How to Stay Relevant in AI: 10 Experts Share Career Advice

In artificial intelligence, yesterday’s expertise can quickly become tomorrow’s outdated knowledge. A framework that once gave a professional an edge may be superseded by a new model, a familiar workflow may become automated and the skills that helped someone advance may no longer be enough to move them forward. For professionals at every stage of their careers, staying relevant means rethinking not just what to learn, but how to learn.

But how do you keep pace with a field that never seems to stand still without spending every waking hour chasing the next breakthrough? And how can experienced professionals distinguish between skills worth investing in for the long term and those they need to continually refresh?

Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, explore what it takes to build lasting expertise in a fast-changing field—from knowing when to challenge established assumptions to recognizing which skills to leave behind.

Learn by Building and Deploying Real Systems

Maitrik Patel, Sr. Engineering Manager at Apple, argues that continuous learning becomes valuable when it is connected to real work.

“The only continuous learning that compounds is learning connected to something real,” he says.

Patel believes the most important lessons emerge through deployment, unexpected failures and investigation. 

“Reading papers and following researchers are useful background,” he says. “The understanding that sticks comes from deploying something, watching it fail in ways you did not expect, and tracing why.”

That approach is particularly important in AI, where a successful demonstration does not necessarily translate into a dependable production system. Data quality, latency, integration challenges and unexpected outputs can expose weaknesses that tutorials never reveal.

“The professionals who stay relevant are the ones who keep shipping things, not just read about them,” he says.

“Spend less time chasing every trend and more time building, testing and reflecting.”

– Andre Shojaie, Founder of HumanLearn

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Learn How to Learn, Not Just Which Tools to Use

Andre Shojaie, Founder of HumanLearn, emphasizes the importance of learning strategies that remain useful as individual technologies change.

“The best AI professionals focus on learning how to learn,” Shojaie says. “The field changes rapidly, but the fundamentals, such as problem-solving, data literacy, systems thinking, and understanding business or user needs, remain valuable.”

That perspective encourages professionals to resist the pressure to master every new model or framework. Instead, they can establish a repeatable learning process: Identify a question, explore the relevant technology, experiment with it and evaluate what the results mean for their work.

“Spend less time chasing every trend and more time building, testing and reflecting,” he says. “Read broadly, follow trusted researchers and practitioners, and dedicate time each week to hands-on learning.”

Equally important, he notes, is to develop skills in communication, ethics, product thinking and change leadership.

“AI will automate many technical tasks, but people who can connect technology to meaningful outcomes will remain in demand,” he says. “The most resilient careers belong to those who can adapt the fastest tomorrow.”

Manage Technical Knowledge Like a Portfolio

Rishi Katdare, Senior Technology Executive at Amazon Web Services (AWS), recommends treating professional knowledge as a portfolio whose components lose value at different rates.

“Treat technical knowledge like a portfolio with different half-lives,” Katdare says. “Some knowledge compounds for years, including systems thinking, security, economics and customer judgment.”

Other skills, including specific frameworks, model APIs and implementation patterns, may depreciate much faster. Professionals who invest equally in every new development risk spending their time repeatedly relearning skills with short useful lives.

“Spend most of your effort deepening durable principles, refreshing fast-moving skills through hands-on use, and reserving time for adjacent capabilities before they become part of your job,” he says.

Periodic reviews can identify which tools merit deeper study and which developments can safely be ignored.

“The goal is not to know every release,” Katdare says. “It is to know what should compound, what should be replaced and when.”

Validate AI Against Business Outcomes

Steve Taplin, CEO of Sonatafy Technology, Inc., argues that the biggest career risk is not technological change itself. It is a reluctance to question established expertise.

“The biggest threat to your AI career isn’t that technology will replace you. It’s that someone willing to challenge everything you know will,” Taplin says.

He cautions against confusing demonstrations, certifications and familiarity with the latest tools for meaningful learning: “That’s not learning. That’s spectating.”

Instead, Taplin urges professionals to take a real business problem, build something against it and determine how to measure success before starting.

“That last step is the one people skip, and it’s what I call the ‘AI Validation Gap,’” he says. “Those who can prove whether AI output is correct, and whether it moved revenue or cost, will stay in demand long after today’s tools are gone.”

The lesson is to make measurable outcomes part of the learning process.

“Your experience doesn’t make you future-proof. Your willingness to make it obsolete does.”

Master the Problem Before the Technology

Hastimal Jangid, Co-Founder of RankRabbit AI, approaches continuous learning from the perspective of problem-solving rather than tool mastery.

“Learn the problems, not the tools,” Jangid says. “Every framework and model you master today will look different in two years, but the questions underneath barely change: What is this system good at, where does it fail and how would I know?”

Jangid recommends building something small and practical every few months. Reading about a technique introduces its terminology, he notes, but applying it to an actual problem reveals its weaknesses, trade-offs and appropriate uses.

“Using it on an actual problem teaches you where it breaks, and that’s what employers and clients pay for,” he says.

He also warns experienced professionals against becoming detached from implementation.

“Stay close to the work, too,” Jangid says. “Judgment gets rusty when you only supervise. Keep one hands-on project going, even if your title says otherwise.”

“Most of us use AI as a source of information. Try using it as a learning partner. ”

– Daria Rudnik, Team Architect and Executive Leadership Coach at Daria Rudnik Coaching & Consulting

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Turn AI Into a Personal Learning Partner

Daria Rudnik, Team Architect and Executive Leadership Coach at Daria Rudnik Coaching & Consulting, recommends using AI not only to complete tasks but also to accelerate the learning process.

“Most of us use AI as a source of information. Try using it as a learning partner,” Rudnik says.

When approaching an unfamiliar technology or subject, professionals can ask AI to explain concepts at different levels, challenge their understanding and help them apply ideas to practical problems. They can request examples, compare competing approaches and explore why a proposed solution might fail.

“Learning should always be an active process of questioning, testing your assumptions and practicing,” she says. “Simply consuming information isn’t enough.”

Rudnik sees particular value in the opportunity to practice even when a human expert is unavailable. A learner can use AI to generate exercises, simulate challenging conversations or work through scenarios that gradually increase in difficulty.

“AI gives us the opportunity to learn faster,” Rudnik says. “But it also helps us practice and test our knowledge and understanding, even when we don’t have direct access to an expert in the field we’re trying to learn.”

Used thoughtfully, AI can make continuous learning more interactive and accessible.

“Anchor your learning in durable fundamentals such as statistics, systems, optimization and software engineering.”

– Dhyey Mavani, Generalist, Agentic AI at Abridge

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Strengthen Fundamentals While Experimenting With New Tools

Dhyey Mavani, Generalist, Agentic AI at Abridge, emphasizes the value of combining technical foundations with practical experimentation.

“Anchor your learning in durable fundamentals such as statistics, systems, optimization and software engineering,” Mavani says, “then continually build small things with the newest tools.” 

These disciplines help professionals understand not just how a particular tool works, but why an approach succeeds or fails.

That foundation becomes especially useful as AI systems grow more complex. Understanding probability helps professionals reason about uncertainty; systems thinking clarifies dependencies; optimization helps frame trade-offs; and software engineering supports reliable implementation.

“The best way to stay relevant is to pair deep foundations with a habit of testing new capabilities firsthand rather than learning them only through commentary,” he says.

The combination avoids two common traps: becoming so focused on theory that knowledge never reaches practical use or becoming so focused on new tools that the underlying concepts remain poorly understood.

Professionals can put this advice into practice by choosing a foundational concept, applying it in a small project and comparing different approaches using current tools. That cycle strengthens both understanding and adaptability.

Review Your Skills for Obsolescence Every Quarter

Bhubalan Mani, a Leader in Supply Chain Technology and Analytics, suggests managing your skills like perishable inventory.

“Every quarter, run an obsolescence review on yourself. List the tasks the newest models now handle well, then write down the value of those skills, the way a planner reserves for aging stock,” Mani says.

Workday’s October 2026 workforce report states that demand for basic AI skills fell 25% after peaking in January, while demand for applied AI skills rose 51% between September 2025 and July 2026. The findings suggest that familiarity with AI tools alone may be less differentiating than the ability to build with them.

“The floor keeps rising, so move your learning one layer up,” he says. “In supply chain, planners who once earned their keep running MRP now earn it by resolving the exceptions the engine cannot.”

Mani recommends working on “unlearning,” too.

“Many prompt tricks from two years ago now hurt results,” he says. “Relevance is not what you know; it is how fast you retire what no longer pays.”

Prioritize Judgment Over Memorization

Egbert von Frankenberg, CEO of Knightfox App Design Ltd., brings a technology leadership and application design perspective to the challenge of staying current.

“In a field that churns this fast, invest in what doesn’t expire,” von Frankenberg says. “Tools and models depreciate; fundamentals compound: probability, optimization, how learning systems generalize, system design and deployment economics.”

For him, strong fundamentals make it easier to learn new frameworks quickly without rebuilding expertise from scratch. He also emphasizes that reading alone is insufficient: “AI is empirical, so you learn by shipping something and watching it fail, which turns fleeting information into durable judgment.”

That judgment becomes more important as AI makes coding, research and information retrieval easier. Professionals still need to decide which problems deserve attention, what evidence supports a proposed solution and when AI is not the appropriate tool.

“Prioritize judgment over recall,” von Frankenberg says.

His advice also extends beyond individual study. “Learn in community and in public; a good network filters a firehose no one can track alone.”

Peer discussions, technical communities and public project write-ups can expose professionals to approaches they might not encounter independently. Sharing work also creates opportunities for feedback and helps make learning visible to colleagues and prospective employers.

“Stop chasing every specific, and become the person who can quickly make sense of whatever comes next.”

Build a Learning Routine Around People, Problems and Time

Satyabrat Chowdhury, Founding Member and Field CTO at CoreStack, recommends organizing continuous learning around three deliberate activities rather than an endless stream of updates.

“I split my learning time across three tracks,” Chowdhury says. “The first is people.”

Conversations with customers, architects and peers reveal how AI performs in actual organizations, including the problems, trade-offs and failures that product announcements rarely capture. Chowdhury says these exchanges offer a practical view of how AI behaves in real environments.

“The second is problems,” he says. “Rather than chase every advancement, I ask which problem a new capability solves, and that question rules out most announcements.”

The third track is time-boxed reading. Chowdhury sets aside a specific period each week to test releases that relate to issues raised by customers or peers. 

“Everything else can wait,” he says.

Chowdhury’s method turns continuous learning into a repeatable routine. The goal is not maximum consumption of information, but a steady flow of useful insights that improve decisions and results.

How to Stay Relevant in AI

• Build and deploy something real. Small projects reveal production challenges and limitations that reading and demonstrations cannot fully teach.
• Develop a repeatable learning process. Combine weekly experimentation with reflection, trusted sources and complementary communication and leadership skills.
• Invest in skills with long useful lives. Strengthen systems thinking, security, economics and customer judgment while refreshing fast-changing technical knowledge.
• Measure business outcomes, not just technical performance. Define success criteria before building and test whether an AI system delivers accurate, useful and measurable results.
• Learn the problem before choosing the tool. Build practical projects regularly and maintain enough hands-on experience to evaluate new technologies independently.
• Use AI to test your understanding. Ask it to explain concepts, challenge assumptions and generate practice exercises, then verify important claims and solve problems yourself.
• Pair fundamentals with experimentation. Use statistics, optimization, systems thinking and software engineering to understand why new capabilities work and where they fail.
• Review skill obsolescence every quarter. Identify tasks becoming automated, retire outdated methods and redirect learning toward evaluation, exception handling and domain expertise.
• Strengthen judgment and professional networks. Share projects, learn from peers and practice deciding which problems merit attention and when AI is inappropriate.
• Structure learning around people, problems and time. Learn from real-world experience, investigate relevant challenges and reserve a consistent weekly period for targeted experimentation.

Building a Career That Evolves

A career in AI comes with a moving target. New tools will emerge, familiar workflows will change and even experienced professionals will find themselves back in learning mode. That uncertainty can be exhausting, but it also creates an opportunity to keep growing, explore new areas of expertise and rethink how you contribute. Every new technology offers a chance to ask better questions, challenge old assumptions and discover where your experience can make the biggest difference.

The advice from the Senior Executive AI Think Tank points toward a sustainable way forward: Stay curious, keep your hands in the work and make learning a regular part of your routine. Some skills will need frequent refreshing, while others will deepen with every project and decision. Knowing the difference takes practice. So does letting go of methods that no longer serve you. Over time, those habits can help you navigate the next wave of AI change with greater confidence—and build a career that continues to evolve alongside the technology.