Tag: AI Adoption

8 real world founder fears in the age of AI – Podcast Video

A D2C founder running an Ayurvedic skincare brand, doing about 8 crore in revenue, put it simply. AI is helping everyone create ads, write product descriptions, launch stores faster. My fear is not the technology. My fear is that AI makes every brand look equally good. If everyone has the same marketing superpower, where does my differentiation come from?

Vineeth Vijayaraghavan, who I have known for over thirty years and who advises VCs, family offices and startups on AI strategy, did not pretend there was a tidy answer. This needs introspection, he said. But he had a reframe worth sitting with.

Most of us think about AI inside a business as either software or marketing. Those are the obvious places. What he is seeing some founders do instead is use AI to build capability they could never have afforded before, inside the core of the business itself. He gave the example of a chemical engineering company where the founder now has six AI agents working alongside him on R&D, something that used to require hiring people the business simply could not access or afford. The same logic applies to a skincare brand. AI for product research and literature review is not a bad place to start, he said, though obviously with a healthy dose of scepticism, especially in a category where claims need real verification.

The differentiation question does not disappear. But it shifts from what does my marketing look like to what am I now capable of building that I was not before.

This was one of eight real founder fears we worked through on a new episode of The Founder Catalyst, recorded this month in Chennai. Instead of the usual interview, we sourced questions directly from founders across industries and went through them one by one, no scripts, no theory.

A custom software services founder in Coimbatore is not worried about his developers using AI. He is worried his US clients will stop asking for large projects and instead ask for one senior engineer doing the work of five, slowly turning his company into a staffing business with shrinking margins. Vineeth’s answer, the volume of smaller, faster engagements is going up industry-wide, and the founders doing well are the ones who can turn around small projects quickly and let those grow into larger ones over time, rather than chasing fewer, bigger contracts.

A recruitment firm placing engineers for startups fears that AI screening and ranking candidates makes recruiters irrelevant. Vineeth flipped this one. As AI competency becomes harder to evaluate, not easier, because a test or a hackathon no longer tells you much, the recruiter who has spoken to hundreds of candidates and can spot what is real becomes more valuable, not less.

A chartered accountancy firm is not afraid of losing clients. Being a regulated profession, that part feels safe. The fear is clients expecting the same work for half the fee. Vineeth’s response, look at firms already doing this well. He described one CA firm in Chennai that grew from 14 to 60 people, with AI handling the routine compliance work and a small number of AI-native young developers building internal tools, while the chartered accountants focus on the judgment calls AI still cannot make. That firm, he added, is now raising serious investment, including from outside India.

We also went through fears from an automobile manufacturing unit near Hosur worried about larger competitors using AI to out-pace them on pricing and forecasting, a legal services firm worried about how junior lawyers get trained if AI does their early work, and a logistics company with 70 trucks wondering whether technology becomes the real competitive edge over operational excellence.

We closed with an imaginary scenario. Three founders in a room, a SaaS founder doing 10 crore in ARR, a services founder with a hundred people, and a manufacturing business doing 50 crore. Which one should be most worried about AI, and which the least? Vineeth’s answer was not about industry at all. It was about culture, and the willingness to act.

Watch the full conversation here:

If you are a founder or business leader trying to make sense of what AI actually means for your business, beyond the hype, this conversation is for you.

About the guest:

Vineeth Vijayaraghavan advises companies, family offices, and VC boards on technology and investment strategy, with a particular focus on AI.

About the host:

Venkatarangan Thirumalai is the author of The Founder Catalyst and writes and speaks on AI, leadership, and founder strategy. More at thefoundercatalyst.com

When I work with founders on this, the conversation rarely starts with the technology. It starts with what people are afraid of losing, and whether that fear points them toward the right question or the wrong one.

June 17, 2026
The CIO had a record year, but her teams didn’t notice.

The CIO had every reason to be pleased. She wasn’t.

Board visibility. CEO approval. Record numbers of internal apps shipped. Her IT team was delivering at a pace that would have been unthinkable twelve months ago.

When I joined the call, I expected good news. I heard hesitation instead.

It took me a few minutes to notice what was missing from her update. She never mentioned the line managers. Not once. I asked her directly: Were the department heads actually seeing productivity gains from these applications?

“Venkat, you guessed it. That’s exactly my problem.”

Her team had been shipping AI-built internal applications continuously for nearly a year. Every department was requesting bespoke tools — automation utilities, reporting dashboards, workflow agents — for specialised use cases that previously would never have cleared a development backlog. The economics had changed. AI made these builds fast and cheap. Requests kept coming. Delivery kept pace.

But the telemetry told a different story. Usage in single digits. For large teams where even ten percent adoption would register clearly, the numbers were flat.

The apps were built. They sat unused.

This is not a story about AI failing. It is a story about a safeguard disappearing.

Before AI entered the picture, cost and engineering bandwidth acted as a natural filter on internal tool requests. Most ideas died quietly in the queue — not because anyone evaluated them and said no, but because the cost of building was real and the wait was long. That friction, however crude, forced a basic form of demand validation. Only requests with genuine organisational pull survived it.

AI has removed that friction almost entirely. And most organisations have not replaced it with anything.

What happens next is predictable. A manager spots a genuine gap — usually a real one. A request goes in. The tool gets built without meaningful involvement from the people who would actually use it. No end-user consultation. No workflow integration thinking. The manager accepts delivery. The team quietly returns to what they already know.

The app joins a growing collection of well-intentioned software that nobody opens.

The frustrating part is that the productivity gains from AI are real, and the evidence is unambiguous. EY’s chief economist, citing Federal Reserve Bank of St. Louis data at Davos 2026, found that generative AI is already saving workers the equivalent of 1.6% of total work hours. A randomised experiment with 1,174 adults (NBER, 2026) found AI closed nearly three-quarters of the productivity gap between higher and lower-educated workers. And a University of Hong Kong study (2026) found that untrained AI access actually hurt performance, while even a brief training intervention significantly improved both adoption and outcomes. The technology delivers. The variable is always the people side.

I have seen this pattern repeat across organisations, and it connects to something I write about in my book. Twenty-five years ago, I built inventory software for my father’s publishing firm — convinced that the clear benefits would drive adoption on their own. The staff resisted. Not because the tool was bad, but because nobody had brought them into the process. The breakthrough came only when the firm’s general manager, a man deeply rooted in the old ways, took a personal interest in learning the system. His endorsement shifted the entire team’s behaviour. The technology did not change. The human dynamic did.

That lesson has not aged. If anything, AI has made it more urgent.

Speed is easy to measure. Adoption is what moves the business

Most AI tool deployments follow the same top-down pattern. A business leader identifies the gap. IT builds the solution. The actual users — the ones whose daily habits need to change — are consulted last, if at all.

Users in large organisations carry significant inertia toward familiar tools and established routines. That inertia does not dissolve because a new application exists. It dissolves through involvement, through relevance, and through time. None of those three things gets faster just because the build cycle did.

The fix is not more training sessions or better change management communications. Those are responses to a symptom.

The deeper problem is that the old economic forcing function has gone, and nothing has taken its place. What needs rebuilding is a lightweight governance layer. Before any internal AI tool gets approved, two questions should have clear answers.

Is there genuine pull from the people who will use it — not just from the manager who requested it?

Does an existing tool or workflow already adequately cover this need?

That gate does not need to be bureaucratic. It needs to exist.

AI has made building cheap. It has not made building the right thing any easier. If anything, it has made that harder — because the friction that used to quietly filter out weak ideas is gone, and most organisations have not noticed yet.

Speed of delivery is easy to measure and tempting to celebrate.

Depth of adoption is what actually moves the business.

If questions like these sit at the intersection of technology and leadership for your organisation, this is what I focus on at The Founder Catalyst. Happy to continue the conversation in the comments or connect directly.

April 29, 2026
What Microsoft got wrong about Copilot?

Underpromise and overdeliver — my message to founders on AI

Microsoft’s own corporate VP of the Office Product Group admitted it publicly this week: “When we first shipped Copilot, foundation models were not powerful enough to use Copilot to command the applications.”

That is a remarkable thing to say out loud. And the right thing to say.

Go back to late 2022. ChatGPT lands. Microsoft moves fast — faster than they should have — and Copilot arrives positioned as the AI-powered assistant that would transform how you work in Word, Excel, and PowerPoint. The demos were impressive. The promises were large. What landed in enterprise inboxes was a chatbot that could summarise a document but not meaningfully act on one.

Users felt misled. Many quietly switched to ChatGPT. Enterprise teams started evaluating Gemini. And then Claude — particularly the disruption that Claude Cowork brought to real productivity workflows — reset the benchmark for what capable AI in the workplace could actually look like. Billions in market value moved. The pressure on Microsoft was real.

They have now course-corrected. Agent Mode is rolling out across Word, Excel, and PowerPoint. Anthropic models sit alongside OpenAI models inside Copilot. The sidebar now shows you every step the AI takes on your document in real time. That is a genuine improvement, not a rebrand.

Is it late? Yes. Is it too late? I don’t think so. Microsoft still owns the surface — Word, Excel, and PowerPoint are entrenched in enterprises and governments in a way that no AI startup can replicate overnight. Adding capable models to that distribution is a credible recovery. Not a comeback story, but a course correction that could hold.

The lesson, though, is not really about Microsoft.

It is about what you promise before you ship.

Every CEO and CTO rolling out an AI initiative right now faces the same pressure Microsoft felt in 2022. The frontier lab demo is extraordinary. The consultant’s deck is compelling. The board wants to see momentum. So you make large claims. You announce something before the reality is ready. And then reality arrives.

The smarter path is quieter. Pilot with a small, honest cross-section of your organisation — not the enthusiasts, the actual everyday users. Observe what actually gets used, not what gets praised in a feedback survey. Understand where the model genuinely helps and where it confidently fails. That gap between demo and daily use is where most AI rollouts go wrong, and no amount of training data or vendor reassurance closes it faster than watching your own people use it for two weeks.

The good news today — which genuinely did not exist two years ago — is that AI coding agents let you iterate and fine-tune far faster than traditional development cycles allowed. The feedback loop is tighter. Use it before you go back to your board asking for the larger budget.

Microsoft overpromised in 2022. They paid for it in user trust and adoption numbers, and they spent two years correcting course. They had the balance sheet and market position to absorb that. Most companies don’t.

Promise less. Deliver more. Then expand.

That is not caution. That is how you build something that compounds.

If you are a founder or CXO working through an AI rollout — what to pilot, how to read real usage signals, when to scale — I work through exactly these decisions at thefoundercatalyst.com. Connect with me at linkedin.com/in/venkatarangan

April 27, 2026
Is Formula 1 telling us something about how to roll out AI?

Stay with me for a second.

Starting this 2026 season, F1 has quietly executed its biggest regulation overhaul in over a decade. The 1.6L V6 turbocharged engine stays. But the electric side of the hybrid system has been roughly tripled in power, and the sport is now targeting a 50:50 split between internal combustion and electric power. The cars are running on Advanced Sustainable Fuels for the first time, and DRS has been replaced by an electrically-driven Overtake Mode.

A serious pivot. And yet F1’s own president has been categorical that the sport will never go fully electric. Not ideology — battery technology simply cannot sustain a Grand Prix, and pushing too fast would break the product itself. There is also a regulatory reality at play. Governments across the EU and elsewhere have been legislating hard on emissions and the long-term future of internal combustion engines. F1 had to respond to that pressure, just as every other industry has. It could not ignore the direction of travel, but it also could not pretend the technology was ready to go all the way.

So here is what F1 has actually done. It read where the regulation is heading. It read where public sentiment is heading. It read where the technology is heading. And instead of picking a side, it built a hybrid architecture where the driver stays in the seat, but the electric system dramatically amplifies what the car can do. Even that has not been smooth, and the FIA is still tuning the rules with teams this month. Imperfect, but directionally right.

This is the pattern I keep coming back to when founders and CXOs ask me how aggressively they should roll out AI inside their business.

The temptation on both sides is to go to an extreme. One camp, in the name of optimisation, wants to rip humans out of workflows and hand entire functions over to agents. The other camp wants to wait, watch, and delay until things settle — play it safe. The data says both are mistakes.

MIT’s NANDA initiative studied this through 2025 and found that around 95% of enterprise generative AI pilots deliver no measurable impact on the P&L, despite $30 to $40 billion spent. The 5% that did succeed shared a common pattern: they kept scope tight, focused on back-office automation where the ROI was clearest, and worked with specialised external partners rather than trying to build everything in-house. Firms that bought and partnered succeeded roughly twice as often as those that built internally.

A Harvard Business School field experiment with BCG consultants sharpened the point further. For tasks inside AI’s current capability frontier, consultants using AI completed 12 percent more work, 25 percent faster, at higher quality. For tasks outside that frontier, the same consultants using AI were 19 percent less likely to get the right answer. Same people, same tools, different task. AI made them worse when the task was not the right fit.

Now layer in the regulation piece. The EU AI Act’s core obligations for high-risk AI systems come into force on 2 August 2026. Article 14 of the Act uses a specific phrase that deserves attention: “human-in-command.” It is not a suggestion. It is a statutory requirement that high-risk AI — which includes recruitment, performance evaluation, credit decisions, medical diagnostics, and critical infrastructure — must be designed so a human can meaningfully supervise, intervene, and override. India is not there yet, but every serious jurisdiction is moving in the same direction.

Put it together, and the picture is clear. The research says narrow deployments work, broad ones fail. The research says AI amplifies humans on the right tasks and actively harms them on the wrong ones. The regulation says, at least in high-stakes domains, a human must remain in the loop by law.

The firms that will win the next few years are not the ones going all-in on autonomous AI, and not the ones hiding from it. They are the ones building a hybrid operating model now, keeping judgment with humans and leveraging AI, and getting good at the handoff long before it becomes mandatory.

F1 worked out that an elegant hybrid is harder to build than a pure ICE or a pure EV. But it is the only architecture that keeps the sport competitive, relevant, and regulation-ready at the same time.

The same logic applies to your business.

If you are a CXO or founder thinking through how to sequence AI inside your organisation without getting ahead of your team, your customers, or the regulators, happy to talk. Reach me at v@thefoundercatalyst.com or connect on LinkedIn: linkedin.com/in/venkatarangan

April 24, 2026
Avoiding AI Overkill: Right Tool for the Right Job

The most expensive mistake in AI adoption is not using it correctly. It is using it on the wrong problem.

I spent 100 minutes and nearly 19,000 tokens asking Claude Code to clean up my Windows PC — old .NET runtimes, NVIDIA CUDA versions, Visual Studio build tools — figuring out what to keep, what to drop, what to upgrade.

It worked. Mostly. With a lot of permission dialogs, a few course corrections, and some very patient clicking on my part.

Then I realised: Bulk Crap Uninstaller and Winget, two free Windows tools, would have done the same job in under ten minutes.

That is not a complaint about the AI. Claude Code did something genuinely impressive. It reasoned through a messy, undocumented environment and wrote shell scripts without me spelling out every step. That kind of judgment in ambiguity is a real capability.

But I had handed a magic hammer to a problem that had always had a perfectly good screwdriver.

The mistake is not reaching for AI when things get complex. It is reaching for it when things are complex but well-mapped. Those are very different situations, and most adoption decisions right now are not making that distinction.

LLMs are extraordinary when the environment is unpredictable, requirements are shifting, and no existing tool can handle the ambiguity. They are expensive, slow, and overkill when the problem is well-understood and reliable tooling already exists.

This shows up everywhere. For founders, it is deploying AI agents for sales outreach before figuring out why your best customers actually buy. For operators, it is building an AI layer on top of a workflow that a better-designed spreadsheet would have solved.

The pattern holds at scale, too. For global logistics teams, it is asking a model to interpret shipment delay patterns every morning, when a dashboard threshold alert would have caught the same issues faster and more cheaply. For large enterprises, it is routine to route quarterly demand forecasts through a model when the data is structured, the variability is low, and a well-tuned statistical model has always been the right tool.

This is where the technology stands today. The boundary between what needs a model and what does not is shifting, and anyone who tells you it is fixed is not paying attention. The right answer next year may not be the right answer now. Stay curious, stay sceptical, and keep re-evaluating.

Not every hard problem is ambiguous. And not every ambiguous problem needs a model.

Use the magic deliberately.

April 7, 2026
Your competitors are going YOLO on AI, are you?

This week, a quiet but important shift happened in the developer tools world. It hints at a wider change in how software itself will be developed and delivered.

Microsoft announced that Visual Studio Code will now ship stable releases every week instead of monthly. Around the same time, developer tools from both Microsoft and Google began introducing a new capability inside coding assistants — AI agents that can act without waiting for human approval. In tools like Copilot and Gemini Code Assist, these agents can execute commands, modify files, retry failures, and continue working until they believe a task is complete. No hand-holding. No checkpoint at every step.

Developers have started calling this “YOLO development.” The phrase borrows from the pop culture line “You Only Live Once“, and in this context means letting AI run without pausing for human sign-off at every turn. That may sound reckless. But beneath the humour, something real is changing — and for founders, the implications are worth understanding clearly.

For most of the history of software companies, development velocity was a function of people. More engineers meant more output. Faster hiring meant faster product. The development loop — write, test, review, ship — was a human loop at every stage, and headcount was the lever you pulled when you needed to go faster. That assumption is quietly breaking down.

What’s emerging looks meaningfully different. A human defines the goal, the constraints, and the expected outcomes. AI agents implement the changes. Automated systems run checks. Humans validate whether the result matches the intent. The human is still essential — but the role has shifted from executor to decision-maker.

For a founder, that distinction matters enormously. It means the ceiling on your engineering output is no longer tied to the size of your team in the way it once was.

But there’s a catch, and it’s worth being honest about it. AI can produce code across hundreds of files in minutes. That speed creates a new kind of problem: if the machine generates changes faster than your team can safely validate them, generation is no longer the constraint. Verification is.

AWS CTO Werner Vogels calls this verification debt — the gap between how fast AI can produce changes and how fast an organisation can confirm those changes are correct, secure, and doing what was intended.

This is where most companies will either pull ahead or fall behind. The bottleneck hasn’t disappeared. It has moved. And the founders who recognise where it now sits — and build systems around it — will have a structural advantage over those still thinking about AI purely as a way to write code faster.

The deeper shift worth understanding is this: software engineering is moving from code-centric to specification-centric. Instead of writing every line, engineers define the objective, the boundaries, and the expected behaviour. The AI generates and iterates on the implementation.

It happened in chip design decades ago. Engineers stopped drawing circuits by hand and began describing systems at a higher level of abstraction. The tooling handled the translation. Productivity leapt. The nature of the work transformed permanently.

Software is entering the same phase now. The future isn’t YOLO. It’s AI on a leash — running fast, but you’re still holding the lead.

For founders, this reframes the opportunity entirely. The question is no longer how do I use AI to write code faster. That’s a commodity improvement. The real question is: what can I now build that I couldn’t before — because development effort and cost are no longer the constraint?

Some businesses will use this to go deeper into their existing product — adding complexity, personalisation, and capability that was previously too expensive to build. Others will use it to serve more customers without proportionally growing their team. Both are legitimate. Both represent compounding advantages that don’t show up immediately but become significant over eighteen to thirty-six months.

The metrics to watch are not about AI adoption. They’re about business outcomes. Has the turnaround time of a key workflow come down? Has the team’s capacity to serve clients increased without a corresponding increase in cost? Has resolution time improved? Has your release cadence changed? These are the numbers that eventually show up in revenue, margins, and growth — and right now, most founding teams aren’t measuring them with enough discipline.

So the real question isn’t whether your developers are going YOLO. It’s whether you, as a founder, are willing to go first.

March 13, 2026
Running Generative AI Models Locally Is Now Easy

Want to explore Generative AI without sending your data to the cloud? Running models locally on your PC or Mac is now surprisingly simple.
Start with Ollama, a lightweight framework that makes it easy to run AI models locally on your computer. It supports both open-weight models and open-source models. Together, these models have made hands-on AI experimentation accessible to anyone with a capable machine, without depending entirely on cloud APIs. Ollama recently added a clean, chat-style desktop app to its command-line interface. I’ve been testing OpenAI’s GPT-OSS 20B open-weight reasoning models, which run locally through Ollama and even support limited web search. For routine tasks — summarising, drafting, quick analysis — it’s remarkably capable.
Sample Output from GPT-OSS model running locally on a PC with Ollama
Another option is Msty Studio, a free tool (for non-commercial use) that gives a visual interface to run these open models on your system. It connects seamlessly with Ollama and supports models from Hugging Face. Ideal for hands-on experimentation before making big AI bets. Why does this matter for founders and CEOs? 1. Data Privacy and Control – Running locally means your business data never leaves your system. For regulated industries, that’s a real advantage. 2. Independence and Learning – Most AI transformations rely on commercial APIs. Testing open models locally helps your team learn, experiment safely, and reduce vendor dependence. 3. Strategic Experimentation – Founders who explore AI hands-on make sharper choices about when to buy, build, or partner. It’s the difference between hearing about AI and understanding it. Two things to note: 1. Large models need powerful hardware — GPUs with high VRAM or plenty of system memory. 2. “Open” doesn’t mean “risk-free.” You’ll handle updates, licenses, and security yourself. Local AI isn’t replacing the cloud — it’s giving leaders another layer of control and insight. I cover these shifts — from cloud dependence to local AI — in my keynotes for decision makers. To invite me for a corporate talk, contact Kural at leadership@kuralkonnect.com

October 6, 2025
Top Three Trends in Generative AI Applications for Enterprises in 2024
Generative AI is revolutionizing the business landscape, enabling enterprises to streamline operations, enhance decision-making, and create innovative solutions. In 2024, several key trends have emerged as organizations integrate generative AI into their workflows. Let’s explore the top three trends shaping the adoption and utilization of generative AI applications in enterprises.
1. Customization Over Off-the-Shelf Solutions
Enterprises are moving away from purchasing off-the-shelf generative AI applications. Many of these pre-packaged solutions are simply wrappers around foundational models. Instead, businesses prefer to take foundational models from industry giants like Microsoft, OpenAI, Google, and other cloud providers and customize them. By doing so, they can seamlessly integrate AI capabilities into their existing enterprise applications, leveraging their deep understanding of their own business processes. This tailored approach allows for greater flexibility, ensuring that the AI solutions align precisely with organizational needs and objectives.
1. Cautious Internal Use with a Shift Toward Customer Integration
In 2024, enterprises are effectively deploying generative AI tools within their internal operations. These applications are being used for a variety of use cases, such as automating workflows, enhancing data analysis, and improving productivity. However, companies are exercising caution when it comes to exposing these AI tools directly to end customers. Concerns about data privacy, security, and the maturity of AI models have contributed to this cautious approach. Nevertheless, this trend is expected to shift in the later months of 2024 and into 2025, as enterprises gain more confidence in the technology and work to mitigate associated risks.
1. Shifting Budget Allocations
Historically, budgets for generative AI applications have been drawn from innovation funds. This trend is changing as AI becomes a more integral part of standard business operations. In the coming months, we will see a shift in budget allocations, with funding for generative AI applications coming from regular software development and maintenance budgets. This change reflects the growing recognition of AI’s value in everyday business functions and the need for sustainable funding models to support its ongoing integration and development. As generative AI continues to evolve, enterprises are adapting their strategies to maximize its benefits. Customization of foundational models, cautious internal use with a gradual move towards customer integration and shifting budget allocations are key trends that will shape the landscape of generative AI applications in 2024 and beyond. By staying abreast of these trends, businesses can harness the full potential of generative AI to drive innovation, efficiency, and growth.
August 12, 2024
Understanding AI’s Role in the Evolving Workplace

As a founder or business leader, understanding the evolving nature of work and AI’s role is crucial. The “2024 Work Trends Index Annual” Report by Microsoft and LinkedIn published on May 8, 2024 offers valuable insights into AI adoption across sectors. Let’s dive into the essential takeaways every business leader should know. Bring Your Own AI A crucial trend from the report is the rise of BYOAI (Bring Your Own AI). Employees across all age groups are increasingly using their own AI tools at work. This trend is widespread, not limited to tech-savvy individuals. In fact, 75% of knowledge workers around the world use generative AI at work, making it a norm rather than an exception. Power Users Lead the Way As employees become more comfortable with AI, power users are emerging as champions of innovation. These individuals are familiar or extremely familiar with AI, using it at least several times a week and saving more than 30 minutes a day. They report substantial benefits from using AI, including a more manageable workload (92%), boosted creativity (92%), improved focus (93%), and increased motivation (91%). The Struggle is Real Despite the advantages of AI, many employees are still struggling to keep up with the pace and volume of work. The report reveals that 68% of employees struggle with the pace and volume of work, leading to burnout for nearly half (46%). This isn’t just a personal issue; it’s a business concern. AI can alleviate these pressures by automating routine tasks, freeing up time for creative and strategic work. As Neuroscientist Michael Platt notes, “AI can help liberate workers from menial tasks, fostering innovation and creativity.” The Training Gap While power users are leading the way in adopting AI, there is still a significant training gap to be bridged. Only 39% of employees who use AI at work have received company-provided training, indicating a need for more support and resources. Conclusion The future of work is here, and AI is leading the charge. As a founder or business leader, understanding the evolving nature of work and AI’s role is crucial. BYOAI is a growing trend, and by prioritizing AI training, upskilling, and reskilling your employees, you can position your business for success in an increasingly AI-driven world. Let’s leverage these insights to foster a more innovative, efficient, and motivated workforce.

May 21, 2024