How to Build Strong AI Teams

When a company deploys an AI transformation, everyone fixates on the technology but here’s what is even more important. It's about the people. Over the years, I've developed a simple but powerful tool to evaluate teams for AI readiness. I call it my Will-Skill Matrix for AI! It’s taking a pre-existing model and customizing it for AI deployments based on 13 years of deployment experience. This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 𝗛𝗶𝗴𝗵 𝗦𝗸𝗶𝗹𝗹, 𝗛𝗶𝗴𝗵 𝗪𝗶𝗹𝗹: These are your champions - they have the technical capabilities and the hunger to drive AI adoption forward. 𝗛𝗶𝗴𝗵 𝗦𝗸𝗶𝗹𝗹, 𝗟𝗼𝘄 𝗪𝗶𝗹𝗹: Often your most technically brilliant people who resist change. They've mastered existing systems and see AI as either a threat or unnecessary complexity. 𝗟𝗼𝘄 𝗦𝗸𝗶𝗹𝗹, 𝗛𝗶𝗴𝗵 𝗪𝗶𝗹𝗹: Your enthusiastic learners. They may not understand neural networks, but they're eager to embrace AI-driven solutions. 𝗟𝗼𝘄 𝗦𝗸𝗶𝗹𝗹, 𝗟𝗼𝘄 𝗪𝗶𝗹𝗹: These team members neither understand AI nor want to adapt to it. They're comfortable in their current roles and see no reason to change. Here's the counterintuitive insight most leaders miss: The "Low Skill, High Will" group is your hidden treasure in AI transformation. I discovered this at one of my employers during a massive data overhaul. My most valuable contributors weren't always the data scientists with impressive credentials. Often, they were business analysts who couldn't code complex algorithms but brought boundless curiosity and deep business knowledge and a will to figure it out. Why does this matter? Because AI implementation isn't just a technical challenge - it's fundamentally a human change management project. In one particularly tough transformation, I spent months trying to convince resistant technical experts to embrace new methods. Meanwhile, I overlooked enthusiastic business teams eager to learn and adapt. The breakthrough came when I finally shifted focus. By empowering the "High Will" groups and pairing them with technical mentors, our implementation timeline was shortened by nearly 40%. This completely changed my approach to building AI teams. The most successful AI implementations don't just depend on having the best algorithms or the most data engineers. They depend on having people throughout your organization who are willing to reimagine what's possible - and who bring others along with them.

How can leaders transform their teams to be AI-first? It starts with mindset. An AI-first mindset means: Seeing AI as an opportunity, not a threat. Viewing AI as a tool to augment teams, not just automate tasks. Using AI to reimagine work, not just optimize work. As leaders, it’s on us to build this mindset within our teams. Here are 5 ways we do this at HubSpot: Use AI daily: Lead by example—trust grows when teams see leaders embrace AI themselves. I use it everyday and share very specific use cases with our company on how I use it. Now every leader is doing the same with their teams. The result is that we will have almost everyone in the company use AI daily by the end of year. Apply constraints: Give clear, focused challenges. We kept headcount flat in Support while growing the customer base by 20%+. Result - the team innovated with AI and over achieved the target. Smart constraints drive innovation. Establish tiger teams: Empower small, agile groups to experiment, innovate, and teach the organization. We have AI Tiger teams in every function - they share progress in Slack channels and there is so much energy with small groups experimenting and learning. Be a learn-it-all: Foster a culture of continuous learning. Share openly about successes and failures alike. We have dedicated 2 full days to learning and scaling with AI this quarter as a company - we have lined up great speakers, ways to experiment and gamified learning. Measure progress and share it: Measure which teams are completing learning modules, using AI everyday and share that openly. A little healthy competition goes a long way in driving AI-fluency. AI isn’t just a technology shift. It’s fundamentally reshaping how work gets done—and that requires shifting our mindset first. Leaders who embrace AI now will unlock creativity, performance, and impact. Are you building an AI-first mindset with your team? #Leadership #AI #Innovation #Mindset #FutureOfWork

AI is coming for your team's jobs. 𝘞𝘳𝘰𝘯𝘨! That's the narrative of fear and redundancy. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆? AI is a massive opportunity to multiply your existing talent, not just replace it. But many companies are getting it wrong. They're either: ► Freezing all spending, scared of making the wrong move. ► Looking at AI as a pure cost-cutting tool (i.e., who can we fire?). Both are paths to slow-growth and eventual failure. There's a 3rd option: 𝗧𝗵𝗲 𝗛𝘂𝗺𝗮𝗻-𝗔𝗜 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗥𝘂𝗻𝘄𝗮𝘆. It's a 90-day strategic plan to turn your current employees into an AI-augmented, high-leverage workforce. Instead of firing your Instructional Designers, you turn them into Human-Machine Performance Analysts. How? ► 𝐒𝐭𝐞𝐩 𝟏: Audit Tasks, Not People. Map every task your team does. Then, use a framework like the Human-AI Task Scale to classify them. What's fully manual? What can AI support? What can be fully automated? ► 𝐒𝐭𝐞𝐩 𝟐: Find the Skill Gaps. You know what can be automated. Now, what adjacent skills does your team need to manage that new reality? This isn't a mystery. The path is from creator to orchestrator. ► 𝐒𝐭𝐞𝐩 𝟑: Execute a 90-Day Runway. Week 1-2: AI Foundations (Prompting, etc.) Week 3-4: Task Automation (Automate one core workflow) Week 5-6: Skill Pivot (Start an adjacent-skill project like data analysis) ...and so on. The result? 𝘠𝘰𝘶'𝘳𝘦 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 "𝘴𝘢𝘷𝘪𝘯𝘨 𝘮𝘰𝘯𝘦𝘺." You're building a team with a 4x output multiple. You're getting a 3x-10x ROI on your investment. (Links to the research in the comments.) You're keeping the institutional knowledge you'd lose from layoffs. 𝗦𝘁𝗼𝗽 thinking about replacing people. 𝘚𝘵𝘢𝘳𝘵 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 𝘢𝘣𝘰𝘶𝘵 𝘢𝘶𝘨𝘮𝘦𝘯𝘵𝘪𝘯𝘨 𝘵𝘩𝘦𝘮. The companies that do this will win the next decade. The others will become a footnote. Need a visual? I mocked up an application (still in progress) illustrating the steps and the ROI. You can find the link in the comments. 👇🏻

Most companies aren’t failing at AI adoption because of the tech. They’re failing because employees are afraid to use it. Tools are rolling out fast. But usage? Still stuck in pilot mode. 52% of employees using AI are afraid to admit it. And when managers don’t model usage themselves, team adoption stalls. One thing is clear: AI adoption doesn’t just happen. You have to design for it. Here are 10 strategies that actually work: 1. Track adoption and set goals. Measure usage patterns and benchmark performance across teams. Make AI part of your performance conversations, like Shopify does. 2. Engage managers. If they use AI, their teams are 2 to 5x more likely to follow. Enable them, train them, and let them lead by example. 3. Normalize usage. More than half of AI users hide it. Reframe the narrative. AI isn’t cheating, it’s table stakes. 4. Clarify policies. Without clear guidelines, people freeze. Spell out what’s allowed and what’s not. 5. Promote early wins. A great prompt that saves hours? Share it. Celebrate it. Build momentum. 6. Share best practices. Run prompt-a-thons. Create internal libraries. Make experimentation part of the culture. 7. Deploy AI agents strategically. Use ONA to spot high-friction workflows. Insert agents where they’ll have the biggest impact. 8. Balance experimentation with safe tooling. Watch what tools employees are adopting organically. Then invest in enterprise-grade tools your teams already want. 9. Customize by role and domain. Sales, HR, engineering, each needs a tailored strategy. Design workflows that reflect the reality of each team. 10. Benchmark yourself. How does your AI usage compare to peers? Track maturity, share progress, and stay competitive. From our work at Worklytics, these are the tactics that move organizations from pilot mode to performance. You can find the full AI Adoption report in the comments below. Which of these 10 is your org already doing and what’s next on your roadmap? #FutureOfWork #PeopleAnalytics #AI #Leadership #WorkplaceInnovation

🎯 The CIO's Organizational Playbook for the AI Era... I recently spoke with a CIO friend about how IT teams are changing. Our discussion made me think about what sets apart IT teams that succeed with AI from those that don’t. I looked over my research and reviewed my interviews with other leaders. This information is too valuable not to share: ✓ Build AI-Ready Capabilities 🟢 Establish continuous learning programs focused on practical AI applications 🟢 Implement cross-functional training to bridge technical/business gaps 🟢 Prioritize hands-on AI workshops over theoretical certifications ✓ Master AI Risk Management 🟢 Develop processes to identify and mitigate technical failures early 🟢 Create a strategic AI roadmap with clear risk contingency protocols 🟢 Align all AI initiatives with broader business objectives ✓ Drive Stakeholder Engagement 🟢 Build a cross-functional AI coalition (executives, HR, business units) 🟢 Communicate AI initiatives with transparency to reduce resistance 🟢 Document tangible benefits to secure continued buy-in ✓ Implement with Agility 🟢 Replace waterfall approaches with iterative AI development 🟢 Focus on quick prototyping and real-world testing 🟢 Ensure infrastructure scalability supports AI growth ✓ Lead with AI Ethics 🟢 Train teams on bias identification and mitigation techniques 🟢 Establish clear governance frameworks with accountability 🟢 Make responsible AI deployment non-negotiable ✓ Transform Your Talent Strategy 🟢 Enhance IT roles to integrate AI responsibilities 🟢 Create peer mentoring programs pairing AI experts with domain specialists 🟢 Cultivate an AI-positive culture through early wins ✓ Measure What Matters 🟢 Set specific AI KPIs that link directly to business outcomes 🟢 Implement continuous feedback loops for ongoing refinement 🟢 Track both technical metrics and organizational adoption rates The organizations mastering these elements aren't just surviving the AI transition—they're thriving because of it. #digitaltransformation #changemanagement #leadership #CIO

AI-driven teams scale fast—or crash hard. The real game-changer? IO psychology, and how it rewires your talent engine 👇 Most leaders focus on AI tools and forget the human element. Big mistake. Industrial-Organizational (IO) psychology is the secret sauce for AI success. It's about optimizing human performance in tech-driven environments. Here's how IO psychology transforms your AI teams: 1. Talent acquisition: Use psychometric assessments to identify AI-ready mindsets. 2. Team composition: Balance technical skills with soft skills for cohesive AI units. 3. Learning agility: Foster adaptability to keep up with rapid AI advancements. 4. Change management: Reduce resistance to AI integration through targeted interventions. 5. Performance metrics: Develop KPIs that align human efforts with AI capabilities. 6. Leadership development: Train managers to lead hybrid human-AI teams effectively. 7. Organizational culture: Build a culture that embraces AI as an enabler, not a threat. Remember: Your AI is only as good as the team behind it. Invest in your people's psychology, and watch your AI initiatives soar. Elevate your human capital to match your technological ambitions.

Been perfecting this small team AI methodology, and it’s incredibly effective. Get 4-8 people together, at least one technical and at least one subject matter expert, and sprint together with paid AI tools (free tools are fun for experiments, not for business). Start with a multi-hour shareout (with AI recording and transcribing!) of the SME sharing the latest in their field, the work the company is doing, the blockers the company has, what you would share if you were onboarding someone new and wanted them to succeed. Then have the dev/SWE/SA/DS do the same. Be sure to include what tools and resources are available, what is actually used in your company’s stack, what’s working, and what’s not. Then run two deep research queries: one on your specific company, one on AI’s impact on your narrow industry. Then brainstorm how to use AI to transform your business (have AI record and transcribe this too! Otter is easy, but there are others). THEN take both deep research outputs and single-person transcriptions, load it as context. Then prompt the AI to brainstorm potential AI ideas, giving the third transcript as an example. In your prompt… Ask for 200 ideas. Ask AI to score and rank the ideas on criteria you care about (potential revenue? Speed to deploy? People required?). Make sure the top 5 are wide in their support (ie not just 5 ideas to improve calendar management). Ask AI to give a mini execution plan for the top 5. Ask for what you might be missing. Ask for ways to make the ideas 5x better. Ask for revolutionary ways to execute the 5 so that they score higher on your criteria. Try and be in person if possible. It's extra magic. We are seeing cutbacks from cost pressures, uncertain economic conditions, and the hope of AI. I am not (yet?) a proponent for 4-day work weeks, but I am a big proponent of smaller teams. This is one way to get ahead. ♻️ Save and share this post with others who could use an AI edge.

Some of the best AI breakthroughs we’ve seen came from small, focused teams working hands-on, with structured inputs and the right prompting. Here’s how we help clients unlock AI value in days, not months: 1. Start with a small, cross-functional team (4–8 people) 1–2 subject matter experts (e.g., supply chain, claims, marketing ops) 1–2 technical leads (e.g., SWE, data scientist, architect) 1 facilitator to guide, capture, and translate ideas Optional: an AI strategist or business sponsor 2. Context before prompting - Capture SME and tech lead deep dives (recorded and transcribed) - Pull in recent internal reports, KPIs, dashboards, and documentation - Enrich with external context using Deep Research tools: Use OpenAI’s Deep Research (ChatGPT Pro) to scan for relevant AI use cases, competitor moves, innovation trends, and regulatory updates. Summarize into structured bullets that can prime your AI. This is context engineering: assembling high-signal input before prompting. 3. Prompt strategically, not just creatively Prompts that work well in this format: - “Based on this context [paste or refer to doc], generate 100 AI use cases tailored to [company/industry/problem].” - “Score each idea by ROI, implementation time, required team size, and impact breadth.” - “Cluster the ideas into strategic themes (e.g., cost savings, customer experience, risk reduction).” - “Give a 5-step execution plan for the top 5. What’s missing from these plans?” - “Now 10x the ambition: what would a moonshot version of each idea look like?” Bonus tip: Prompt like a strategist (not just a user) Start with a scrappy idea, then ask AI to structure it: - “Rewrite the following as a detailed, high-quality prompt with role, inputs, structure, and output format... I want ideas to improve our supplier onboarding process with AI. Prioritize fast wins.” AI returns something like: “You are an enterprise AI strategist. Based on our internal context [insert], generate 50 AI-driven improvements for supplier onboarding. Prioritize for speed to deploy, measurable ROI, and ease of integration. Present as a ranked table with 3-line summaries, scoring by [criteria].” Now tune that prompt; add industry nuances, internal systems, customer data, or constraints. 4. Real examples we’ve seen work: - Logistics: AI predicts port congestion and auto-adjusts shipping routes - Retail: Forecasting model helps merchandisers optimize promo mix by store cluster 5. Use tools built for context-aware prompting - Use Custom GPTs or Claude’s file-upload capability - Store transcripts and research in Notion, Airtable, or similar - Build lightweight RAG pipelines (if technical support is available) - Small teams. Deep context. Structured prompting. Fast outcomes. This layered technique has been tested by some of the best in the field, including a few sharp voices worth following, including Allie K. Miller!