Hello and welcome to Modern CEO! I’m Stephanie Mehta, CEO and chief content officer of Mansueto Ventures. Each week this newsletter explores inclusive approaches to leadership drawn from conversations with executives and entreprene…
One cold Friday night a few years ago, I collapsed to the ground in the arrivals hall of a small French airport. I started sobbing and couldn’t stop. It took physical collapse for me to acknowledge that I was burned out and that my work life was unsus…
One of the best days of Gabriella’s career was also one of her hardest days as a parent. Gabriella, who asked for a pseudonym to protect her children’s privacy, had just filmed the launch video for her new company. On the train ride back home, she got…
It’s graduation season and my email inbox is flooded with inquiries from students entering the workforce, looking for career advice. How do I land my dream job? What should I do at the company where I’ve been recently hired to get where I really want to be? How do I go from what I have to do to what I want to do?
What I’ve gathered from these students is not much different from what we more seasoned professionals struggle with day in and day out. How do we square the incongruence between our duty—the thing we have to do to survive, pay our bills, and keep the lights on—and our conviction—the thing we feel called to do? The job, of course, is our duty. The gift is our conviction. For most of us, the two seem as far apart as east and west, and never the twain shall meet. For only a few lucky ones, their job and their gifts coexist, at least, that’s what we’ve told ourselves.
But what if that’s not the case at all? What if we could have our cake and eat it, too? We invited Najoh Tita-Reid onto the latest episode of the From the Culture podcast to help us explore this tension. She is the former global chief growth officer at Mars Petcare, former global CMO at Logitech, and former VP of marketing at Bayer Consumer Care—a three-decade-plus veteran. Yet despite her incredible resume of leading big brands, she recently walked away from all of it, not because the work was bad but because her conviction was bigger.
Tita-Reid had been working on her gift right alongside her duty for quite some time before she left the C-suite. She didn’t see the two as a mutually exclusive proposition, but more as a game of catch-up. Her corporate duty had been hard at work long before her gift began to manifest. It took years before she realized her conviction—her ability to peek around the corner and see change.
Like a canary in a coal mine, as Tita-Reid puts it, she’s been able to sense shifts long before they happen. This ability started as a whisper and increasingly got louder, but by the time it registered that she was a “canary,” she was deep into her marketing career and her conviction seemed underdeveloped relative to her duty skills. So, she’d wake up at 5 o’clock to do the conviction work before the duty work began. For her, that meant teaching herself AI from independent instructors, on her own time, on her own dime, while her C-suite job was still going.
The duty kept her solvent. The conviction kept her alert. Before long, she was bringing her newly developed canary skills to her marketing work, and it helped her rise through the ranks and up the corporate ladder, until her conviction and her duty were equally yoked. That’s when Tita-Reid realized that her conviction could lead her duty, so that the curiosity of her gift could actually become her duty. That is when she decided to disembark the traditional corporate train and ride her convictions into the sunset.
As a career marketer myself, I relate to this deeply. I was a few years into my career before I realized my conviction. I became insatiably curious about the social sciences and their application to behavioral adoption. I wanted to study it, teach it, and practice it. By the time I became aware of it, I was already running a full department at an advertising agency, and, like Tita-Reid, my duty skill set far surpassed my curiosity.
So, I did exactly what she did: I began to work on my conviction before and after work. I read nonstop—Kahneman, Ariely, Thayler, Lowenstein. One scholar led me to another and helped me build a theoretical repertoire. I taught classes about my learnings on the weekends, at night, and even in the early mornings. And the more I did it, the closer these two disparate worlds became. I even got a doctorate in the conviction while working my duty. This went on for over a decade before my conviction and my duty were parity, and it was at this point that, like Tita-Reid, I, too, allowed my conviction to lead me.
So, I say to you what Tita-Reid told us and what I tell my students: Do your duty while developing your conviction skill set. Work your 9-to-5 and your 5-to-9 so that before long, your 5-to-9 becomes your 9-to-5. This is not a side hustle, but an investment. You’re investing in yourself today to realize the interest tomorrow.
Because of those many years of investing in myself while also investing in my place of work (my duty), I can truly say that I’m now living in my gift—and it is a gift. I get to teach at one of the best schools in the world (the University of Michigan), work with some of the biggest brands in the world (Google, TikTok, and McDonald’s), and put ideas in the world through platforms like this article you’re reading, books, and stages. It’s not a dream; it’s compound interest, and it’s available to you, too.
Check out our full conversation with Najoh Tita-Reid on the latest episode of From the Culture here.
I used to think I was a great salesperson because I had all the right answers. I knew my product inside and out. I could explain every feature, every benefit, every reason someone should say yes. And I did what most people do—I led with that. Confiden…
In a recent survey of senior leaders at large U.S. and U.K. professional services firms, 61% said they had abandoned at least one AI project in the past year because their people lacked the skills to deliver it. Deloitte’s “2026 State of AI in the Enterprise” report, based on a survey of more than 3,200 business and IT leaders across 24 countries, found that insufficient worker skills are now the single “biggest barrier to integrating AI into the business.”
There is no quick or easy solution to this problem. While it is possible to bring in new hires or contractors with the short-term capabilities you need, this approach is not sustainable in the long term as it is both expensive and creates critical dependencies. And it is equally impossible to flip a switch to develop these capabilities in-house overnight. But businesses can start the vital process of building those skills systematically. And there is no better time to begin than now. Organizations that get ahead of the pack in this critical area will build an advantage over their peers that will compound every quarter.
The Capability Stack
Organizational AI capabilities emerge from four mutually reinforcing layers of expertise.
Technical depth. This is the specialized engineering capability that builds and maintains AI systems: machine learning engineering, data engineering, AI security, model evaluation, and related disciplines. Without sufficient technical depth, the wrong things get built and bought, and the organization creates risk that it doesn’t understand.
Domain application. This layer is where AI strategy meets business reality. It consists of the capability to apply AI within a specific business function. And it relies on people who understand not just what the technology can do, but where it creates value in a particular operational context.
General workforce fluency. This is the baseline capability that every knowledge worker needs: sufficient understanding to use AI tools productively, to recognize when outputs are unreliable, and to contribute usefully to conversations about how AI is being deployed in their area. Without this general fluency, adoption stalls, misuse spreads, and employees remain dependent on a small group of specialists.
Organizational infrastructure for learning. This is the layer that sustains the other three: the systems, incentives, and management behaviors that determine whether capability grows or erodes. It includes how learning is funded, how time for development is protected, how reskilling pathways connect to real roles, and how managers are held accountable for the capability development of their teams. Without this layer, every investment in the first three decays.
The 90-day plan that follows works through all four layers simultaneously.
The 90-Day Plan
Days 1-30: Map
The goal of this phase is to understand what you have, what you need, and where the gap between them will hurt you first.
1. Define the capability model. Use the capability stack to define what AI capability means for your organization. Be specific. What does technical depth mean in your business? Which roles require domain application? What level of AI fluency should every knowledge worker have? The shared model needs to be explicit and agreed on.
2. Identify the workforce baseline. Assess existing employees against the capability model. Use a combination of self-assessment, manager assessment, and skill validation—and treat all three with appropriate skepticism. None of these tools is perfect, but that’s okay: the goal is not a perfect picture, just a better one.
3. Map capability demand to the strategy. Take your AI strategy and the innovation portfolio it has produced, and decompose them into the specific capabilities required at each layer of the stack. This is the demand side of the equation, and it is typically missing from AI strategies altogether. Organizations approve ambitious AI portfolios and then discover, months later, that they don’t have the people to staff them. The demand map prevents that discovery from arriving as a surprise.
4. Identify the highest-leverage gaps. The gap between current state and required state will normally be large. You will not close it completely in a quarter, and attempting to do so will dilute the impact of investment across the board. Prioritize ruthlessly. Identify the handful of capability gaps that will most directly constrain the AI initiatives already in flight or about to launch. If your innovation pipeline has three experiments ready to go and two of them require data engineering capabilities that you don’t have, then that’s where the first thirty days of investment should be directed.
5. Audit how learning currently works. Map the current state of organizational learning. The infrastructure layer of the capability stack depends on it. Flag the parts of the system that will scale into the AI era and the parts that need to be rebuilt or replaced.
For a practical guide to building the AI innovation portfolio against which capability requirements should be mapped, see “How to build an AI innovation pipeline that creates real long-term value.”
Days 31-60: Build
In this phase, the organization begins closing the gaps previously identified while also laying the foundations for ongoing and systematic workforce development.
1. Launch the core technical hiring push. For the small number of roles that the organization genuinely cannot develop internally on the required timeline, run a focused external hiring effort. Be disciplined about which roles you select. Reserve external hiring for the positions where internal technical expertise of the required depth truly cannot be developed in the available window. For everything else, build from within.
2. Stand up the reskilling program. For the much larger population of employees who can move into AI-adjacent roles with the right investment, build a structured reskilling program tied directly to the capability model. The program should connect to real roles on the other side. Reskilling efforts fail when they become training programs with no path to a new job.
3. Drive baseline fluency across the workforce. Roll out a broad AI fluency program for the general knowledge-worker population. Tie completion to specific behavioral expectations, not just attendance.
4. Build the partner ecosystem. Identify the external partners—universities, training providers, specialist consultancies, managed service providers—that can accelerate the building of capabilities where internal investment alone cannot move fast enough. Partnerships should be structured with clear deliverables and explicit transfer-of-capability expectations. A partner that builds your capability is an investment, while a partner that performs the work without transferring the capability is a dependency-in-waiting.
5. Redesign the highest-leverage roles. Select two or three of the roles that will be most comprehensively transformed by AI in your organization. Redesign them deliberately, working with the people who do that job today. Ask practical questions. What parts of the job should AI take on? What parts should the human retain and do better? What new responsibilities emerge when routine work is automated? The redesigned role can serve as a template for the broader workforce transformation and as a concrete demonstration that capability development leads somewhere real.
6. Make managers accountable for capability development. Your middle managers are the transmission mechanism for every capability program you launch—if their teams aren’t developing, the programs aren’t working. So make your managers accountable for success. Success needs to be specific and measurable: employees reskilled into new roles, team fluency levels achieved against the capability model, learning time protected against competing demands, and internal moves into AI-critical positions. Managers who consistently develop their teams’ capabilities should be recognized and rewarded. The signal this sends through the organization is more powerful than any training program.
For more on why AI reskilling demands organizational transformation rather than individual training, see “What AI reskilling really requires.”
Days 61-90: Embed
Now it’s time to lock the changes into the operating fabric of the organization so that building workforce capabilities specific to AI becomes a permanent discipline rather than a one-off initiative that fades when the next priority arrives.
1. Operationalize capability reviews. Make capability a recurring item in talent reviews, business reviews, and board reporting. Build a capability dashboard, updated on a defined cadence, that tracks the state of each layer of the capability stack against the demand map from Phase 1. This turns a set of programs into a managed discipline, with the same rigor as that applied to financial performance or operational metrics.
2. Make learning a standing expectation. The test of whether an organization is serious about capability development is what happens when learning time collides with operational demand. In most organizations, learning loses. The fix is structural: Define the learning time expectation, make it visible, and hold managers accountable when it isn’t protected.
3. Track the flow of capability, not just the snapshot. If you only measure the stock of capability, you will miss the trends that determine whether you’re building momentum or losing ground. Track the indicators that reveal direction: internal moves into AI-critical roles, retention in those roles, reskilling throughput and placement rates, external hires converted to productive contributors, and the rate at which fluency programs change actual behavior rather than just accumulating completions.
4. Stress-test the capability with real work. Deploy the newly developed capability on an active AI initiative from your innovation pipeline and watch what happens. Where the capability holds under operational pressure, scale the playbook that produced it. Where it breaks—where the reskilled engineer can’t handle production complexity, where the fluent marketer still can’t evaluate model outputs—fix the upstream investment before you scale it.
5. Treat AI-critical roles as organizational infrastructure. Every AI-critical role in your organization is, to some degree, a new role—one that didn’t exist five years ago and may not have an established internal talent pipeline. That means every such role is a potential single point of failure. If your lead ML engineer leaves and there’s no one behind them, you don’t just have a vacancy—you have a capability collapse that can stall an entire portfolio of initiatives. Build succession depth for these roles the way you would for any other critical piece of infrastructure: Identify the successors, invest in their development, and make the pipeline visible.
6. Iterate. By day 90, the data is available. Which hires worked? Which reskilling pathways produced employees ready to do the job? Which fluency programs changed behavior rather than just generating completion certificates? Use the evidence. Reshape the next cycle based on what you’ve learned.
For a deeper look at how AI is redefining the management roles on which capability development depends, see “AI and the death (and rebirth) of middle management.”
Conclusion
This 90-day plan will not solve every capability problem. But what it will do is get you started on building the system that keeps capability growing long after the initial push. And this is more important than ever, because in the AI era, the workforce you have today is never the workforce you will need tomorrow.
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