Key Questions to Shape AI Strategy

Don't do AI - your business isn’t ready for it. Adopting AI isn't a simple plug-and-play; your operating model needs to shift. As a business leader, you first need to answer the question: "How will the AI-reinvented version of our business look?" Consider this: ➡️ Value Realization 1. Which high-value use-cases could significantly transform our business? 2. How will customer experience evolve with AI implementation? 3. What KPIs will measure the success of AI initiatives? ➡️ People 4. How will roles and responsibilities within our team change as AI takes on repetitive tasks? 5. What new skills and capabilities will our employees need to learn? 6. How will we manage change and employee resistance during AI adoption? ➡️ Process 7. How should our current processes evolve to integrate AI effectively? 8. What process will the business use to approve budgets for AI initiatives? 9. How will we train and inform both technical and non-technical staff about AI? ➡️ Technology 10. What infrastructure upgrades are required to facilitate AI integration? 11. How will we handle data privacy and security in our AI operations? 12. How will IT enable the business function through AI? ➡️ Leadership 13. Which executive will champion our AI Centre of Excellence (CoE)? 14. What will AI governance look like? 15. What strategies will we implement to ensure Responsible AI practices? These questions are your roadmap. Answering them will position your business not just to survive, but to lead in the AI-driven future. 📈 The next step? Start exploring these questions, develop a clear vision, and take strategic action. Companies like Microsoft and JPMorgan have thrived by methodically addressing these questions before scaling AI. Others rushed in with flashy projects that failed to deliver ROI. Embracing AI isn't just advantageous - it's essential, for all businesses. But the way you navigate this transition will determine whether your business thrives or becomes another cautionary tale. Follow for more executive-level insights on navigating AI successfully.

Leaders, here’s how to define your AI vision: The hardest part of AI adoption is deciding what role AI should play in how your organization operates. What’s missing is a shared AI vision: a clear, organization-level understanding of where AI should create leverage, where it should be constrained, and what success actually looks like in operational terms. Without that clarity, AI adoption fragments. Each function optimizes for its own needs - data gets duplicated, governance becomes reactive. As a result, leaders see activity, but not progress. Before choosing tools, vendors, or pilots, leadership teams need to answer a small number of foundational questions together: 1/ Where should AI change decisions, not just automate tasks? Identify the decisions that slow the organization down, introduce risk, or rely too heavily on manual judgment. If AI doesn’t meaningfully alter how those decisions are made, it’s not strategic. 2/ Where should AI explicitly not operate without human oversight? Defining boundaries is as important as defining ambition. Leaders need clarity on which areas require escalation, review, or human accountability, especially where consequences are material. 3/ How will we recognize progress beyond activity metrics? Success is not the number of pilots, tools, or models deployed. It is operational change: fewer handoffs, faster resolution, clearer ownership, and more consistent outcomes. These questions create alignment across teams. They prevent AI from becoming a collection of disconnected experiments. Most importantly, they give technical teams a clear mandate to design systems that serve the organization’s intent. In Part 2, I’ll explore what comes next once that vision is defined: how organizations translate intent into operating models, governance, and execution without losing momentum or control.

If your AI vendor only shows the gloassy demo and can’t explain how it works in plain English, you need to keep asking more questions. If you’re a senior leader considering AI for your business, this article gives you six simple but critical questions that cut through the hype: how the system actually works, how it fails, whether it’s reliable, explainable, and capable of ROI at scale. It’s written for non-technical leaders — no jargon, no PhD required — just the practical, second-layer questions that will help you decide if an AI tool is commercially worth your investment.

5 on AI: The Executive AI Readiness Test Before your organization spends another dollar on AI, your leadership team should be able to answer these 5 questions. Most can’t. 1. What problem are we actually solving? Not “we want to use AI.” What specific decision, process, or capability are you trying to improve — and how will you measure success? Vague ambition produces expensive pilots that go nowhere. 2. Who owns AI outcomes in this organization? If the answer is “IT” or “it depends,” you have a governance problem. AI initiatives that lack clear executive ownership fail at a predictable rate. 3. What’s our data reality? AI is only as good as the data behind it. Do you know the state of your data infrastructure? Most executives are surprised by what they find when they actually look. 4. How will we manage the risk? Bias. Hallucination. Regulatory exposure. Reputational damage. These aren’t hypotheticals — they’re outcomes that have already hit real companies. What’s your framework for catching problems before they become headlines? 5. Are we building capability or dependency? Buying an AI tool is easy. Building an organization that knows how to evaluate, deploy, and evolve AI over time is a strategic asset. Which one are you doing? These five questions form the backbone of how I open every executive training engagement. The answers — or the absence of them — tell you almost everything about where a company actually stands on AI. Which of these would be hardest for your leadership team to answer?

𝐃𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 In my previous post, I discussed the Enterprise AI Talent Stack and the talent architecture organizations need to scale AI.  But hiring the right talent is only the first step. Once those capabilities are in place, the next critical question becomes: How does the organization actually run AI as a function? This is where many enterprises struggle. Even with strong AI talent, organizations often face the same pattern: * AI initiatives emerge across different teams * Ownership of models in production becomes unclear * Governance is applied too late in the lifecycle * Scaling beyond experimentation becomes difficult The missing piece is usually a clearly defined AI Operating Model. The operating model defines how AI work flows through the organization—from idea to production to long-term oversight. A strong enterprise AI operating model typically answers four critical questions: 1. How Are AI Use Cases Prioritized? AI resources are finite. Not every opportunity should be pursued. The operating model should define: * How business teams propose AI use cases * How initiatives are evaluated for value and feasibility * Who ultimately prioritizes investment Leading organizations treat AI initiatives as a portfolio, balancing impact, risk, and strategic alignment. 2. Who Owns AI Systems After Deployment? One of the most common gaps in enterprise AI is post-deployment ownership. The operating model must clearly define: * Who monitors models in production * Who is accountable for model drift or performance degradation * Who manages updates as data, markets, or regulations evolve Without lifecycle ownership, even well-built AI systems degrade over time. 3. How Is Governance Embedded Across the Lifecycle? Governance cannot be a final checkpoint before deployment. A mature operating model integrates governance across: * Use case approval * Model development and testing * Validation and risk assessment * Production monitoring and auditability This ensures AI systems remain trusted, compliant, and aligned with enterprise risk appetite. 4. How Do Business Teams Access AI Capabilities? AI should not remain confined to a central team. The operating model should create clear pathways for business units to: * Propose AI opportunities * Collaborate with AI teams * Integrate AI solutions into operational workflows Many organizations adopt a hub-and-spoke model, where a central AI function provides standards, governance, and platforms while business units drive use case innovation. Scaling AI is not just about building models. It’s about designing an operating model that clarifies: * Decision rights * Lifecycle ownership * Governance integration * Collaboration between business and technology teams Because at enterprise scale, AI success is as much an organizational design challenge as it is a technological one.

Most companies start their AI strategy with the wrong question. And that mistake is costing them millions in stalled initiatives. According to McKinsey, 78% of companies are already using GenAI. Yet only 1% describe their AI strategy as mature, and many report no meaningful bottom-line impact. The problem isn’t technology. It’s direction. Here’s the boardroom scenario playing out across industries: The CEO returns from a conference. The slides promise trillions in AI impact. The board asks about strategy. The CTO is already piloting tools. So the question becomes: “Where can we apply AI?” Wrong question. The better question: “What business problems have plagued us year after year and which ones, if solved, would change everything?” That reframe changes how AI gets deployed. Here’s the framework that separates AI leaders from AI experimenters: 1/ Start With the Problem List → Pull your last 3 years of QBRs, board decks, and executive off-sites → Identify the problems that appear every year → Those are your AI candidates, not vendor tools Reality: A problem that survives three budget cycles has real organizational weight. 2/ Score on Two Axes: Pain Depth × Data Availability → Pain depth: what does this cost annually in revenue, time, or talent? → Data availability: do we have the data to deploy a solution? Reality: Most failed AI projects score high on hype and low on impact. 3/ Price the Status Quo Before the Solution → What is chronic inaction costing per quarter? → That number becomes your ROI floor Reality: Organizations seeing real AI returns typically quantified the problem cost before selecting the technology. 4/ Separate Chronic From Acute → Acute problems feel urgent but often resolve themselves → Chronic problems quietly drain revenue and talent every year Reality: If the problem appeared this quarter, AI probably isn’t the solution. 5/ Identify the Problem Owner (and the Friction) → Every chronic problem has a frustrated executive living with it → Look at exit interviews, NPS feedback, and operational complaints Reality: High-ROI AI opportunities often hide in employee and customer friction. 6/ Build the Problem → Outcome → Metric Chain → Problem: customer escalations spike every Q4 → Outcome: predictive routing reduces escalations → Metric: CSAT, handle time, agent overtime cost Then go find the AI solution. Reality: Gartner identifies unclear business value as the top reason AI projects die after proof of concept. Here’s what the problem-first approach unlocks: → Built-in executive sponsorship → Pre-existing ROI benchmarks → Organizational urgency beyond vendor hype → A board narrative grounded in finance, not technology The companies seeing real AI returns didn’t start with tools. They started with chronic business problems. The question isn’t: “Where can AI go?” It’s: “What has been breaking us and for how long?” Answer that first. Everything else follows. Save this for future reference.

We’ve entered the era of “AI vaporware”. Big claims, fragile tech, and minimal insight into the data that powers it. If you're a B2B buyer, read this 👇 before you invest $50,000/yr on fancy new AI tech: We all know how quickly the tech landscape can shift. Just a few weeks ago, Xandr (a $1B DSP used by some martech platforms) suddenly shut down. Not because it wasn’t working. Microsoft simply sunset it to focus on its own advertising ecosystem and first-party data strategy. Now we’re seeing a new wave of risk: this time, dressed up as AI innovation. Fast launches. Flashy claims. Shaky foundations. But with AI, it's 10x faster. "AI-powered!" everyone screams. Sure. But powered by what? Trained on what? Is it built to last, or built to raise a Series F? If you're evaluating new AI vendors, here are the questions I'd ask before signing on the dotted line (shout out to Chad Holdorf): 1. Model & Intelligence - Can I trace how the model makes decisions? - What training data was used? Is it proprietary or public? - How is model performance tracked and improved? - Can models be tuned or retrained for our use cases? 2. Infrastructure & Ownership - Who owns the infrastructure and hosting? - What happens if the provider changes cloud vendors or LLMs? - Is it multi-cloud or locked to one ecosystem? 3. Security & Compliance - How is data handled? Is it encrypted at rest and in transit? - Does it meet our compliance standards (SOC 2, GDPR, etc)? - Can I audit or delete my data? 4. Integration & Extensibility - Can it connect to my tools (CRM, MAP, CDP)? - Does it expose APIs for other systems to use? - Is there a roadmap for more ecosystem support? 5. UX & Governance - How do users interact with it—chat, UI, workflow? - Are there guardrails for bad outputs or hallucinations? - Who controls permissions, access, and audit trails? 6. Business Impact - What metrics or outcomes has it improved for others? - Can it reduce cost, increase speed, or drive revenue? - Does it scale across teams or stay in a silo? Remember... “AI-first” without infrastructure is just AI branding. If the tech is built on weak systems, the smartest model in the world can’t save it.