How AI Tools Transform Resume Writing for Success?

On an average, HR managers and recruiters go through a resume in almost six to seven seconds. It’s a really short time and shows that your resume must be outstanding and unique to catch their eye. Using difficult fonts, flashy designs, and a bad layout can become a reason for you to miss out an opportunity, even if you are well-qualified for that role.

Your resume tells about your past work history, skills, hobbies, competencies, etc. Just like many other industries, Artificial Intelligence (AI) can help you with writing your resume. Most people make silly mistakes or are unable to include all necessary information about themselves in their resume. An AI job search tool can help craft a flawless resume for you apart from just searching jobs.

How AI Tools Transform Resume Writing?

Instead of doing it by yourself, when you take the help of AI, it will ensure that your resume has the right format and headings. 

Also, AI goes through the job posting and optimizes your resume based on it so that you have an edge over other candidates. This is how AI is transforming the art of writing resumes.

Suggest Ideal Templates

Most people choose a template for their resume and keep using it for all future applications. This is not the correct way because recruitment trends keep changing and not all organizations are looking for a similar thing.

A template may be good for a particular job opportunity but it doesn’t mean that it will work everywhere. AI tools suggest templates depending on the company you’re applying to. The right template will ensure clarity and visual appeal, highlighting relevant skills to impress HRs.

Analyzes Job Descriptions & Optimizes Your Resume Accordingly

You should never use the same resume for different job opportunities as every role demands different skills. AI tools carefully go through job descriptions and understand the requirements. They optimize your resume with several keywords and skills that recruiters are looking for. 

Also, these tools will place relevant terms in such a way that recruiters surely see them while going through your resume. Using a single resume does not work anymore and you should use AI tools if you want a perfect resume based on the role you’re applying for.

Focuses on Your Top Skills & Achievements

Many people don’t put emphasis on their top skills and previous achievements when creating their resume. Recruiters won’t put in the effort to read every single word of your resume and it’s your duty to showcase your skills and experience in a way that they have high visibility. 

When you use an AI job search tool, it will help you in highlighting the in-demand skills you have and your past work history relevant to the role. Even if you are well-qualified for a job, if your resume does not showcase your skills properly, you’ll miss out.

Helps With Proper Design & Formatting

No one likes to go through outdated or poorly-designed resumes. Your resume should have a visually-appealing design and must be easy-to-read by recruiters. 

AI will recommend design ideas that are trending so that your resume becomes attractive. Also, it helps with proper formatting where every section is highlighted and all the important skills and information is clearly visible.

Detects Error & Silly Mistakes

Many times people are applying to multiple opportunities at the same time which increases the chances of making errors or mistakes. You have to make slight changes to your resume depending on each individual job opening and it is common to make mistakes doing this.

AI goes through your entire resume and alerts you regarding any spelling, grammar, or other mistakes. Attention-to-detail is an important skill that recruiters look for and if your resume is filled with mistakes, recruiters will reject it right away.

Provides Feedback to Improve Your Resume

AI tools will give you personalized suggestions and constructive feedback after going through your resume so you can improvise it. 

Based on your existing resume, skills, work experience, and the industry you work in, you’ll get the best suggestions to create a better resume. You’ll get to know several keywords and skill sets that are in high demand so that you can optimize your resume accordingly.

Conclusion

Millions of people are out there searching for jobs and the competition is intense. As part of the recruitment process, hiring managers first look at the resume of candidates and it is important to have the perfect one if you want to gain an edge over others.

You’re bound to fail if your resume is outdated and you don’t talk about your skills and experience properly. Leveraging technology is a great option as an AI job search tool will help you improve your resume other than just finding potential job opportunities for you. With an attractive, informative, and error-free resume, your chances of getting a job go up significantly.

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Artificial intelligence (AI) has made quite a name for itself in the previous year. However, lately, its pitfalls have been dominating most of the conversation. By now, we are all familiar with the failed, yet viral, Willy Wonka Glasgow experience, and have witnessed the harm AI can cause to an event and its attendees. Unfortunately, when it comes to using AI technology, “Pure Imagination” needs some limitations. 

Despite the 2017 Fyre Festival, event planners weren’t able to avoid the temptation of AI to prevent creating the next viral event hoax. The event industry can greatly benefit from technology advancements and AI, as long as event marketers and planners can implement a system of checks and balances to avoid relying too heavily on it. While it’s unlikely that this is the last instance of a misrepresented event, we can focus on learning from these mishaps, ultimately improving event experiences for everyone involved. Event planners can lean into these mistakes and turn them into lessons on how to ensure event goers aren’t led astray, address concerns and questions about the integration and use of AI, and work to prevent another viral hoax and fraudulent event. 

The Emergence of AI in Event Planning

AI has permeated nearly every industry, offering a wide range of possibilities for efficiency and innovation. Unsurprisingly, event planners have eagerly embraced AI to optimize their projects. It’s impossible to believe that event marketers won’t be leaning into the technology, so it’s imperative that they’re doing so responsibly and truthfully. The promise of AI lies in its ability to analyze vast amounts of data, predict trends, and automate tedious tasks, therefore freeing up time for planners to focus on what they do best: creativity and strategy. 

While these tools have great potential to lighten workloads, it’s essential to recognize that AI cannot completely replace the human touch in marketing efforts. Authenticity, personalization, and emotional connection are key factors in marketing that simply cannot be replicated by AI. Event marketers can find the balance with this by giving AI limitations to ensure it’s grounded in reality. With proper guardrails in place, event marketers and planners can ensure that ideas generated by AI are realistic in practice and geared towards the correct audience. 

Where Marketers Are Missing the Mark

One of the critical mistakes marketers can make is relying on AI for content creation without integrating it into the broader objectives of event planning. Authenticity in events hinges on a blend of AI-driven content development and personalized experiences. While AI can certainly generate compelling marketing copy and visuals, it lacks the intuitive understanding of human emotions and cultural nuances that are essential for creating memorable experiences.

AI tools won’t grasp the entire picture of the event – it doesn’t understand the limitations or small logistics that need to be considered. If teams plan to use AI to develop marketing visuals for an event, as was the case in the Willy Wonka debacle, there needs to be human oversight in the process. Without it, the event planners risk eroding the trust they previously worked to build with their customers. 

The Key to Successful AI Training and Integration

For AI to truly enhance event planning, it must be trained to understand and cater to the audience’s preferences. By leveraging high-quality data inputs, AI-generated output becomes more reliable, leading to more relevant marketing materials and enriched event experiences.

Integrating proprietary human inputs alongside external data sources allows AI to better grasp the nuanced needs of diverse customer segments, ensuring that events resonate authentically. This hybrid approach combines the analytical power of AI with the creativity and empathy of human planners, resulting in events that are both innovative and emotionally resonant.

As we navigate the intersection of AI and event planning, it’s imperative to ground AI systems in reality to avoid the pitfalls of overpromising and under delivering. The Willy Wonka experience serves as a cautionary tale, highlighting the importance of employing AI within a structured framework that aligns with marketing objectives.

When wielded effectively, AI serves as a powerful tool to craft compelling content and deliver personalized experiences, meeting or even exceeding the expectations of target audiences set by advertisements. By learning from the missteps of technological overreach and embracing AI carefully, event planners can unlock their full potential to create memorable and authentic experiences for attendees.

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Whether you’re choosing a restaurant or deciding where to live, data lets you make better decisions in your everyday life. If you want to buy a new TV, for example, you might spend hours looking up ratings, reading expert reviews, scouring blogs and social media, researching the warranties and return policies of different stores and brands, and learning about different types of technologies. Ultimately, the decision you make is a reflection of the data you have. And if you don’t have the data—or if your data is bad—you probably won’t make the best possible choice.

In the workplace, a lack of quality data can lead to disastrous results. The darker side of AI is filled with bias, hallucinations, and untrustworthy results—often driven by poor-quality data.

The reality is that data fuels AI, so if we want to improve AI, we need to start with data. AI doesn’t have emotion. It takes whatever data you feed it and uses it to provide results. One recent Enterprise Strategy Group research report noted, “Data is food for AI, and what’s true for humans is also true for AI: You are what you eat. Or, in this case, the better the data, the better the AI.”

But AI doesn’t know if its models are fed good or bad data— which is why it’s crucial to focus on improving the data quality to get the best results from AI for IT use cases.

Quality is the leading challenge identified by business stakeholders

When asked about the obstacles their organization has faced while implementing AI, 31% of business stakeholders involved with AI infrastructure purchases had a clear #1 answer: the lack of quality data. In fact, data quality ranked as a higher concern than costs, data privacy, and other challenges.

Why does data quality matter so much? Consider OpenAI’s GPT 4, which scored in the 92nd percentile and above on three medical exams, which failed two of the three tests. GPT 4 is trained on larger and more recent datasets, which makes a substantial difference.

An AI fueled by poor-quality data isn’t accurate or trustworthy. Garbage in, garbage out, as the saying goes. And if you can’t trust your AI, how can you expect your IT team to use it to complement and simplify their efforts?

The many downsides of using poor-quality data to train IT-related AI models

As you dig deeper into the trust issue, it’s important to understand that many employees are inherently wary of AI, as with any new technology. In this case, however, the reluctance is often justified.

Anyone who spends five minutes playing around with a generative AI tool (and asking it to explain its answers) will likely see that hallucinations and bias in AI are commonplace. This is one reason why the top challenges of implementing AI include difficulty validating results and employee hesitancy to trust recommendations.

While price isn’t typically the primary concern regarding data, there is still a significant price cost to training and fine-tuning AI on poor-quality data. The computational resources needed for modern AI aren’t cheap, as any CIO will tell you. If you’re using valuable server time to crunch low-quality data, you’re wasting your budget on building an untrustworthy AI. So starting with well-structured data is imperative.

Four facets of high-quality, trustworthy data for IT use cases

To understand why the quality of data matters, let’s look at AI in IT—an area that has value for nearly every industry. New AI models for IT can reduce the number of help tickets, dramatically lower the time needed to resolve problems and help you make better decisions by proactively highlighting potential issues before purchasing new software. In a field where a mistake can cost your organization millions of dollars at scale, a good AI solution is worth its weight in gold. But how do you ensure that it’s using good data?

The first thing to consider is the breadth of data. More data across more sources typically makes an AI more trustworthy, as long as you’re collecting good data. Think of it this way: a single restaurant review can offer a glimpse into its quality, but a restaurant with numerous reviews provides a more accurate assessment, allowing you to make a more informed decision. Was the one negative issue an outlier? Or is there a pattern that should be identified and evaluated?  Similarly, an AI trained for IT on 10,000 data points collected every 15 seconds from endpoints will be more useful than an AI trained on 800 data points every 15 minutes.

Next, focus on data depth. The amount of data a model has from IT endpoints can make a significant difference. In one instance, a company had 3,000 systems crash after a software patch didn’t play nice within the existing setup. The IT team quickly resolved the issue using a patented AI that identifies correlations between their system changes and device anomalies. This process was possible because the AI had been trained on their unique datasets, including historical data.

As AI trained for IT collects data, it’s crucial that the data is well-structured and as clean as possible. Most data sets will invariably have some noise—data that’s meaningless, irrelevant, or (in some cases) even corrupt, but training AI on high-quality, well-structured label makes all the difference. 

Finally, don’t forget about your people. AI is simply a tool. Change management and the impact of AI on humans are invaluable considerations when making decisions about introducing AI capabilities and use cases and (perhaps most important) evaluating if the AI you’re using for IT is delivering the most useful results for your organization. As AI continues to transform nearly every industry, think of data as the ingredients in the best AI recipe. If the ingredients are bland, the power and nuance of AI is lost. AI that’s fed robust, rich data, however, delivers on all the promises and opportunities of well-trained models. From there, the results will follow.

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Marketing organizations are increasingly adopting artificial intelligence to help analyze data, uncover insights, and deliver efficiency gains, all in the pursuit of optimizing their campaigns. The era of AI augmentation to assist marketing professionals will continue to gain momentum for at least the next decade. As AI becomes more pervasive, this shift will inevitably reshape the makeup and focus for marketing teams everywhere.

Humans will retain control of the marketing strategy and vision, but the operational role of machines will increase each year. Lower-level administrative duties will largely disappear as artificial intelligence tools become more deeply entwined in the operations of marketing departments. In the same way, many analytical positions will become redundant as smart chatbots assume more daily responsibilities.

However, the jobs forecast is not all doom and gloom because the demand for data scientists will explode. The ability to aggregate and analyze massive amounts of data will become one of the most sought-after skillsets for the rest of this decade. The fast-growing demand for data analysis will remain immune to economic pressures, and those kinds of job positions will be less susceptible to budget cuts.

Effects of the AI Rollout on Marketing Functions

As generative AI design tools are increasingly adopted, one thorny issue involves copyright protection. Many new AI solutions scrape visual content without being subjected to any legal or financial consequences. In the year ahead, a lot of energy and effort will be focused on finding a solution to the copyright problem by clarifying ownership and setting out boundaries for AI image creation. This development will drive precious cost and time savings by allowing marketing teams to embrace AI design tools more confidently, without the fear of falling into legal traps.

In addition, AI will become more pivotal as marketing teams struggle to scale efforts for customer personalization. The gathered intelligence from improved segmentation will enable marketing executives to generate more customized experiences. In addition, the technology will optimize targeted advertising and marketing strategies to achieve higher engagement and conversion levels.

By the end of 2024, most customer emails will be AI-generated. Brands will increasingly use generative AI engines to produce first drafts of copy for humans to review and approve. However, marketing teams will have to train large language models (LLMs) to fully automate customer content as a way of differentiating their brands. By 2026, this practice will be commonplace, enabling teams to shift their focus to campaign management and optimization.

AI Marketing Trends Impact Vertical Industry Groups

In addition to affecting job roles, the AI revolution is expected to supercharge marketing functions across nearly every type of industry. Two obvious examples include the retail and healthcare sectors. The retail industry has been quick to integrate AI to deliver efficiencies and increase sales. One emerging innovation is to combine neural networks with a shopper and a product to create new retail marketing experiences. For example, starting in 2024, you can expect an AI assistant to showcase an item of clothing on a model with similar dimensions to see exactly how it will look in various poses. Most industry watchers believe that such immersive, highly personalized virtual experiences will be the future of retail.

AI is also creating a radical new reality for the healthcare industry. For instance, digital twins are becoming increasingly ubiquitous for researchers, physicians, and therapists. A digital twin is a virtual model that accurately replicates a physical object or system. In this way, users can simulate physical processes through digital twins to test various outcomes without involving actual products or people, which greatly reduces operational costs and risks to public safety. For example, AI-powered digital twins could usher in new ways of marketing healthcare services for an aging population, by allowing people to live independently for longer. Or such twins might be used for future drug development projects.

AI will also play a pivotal role in the early diagnosis of potential health issues. For example, full-body MRIs will tap into the ability of AI to identify, analyze, and predict data patterns to help diagnose diseases long before any symptoms are visible to the human eye. In addition, AI will take a more prominent role in assisting medical staff to understand and interpret findings and provide treatments and care recommendations. All of these AI benefits will help sales and marketing teams to craft new messages that can communicate such considerable advantages to consumers.

Artificial intelligence engines have already upended marketing practices based on their extraordinary capacity for data analysis and efficiency, and this growth trend is only expected to continue in the coming years. To keep up with these technical developments, marketing professionals should become more comfortable using the AI tools which are rapidly remaking the entire marketing landscape.

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Hardly a week goes by without news of a ‘technical issue’ or outage. Sainsbury’s, Marks & Spencer and Tesco Bank are just some of the well-known brands that have experienced tech meltdowns in recent weeks. 

We’ve come to expect IT crashes as a part of life, but if handled poorly, they can snowball into a major crisis, tarnishing a company’s reputation, eroding consumer trust and resulting in lost sales. 

Here are five safeguards brand owners can put in place to protect their sites and minimise fallout from an IT crash.

Get your comms in order

Tech glitches are much easier to address – and fix when everyone’s in the loop, from your software developers to your marketing team to your customer base. Making your users aware of problems is paramount. 

All too often, a bad situation can quickly get worse when details of an outage spread like wildfire across social media (and for some unfortunate brands, this could be the first time they are made aware of the problem). Don’t bury your head in the sand. Be proactive and acknowledge the problem; email subscribers or post a message on social media, reassuring users that you are dealing with the problem and outlining expected timeframes for resolution. 

It’s also a great opportunity for your brand to establish or reinforce their ‘personality’ – using a heartfelt apology or humour, for example, if that tone is appropriate. But don’t promise what you can’t deliver – make sure that timeframes and assurances can be met. This is particularly important if there has been a glitch that could affect customer data.

Test, test and test again

We’ve come across many brands that don’t have a back-up structure in place or rollback code, where a system can revert to its previous state. While there’s no standard way to build a system, in our experience, the lack of robust software and website testing often comes down to cost, particularly with smaller brands and start-ups. It can also be due to not having regular access to a software development team or thinking an outage will never happen. Trust me, it can. 

Legacy brands have the resources and teams in place as their sites require more complex and continual updates, but we’ve had to battle with such clients when it comes to improving the testing phase.

Don’t underestimate this – testing is vital to ensure your website works. Typically, this involves a build phase, a testing phase and a quality assurance (QA) check, which aims to identify any issues across the entire build. Too often, testing and QA are the first things to go when brands want to cut costs. It could be an expensive mistake.           

Be alert

Do an audit of your site at least once a year with penetration (PEN) testing, where you look for any vulnerabilities in any of your systems. It’s not a guarantee that your site is absolutely secure and glitch-free, but as a brand, at the very least, you will have tried to identify any potential issues and protect your site and stored data.  Being proactive with system security alongside testing and QA reduces the risk of outages drastically.

Alongside PEN and code testing, you need to know when systems go down. There is nothing worse than a customer notifying you that your website or platform doesn’t work. 

Setting up monitors for your systems to notify you is the first step of your action to an outage. Depending on your user, you can even make these publicly accessible, like Slack (https://slack-status.com/), and other platforms so your users are aware of this issue as it happens.     

Consider your site structure

It is possible to limit outages to specific parts of a site, but it will depend on how your website or platform is built and whether different parts of it are hosted on separate services, for example.  This approach could help contain the fallout from an outage. Take X, formerly Twitter: its likes and tweets are kept separate where microservices are used for each, so if ‘likes’ were to go down, tweets would still be visible. We would advise this type of structure for brands that would benefit from such an approach. A microservices set up would benefit anyone that’s creating a platform for users need to complete things in, such as banks, ecommerce but not needed for things like marketing websites and ‘brochure’ websites.

Post-crash follow ups

The level of testing required and the number of times this is needed will depend on the size of the company, its user base and its product.  It is also essential to take tech developments into account, all of which can impact even the most robust of sites. We recommend PEN tests once a year, but above all, be vigilant, take customers seriously and respect their data. 

If you’ve suffered an outage, there’s nowhere to hide. Being proactive rather than reactive shows that you care, which can make a big difference to your reputation.

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With 2024 well underway, we are already witnessing how generative artificial intelligence (GenAI) is propelling the cybersecurity arms race among organizations. As both defensive and offensive players adopt and operationalize finely-tuned Large Language Models (LLMs) and Mixture of Experts (MoE) model-augmented tools, the approach organizations take toward cybersecurity must evolve rapidly. For instance, GenAI-powered capabilities such as automated code generation, reverse engineering, deepfake-enhanced phishing, and social engineering are reaching levels of sophistication and speed previously unimaginable.

The urgency to rapidly adopt and deploy these AI-augmented cybersecurity tools is mounting, and organizations that are reluctant to invest in and adopt these tools will inevitably fall behind, placing themselves at a significantly higher risk of compromise. While it is imperative for organizations to move swiftly to keep pace with this rapid advancement, it is equally crucial to acknowledge the intricate nature of GenAI and its potential to be a double-edged sword. To avoid the perils of AI and leverage its benefits, organizations must comprehend the importance of keeping abreast of its advancements, recognize the dual capacity for good and harm inherent in this technology, and implement internal processes to bridge knowledge gaps and tackle AI-related risks. To counteract known and emerging AI-related threats, such as data leakage, model poisoning, bias, and model hallucinations, it is essential to establish additional security controls and guardrails before operationalizing these AI technologies.

Keeping pace with adversaries

The challenge posed by AI-powered security threats lies in their rapid evolution and adaptability, which can render conventional signature and pattern-based detection methods ineffective. To counter these AI-based threats, organizations will need to implement AI-powered countermeasures. The future of cybersecurity may well be characterized by a cyber AI arms race, where both offensive and defensive forces leverage AI against one another.

It is widely recognized that cyber attackers are increasingly using GenAI tools and LLMs to conduct complex cyber-attacks at a speed and scale previously unseen. Organizations that delay the implementation of AI-driven cyber defense solutions will find themselves at a significant disadvantage. They will struggle not only to adequately protect their systems against AI-powered cyberattacks but also inadvertently position themselves as prime targets, as attackers may perceive their non-AI-protected systems as extremely vulnerable.

Advantages versus potential pitfalls

When appropriately implemented, safeguarded, and utilized, technologies like GenAI have the potential to significantly enhance an organization’s cyber defense capabilities. For instance, foundational and fine-tuned (LLMs) can expedite the processes of cyber threat detection, analysis, and response, thus enabling more effective decision-making and threat neutralization. Unlike humans, LLM-augmented systems can quickly identify new patterns and subtle correlations within extensive datasets. By aiding in the swift detection, containment, and response to threats, LLMs can alleviate the burden on cybersecurity analysts and diminish the likelihood of human error. Additional benefits include an increase in operational efficiency and a potential reduction in costs.

There is no doubt that technologies such as GenAI can provide tremendous benefits when used properly. However, it is also important not to overlook the associated risks. For instance, GenAI-based systems, especially LLMs, are trained on very large datasets from various sources. To mitigate risks such as data tampering, model bias, or drift, organizations need to establish rigorous processes to address data quality, security, integrity, and governance. Furthermore, the resulting models must be securely implemented, optimized, and maintained to remain relevant, and their usage should be closely monitored to ensure ethical use. From a cybersecurity perspective, the additional compute and data storage infrastructure and services needed to develop, train, and deploy these AI models represent an additional cyber-attack vector. To best protect these AI systems and services from internal or external threat actors, a comprehensive Zero Trust Security-based approach should be applied.

Adopting AI for Cybersecurity Success

Considering the breakneck speed at which AI is being applied across the technology and cybersecurity landscape, organizations may feel compelled to implement GenAI solutions without an adequate understanding of the investments in time, labor, and expertise required across data and security functions.

It may seem counterintuitive, but a sound strategy for incorporating artificial intelligence (which, on its face, would seem to offset the need for human efforts) involves no small amount of human input and intellect. As they adopt these new tools, CTOs and tech leadership will need to consider:

• AI advancement – It is an absolute certainty that GenAI will continue to be a fluid, constantly evolving tool. Engineers and technicians will need to stay abreast of its shifting offensive and defensive capabilities.
• Training and upskilling – Because AI will never be a static technology, organizations must support ongoing learning and skills development for those closest to critical AI and cybersecurity systems.
• Data quality and security – Artificial intelligence deployed for cybersecurity is only as good as the data that enables its learning and operation. Organizations will require a robust operation supporting the secure storage, processing, and delivery of data-feeding AI.

Undoubtedly, leaders are feeling the urgency to deploy AI, particularly in an environment where bad actors are already exploiting the technology. However, a thoughtful, strategic approach to incorporating artificial intelligence into cybersecurity operations can be the scaffolding for a solid program that greatly mitigates vulnerabilities and protects information systems far into the future.

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The intersection of artificial intelligence (AI) and digital advertising to create truly engaging experiences across global audiences and cultures is reaching an inflection point. Companies everywhere are leveraging powerful trends in AI, machine learning and apps for performance marketing.

Today’s AI and machine learning technologies are allowing apps to understand speech, images, and user behavior more naturally. As a result, apps with AI capabilities are smarter and more helpful, and companies are using these technologies to create tailored experiences for customers, regardless of language or background. AI is leveling the playing field by making advanced data tools accessible to anyone, not just data scientists.

Kochava has incorporated AI and machine learning across our diverse solutions portfolio for years, such as within our advanced attribution and fraud prevention products. We have also adopted advanced technologies, like large language models (LLMs) to develop new tools.

Many organizations are instituting internal restructuring with a focus on enhancing the developer experience. The aim is to leverage the full potential of AI for smart applications, providing universal access to advanced tech tools, while adapting to changes in app store policies. Engineering teams are spearheading the development of self-service platforms managed by product teams. The primary objective is to optimize developers’ workflows, speeding up the delivery of business value, and reducing stress. These changes improve the developer experience which can help companies retain top talent.

From an overall organizational structure perspective, in pursuit of a more efficient and effective approach, Kochava is focused on enhancing developer experiences, leveraging AI for intelligent applications, democratizing access to advanced technologies, and adapting to regulatory changes in app marketplaces.

Reimagining the Future

The software and applications industry is one that evolves particularly quickly. The app market now represents a multibillion-dollar sector exhibiting no signs of slowing. This rapid growth and constant change presents abundant opportunities for developers to build innovative new applications while pursuing their passions. For app developers, monitoring trends provides inspiration for maintaining engaging, innovative user experiences.

As AI integration increases, standards will develop to ensure AI can automatically interface between applications. It will utilize transactional and external data to provide insights. Applications will shift from set features to AI-driven predictions and recommendations tailored for each user. This advances data-driven decision making and transforms the experience for customers, users, teams, and developers.

Democratizing access to generative AI across organizations has the potential to automate many tasks, lower costs and create new opportunities. It changes the competitive landscape by making vast knowledge more accessible to anyone through natural language. Increased access to information is a big trend.

Changes in regulations are also allowing third-party app stores on many operating systems. This reflects a shift that provides developers new opportunities and challenges as larger publishers set up their own markets.

Trends Transforming How We Work

Intelligent Applications: With increasing AI integration, standards will develop to ensure AI can automatically interface with other applications. It utilizes transactional and external data to provide application insights. Shifting from procedural features to AI-driven predictions and recommendations personalized for users advances data-driven decision-making, transforming customer, user, product owner, architect, and developer experiences.

Generative AI Democratization: Democratizing access to generative AI across the organization. With broad task automation potential, it reduces costs while fostering growth opportunities. This transforms enterprise competitiveness and facilitates democratized information and skills access across roles and functions. Enabling natural language interface access to vast knowledge equitably is key.

Third-Party App Stores: Regulation changes may allow third-party app stores on operating systems, anticipated to increase larger publishers’ establishment in these markets. This reflects an evolving app store ecosystem with new opportunities and challenges for both platform operators and developers.

AI can help marketers think outside the box to find those gems that standard sources aren’t turning up. Exercise AI’s power to maximize your marketing campaigns and connect with customers worldwide!

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In an era where technology seamlessly integrates into every facet of our lives, the vision of the future of transportation, once dreamt in the mid-20th century, is becoming a reality. Landscapes are evolving, with the promise of enhanced connectivity, ease of travel, and the development of sprawling metropolises aimed at fostering a more harmonised society. This transformative period in transportation is not just about sleek designs, improved fuel efficiency, or advanced safety systems; it is about the underlying digital revolution that has turned vehicles from mechanical wonders into sophisticated, software-driven entities.

The marvel of modern vehicles extends far beyond their aesthetic appeal or physical innovations. Today, vehicles are commonly referred to as data centres on wheels, equipped with digital interfaces that constantly communicate with manufacturers, receive over-the-air (OTA) software updates, and integrate advanced safety features, like LIDAR systems, to navigate complex environments. The once direct mechanical connection between the accelerator and the engine has been replaced by a digital command centre, where a simple press of a pedal is translated into a series of computations that ensure optimal performance and safety.

However, this digital evolution brings with it a looming shadow of vulnerability. The very systems that make modern vehicles a marvel of technology also exposes them to a myriad of cybersecurity threats. In recent years, the automotive industry has witnessed a concerning trend: an increase in cyber-attacks targeting not just the vehicles but the entire ecosystem surrounding their development, production, and maintenance. The 2021 attack on KIA Motors by the DopplePaymer group is a stark reminder of the potential consequences of inadequate cybersecurity measures. While no direct harm to drivers was reported, the incident underscored the risks of operational downtime, revenue loss, and eroding customer trust.

The question then becomes, what lies ahead? The potential targets for cyber-attacks are not limited to consumer vehicles but extend to government and municipal mass transit systems. The stakes are exponentially higher, with the threat landscape encompassing espionage, state-sponsored activities, and the emerging menace of AI-driven cyber threats. The complexity of modern vehicles, often containing upwards of 100 endpoints, including infotainment systems that store personal data, demands a cybersecurity strategy that transcends traditional approaches and international borders.

Aston Martin’s F1 team provides a great example of the intricate cybersecurity needs of ultra-modern, high-tech vehicles and their creators. These highly complex vehicles illuminate the imperative for a holistic cybersecurity framework that addresses the challenges faced across the entire product lifecycle, from pre-production to post-production. The Aston Martin F1 team, known for their cutting-edge technology and pursuit of perfection, exemplifies the critical need for advanced cybersecurity measures capable of thwarting AI-driven threats and protecting the intricate network of systems and applications that underpin the performance of these high-speed machines.

While protecting an F1 vehicle can be accepted as an extreme example of a connected vehicle with each endpoint generating large sets of data, many of these technologies are likely to find their way into consumer, municipality, government, and even mass-transit vehicles down the road.

The cybersecurity of modern vehicles is indeed a data problem. 

Protecting this data requires a proactive approach, one that involves hunting for threats, deceiving potential attackers, and adopting a mindset that places vehicle cybersecurity on par with data security across the rest of the organisation. It’s about creating a resilient shield around the digital and physical aspects of transportation, ensuring that innovation continues to drive us forward, not backward into an age of vulnerability.

As we navigate this digital frontier, the automotive industry must prioritise cybersecurity as a foundational element of vehicle design and functionality. The collaboration between cybersecurity experts like and automotive giants is a step in the right direction, but it is only the beginning. The path forward requires a concerted effort from manufacturers, suppliers, cybersecurity professionals, and regulatory bodies to establish robust standards and practices that safeguard our vehicles and, by extension, our society. The future of transportation depends not just on technological advancements but on our ability to protect and secure these innovations against the ever-evolving threats of the digital age.

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You may have seen the acronym ‘RAG’ floating around in relation to artificial intelligence. What the heck is RAG and why is everyone talking about it? 

RAG stands for Retrieval-Augmented Generation and combines a generative model with a retrieval system to enhance or augment (as the name suggests) AI responses with more accurate and current data. So this means there are two portions to it: the generative model, which generates human-like text, and the retrieval system, which supplements the generative model’s output. 

As with any emerging technology, before implementing it within your organization, it’s wise to understand it, as well as its potential benefits, and truly consider why you should – or should not – use it. Let’s explore what RAG is and the impact it can have on your business. 

The RAG Process

There are four components to the process flow of a RAG process: query processing, retrieval, integration, and generation. These components are what allow you to truly specialize a Large Language Model (LLM) with a knowledge base of your choosing.. 

• Retrieval: The retriever is a component that searches and selects relevant information from a large database or knowledge base based on the input query.
• Knowledge Base: This is the collection of data or information sources that the retriever accesses to find content relevant to the query.
• Re-ranker/Selector: The re-ranker or selector evaluates and chooses the best output from the generated responses, ensuring relevance and quality.
• Generation: This component integrates the retrieved information into the language generation process, synthesizing it with the input to produce a coherent response.

Now that we’ve outlined the process RAG uses to produce more effective AI responses, let’s examine why RAG is more effective than other models. 

Benefits of RAG

There are numerous benefits of implementing RAG, but the major benefits include preventing hallucination, control over the knowledge base used and flexibility in updating information like price changes or product stock.

Preventing Hallucination: RAG reduces the occurrence of generating false or nonsensical information by grounding responses in verified data, enhancing the accuracy and reliability of AI-generated content, crucial for areas where precision is vital.

Control Over the Models Knowledge: RAG allows precise control over the information sources, enabling organizations to tailor content generation to their specific standards and requirements, thus ensuring consistency and alignment with organizational values.

Flexibility on Updating Information: With RAG’s ability to process real-time data, it excels in applications requiring current information, such as AI sales agents and market analysis, ensuring that businesses can offer accurate, timely data to their clients.

Future of RAG and AI

RAG holds great potential for revolutionizing customer interactions, particularly in the sales function. Businesses would be wise to take advantage of all these benefits – or risk losing out to the competition. If you are just dipping your toes into the RAG pool, my recommendation would be to start with one narrow use case and expand from there. Starting small versus going far and wide out of the gate can help you avoid mistakes down the road.

I anticipate RAG and AI as a whole will improve even more regarding emotional intelligence. For example, it’s likely AI will be able to interpret emotions from tone and facial expressions. This can have far-reaching implications not only across many functions of your business but also across industries. It will be important for leaders to watch this space and keep up with the latest RAG and AI trends to most effectively implement it within your business. 

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The rise of Generative AI (GenAI) has enormous potential for the banking and finance industries. By utilizing GenAI, banks and credit unions speed applications from submission to approval, save time and effort, and deliver a desirable customer experience.

A recent report from the Society for Human Resource Management (SHRM) and The Burning Glass Institute details how GenAI will have an outsized role on the banking and finance industries. The report lists Morgan Stanley, Bank of America and Northwest Mutual as some of the organizations that are most likely to capitalize on the implementation of GenAI. Their study also measures GenAI exposure among several different professional industries; “investment banking and securities dealing and brokerage” measured third highest while “mortgage and nonmortgage loan brokers” ranked highest overall. If SHRM and The Burning Glass Institute are so convinced that GenAI will profoundly alter how financial institutions operate, what will that change look like and why does it matter?

GenAI is distinct from other forms of automation by its ability to automate what is typically considered knowledge work. This represents a sea change in how professional industries, including financial services, will implement automation technology in their workplaces. In fact, financial services are especially dependent on repetitive manual processes requiring specialized knowledge. Processes like loan underwriting and credit card applications require knowledge workers to manually input data and individually connect with customers or members, which takes up the majority of workers’ time and tasks.  GenAI excels in automating repetitive, manual tasks—such as data processing and pattern identification—streamlining operations and freeing up valuable time for knowledge workers.

The applications of GenAI within financial services manifest in both evident and nuanced ways, each offering distinct advantages to forward-thinking institutions. Many industries have begun employing GenAI solutions as chatbots for customer service, and financial services are no exception. GenAI-powered chatbots, operational around the clock, offer an immediate response to customer inquiries, significantly reducing the need for direct intervention by skilled professionals and enhancing service efficiency.  However, these solutions become even more compelling for financial institutions when embedded in the bank or credit union’s broader systems. For example, a loan applicant can interact with a GenAI-enabled chatbot and get a real-time status update on their loan status by providing a few identifying details. In this way, GenAI increases efficiency while also directly improving the customer or member experience.

As lending processes are vitally important to any bank or credit union, they are also ripe for GenAI enhancement. Much of lending involves knowledge workers reaching out to customers and acquiring all the documents and data necessary to complete the loan application. This can be a tedious process that can take days or weeks to complete. With GenAI solutions, not only can financial institutions instantly reach out to applicants requesting documents, but those same solutions can also automatically identify all necessary documents by analyzing the application status without a human ever laying eyes on it. Some tools can even go one step further by identifying the document category, extracting data from the documents and validating them against the loan application instantly. Imagine your own knowledge worker advancing loan processing overnight and over the weekend!

Additional GenAI solutions go beyond immediate revenue-generating processes for bank and credit unions. These tools can assist with marketing efforts by creating personalized offers based on account information. GenAI-enabled marketing tools can detect patterns in account behavior and automatically generate personalized offers based on customer preferences. Just like with lending processes, GenAI tools can then automatically reach out to customers and members via email, SMS or other communication channels. These marketing efforts again increase efficiency, improve the customer or member experience and create more lending opportunities.

GenAI’s distinctive ability to automate professional tasks, particularly in the realm of financial services, where repetitive manual processes dominate, is reshaping traditional workflows. The adoption of GenAI solutions, from chatbots enhancing customer service to automating lending and revolutionizing marketing efforts, signifies a paradigm shift towards efficiency, improved customer experiences and increased lending opportunities. 

GenAI technology is novel, and its implementations are sure to evolve further in the coming months and years. However, its potential for financial services is undeniable. In order for banks and credit unions to take full advantage of this nascent technology, financial institutions need to create AI policies, complete digital transitions and start exploring and investing in GenAI use cases now. 

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