LLMs excel in analyzing a wide array of data sources and streamlining regulatory tasks via advanced document analysis. Their ability to process and extract insights from unstructured text data is particularly valuable. In the financial sector, for instance, LLMs enable companies to tap into previously underutilized data sources like news reports, social media content, and publications, opening new avenues for data analysis and insight generation.

The impact of generative AI is also profoundly felt in software engineering, a critical field across all industries. The potential for productivity improvements here is especially notable in sectors like finance and high-tech. An interesting trend in 2023 is the growing adoption of AI coding tools by traditionally conservative buyers in software, such as major banks including Citibank, JPMorgan Chase, and Goldman Sachs. This shift indicates a broader acceptance and integration of AI tools in areas where they can bring about substantial efficiency and innovation.

How can businesses harness the potential of generative AI while addressing potential ethical concerns and biases?

The principles of ethical practice and safety should be at the heart of implementing and using generative AI. Our core ethos is the belief that AI must be secure, reliable, and efficient. This means ensuring that our products, including evoML and Artemis AI, which utilize generative AI, are carefully crafted, maintained, and tested to confirm that they perform as intended.

There is a pressing need for AI systems to be free of bias, including biases present in the real world. Therefore, businesses must ensure their generative AI algorithms are optimized not only for performance but also for fairness and impartiality. Code optimization plays a crucial role in identifying and mitigating biases that might be inherent in the training data and reduces the likelihood of these biases being perpetuated in the AI’s outputs.

More broadly, businesses should adopt AI governance processes that include the continuous assessment of development methods and data and provide rigorous bias mitigation frameworks. They should scrutinize development decisions and document them in detail to ensure rigor and clarity in the decision-making process. This approach enables accountability and answerability.

Finally, this approach should be complemented by transparency and explainability. At TurinTech, for example, we ensure our decisions are transparent company-wide and also provide our users with the source code of the models developed using our platform. This empowers users and everyone involved to confidently use generative AI tools.

The Need for Sustainable AI:

Sustainable AI is becoming increasingly important. What are the environmental and ethical implications of AI development, and why is sustainability crucial in this context?

More than 1.3 million UK businesses are expected to use AI by 2040, and AI itself has a high carbon footprint. A University of Massachusetts Amherst study estimates that training a single Natural Language Processing (NLP) model can generate close to 300,000 kg of carbon emissions.

According to an MIT Technology Review article, this amount is “nearly five times the lifetime emissions of the average American car (and that includes the manufacture of the car itself).” With more companies deploying AI at scale, and in the context of the ongoing energy crisis, the energy efficiency and environmental impact of AI are becoming more crucial than ever before.

Some companies are starting to optimize their existing AI and code repositories using AI-powered code optimization techniques to address energy use and carbon emission concerns before deploying a machine learning model. However, most regional government policies have yet to significantly address the profound environmental impact of AI. Governments around the world need to emphasize the need for sustainable AI practices before it causes further harm to our environment.

Can you share some insights into how businesses can achieve sustainable AI development without compromising on performance and innovation?

Sustainable AI development, where businesses maintain high performance and innovation while minimizing environmental impact, presents a multifaceted challenge. To achieve this balance, businesses can adopt several strategies.

Firstly, AI efficiency is key. By optimizing AI algorithms and code, businesses can reduce the computational power and energy required for AI operations. This not only cuts down on energy consumption and associated carbon emissions but also ensures that AI systems remain high-performing and cost-effective.

In terms of data management, employing strategies like data minimization and efficient data processing can help reduce the environmental impact. By using only the data necessary for specific AI tasks, companies can lower their storage and processing requirements.

Lastly, collaboration and knowledge sharing in the field of sustainable AI can spur innovation and performance. Businesses can find novel ways to develop AI sustainably without compromising on performance or innovation by working together, sharing best practices, and learning from each other.

What are some best practices or frameworks that you recommend for businesses aiming to integrate sustainable AI practices into their strategies?

Creating and adopting energy-efficient AI models is particularly necessary for data centers. While this is often overlooked by data centers, using code optimization means that traditional, energy-intensive software and data processing tasks will consume significantly less power.

I would then recommend using frameworks such as a carbon footprint assessment to monitor current output and implement plans for reducing these levels. Finally, overseeing the lifecycle management of AI systems is crucial, from collecting data and creating models to scaling AI throughout the business.

Final Thoughts:

In your opinion, what key takeaways should business leaders keep in mind when considering the optimization of AI code and the future of AI in their organizations?

When considering the optimization of AI code and its future role in their organizations, business leaders should focus on several key aspects. Firstly, efficient and optimized AI code leads to better performance and effectiveness in AI systems, enhancing overall business operations and decision-making.

Cost-effectiveness is another crucial factor, as optimized code can significantly reduce the need for computational resources. This lowers operational costs, which becomes increasingly important as AI models grow in complexity and data requirements. Moreover, future-proofing an organization’s AI capabilities is essential in the rapidly evolving AI landscape, with code optimization ensuring that AI systems remain efficient and up-to-date.

With increasing regulatory scrutiny on AI practices, optimized code can help ensure compliance with evolving regulations, especially in meeting ESG (Environmental, Social, and Governance) compliance goals. It is a strategic imperative for business leaders, encompassing performance, cost, ethical practices, scalability, sustainability, future-readiness, and regulatory compliance.

As we conclude this interview, could you provide a glimpse into what excites you the most about the intersection of code optimization, AI, and sustainability in business and technology?

Definitely. I’m excited about sustainable innovation, particularly leveraging AI to optimize AI and code. This approach can really accelerate innovation with minimal environmental impact, tackling complex challenges sustainably. Generative AI, especially, can be resource-intensive, leading to a higher carbon footprint. Through code optimization, businesses can make their AI systems more energy-efficient.

Secondly, there’s the aspect of cost-efficient AI. Improved code efficiency and AI processes can lead to significant cost savings, encouraging wider adoption across diverse industries. Furthermore, optimized code runs more efficiently, resulting in faster processing times and more accurate results.

Do you have any final recommendations or advice for businesses looking to leverage AI optimally while remaining ethically and environmentally conscious?

I would say the key aspect to embody is continuous learning and adaptation. It’s vital to stay informed about the latest developments in AI and sustainability. Additionally, fostering a culture of continuous learning and adaptation helps integrate new ethical and environmental standards as they evolve.

Leslie Kanthan

Chief  Executive Officer and Founder at TurinTech AI

Dr Leslie Kanthan is CEO and co-founder of TurinTech, a leading AI Optimisation company that empowers businesses to build efficient and scalable AI by automating the whole data science lifecycle. Before TurinTech, Leslie worked for financial institutions and was frustrated by the manual machine learning developing process and manual code optimising process. He and the team therefore built an end-to-end optimisation platform – EvoML – for building and scaling AI.

The post AI-Tech Interview with Leslie Kanthan, Chief Executive Officer and Founder at TurinTech AI first appeared on AI-Tech Park.

Rishabh, what inspired you to co-found Opaque Systems, and how does your background in computer science and cryptography contribute to the company’s success?

The founding of Opaque Systems goes back to the RISELab at UC Berkeley. While at UC Berkeley, I and a team of world-renowned security and systems researchers and practitioners – including Raluca Ada Popa (UC Berkeley professor and co-founder of Opaque Systems), Ion Stoica (co-founder of Databricks), and industry visionaries, Wenting Zheng, and Chester Leung — developed MC2, an open source project focused on enabling multi-party analytics and AI on confidential data. The massive enterprise demand for these capabilities led to five years of extensive research that became the basis for the Opaque Systems you see today. 

While receiving my Ph.D. in computer science, I spent years researching confidential computing and the seemingly untouchable challenge of making collaboration on encrypted data possible. I saw the market need and dedicated myself to developing a platform where multiple organizations could securely collaborate on data throughout its entire lifecycle and unlock aspects of data that had previously been hidden from organizations. In addition to my background and my co-founders’, we received early access to Intel’s SGX hardware enclaves for confidential computing — years before it became commercially available — which gave us a head start on innovation and research.

Can you tell us more about Opaque Systems and its mission to provide a confidential computing platform for collaborative analytics, AI, and data sharing at scale?

Our mission is to help organizations protect their sensitive data and realize the massive opportunity to leverage this untapped treasure trove of data. Much of what we’ve been doing since the launch of Opaque’s Confidential Computing Platform in December of 2022, has been focused on enabling use cases for secure data sharing, multi-party analytics and Data Clean Rooms (DCRs) for financial services organization, in AdTech, healthcare and more. 

At the recent Confidential Computing Summit we also demonstrated the role of confidential computing for privacy-preserving LLM’s, with the recent AI advancements. Our latest innovations to the Opaque Confidential Computing platform are focused on protecting the confidentiality of data used in LLMs (confidential AI) and zero-trust DCRs optimized for Microsoft Azure Confidential Computing to enable secure multi-party collaboration on encrypted/confidential data. These innovations allow organizations to quickly and securely use LLMs to analyze confidential data without sharing or revealing the underlying raw data — keeping data encrypted and secure throughout its lifecycle. It is an important step towards a future where confidential computing becomes the standard for computing, and we’re really excited about its potential. 

Could you explain the concept of confidential computing and its significance in today’s data privacy landscape?

With data privacy regulations becoming more stringent, securing confidential and sensitive data is an urgent priority for organizations globally. Confidential computing — projected to be a $54B market by 2026 by the Everest Group — provides a solution using TEEs or ‘enclaves’ that encrypt data during computation, isolating it from access, exposure, and threats. When used to its full potential, confidential computing can help solve today’s biggest data privacy concerns by providing a secure data processing environment and ensuring organizations comply with new privacy regulations and protect against growing attack surfaces.

We’ve taken that further with the Opaque Confidential Computing Platform by enabling secure multi-party analytics and AI in these enclaves. Through our platform, financial institutions, for example, can be confident that their data remains confidential even when pooling data sets together from different business units/organizations to fight financial crime better and prevent money laundering — as just one example use case. 

The Confidential Computing Consortium and Opaque Systems recently co-hosted the inaugural Confidential Computing Summit. What were the main objectives of this summit, and what were the highlights?

The inaugural Confidential Computing Summit earlier this summer addressed the state of the industry and highlighted innovations and breakthrough use cases involving confidential data. Our main objective was to showcase confidential computing’s potential and educate the industry on its robust use cases. The Summit aimed to bring together the premier technology providers, researchers, as well as existing users to help organizations advance confidential computing. While we had some of the biggest names in confidential computing innovation speak, some highlights included our panels on confidential computing’s trending applications and use cases, the surging demand for DCRs, and the state of Generative AI security and privacy.

The Opaque Systems team also launched innovations to our Confidential Computing Platform, enabling confidential AI and zero-trust DCR capabilities. Through privacy-preserving generative AI, our new confidential AI functionality enables an enterprise to use LLMs on sensitive data while still preserving its confidentiality — one of the biggest issues in today’s business world as more organizations use LLMs like GPT-4. Our zero-trust DCRs, on the other hand, are geared towards enabling multi-party collaboration on confidential data to unlock its business value without sharing or revealing the underlying raw data. This is particularly applicable to the AdTech industry, where collaboration on sensitive datasets can unlock net new use cases and opportunities for clients.

With data privacy regulations becoming more stringent, how does Confidential Computing address the urgent need to secure confidential and sensitive data for organizations globally?

As organizations look to enhance data privacy and adhere to more stringent regulations, they must go beyond data encryption at rest or in transit alone. It’s imperative to adopt solutions such as confidential computing that keep data encrypted/protected when in use — in memory and during computation — to prevent the exposure of confidential data to unauthorized parties. 

Data security is at greater risk, especially with the rise in generative AI models and LLMs. The big privacy problem with LLMs is that unwanted parties (such as the service provider) can gain access to sensitive information such as proprietary code, confidential customer data, or other forms of IP if they’re not secured and encrypted during model processing. Organizations with proprietary models that are running LLMs can also run into IP issues in some cases, if they need to deploy their proprietary model within the customer organization — opening the doors to increased data exposure and risk.

Confidential computing allows organizations to use groundbreaking confidential AI capabilities to solve data privacy concerns. Opaque’s platform, for instance, provides a safeguard against the exposure of confidential data to unauthorized parties through multiple layers of protection for sensitive data against potential cyber-attacks or data breaches through a powerful combination of secure hardware enclaves and cryptographic fortification.

The Everest Group forecasts that the Confidential Computing market will reach a $54B market opportunity by 2026. How do you see this market evolving, and what factors will contribute to its growth?

Over the last few years, organizations have realized the benefits of sharing data across teams and organizations. With the growing amount of sensitive data rising within businesses daily, the need for confidential computing has never been greater. One of the biggest reasons confidential computing rose to the top of the market was the initial support from all major cloud vendors, including Microsoft Azure, Google Cloud, and Amazon Web Services, and major chip manufacturers, such as Intel and AMD. Now, in the era of generative AI, we’ll see confidential computing continue to grow and become a must-add tool for all industries looking to collaborate on sensitive information.

Who attended the Confidential Computing Summit? What were the key topics discussed, and how did they relate to secure data sharing, data processing, multi-party analytics and AI, privacy-enhancing technologies, and confidential computing cloud platforms?

The majority of the attendees were technical experts and those whose roles fell into the categories of innovator, executive, regulator, business leader, security expert, data scientist, data analyst, AI/ machine learning practitioner, data privacy expert, or researcher.

Attendees heard from experts from Google, Microsoft Azure Confidential Computing, VMware, Accenture, PwC and Ernst and Young, among others, and left with a greater knowledge of confidential computing, its emergence as a key solution for the future of data security and privacy, and its many powerful use cases across industries, including but not limited to financial services, insurance, healthcare, manufacturing, AdTech, and Web3.

As a co-founder of Opaque Systems, what were your expectations for the Confidential Computing Summit, and how do you envision the summit contributing to the adoption and advancement of confidential computing?

We had really high hopes for the Confidential Computing Summit, and I’m happy to say we exceeded those expectations and had an exceptional turnout in San Francisco. Opaque Systems and the Confidential Computing Consortium (CCC) saw the summit as a way to showcase confidential computing’s potential and educate attendees on its powerful use cases and shed light on some of the creative innovations happening in academia, as well as at some of the top companies in the market. We believe that greater visibility and more understanding of confidential computing would, in turn, lead to greater industry awareness and understanding that this is the key to addressing ongoing data privacy and regulation concerns.

Opaque Systems commercializes the open-source MC2 technology invented at UC Berkeley. Could you explain how this technology enables secure data sharing and analysis among multiple parties while maintaining confidentiality?

MC2 (Multi-Party Collaboration and Coopetition) enables rich analytics and machine learning on encrypted data, ensuring that data remains concealed even when processed. Through a temporary “black box” method via secure enclaves, the data being used remains confidential to the server running the job. It may sound contradictory, but it’s true: multiple data owners can jointly run analytics or run ML models on their collective data without revealing that data to anyone else. This alleviates concerns about offloading confidential workloads to untrusted third parties or cloud providers. MC2 solves the tension between expanding cloud adoption, the need for data sharing, and the increasing concern over data privacy. 

Can you elaborate on the secure hardware enclaves and cryptographic fortification utilized by the Opaque Platform to ensure secure, fast, and scalable computations?

Opaque uses confidential computing technology to leverage specialized hardware. Our privacy-enhancing technology ensures that datasets are encrypted throughout the entire analysis process — allowing multiple parties to collaborate on analytics and AI use cases without compromising the confidentiality of the individual data. The data remains encrypted and protected during its entire lifecycle. The specific innovation breakthroughs for our platform include:

• High-performance analytics and AI/ML on encrypted data using familiar tools. The ability to isolate sensitive data in TEEs, including enclaves and confidential VMs, and perform collaborative, scalable analytics and machine learning directly on encrypted data using familiar tools such as Apache Spark and notebooks.
• Inter- and Intra-company collaborative analytics, AI/ML, and data sharing. Multiple data teams within and across organizations can collaborate to securely share encrypted data, perform collaborative analytics and ML on data while encrypted and enforce data and computation policies. Each party is privy only to their specific data and insights.
• Multidimensional scaling across enclaves, data sources, and multiple parties. Provides a simplified data management approach with secure access across enclave clusters and the ability to automate cluster orchestration, monitoring, and management across multiple workspaces without operational disruption.

How does the Opaque Platform address the challenges of maintaining data confidentiality and protecting data end-to-end, especially in industries such as financial services, insurance, healthcare, manufacturing, adtech, and web3?

Our technology allows multiple organizations and data owners to collaborate securely while remaining in regulatory compliance. Our Confidential Computing Platform opens up significant opportunities for collaboration where various barriers previously existed for value extraction. For example, financial institutions can now collaborate on sensitive data for regulatory and commercial reasons — without exposing the data itself — and analyze joint data to identify and prevent fraud, and deepen their insights into money laundering activity and risk measurement. In healthcare, we’re seeing use cases around collaborating on sensitive data for faster drug discovery, patient profiling, and better insights into quality of care.

As I mentioned earlier, our new data clean room innovations lean heavily into Adtech, as it’s a primary use case for confidential computing and DCRs. Opaque’s AI-driven Platform functions as a secure data room and empowers AdTech companies to unlock the value of confidential and sensitive data by enabling useful analytics and machine learning on fully encrypted data at scale. This ensures encryption not just at rest and in transit but also in use — allowing users to share insights from valuable data without compromising the privacy of their first-party data.

In your opinion, what is the future of confidential computing, and how do you see Opaque Systems contributing to its development and adoption?

Confidential computing is the future of data collaboration and analysis. Without a secure platform that protects data privacy from end-to-end, organizations will be pigeon-toed into silos and unable to innovate to their full potential. 

This is becoming particularly necessary for preserving the security and privacy of data used in AI models and LLMs. With Opaque, our Confidential Computing Platform allows customers to encrypt their prompts and sensitive data to derive the benefit of the generative AI without exposing private and proprietary information. This is done alongside policy control, auditing transparency, and full data custody. With broader support for confidential AI use cases, the Opaque platform will also provide safeguards for machine learning and AI models to execute encrypted data inside Trusted Executions Environments (TEEs), preventing exposure to unauthorized parties.

Are there any partnerships or collaborations that Opaque Systems has formed to enhance its confidential computing capabilities or expand its reach?

We just recently announced a strategic partnership with Roqad, an identity solutions provider, to enable seamless multi-party data collaboration through data clean rooms (DCRs) while still adhering to evolving privacy rules and regulations. We also closely collaborate with leading global professional services firms as well as tech giants such as Intel and Microsoft Azure; Accenture co-presented with us at the Confidential Computing Summit regarding our Data Clean Room solution.

Rishabh Poddar

CEO and Co-founder of Opaque Systems

Dr. Rishabh Poddar is the CEO and Co-Founder of Opaque Systems, a confidential computing platform for collaborative analytics, AI, and data sharing at scale. Rishabh holds several US patents and has authored over 20 research papers in computer security, systems, and applied cryptography. Rishabh received his Ph.D. and MS degrees in Computer Science from UC Berkeley and an undergraduate degree in Computer Science from the Indian Institute of Technology (IIT) Kharagpur. Previously, Rishabh was a researcher at IBM Research and a management consultant at The Boston Consulting Group.

The post AITech Interview with Rishabh Poddar, CEO and Co-founder of Opaque Systems first appeared on AI-Tech Park.

1. Raj, please share key insights into your role as the Founder and CEO of Techwave and your journey contributing to its rapid growth.

As the Founder and CEO of Techwave, my journey has been one of relentless pursuit of excellence and innovation. Steering Techwave from its inception to becoming a global leader in digital transformation services has been both challenging and rewarding. My role has demanded a visionary outlook to foresee industry trends, a strategic mindset to navigate market dynamics, and a people-first approach to leadership. This trifecta has been crucial in contributing to Techwave’s rapid growth. We’ve expanded our global footprint, diversified our service offerings, and nurtured a culture that champions innovation, inclusivity, empathy, and continuous learning. My leadership philosophy has always been about empowering our teams, fostering a collaborative environment, and placing our clients at the center of everything we do.

2. What notable accomplishments has Techwave achieved under your leadership, particularly in terms of expansion, capitalization, and employee engagement initiatives?

Under my stewardship, Techwave has achieved remarkable growth, a testament to our innovative solutions, customer-centric approach, and the dedication of our global workforce. We’ve significantly expanded our presence, now operating with a prominence presence in 11 countries across the globe and serving a diverse client base across industries. Our workforce has grown to over 3000 associates, a reflection of our robust expansion and capitalization strategies.

Our employee engagement initiatives, particularly the SPARK framework, underscore our commitment to creating a vibrant and inclusive work culture. This framework focuses on engaging employees, fostering community engagement, and promoting diversity, which has significantly contributed to our high levels of employee satisfaction and retention.

Our corporate social responsibility efforts, like supporting the Houston Food Bank and participating in Primiethon – The Hope Run, reflect our commitment to making a positive impact in the communities we serve. These initiatives, alongside our accolades such as Asia’s Best Employer Brand Award and the President’s Volunteer Service Award, highlight our achievements in fostering a culture of excellence and community service.

3. How has Techwave positioned itself to stay ahead in a competitive digital landscape?

In a rapidly evolving digital landscape, staying ahead requires agility, foresight, and a commitment to innovation. At Techwave, we’ve positioned ourselves at the forefront of digital transformation by continuously investing in emerging technologies and nurturing a culture that embraces change. Our R&D efforts are focused on leveraging AI, machine learning, cloud-native technologies, and blockchain to develop solutions that address our clients’ most complex challenges of today and tomorrow.

We prioritize understanding our clients’ unique needs and market dynamics, which enables us to tailor our solutions for maximum impact. Our approach to innovation is not just about adopting new technologies but integrating them in ways that redefine business processes, enhance customer experiences, and drive sustainable growth.

4. Could you highlight Techwave’s approach to trending topics such as Platform Engineering, Cross Border Payments, and Artificial Intelligence Modelling Language?

Our approach to platform engineering, cross-border payments, and AI modelling language is rooted in our deep industry knowledge and technical expertise. In platform engineering, we focus on building scalable and resilient systems that support our clients’ business strategies. For cross-border payments, we leverage blockchain and other fintech innovations to offer secure, efficient, and compliant solutions. In AI, we’re pushing the boundaries of what’s possible with advanced modelling techniques that enhance decision-making and operational efficiencies for our clients.

These areas are critical to our mission of delivering comprehensive digital transformation solutions. By focusing on these domains, we ensure that our clients are well-equipped to navigate the complexities of the digital economy.

5. What are the essential core components for a successful omni-channel strategy, and how do they contribute to an integrated customer experience across channels?

A successful omni-channel strategy hinges on seamless integration, personalization, and real-time engagement. At Techwave, we understand that an integrated customer experience across all channels is paramount. We leverage data analytics and AI to gain insights into customer behaviors and preferences, which allows us to deliver personalized experiences. Our technology stack, designed for agility and scalability, supports the seamless integration of various channels, ensuring that our clients can offer their customers a cohesive and engaging experience.

6. How does technology play a critical role in ensuring a seamless digital customer experience through omnichannel integration at Techwave?

Technology is the cornerstone of creating a seamless digital customer experience. At Techwave, we use a combination of cloud computing, AI, and machine learning to ensure that all customer touchpoints are integrated, providing a unified and personalized customer journey. Our investments in technology enable us to automate processes, analyze vast amounts of data, and deliver insights that drive better customer engagement and loyalty.

7. What challenges do businesses typically face during the implementation of omnichannel strategies, and what effective solutions and best practices has Techwave adopted?

Implementing an omni-channel strategy comes with its set of challenges, including data silos, platform integration, and maintaining a consistent brand experience. At Techwave, we tackle these challenges head-on by employing a holistic approach that includes robust data management practices, leveraging cloud-native technologies for flexibility and scalability, and adopting a customer-centric mindset that informs all our strategic decisions.

8. Can you provide insights into how Techwave navigates the complex landscape of Cross Border Payments and its approach to ensuring success in this area?

The complexity of cross-border payments requires a nuanced approach that balances efficiency, compliance, and security. At Techwave, we employ the latest in fintech innovations, including blockchain, to streamline the process while ensuring adherence to global regulatory standards. Our expertise in financial technologies enables us to design payment solutions that are not only secure but also enhance the customer experience by reducing transaction times and costs.

9. In terms of Enterprise Digital Services, how does Techwave drive innovation in a competitive climate, combining technical knowledge and breakthrough analytics?

Innovation is at the heart of Techwave’s strategy for enterprise digital services. We combine our deep technical knowledge with insights from data analytics to develop solutions that anticipate market trends and meet our clients’ evolving needs. Our commitment to R&D, along with a culture that encourages experimentation and learning, allows us to stay ahead of the curve and deliver cutting-edge solutions that drive value for our clients.

10. What emerging trends and innovations do you anticipate in the digital customer experience and omnichannel integration, and how is Techwave preparing to adopt them proactively?

The future of digital customer experience is likely to be shaped by advances in AI, augmented reality, IoT, and other emerging technologies. At Techwave, we are actively exploring these technologies to understand how they can enhance the omni-channel experience. We are particularly focused on how AI and machine learning can further personalize customer interactions, making them more engaging and effective. Our proactive approach to adopting these technologies ensures that we are ready to integrate them into our solutions, keeping our clients ahead in a rapidly changing digital landscape.

In conclusion, as we navigate the ever-evolving digital landscape, our commitment at Techwave remains unwavering: to deliver innovative solutions that drive growth, enhance customer experiences, and contribute positively to our communities. Our achievements and our approach to the future reflect our dedication to being a leader in digital transformation, poised to meet the challenges and opportunities of the digital age. With focus on employee well-being, community service, and continuous learning underscores my vision for Techwave to be a leader not just in technology, but in creating a sustainable and inclusive future.

Raj Gummadapu

Co-Founder and Chief Executive Officer at Techwave

Co-Founder and Chief Executive Officer, Techwave Raj Gummadapu is a visionary enterprise transformation leader with over 20 years of experience in the IT & engineering solutions business. With his inherent dynamism and deep people-first approach to work, Raj has been instrumental in leading Techwave’s expansion and diversifying its strength across multiple growth avenues.

He has been instrumental in inculcating a strong client focus and developing various business solutions that would enable organizations to harness the potential of a data-driven, digital future. He is strongly motivated by his desire to make digitalization accessible to enterprises across the world and empower them with powerful, secure and affordable tech solutions.

As the Chief Executive Officer of Techwave, Raj has been enabling Techwave to rise as a leading global digital transformation partner by expanding its robust service offerings, delivery strength and customer-focused innovation. He has played an instrumental role in laying the foundation of Techwave’s client-driven approach to business and fostered a culture of diversity and learning in the company. Previously, he has held key leadership positions in companies like Sysco & Deloitte and has led the charge for Business Intelligence and Performance Management Practices. Raj has earned his Bachelor of Commerce from the prestigious Osmania University in Hyderabad, India. He then went on to become a Chartered Accountant with the premier Institute of Chartered Accountants of India (ICAI) and holds the title of a Certified Public Accountant (CPA). With his strong finance background, Raj seamlessly infuses his business acumen and multidisciplinary thinking to develop holistic solutions for customers and partners at large.

The post AITech Interview with Raj Gummadapu, Co-Founder and Chief Executive Officer at Techwave first appeared on AI-Tech Park.

Sean, can you introduce yourself to our audience and share the inspiration behind founding Blink?

I’m Sean and I am the co-founder and CEO of Blink, the super-app for frontline workers. Blink brings all the benefits of digital connection to the frontline worker – bringing everyone and everything together, closing the connection gap that exists between desk-based and frontline colleagues. Our app gives employees a single entry point to everything they need to support their working life – communications, productivity tools, people and processes, all through one app on their phones.

Blink was born out of challenges I experienced at a former company of mine. A large proportion of our workforce was remote and met only occasionally for quick company updates. I noticed that so many team members were struggling to feel connected to each other and their team leaders, and the entire culture suffered as a result.

I made it my aim to change that experience and, after listening to what my colleagues said would genuinely have the most impact, set about creating technology that would deliver meaningful, positive change to workers’ roles.

This turned out to be the very first iteration of Blink. And while there were a few bumps in the road getting our tech to hit the mark, we eventually nailed our proposition and target market, and quickly won our first major client, launching to 24,000 workers at Stagecoach, in 2019. 

Blink extends the digital revolution to every worker – so everyone feels connected, but also allows companies to digitise their entire end-to-end operations – lowering costs and increasing staff satisfaction. It’s true win-win.

Today, we serve more than 300 leading frontline-first companies like Domino’s, Tonkotsu, Saint Luke’s Hospital, Elara Caring and Falck Ambulance.

What’s the overarching vision that drives Blink’s mission and development?

Our mission is to create connected workforces in which everyone thrives. Put simply, that means helping frontline-centric companies to become better and more effective places to work, which is precisely what they need to do to become a more successful company overall.

Culture and productivity are the top priorities for most CEOs – Blink directly transforms both for the largest part of the workforce.

Technology is the driving force behind making this possible – and by harnessing tech that is specifically designed with frontline workers in mind, we can continue to improve the working lives of individuals the world over. That is the vision that gets me out of bed every morning. 

What benefits do companies gain from utilizing Blink’s product?

Frontline workers face numerous different challenges on a daily basis: isolation, working in silos, a lack of connectivity and collaboration, no direct channel of communication with their superiors, limited access to flexible working, no meaningful way to share feedback or ask for support, outdated reporting and forms – the list goes on. Blink puts all this – and a lot more – in the palm of their hands. Taking away huge amounts of friction, frustration and cost by simplifying how people work.

For example, by offering single sign-on, mobile-enabled access to all critical tech applications, we greatly improve accessibility and uptake. We allow employers to build easy-to-access content hubs so workers can access important information and resources whenever they need it. We replace paper trails with simple and intuitive mobile forms, helping employees improve efficiency, and we eradicate convoluted IT support tickets so that time-poor workers are not put off asking for assistance. Secure mobile-first chats facilitate better communication, while mandatory reads can ensure employees don’t inadvertently delete important updates. 

All of these tools – which are available through our super-app – help ensure frontline employees’ needs are being met and that they are valued and respected by their employers, something which can have a transformative impact on company culture, leading to greater productivity, higher retention rates and overall business growth. 

Can you highlight some distinctive features that define Blink’s platform? How does Blink distinguish itself from competitors in the market?

First, this is about simplicity. You won’t find a better mobile-experience in the enterprise. We’re the only platform to bring together all the elements workers need, and securely make them available through one app.

Another thing that makes Blink unique is our ability to collate a vast array of data points on a company’s workforce – such as how its existing technology, systems and tools are used by frontline workers – and how staff are feeling and behaving. We bring all this together with turnover data, cohort analysis and even new-joiner surveys, to tell leaders exactly what they can do to improve their operations.

What are some prevalent pain points customers approach Blink with?

It’s interesting because so often customers come to us and say they have an ‘engagement’ problem – but they struggle to put their finger on what that problem actually is or how they can address it. All they know is that their teams feel disjointed and dissatisfied, which is ultimately impacting their overall culture and performance. 

And yet, when I talk to frontline workers, no one tells me they want to feel more ‘engaged’ at work. What I do hear them say is that they want to feel more involved, have more transparency and more autonomy. They want to feel that their employer understands their needs and has their back – even if they’re not physically seeing them on a day-to-day basis. 

But they also want more convenience – by providing convenient and easy to use technology people feel more valued and empowered.

It seems to be that ‘employee engagement’, at least for frontline workers, has been misunderstood to be mainly about culture initiatives, when actually it’s largely about how people interact day-to-day with the company.

And in most cases, there’s no single issue to blame. It’s a combination of factors – poor communication, outdated processes, a lack of 360 feedback – that lead to an overall sense of frontline disconnection and discontent. So what Blink does is take all the challenges and pain points felt by frontline workers, and provides technology-driven solutions that ease these points of tension.

How do you anticipate AI influencing employee engagement in the future? Which areas do you foresee being most affected?

Perhaps one day we’ll all be working for an AI boss – one that knows how we like to be managed, how we like to work and is able to get the best from us? In the short term, every part of the employee lifecycle will be improved with AI – from better hiring, smoother on-boarding, easier access to information, better performance analysis, training, fairer pay – you’ll see every software company improve their products and services using AI.

Matching supply and demand of work is another area we’re already seeing AI make a big impact – so companies have the people they need, and workers get access to working patterns that suit them.

We’re already able to target communication – time, channel, language – to the right person at the right time. We’re already seeing AI-generated video that is able to read news updates in any language. This will become an extension of the employer brand and you’ll see companies invest in their ‘avatar’ with just the right tone of voice.

For us individually, the most noticeable short-term benefit will probably be around information retrieval, summarisation and assistants – that enable everyone to be more effective at their jobs.

For me, I’d really like to see an AI coach – who can monitor my work, interactions, colleague feedback, and help me become a better CEO. If everyone had a coach who could teach us things and help us be more effective, that would be pretty impressive.

What are your predictions for how work will shift over the next few years?”

We’re going to continue to see a rise in the status, pay and demand for frontline workers. As a society, over the past 50 years we’ve over-rotated towards academic qualifications and desk-based work. The AI-powered productivity gains for desk-based workers, at the same time as a shortage of frontline workers – nurses, hospitality, electricians – will mean many people will be better off financially if they learn a trade or new qualification in a frontline industry.

This power-shift will also drive a culture change in frontline organisations – with a focus more on the workers, and the organisation existing to serve those who serve its customers. This means more transparency, more empowerment and more inclusivity.

For desk-based workers, I’m a fan of the adage that AI won’t take your job – someone who knows how to leverage AI will take your job. So we’ll see an acceleration of those who are most adept at using technology getting ahead at work.

Lastly, could you offer a preview of upcoming product upgrades that your customers can anticipate?

We’re injecting AI into every aspect of our system – from content creation through to predictive analytics that will be able to foresee when and why workers might leave, and even suggest training for managers to help prevent this happening. We’re looking at using AI to create personalized video, summarise data, answer questions and end the need for lengthy forms. We’re delivering content in people’s native language and personalising the experience based on people’s working hours, location, role, and much more. We’re looking at how we can match people to shifts intelligently, and how we can be the CEO-coach that helps leaders improve their operations based on our unique view of the entire employee experience. Our position with a view over the end-to-end employee lifecycle has given us a unique perspective and unique data set that we are now leveraging.

Sean Nolan

CEO and co-founder, Blink

Driven by his early encounters with remote work challenges, Boston-based CEO Sean Nolan co-founded Blink in 2014. This all-in-one “super-app” empowers frontline workers with the digital tools, connections, and resources they need on their personal phones. Blink bridges the gap between management and millions of individuals across industries, from bus drivers to healthcare workers, fostering engagement, belonging, and a 26% reduction in staff turnover. With over 300 clients, and used seven times daily by 300,000+ workers, Blink is revolutionizing the frontline experience, one tap at a time.

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Terry, can you please share a bit about your professional journey and how you became involved with Playsee, especially in the context of the development of AILEX?

Over the past five years I’ve worked in digital marketing, including social media marketing. When I joined Playsee, I quickly became involved in the product department and naturally became the Product Manager for AILEX from ideation to launch.

We wanted to create an interactive feature for users to discover and connect with their local community and spots. By simply chatting with AILEX, users can find new places to go or communities that share their interests in their area. 

How does AILEX leverage both AI technology and local knowledge to provide users with personalized lifestyle recommendations?

AILEX, which stands for AI location expert, is powered by AI and local knowledge from content around the user. AILEX makes lifestyle recommendations after considering a user’s input and what they are interested in doing and learning more about. Lifestyle recommendations can come in the form of videos, hot spots, or communities that are close in proximity and of interest to users.

In what ways does AILEX facilitate engaging conversations with users? How does this enhance the overall experience of users?

AILEX is a chatbot where users can engage in conversation with it. With a friendly personality, users can simply have a fun conversation with AILEX. While engaging in conversation, AILEX gets to know the user and will be able to provide accessible lifestyle recommendations where users can go to and experience.

AILEX asks users about their preferences and tailors recommendations accordingly. Can you elaborate on how this personalized approach sets AILEX apart from other AI-driven features on the market?

The differentiating factor would be AILEX’s ability to understand a user’s needs as it continues to get to know them, while also providing suggestions that are local and accessible.

AILEX directs users to specific Playsee communities. How does this feature encourage community engagement and connection among users with similar interests?

When a user chats with AILEX, the chatbot also identifies a community that would be suitable in terms of interest to users and shares it with them. AILEX also identifies why the community may be relevant. This way when users are interested in a certain topic they can find a community and connect with people that are like-minded and share the same interests and passions.

AILEX takes weather into account when making recommendations. How does this enhance the user experience, and what challenges did the team face in implementing this dynamic aspect?

The ability to take weather into consideration enables more suitable suggestions for the user. For example, if it’s sunny, AILEX would encourage users to go outside to enjoy the sun by sharing spots or activities that are best suited for a sunny day near them. 

How does AILEX contribute to the local exposure of small businesses, and what benefits can businesses anticipate from being featured in AILEX’s recommendations?

By prioritizing lifestyle recommendations close by, local businesses are able to get more exposure from people who may not frequently visit or were never aware about them in the first place. In return, AILEX can help small businesses gain more local foot traffic.

As a product manager, what advice do you have for individuals or businesses looking to integrate AI-driven features into their products or services for a more personalized user experience?

Businesses or individuals looking to integrate AI-driven features into their operation or social lifestyle can look into using AI features such as AILEX, which provide more personalized experiences to reach the right customers and allow users to discover great local gems. 

Any final thoughts or messages you would like to share with our readers regarding the future of Playsee, AILEX, or the broader landscape of AI-driven social applications?

We’re always working on enhancing Playsee to enable our users to have a better experience and build connections with their local community. It’s our mission to bring accessibility to everything and make it easier for people to create meaningful connections to where they are in life. We are releasing an update to AILEX soon so be on the lookout!

Terry Hu

Product Manager at Playsee

Terry Hu is the AI Product Manager at Playsee, a social media platform that enables discovery and community connections within your surrounding area. Terry’s work has a focus on the app’s AI feature AILEX – an AI chatbot and friendly companion that provides local lifestyle suggestions to users.   

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Colin, could you elaborate on the concerns you’ve raised regarding AI’s impact on elections?

Answer: When it comes to AI and its impact/role in elections, the challenge is misinformation, generated by deep fakes (e.g. someone’s image and voice being used to propagate false opinions and incorrect information), bot accounts on social media propagating incorrect and/or misleading information and people’s susceptibility these types of behaviors. In practical terms this means that we all need to be more skeptical of what we see, read, and encounter online and be able to verify what we see and hear online.

How does AI contribute to the dissemination of misinformation and disinformation during electoral processes, in your view?

Answer: AI contributes to the dissemination of misinformation and disinformation by enabling the creation and spread of convincing fake content, such as deepfakes, and by personalizing and optimizing the delivery of content on social media platforms. These capabilities can be exploited to create false narratives, impersonate public figures, and undermine trust in the electoral process.

Can you provide examples of how AI technologies, such as deepfakes and social media manipulation, undermine the integrity of elections?

Answer: Examples include:

• Deepfakes: AI-generated videos or audio recordings that convincingly depict real people saying or doing things they never did, which can be used to create false impressions of candidates or mislead about their positions.
• Social Media Manipulation: The use of bots and algorithms to amplify divisive content, spread falsehoods, and manipulate trending topics to influence political discourse.
• Personalized ads:The creation and use of political ads designed to mislead, convince others of false information, and/or get them to take actions that may be against their best interests and benefit someone else unbeknownst to the viewer of the ad.

What specific measures do you recommend to combat the threat of AI interference in elections?
Answer: I do not pretend or purport to have all the answers or even any answers, per se. What I can suggest is that measures including developing and enforcing strict regulations on political advertising and the use of personal data for political purposes, implementing robust and verifiable fact-checking and content verification mechanisms to identify and label or remove false information, and encouraging the development of AI systems that prioritize transparency, accountability, and the detection of manipulative content may be useful.

In your opinion, how can transparency and accountability in AI algorithms help prevent their misuse in the electoral context?

Answer: Enhancing transparency involves making the workings of AI algorithms more understandable and accessible to regulators and the public, including disclosing when and how AI is used in content curation and distribution. Accountability measures include holding platforms and creators legally and ethically responsible for the content disseminated by their AI systems so as to ensure that there are mechanisms to challenge and rectify misleading or harmful outputs.

How do enhanced security measures contribute to safeguarding electoral systems from AI-related threats, Colin?

Answer: Improving the security of electoral systems against cyber threats by enabling more robust systems of detecting, preventing, and eliminating such threats can help ensure elections are more reliable and trusted. Additionally, securing the infrastructure against AI-powered attacks by engaging in regular security audits can help as well.

Could you explain how AI tools for misinformation detection and management can mitigate the impact of false information on voters, as you’ve suggested?

Answer: AI tools are very good at analyzing vast amounts of data. Because of this, such tools could be used/tools could be developed to better detect patterns indicative of misinformation, such as the spread of known false narratives or the abnormal amplification of content. Such tools, if used regularly and correctly*, could help platforms and regulators quickly identify and mitigate the spread of false information and ensuring more accurate information being disseminated online. *However, such tools could also be used to do the opposite, e.g. to ensure that only misinformation is shared, so who has control and use of such tools is a critical factor to account for as well.

From your perspective, how crucial is collaboration between AI companies, governments, and regulatory bodies in addressing AI challenges in elections?

Answer: Combating the risks posed by AI requires thoughtful and systematic collaboration between governments, regulatory bodies, AI companies, and, importantly, AI experts. The use of experts to supplement what these various entities know about AI currently is important as AI continues to evolve and each of us are learning as we go along. Collaboration is crucial for developing standards and norms for the ethical use of AI in elections, sharing best practices for detecting and countering misinformation, and coordinating responses to emerging threats. This includes joint efforts to improve public understanding of AI’s role in information dissemination and to develop technologies that support electoral integrity.

What strategies do you propose to enhance public awareness and education regarding AI’s potential misuse in electoral processes?

Answer: While repeating what I said earlier about not having THE answer, some strategies that strike me as potentially being helpful include launching public education campaigns to inform citizens about how AI can be used and misused in electoral contexts, teaching critical digital literacy skills to help individuals identify misinformation, and being more active and systematic in promoting and creating a better understanding of the mechanisms behind AI-driven content curation and recommendation systems.

What are the essential components of a multi-faceted approach to ensuring the integrity of elections amidst AI challenges?

Answer: Components would likely include:

• Legal and regulatory frameworks that address AI-specific challenges in elections.
• Technological solutions to detect and mitigate misinformation and secure electoral processes.
• Educational initiatives to enhance public understanding and resilience against misinformation.
• International and systematic cooperation to tackle better understanding and adaptation to the age of AI

Colin Levy

Director of Legal at Malbek

Colin S. Levy is an experienced lawyer, legal tech expert, and the author of The Legal Tech Ecosystem, available via Amazon.  Throughout his career, Colin has seen technology as a key driver in improving how legal services are performed. Because his career has spanned industries, he witnessed myriad issues, from a systemic lack of interest in technology to the high cost of legal services barring entry to consumers. Now, his mission is to bridge the gap between the tech world and the legal world, advocating for the ways technology can be a useful tool for the lawyer’s toolbelt rather than a fear-inducing obstacle to effective legal work. Colin is a sought-after writer and speaker.  He is often asked to be a guest on legal tech podcasts, contributes to articles, blog posts, and other types of content published on various law outlets, and enjoys interviewing leading leaders in the legal and legal tech spaces.

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Hello Bill, we’re delighted to have you with us, could you provide an overview of your professional journey leading up to your current role as Chief Revenue Officer at BlueCloud?   

I come from a family of entrepreneurs, finding ways to help support the family business from an early age. My first job was cleaning cars at my parent’s car rental company in Buffalo, NY. We worked hard and constantly discussed business, outcomes, and the variables that could be controlled to help drive the company KPIs in the right direction. It was a central part of my life. As I progressed through my academic journey, my focus was on financial and accounting management. However, my practical experiences led me away from the traditional paths of corporate and public accounting and towards a career in sales within the financial services sector. Over the years, I gained extensive exposure to businesses of all sizes, from small enterprises to corporate giants like General Electric. This diverse background equipped me with a comprehensive understanding of financial operations, laying the groundwork for my transition into business intelligence and analytics. Embracing emerging technologies, I navigated through various roles spanning sales, customer success, and solution engineering across multiple organizations. Despite experiencing success in different environments, I continually sought challenges that would leverage my financial expertise and keep me at the forefront of technological innovation. My journey eventually led me to ThoughtSpot, where I spearheaded market expansion efforts and rose through the ranks to manage multiple regions. However, it was my alignment with BlueCloud’s vision and values that ultimately drew me to my current role. Here, I’ve found the perfect combination of my diverse skill set and passion for driving business outcomes through transformative technologies. Throughout my career, I’ve remained committed to embracing diverse perspectives and delivering tangible results in every endeavor. By constantly challenging myself and leveraging my expertise, I’ve been able to carve a fulfilling career path that continues to inspire and excite me.

In your extensive experience, what specific challenges do IT companies and consulting firms encounter when adapting to the rapidly evolving digital landscape?  

I’ve observed that one of the primary challenges is the necessity for clearly defined business values and a willingness to embrace change. This dynamic closely mirrors the fundamentals of a standard sales process. Just as in sales, it’s crucial to identify and understand the pain points driving the need for change. While it may seem tempting to stick with legacy technology, the risks associated with maintaining outdated systems can be just as significant, if not more so, than keeping pace with the evolving technological landscape. At its core, navigating this landscape requires effective change management and risk mitigation strategies. Moreover, it involves bridging the gap between technical solutions and non-technical stakeholders within organizations. For IT companies and consulting firms like ours, this often entails dedicating time and resources to ensure that stakeholders comprehend how technology integration aligns with and supports their overarching business objectives. Ultimately, the conversation must revolve around the delivery of tangible business outcomes and value rather than merely implementing cutting-edge technology for its own sake. If we fail to address this fundamental aspect, we risk providing solutions that lack meaningful impact and fail to meet the client’s objectives. Therefore, our challenge lies in consistently facilitating discussions that center on the alignment of technology with specific business needs and desired outcomes.

From your vantage point, what is the efficacy of digital transformation, particularly through AI implementation, in mitigating job cuts and enhancing process efficiency? 

Digital transformation has historically been risky, requiring not just technology implementation but also organizational buy-in and the ability to show real value. When it comes to reducing job cuts and improving process efficiency, these goals are separate but connected. AI implementation, much like past shifts such as the introduction of the assembly line, often automates tasks once done by highly specialized workers. Today, as AI takes over more tasks, these workers must adapt by continually learning new, specialized skills to stay relevant. This shift naturally boosts process efficiency, as tools like workflow automation and generative AI streamline tasks such as content creation. While increased efficiency may initially lead to job cuts, it also creates opportunities for workers to move into roles that use their expertise in new ways, adding more value to the organization. The key is to embrace transformation and actively help employees evolve their skills to meet the evolving digital landscape’s demands. Overall, this transformation benefits the global economy by encouraging growth, efficiency, and specialization.

What are the primary hurdles faced by IT professionals in acquiring the requisite skills and expertise for successful digital transformation initiatives?   

The technology landscape is constantly evolving with new advancements, making it challenging to stay updated. While traditional concepts like data warehousing and data lakes have seen improvements, emerging technologies like AI and ML pose more intricate challenges. Historically, these technologies have faced implementation hurdles, especially in transitioning them to production efficiently. To tackle these challenges, IT professionals need a deep understanding of what to prioritize, what pitfalls to avoid, and how to leverage these technologies effectively. Partnering with organizations such as BlueCloud can provide valuable guidance and insights as they stay abreast of the changing landscape and offer future-proof solutions. In this fast-paced environment, the focus should be on building a flexible and adaptable foundation rather than constantly chasing the latest technology trends. By providing targeted support and guidance, organizations can empower IT professionals to navigate digital transformation successfully.

In your view, what strategies should organizations employ to effectively combat the shortage of IT skills and transformational expertise within their workforce?   

To effectively address this challenge, organizations need to adopt a strategic approach that encompasses several key aspects. Firstly, they should consider partnering with an organization like BlueCloud that offers productive, cost-effective, and high-quality services. These partnerships can provide valuable support in navigating the complexities of digital transformation initiatives and help organizations acquire the necessary IT skills for specific projects and engagements. Secondly, organizations must recognize the importance of embracing a remote and borderless culture, especially in today’s rapidly changing landscape. By doing so, they can tap into a global talent pool and overcome geographical constraints in sourcing the required expertise. This shift towards a more flexible and decentralized work environment is crucial for accessing the right skill sets, regardless of location. Lastly, organizations need to adapt to the “new normal” and acknowledge that traditional methods of talent acquisition may no longer suffice. Instead of focusing solely on proximity to physical offices, they should prioritize skills and certifications, regardless of geographical location. This approach enables organizations to find the right talent efficiently and effectively, thereby mitigating the impact of the IT skills shortage on their transformational initiatives.

How does BlueCloud contribute to facilitating digital transformation within organizations, particularly through the integration of AI services, data engineering, and cloud operations?   

BlueCloud plays a pivotal role through our comprehensive suite of services, particularly in the integration of AI services, data engineering, and cloud operations. At its core, BlueCloud provides a highly skilled and adaptable workforce that can be scaled to meet the specific needs and requirements of each organization. This workforce is continuously trained and updated to stay knowledgeable on the latest technologies and trends in the industry. BlueCloud also maintains strategic partnerships with leading organizations such as Snowflake, ensuring access to cutting-edge tools and technologies. By leveraging these partnerships, BlueCloud can offer best-in-class solutions that drive efficiency and innovation within organizations. Furthermore, BlueCloud serves as both a guiding light and an execution arm for organizations embarking on digital transformation initiatives. Our expertise in areas such as generative AI and high-quality data engineering enables organizations to automate processes that were once reliant on highly specialized resources. In addition to technical expertise, BlueCloud provides valuable support in optimizing the value delivered to customers. This includes assisting with funding mechanisms, outlining business cases, and publicizing key strategic initiatives. By aligning with BlueCloud, organizations can stay ahead of their competitors and achieve their digital transformation goals more effectively.

Looking ahead, what key trends do you anticipate shaping the digital landscape, and how should organizations prepare to navigate them effectively? 

One notable trend is the increasing automation of highly specialized skill sets, which is transforming processes that previously required months of manual work by data scientists. This automation is now accessible without creating or buying “black box” solutions, offering organizations greater efficiency and transparency. Another significant trend is the rise of multi-cloud capabilities, allowing organizations to seamlessly integrate data from various sources while leveraging AWS, Azure, Google Cloud, or a combination of the three within a unified platform such as Snowflake. This enables organizations to leverage diverse data sources and technologies to drive outcomes effectively. In terms of organizational focus, there’s a shift towards profit through efficient revenue generation, data monetization, and revenue optimization, moving away from a sole emphasis on cost optimization or revenue at any cost. Organizations are prioritizing initiatives that drive tangible business value, mitigate risks, and optimize revenue streams. To effectively prepare for these trends, organizations should collaborate closely with their business stakeholders to identify specific challenges and opportunities. Establishing an innovation mindset within the organization can foster a culture of experimentation and openness to new ideas. Moreover, organizations should actively seek input from business users, as their insights can uncover valuable opportunities for leveraging emerging technologies. Finally, organizations can prepare by embracing automation tools and machine learning models to streamline processes such as content creation and ad generation. By leveraging these technologies, organizations can unlock new efficiencies and opportunities for growth. Overall, preparing for the evolving digital landscape requires a proactive approach, characterized by collaboration, innovation, and a willingness to embrace emerging technologies to drive meaningful business outcomes.

Could you share insights into your personal strategies or approaches for successfully leading a technology-focused company like BlueCloud?  

I firmly believe in taking a holistic approach to leadership, especially within a technology-focused company like BlueCloud. We see our mission as nothing less than changing the world, and this mindset guides our every decision and strategy. One key aspect of my approach is embracing diversity and a borderless mindset. Recognizing that talent exists across the globe, we adopt a multicultural approach that values ideas from all corners of the world. This openness allows us to tap into an array of perspectives and talents, driving innovation and creativity within our organization. Additionally, I prioritize fostering a culture of open-mindedness and hard work among our team members. We encourage a mindset that seeks out not just technological solutions, but solutions that deliver tangible business value. By keeping this focus on value creation at the forefront of our efforts, we ensure that everything we do is aimed at achieving success for our clients. Ultimately, my personal strategy revolves around these core principles: embracing diversity, fostering open-mindedness, and relentlessly pursuing value-driven solutions. By staying true to these principles, I believe we can continue to drive positive change and success for both BlueCloud and our clients.

What advice would you offer to IT professionals and leaders striving to maintain relevance and innovation amidst the evolving digital landscape?   

The evolving digital landscape presents both opportunities and challenges for IT professionals and leaders seeking to maintain relevance and drive innovation. My advice stems from recognizing the shifting dynamics within the industry. Firstly, it’s crucial to understand that the macroeconomic climate has impacted product companies, particularly those operating on a Software as a Service (SaaS) model. Valuation processes have evolved, and what may seem like a promising technology from the outside may not necessarily ensure sustainability for the company offering it. Therefore, IT professionals need to exercise caution and consider the broader landscape before investing time and resources into learning or implementing a particular technology. In navigating this landscape, I recommend relying on trusted partners who conduct thorough evaluations behind the scenes. These partners can provide insights into the viability and long-term prospects of various technologies, helping IT professionals make informed decisions. Furthermore, when considering working with a product company, it’s essential to assess the entirety of the landscape, not just the functional aspects of the product. While functional capabilities are crucial, they should be evaluated alongside other factors such as company sustainability, market trends, and alignment with long-term business goals. By taking a comprehensive approach and leveraging trusted partners for guidance, IT professionals and leaders can navigate the evolving digital landscape effectively, maintaining relevance and driving innovation in their organizations.

Lastly, is there any final message or insight you would like to impart to our audience regarding digital transformation, AI integration, or the future trajectory of technology in business? 

I’d like to emphasize the importance of maintaining an open-minded approach when it comes to digital transformation, AI integration, and the future trajectory of technology in business. The rapid advancements in technology have made innovation strategies more accessible and tangible than ever before. One key aspect of this open-mindedness is recognizing that innovation strategies that may have seemed impractical or out of reach in the past are now achievable in a cost-effective way due to the advancements in technology. The return on investment for certain approaches has become more tangible and easily achieved, making it reasonable for organizations to adopt innovative strategies and technologies. Moreover, it’s essential to look beyond one’s own industry and consider how innovation strategies from other sectors can be adapted and applied. Concepts and use cases from industries such as manufacturing, retail, healthcare, and financial services can often be adjusted and tailored to fit other sectors with minor tweaks. In essence, my message is to remain open and receptive to new ideas and approaches. Success in the IT space has often been synonymous with adaptability and the ability to pivot, and this remains true as we navigate the evolving landscape of technology in business. By embracing innovation and staying open to new possibilities, organizations can position themselves for success in the future.

Bill Tennant

Chief Revenue Officer of BlueCloud

Bill Tennant stands at the forefront of BlueCloud as the Chief Revenue Officer, where his nearly 20 years of experience across various industries fuel the company’s growth and innovation. With a decorated background featuring honors like TBBJ 40 under 40 and the CRN Next-Gen Solution Provider Leader, Tennant’s leadership has been pivotal in securing BlueCloud’s position as a partner of choice for tech giants. His vision has led to milestones such as the Snowflake Elite Services Partner Designation and numerous awards for channel partnerships. Tennant is an advocate for leveraging Generative AI, Machine Learning, and Data Governance to deliver business value and is open to discussions about joint ventures that push the boundaries of technology and strategy.

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Jim, as the CEO of Conversica, a leading AI provider for sales and marketing revenue teams, how do you see personalization and recommendations playing a crucial role in enhancing customer experience during their shopping journeys?

At Conversica, we understand the importance of personalization and recommendations in improving the customer experience during any and all shopping journeys. Leveraging AI-driven personalization allows us to tailor interactions based on individual preferences,
creating targeted and relevant experiences. However, BE AWARE. One of the main concerns with AI for retail is the intrusion of privacy. Customers might perceive generative AI and similar as being too intrusive, especially since it collects data over time to monitor purchasing behaviors and make product recommendations.
Our recommendation engines, powered by AI, analyze customer behavior and history to suggest products aligned with their interests, streamlining the shopping process and increasing engagement. We leverage partner AI technologies, like Salesforce Einstein as well. Where there are many choices, our commitment is to provide Conversational AI tools that not only automate processes but also elevate the overall customer experience, making the shopping journey more enjoyable and efficient for each individual customer. BUT, we do so, with a privacy-first approach.

Chatbots have become integral in customer service. Could you share insights on the impact of chatbots in improving customer interactions and support, especially in the context of sales and marketing during the recent holiday season?

Chatbots have become vital in revolutionizing customer interactions, particularly in the realms of sales and marketing, and especially during the holiday seasons. At Conversica, we’ve witnessed the transformative impact of chatbots in enhancing customer support with simple FAQ – frequently asked questions. By deploying AI-driven chatbots, we enable real-time engagement, providing instant responses to customer queries. In our case, our focus is to guide customers through the sales process efficiently. This not only improves responsiveness but also ensures a seamless and personalized experience, crucial during the high-demand holiday season. Chatbots excelin handling repetitive tasks, allowing human agents to focus on complex inquiries – more than just FAQ but rather the infrequent questions. The result is increased efficiency, improved customer satisfaction, and ultimately, a more successful sales and marketing strategy during the shopping rush. Conversica is dedicated to harnessing the power of chatbots to elevate customer interactions, making the shopping seasons a smoother and more enjoyable experience for both businesses and their customers.

With the increasing reliance on AI in the sales and marketing domain, how does Conversica address the concerns related to security and privacy, ensuring that customer data is handled responsibly and securely?

Compliance, Data Security, Privacy, and Transparency are core tenants. Our AI use cases are certified for SOC 2 Type II, ISO 27001, CCPA, COPPA, GDPR, PCI-DSS, and HIPAA/HITECH. Conversica recognizes and shares the excitement over current and potential uses of AI as well as the concerns regarding how and for what purposes AI is and may be used and the resulting consequences. Because our clients and their customers share these concerns, we have provided Conversica’s Responsible Use of AI policies. As a veteran of AI for over 15 years, we know that it’s a must to prioritize the security and privacy of customer data in our AI-driven sales and marketing solutions. We have implemented, for example, strong encryption, access controls, and authentication measures to protect data during storage and transmission. Complying with regulations and following industry best practices, we ensure responsible and transparent handling of customer information. Our commitment is to maintain high standards of security and privacy, allowing businesses to leverage AI benefits confidently and responsibly.

Navigating AI-driven sales can be a challenge for some businesses. What strategies or best practices would you recommend for sales and marketing teams to effectively integrate and leverage AI in their operations during peak shopping seasons?

Navigating AI-driven sales during peak shopping seasons can be challenging, but adopting effective strategies is key. Firstly, it’s crucial to align AI tools with specific business goals, identifying areas where automation can enhance efficiency in order to increase lead generation, improve discovery of products and services, or boost sales conversions.
Training teams on AI usage and fostering a culture of collaboration between AI and human agents is essential. Utilizing AI for data analysis can provide valuable insights to segment your customer base effectively. This allows for more targeted campaigns and messaging, improving engagement and conversion rates. Use AI to analyze customer data and behavior, enabling personalized communication. This could mean tailored product recommendations or customized messaging based on past interactions. AI can help automate the lead scoring process, identifying and prioritizing leads based on their likelihood to convert. This ensures your team focuses on the most promising prospects
during high-volume periods. Additionally, employing conversation automation for real-time customer engagement during peak seasons helps manage increased demand.

What future trends do you foresee in AI for e-commerce? How do you anticipate technology advancements shaping the landscape of sales and marketing processes on online platforms?

In the future of AI for e-commerce, expect more personalized experiences, improved recommendation engines, and advanced chatbots that will be referred to as digital concierges. AI analytics will provide deeper insights into customer behavior, and innovations like voice-activated commerce and visual search or augmented reality shopping will become prominent. These advancements will enhance efficiency and reshape the landscape of online sales and marketing processes.

Personalization is often a key factor in successful marketing campaigns. Can you provide examples or case studies where Conversica’s AI solutions have significantly contributed to personalized and effective marketing strategies?

Conversica’s AI has proven effective in personalized marketing. For instance, our AI assistants engage leads in natural conversations, boosting engagement and conversions resulting in a 24x ROI across the entire network. Conversion rates improve by an order of magnitude, increasing opportunities at every point in the funnel. The AI analytics help businesses analyze customer data for targeted campaigns, while chatbots provide real-time support, enhancing customer satisfaction. These examples showcase how Conversica’s AI contributes to personalized and successful marketing strategies.

With chatbots and customer service on the rise, how does Conversica ensure that its AI solutions are adaptive and responsive to the evolving needs and preferences of consumers in the fast-paced e-commerce environment?

Conversica keeps its AI solutions adaptable by staying tuned to consumer trends, and leveraging the latest in AI technology. We regularly update and improve our chatbots based on feedback and customer interactions, ensuring they remain responsive to the evolving needs and preferences in the fast-paced e-commerce environment. Core technologies, such as GPT 4.0, are leveraged to ensure the most human-like experience.

As AI continues to evolve, how do you envision the future collaboration between AI technologies and human teams in sales and marketing? What role do you see for AI in enhancing the productivity and creativity of human professionals?

Marketing teams produce more high-quality leads to their sales counterparts, getting more for their dollar, ultimately reducing the cost per lead and cost per customer. Sales
teams close more business faster. Both of these are a function of simply being able to focus on more high quality opportunities produced via AI automation.
As AI continues to evolve, the future collaboration between AI technologies and human teams in sales and marketing is poised to be synergistic. AI will take on routine tasks, allowing human professionals to focus on more complex and creative aspects of their roles. People will naturally have to reskill themselves for more high-value activities. People won’t be replaced by AI, but duties and in some cases entire roles will.
AI-driven analytics will provide valuable insights, aiding human teams in making informed decisions and crafting targeted strategies. The combination of AI’s efficiency in data processing and human creativity will lead to more innovative and effective marketing campaigns. Overall, AI is expected to enhance productivity by automating repetitive tasks and augment creativity by providing data-driven insights, fostering a collaborative and dynamic relationsahip between AI and human professionals in sales and marketing.

Considering the diverse nature of e-commerce platforms, how does Conversica tailor its AI solutions to cater to different industries and markets? Are there specific challenges or opportunities that arise in customizing AI for various business domains?

E-commerce involves buying and selling without the sales person. The holy grail is for the e-commerce experience to involve a digital concierge which removes friction anywhere and everywhere in the digital journey.
Conversica tailors its AI solutions to suit diverse e-commerce platforms by understanding the unique needs of different industries. We customize our AI conversations to leverage specific business domain data, addressing the challenges and opportunities each industry presents. This process requires a careful understanding of market nuances to ensure effective customization. Our goal is to provide tailored AI solutions that meet industry-specific requirements and unlock revenue opportunities for businesses in various domains.

Could you share any success stories or notable examples where Conversica’s AI solutions have made a significant impact on the performance and outcomes of sales and marketing campaigns for businesses?

Conversica’s collaboration with KEMP Technologies resulted in generating over $100,000 of opportunities within a month. But what is important for all marketers to understand is that for every AI automation dollar spent, 48 dollars in revenue are produced. When you take both revenue and people cost savings into account AI automation can deliver a 50x ROI (that’s an order of magnitude more than your typical Marketing ROI target). KEMP Marketing initiated the exploration by deploying
Conversica’s AI Assistant, Olivia, to engage with incoming leads, especially those from their ‘freemium’ product’s dedicated website. The freemium product attracted many users for trial purposes, and Conversica became the first point of contact for these leads. Additionally, Olivia was utilized to reconnect with older and inactive leads, providing a means to reengage with prospects and customers who had longer-term projects. This approach proved effective in situations where individuals initially showed basic interest on the website but did not directly engage with KEMP, either due to lower project priority or to explore other options first.

Jim Kaskade

CEO of Conversica

Jim currently leads Conversica, a PE-backed category creator and leader in AI-enabled augmented workforce solutions. Prior, he led Janrain, PE-backed category creator of Consumer Identity & Access Management (CIAM) & acquired by Akamai.

Prior to this Jim led CSC’s (now DXC’s) newly formed Digital Applications business in the Americas, a team of over 7K. The convergence of disruptive technologies such as analytics, mobile, cloud & cyber security were leveraged to help clients become digital businesses. Prior to this, he led CSC’s global Big Data & Analytics (BD&A) business unit, CSC’s fastest growing business of over 1K.

As a senior executive/CEO, Jim Kaskade has built companies in Big Data, Cloud Computing, Software as a Service (SaaS), Online & Mobile Digital Media, Advertising, & Semiconductors. He has also spent 10+ years in leadership roles developing enterprise data warehousing, data mining, and business intelligence solutions.

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