Hands-on experience and microlearning accelerates AI skill acquisition
The world moves fast. AI moves faster. If your team is stuck in the past with traditional training paths, you’re already behind. Our current university systems weren’t built for this level of rapid change. They move too slowly, and they’re not designed to update in real-time. So, if you’re relying on long courses or formal credentials to build AI capability internally, you’re letting time and opportunity slip. That’s not how transformation happens.
Justice Erolin, CTO at BairesDev, said it well: traditional education can’t handle AI’s speed. You need practical experience, work that gets your team directly involved with current tools, models, and data. Pair that with short bursts of focused learning, microlearning, and you’ve got something powerful. Small, intense, and continuous. It fits the rhythm of real work, and it scales inside your organization without slowing down operations.
For executives, this is about speed and precision. You don’t need mass retraining. You need targeted, high-impact capability-building that lets your engineers solve today’s AI problems tomorrow, not three quarters from now. When teams work on live projects alongside peer-led knowledge sharing, they stop waiting to be told how AI fits in. They learn by doing. That’s where leverage happens.
Upskill existing employees to build AI-ready teams
You don’t need a room full of PhDs to start using AI effectively. What you need are people who understand your data, your business, and how to move fast. Mike Loukides, VP at O’Reilly Media, hit the mark: with focused training, most organizations already have people who can do this work. Especially your data engineers. They know how to build and manage the pipelines. They’re close to the ground, and they move fast when you don’t slow them down with red tape.
This approach matters. Hiring AI “experts” is expensive, and most of them don’t know your business. Even worse, the skills they bring become outdated, quickly. AI doesn’t have a five-year roadmap anymore. It has a five-week roadmap. Training the right people already inside your company lets you skip the onboarding ramp and get straight to shipping usable results.
As a decision-maker, you own the pace. Don’t default to outside hires as a shortcut. Invest in the people who know your infrastructure, your customers, and your deadlines. Give them tools. Give them access. Let them run. You’ll get faster integration, better alignment with your goals, and you’ll do it without adding unnecessary complexity to the organization structure. That’s how you stay sustainably competitive.
Continuous learning is essential for staying current in AI
If there’s one thing true about AI right now, it’s that the truth doesn’t stay the same for very long. New models, new methods, new problems, every month, something shifts. It’s not a one-time adjustment. This is a system in motion, and if your learning infrastructure isn’t built to move with it, you’re not going to keep up.
David Brauchler, Technical Director and Head of AI and ML at NCC Group, described it simply: progress in AI is consistent, not occasional. It builds on itself constantly. Mike Loukides from O’Reilly Media added that even if you hire external experts, their knowledge starts aging the day they walk in. This isn’t a space where you can train once and call it done.
For leaders, this means rethinking how learning fits into your operating model. Internal knowledge doesn’t grow by accident. It grows by backing continuous access to real-time updates, team-led learning loops, and open knowledge sharing. When you cut learning into the daily workflow, small releases, internal talks, regular exposure to live tools, you get compound progress. Static skill sets don’t survive here. The edge belongs to the companies that never stop improving inside.
Cross-functional collaboration and hands-on experimentation drive innovation
AI isn’t just a tech initiative. It interacts with decisions across products, customers, operations, risk, everything. You won’t get the full value of AI if decisions around it happen in isolated teams. Collaboration across departments creates friction sometimes, but it also creates better execution.
Vamsi Duvvuri, Technology, Media & Telecommunications Leader at EY Americas, points to something important: when you bring together people from different teams, especially those who don’t normally work together, you uncover assumptions, spot gaps, and build more resilient solutions. During AI experimentation, that mix of input leads to smarter models and fewer blind spots. It also pushes teams to create tools that scale across the organization, not just solve isolated use cases.
If you’re running the business, this shouldn’t just be allowed. It should be a priority. Set up cross-functional sprints. Put people from opposite sides of the organization on the same AI test cases. Let them work on shared outcomes. This builds competence fast, and aligns new capabilities with your actual business needs. It breaks echo chambers and supports innovation without overengineering the culture.
Incorporating innovative talent accelerates AI integration and cultural shifts
There are moments in technology where outside perspective is just as valuable as internal expertise. AI is one of those moments. If you only rely on internal hires that fit the existing mold, you’ll reinforce the habits you’re trying to evolve. To move faster, and differently, you need people who challenge the assumptions your teams haven’t questioned yet.
Vamsi Duvvuri, EY Americas’ Technology, Media & Telecommunications Leader, makes this clear, bringing in challenger hires or executing acquihires of focused AI teams doesn’t just fill gaps. It reshapes thinking. People from startups or experimental teams often operate without the friction of legacy assumptions. They bring speed, clarity, and simplicity in how they solve complex problems. Their mindset helps establish a learning culture that pushes others to think and act in ways that align with where tech is actually going, not where it used to be.
For executives, this isn’t about hiring for mass. It’s about strategic bets. A few well-placed individuals or small teams can become strategic multipliers. They raise the standard. They accelerate peer learning. If you want transformative change instead of incremental adaptation, bring in people who refuse to settle for existing structures. Then give them enough scope to influence critical projects.
Soft skills and human judgment are crucial for effective AI collaboration
AI can process data, but it doesn’t understand context, goals, or nuance. That’s where your people matter. Technical competence is essential, but it’s not enough. The ability to make smart trade-offs, ask good questions, and know when not to use AI, that requires critical thinking, industry knowledge, and leadership judgement.
Justice Erolin, CTO at BairesDev, focused on this balance. Knowing how to push AI to generate outputs is one thing. Knowing when and where to apply those outputs responsibly inside the business is another. As AI embeds itself deeper into core workflows, soft skills, strategic thinking, communication, cross-functional understanding, are now operational requirements, not secondary traits.
If you’re leading transformation, invest in these skills with intention. Technical training gets you tools. Soft skills make those tools useful. The teams that win with AI won’t just be the ones that know how to use it, they’ll be the ones that know why, when, and with what constraints. Prioritize that judgment. It scales faster than code.
Core AI-related skills are essential across all roles
AI isn’t something a single team owns. It’s becoming a horizontal capability that influences every unit, engineering, marketing, product, compliance. To support that kind of integration, there are baseline skills everyone working around AI should understand.
Justice Erolin, CTO at BairesDev, laid out the essentials: prompt engineering for generative models, evaluation and fine-tuning of AI outputs to align with business logic, MLOps for deploying systems at scale, and a deep grasp of AI ethics and governance, how to manage bias, ensure explainability, and protect data privacy. Technical frameworks like TensorFlow, PyTorch, and LangChain aren’t just for specialists now; anyone working closely with AI needs a working knowledge of how they function.
For leadership, investing in these universal competencies is not about turning everyone into AI experts. It’s about equipping your teams with the foundational understanding required to navigate risks, spot opportunities, and contribute to AI strategy. Whether your focus is scale, speed, or stability, this knowledge reduces execution drag by ensuring teams speak the same language. It removes friction from decision-making and aligns more people with the outcomes that matter.
Single-event AI training is insufficient; ongoing education is critical
Training your teams once and calling it complete is a mistake. With AI, capability decays faster than expected because the field doesn’t pause. New models appear. Tools change. Best practices from six months ago are no longer best. If your learning systems don’t evolve, your teams fall behind, even when they’re full of smart, capable people.
Vamsi Duvvuri from EY Americas warned against this trap directly: when companies rely on isolated training events, a workshop, a certification, or a one-off coaching session, they create a false sense of readiness. They tick the box without actually building durable skill. The result? Stagnation.
Executives need to treat ongoing learning as a strategic discipline. Not just access to new content, but cycles of reflection, guided experimentation, and exposure to disruptive talent that sets new expectations across teams. The organizations that outperform aren’t necessarily the ones with more resources. They’re the ones that treat learning as continuous infrastructure. If you build that mindset into your teams, everything else scales with less resistance.
Application teams must address new security risks introduced by AI
As AI becomes embedded across software systems, application teams face security concerns that didn’t exist in traditional architectures. AI doesn’t differentiate between trusted and untrusted inputs the way developers might expect. That creates risk. It opens up new vectors for unreliable outcomes, data leakage, and compromised logic.
David Brauchler, Technical Director and Head of AI and ML at NCC Group, made the case clear: AI requires updated threat modeling. Old security frameworks aren’t enough. You need to think about how AI interacts with content, how it’s trained, and how it’s deployed. And early-stage experiments, especially those conducted in low-sensitivity environments, are the best place to safely develop these security practices before critical exposure occurs.
For executives, the takeaway is operational priority. If you’re building AI tools without simultaneously building new guardrails, you’re leaving holes that attackers and regulatory scrutiny will eventually find. Align your engineering, security, and compliance teams early in the development cycle. Secure what you scale. Fixing AI-related risk downstream costs more than preparing for it upfront.
Organizational adaptability is the ultimate determinant of success in the AI era
You can have all the latest models and tools, but if your teams can’t adapt, they become obsolete fast. This is one of the most underestimated realities in AI transformation. Your real competitive edge doesn’t come from the codebase. It comes from how quickly your people update what they know, drop what no longer works, and absorb what’s next.
Justice Erolin, CTO at BairesDev, said it plainly: the companies that win will be the ones with the most adaptable teams. Execution speed, learning velocity, and willingness to challenge internal norms, these aren’t soft traits. They’re success criteria now. BairesDev has embedded this mindset into its own operations, helping global partners do the same.
As a senior executive, your job is to make sure adaptability isn’t accidental. It’s built into incentives, structure, and leadership behavior. Create conditions where learning is constant, experimentation is encouraged, and failure doesn’t trigger paralysis. The only real risk in this environment is standing still. Everything else can be adjusted.
Final thoughts
You don’t need to chase every breakthrough in AI to stay competitive, you need a team that can keep pace with change. That means building systems where learning isn’t a box to check but a part of how your people work, think, and evolve. Traditional training models won’t get you there. Siloed teams won’t move fast enough.
What matters now is adaptability. Invest in hands-on development, cross-functional collaboration, and lightweight learning infrastructure that fits into real work. Bring in talent that challenges norms. Prioritize security as AI systems scale. And above all, make speed-to-capability a strategic focus.
The landscape will keep shifting. The difference between companies that thrive and those that stall isn’t who has the biggest models, it’s who learns and moves faster than the rest. You control that advantage.
