Practice Curiosity Every Day

The one thing I never want us to lose is curiosity—not as a value on a slide, but as a discipline we practice. The biggest threat isn’t time or bandwidth. It’s quiet hesitation—the worry that trying something new is a distraction, that being wrong is a setback, and that experimenting is time wasted. It isn’t. Stay curious. Don’t let that hesitation temper it. - Kimberly Bloomston, 6sense

Create Safety Around Learning Curves

Build psychological safety around the learning curve by creating deliberate forums for collective signal extraction and rewarding cross-pollination of failures and best practices alike. This makes upskilling a force multiplier that improves team efficiency and the quality of outcomes for customers without the “adopt or perish” mandates that fuel fear-based compliance, not capability. - Bala Vadlamani, BSV Advisory & Media

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Tie Skill Development To Meaningful Work

A key leadership practice is recognizing that skill-building is deeply personal. AI can recommend what to learn, but it can’t understand individual aspirations, fears or context the way a leader can. By taking the time to tailor development paths and connect them to meaningful work, leaders keep teams both engaged and productive, and learning becomes part of execution, not a distraction from it. - Brij Mohan, LPL Financial

Build ‘Teach What You Learned’ Into Shipping

Make “teach what you learned” part of shipping, not a separate initiative. When someone uses AI to solve something faster, debug something tricky or try a new approach, they share a quick note about what they tried, what worked and what surprised them. A Slack post, a Loom, a PR comment—whatever fits the flow. No polish required. - Sameer Kankute, BerriAI

Connect Continuous Learning To Real Projects

Make learning part of the work, not a separate event. Leaders should encourage small, continuous skill-building tied directly to current and planned projects so teams learn new, specific and relevant AI capabilities while delivering outcomes, rather than pausing execution to retrain. - Eric Helmer, Rimini Street

Build Daily AI Experimentation Habits

AI is changing how work gets done, but success depends on building new habits, not just knowledge. Teams should regularly ask if AI can help and test it in their workflow. Even 15 minutes a day builds familiarity. Leaders play a key role by encouraging consistent use, turning AI into a natural part of daily work and strengthening skills over time. - Ann Blakely, Baker Tilly

Start With Business Outcomes

Work backward from customer value. Before chasing any AI tool or skill, have teams name the business outcome and prove it on a real problem in a short timeframe. If a metric moves, invest deeper; if not, document and move on. This keeps learning embedded in execution, allowing teams to compound capabilities without losing shipping velocity. - Hemanga Nath, Amazon

Align AI Policies With A Shared ‘North Star’

With staff buy-in, create one overarching objective as to what the company will achieve through AI, and ensure your policies and procedures are aligned with enabling that outcome. Make it a meaningful achievement. Most organizations have business units that are each running in their own directions with shiny new use cases. Make sure they are linked with your overarching North Star with AI. - Frank Carnevale, iGreenTree Inc.

Model ‘Learning By Doing’

The best leaders model “learning by doing.” They use AI themselves, make upskilling a priority and create a safe environment for teams to share both successes and failures. Giving people time to learn may feel like a slowdown, but it often comes back tenfold in better execution. - Heather Bassett, Xsolis

Turn Human Oversight Into Continuous Learning

One effective leadership practice is to treat human-in-the-loop systems as continuous learning engines, not just oversight checkpoints. Define clear thresholds for human intervention and capture those moments as feedback to improve both AI performance and team expertise. This embeds skill-building into daily work, shifting teams toward higher-value roles without slowing execution. - Michael Ringman, ibex

Incentivize Cross-Team Knowledge Sharing

Given the pace of AI model innovation, leaders should follow a three-principle approach of cross-team collaboration, incentivized learning with sharing, and adopting a mindset of accepting, sharing and embracing failures. Cross-team collaboration promotes the sharing of knowledge and use cases. By incentivizing learning with sharing, an effective AI-driven knowledge base will scale learning for organizations. - Buyan Thyagarajan, Eigen X

Pair Junior And Senior Team Members

Pair senior and junior people on real client work. Learning happens fastest under real conditions. You don’t need a formal training program for every new tool. Give people room to experiment inside actual delivery. They stay sharp, and the work keeps moving. - Stoyan Mitov, Dreamix

Build Both Breadth And Depth

Encouraging T-shaped development—deep expertise in one domain and working knowledge across several—keeps teams adaptable as AI absorbs routine specialization. Leaders who rotate engineers across problem spaces build the cross-functional judgment AI tools can’t replicate. Breadth becomes the competitive skill; depth remains the anchor. - Kevin Cushnie, MC Systems

Manage Change, Not Just Upskilling

One effective leadership practice is investing in change, not just employee upskilling. Leaders should clearly communicate why AI is being adopted, define what success looks like and create continuous feedback loops. This approach not only helps teams build relevant skills in real time, but it also improves clarity and oversight and integrates learning into daily workflows without slowing execution. - Joe Depa, EY

Strengthen Human Skills AI Can’t Replace

AI will continue to replace the repeatable. The teams that stay relevant aren’t the ones that learn about AI—they’re the ones that continuously clarify what human judgment, creativity and accountability still mean in their specific domain. Soft skills and attention to detail will be required more than ever. - Austin Berglas, BlueVoyant

Build AI Agents Alongside Your Team

I hold my Fridays open to build agents alongside anyone who wants to learn—not to lecture, but build and learn alongside them. The “aha” moment is the unlock. But it’s on me to make sure people get there. Until they do, the skepticism isn’t really about the technology. It’s self-preservation dressed up as skepticism. - Paul Deraval, NinjaCat

Publish Clear AI Adoption Principles

Publish a clear team manifesto that separates AI adoption from execution pressure. The standard stays the same: quality and speed remain the same or improve. When that expectation is explicit, teams stop treating learning and delivery as competing priorities and start treating AI fluency as part of the job, not a distraction from it. - Maitrik Patel, Apple

Preserve Critical Thinking Through Manual Practice

Create a “weekly challenge” ritual where team members solve one problem without AI assistance. This builds critical thinking muscles that atrophy when we overrely on AI tools. Start with low-stakes scenarios—a code review, a design critique or troubleshooting done manually. Skills sharpen when we regularly exercise them, and this practice takes just an hour while keeping human judgment sharp. - Nithin Sonti, BrowserOS

Create A Centralized AI Ecosystem

Three aspects matter. First, build a centralized ecosystem to develop AI use cases. Second, design interoperable agents that collaborate to achieve outcomes. Third, use AI to augment knowledge, not replace judgment. Experience must guide decisions and ensure people solve problems using insights, not just echo AI outputs. - Hari Sonnenahalli, NTT Data Business Solutions