Tips for Preparing Boards for Digital Transformation

To perform their duties responsibly, boards must function as Humans + AI. Adopting new working structures and evolved governance structures incorporating AI can lead to substantial performance improvement. Much of my current work with boards is on strategic framing for AI and in AI-augmented decision-making, but there is considerably more potential. A very nice HBR piece brings real-world insights to bear. The first finding was that directors and chairs largely failed to recognize the value and potential of AI in their work. However still many boards and directors are using AI in useful ways. MEETING PREPARATION Directors who use LLMs reported significantly improved understanding of agenda items and reduced workload. One director across five Danish boards uses AI to structure presentations and run simulations; another in Switzerland uses it to refine board discussion questions from the board book. SCENARIO PLANNING GenAI, used well, can be an excellent tool for rapid scenario planning. One board in Austria used an LLM to analyze geopolitical risk in an acquisition proposal. This led to it rejecting the deal, and resulted in management attaching scenario analyses to future proposals. ADDITIONAL PERSPECTIVES Boards in Finland and the Netherlands used AI to test their own strategic conclusions, finding significant overlap between AI-generated insights and their human decisions. This boosted both their confidence in the decisions and their trust in AI’s utility, particularly for validating or challenging complex judgments. IMPROVING BOARD DYNAMICS AI can offer real-time feedback on boardroom dynamics. For example, a Swiss industrial company uses AI to analyze speaking time, tone, and engagement during meetings, creating recommendations for better group engagement. The article addresses potential risks: 🔐 Information leaks. These stem not from AI itself but from poor data governance, which can be mitigated with proper access controls and security training. ⚖️ Sample bias. Regular audits and user awareness are key to avoiding flawed, discriminatory, or incomplete insights. 🧭 Anchoring in the past. AI can be overly reliant on historical data. Scenario simulations and reasoning models can help boards anticipate and adapt to future shifts. And concludes with recommendations on learning to use AI well: 1️⃣ Create engagement. Chairs should start with one-on-one conversations to assess AI literacy and follow up with tailored training to build confidence and interest. 2️⃣ Practice collective experimentation. Boards should test AI tools together in low-stakes settings, debrief their experiences, and gradually integrate AI into governance processes. 3️⃣ Maintain momentum. Chairs must lead by example, celebrate AI use regardless of outcomes, and embed AI progress into board evaluations. I am currently working on a 'GenAI in the Boardroom' mini-report that I will be sharing soon, addressing these and a range of other issues and possibilities.

Most digital transformations don't fail because of the tech. They fail because of the 'silent resistance.' Here is how we solved for that at a 20,000 FTE multinational. I used to Chair the Infrastructure Change Control Board (ICCB), a brainchild of their visionary MD. It was a perfect governance measure at a time when GRC practices were still maturing in the Indian corporate scene. ICCB did the following things right : ✅ Cross-Functional Representation : Including members from Sales, Transitions, HR, Security, Finance and Legal in addition to IT & Infra, it ensured that enterprise interdependencies were deliberated ✅ Risk based Tiered Ranking : Change requests mapped to the operational risk rating framework, thereby following a standard tiering methodology (eg Significant, Minor, Emergency) with associated actions, implementation schedules, controls ✅ Post Implementation Reviews : Regular status review of approved changes to ensure adherence to schedule, sign-offs, dependency checks and also analysis of delayed / failed projects. It was a classic case on how governance, done right, doesn't slow things down, but enhances efficiency by advance planning and analysis of the required steps and cross-dependencies, thereby reducing "rework" caused by failed changes. Why are the above important? Most of us have seen enthusiastically designed automation or transformational programs - technically sound, strategically aligned, having the governance structure in place and budget allocated - failing to execute.   The Real Barrier? The Human Element. It’s rarely a lack of skill. It’s often 'Silent Resistance' born from: ▪️Communication Gap : Often the leadership fail to communicate or explain the link of the 'why' of #automation to the broader business vision ▪️ Anxiety : There's angst of a probable downsizing due to automation, specially with AI projects, that stall adoption ▪️Exclusionary Engagement : When the support functions feel detached, they (quietly) deter implementation. Board & executive level success factors for transformation / automation programs include : ✔️ Communication Plan - customized to, but covering all stakeholders ✔️ Training - as a capability builder where people learn to improve through continuous usage, rather than passing an one-time assessment test ✔️ Accountability - Identify champions within each business function to guide, monitor, provide feedback and ensure successful adoption ✔️ Support - Set up a team to act on feedback and regularly report back improvements to the relevant governance council. ✨ An effective change management process is the bridge that can shift a departmental initiative into an 'Institutional Process'. What's your biggest hurdle in driving cultural acceptance for large-scale automation? Let's discuss in the comments.   #ChangeManagement #StakeholderEngagement #technology #DigitalTransformation #BoardGovernance

7 questions every board should ask before claiming AI readiness. Spoiler: It’s not “Do we have the tech?” Most AI failures aren’t technical. They’re cultural, political, and invisible. Boards have to lead the change, not the tools. 1️⃣ Is our culture ready for AI-scale change? About 70% of transformations stall or underdeliver. AI is even more fragile without cultural readiness. If the middle stalls, strategy dies before delivery. Fund the change, not just the tools and pilots. Reward behaviors that ship AI, not status reports. 2️⃣ Do employees trust our AI intentions? Up to 60% of employees distrust internal AI plans. Fears: layoffs, surveillance, biased decisions, errors. Distrust drains adoption, output, and brand goodwill. Give clear intent, guardrails, and shared upside. Let teams co-design workflows before rollout. 3️⃣ Can we prove our AI data is secure? 78% of breaches trace to weak controls and handling. GDPR and the EU AI Act raise the price of failure. Fines can reach 7% of global turnover. And headlines. Prove lawful data use, retention, and vendor paths. Be audit-ready before the first use case ships. 4️⃣ Will our AI use stand up to ethics tests? 56% of customers walk over perceived unethical AI. Ethics is a market signal, not a press release. Bias and opacity create legal and trust exposure. Build red lines, testing, and escalation paths. Hold the line when targets tempt shortcuts. 5️⃣ Who owns an AI mistake when it happens? Who signs their name to AI decisions that go wrong? Personal liability is moving toward executives. ‘The vendor did it’ will not survive scrutiny. Name owners, forums, and incident playbooks. Run failure drills before the real incident. 6️⃣ Can we explain any AI decision clearly? Explainability is now an investor and regulatory ask. Boards must defend a hard AI call in plain words. If you can’t explain it, you can’t defend it. Log decisions, data, and model versions by default. Practice the briefing before you need the briefing. 7️⃣ Do we control AI risk in our supply chain? About 65% of AI risk rides on third parties. Opaque models and weak clauses become your liability. Audit the stack: data, models, and human review. Contract for transparency, testing, and remedies. Replace vendors who won’t meet your standard. AI readiness is not a project. It’s a habit. Governance is a daily practice, not a deck. Lead before regulators and headlines do. Is your firm AI-ready?

Real talk...most boards are flying blind on #AI. We still have leaders of orgs that think AI is a "bubble". They're approving AI budgets, reviewing AI risk disclosures, and evaluating AI-driven acquisition targets — but this isn't a technology problem. It's a governance problem. Three things change when a board gets AI-literate: 1. Better capital allocation. AI-literate leaders ask harder questions about build vs. buy, model lock-in risk, and whether the $5M "AI transformation" is actually a dashboarding project. They catch the gap between a vendor demo and production-grade infrastructure. 2. Sharper risk oversight. Data privacy, model hallucination, regulatory exposure — these aren't abstract risks anymore. A board that understands how LLMs actually work can pressure-test management's AI risk framework instead of rubber-stamping it. 3. More informed M&A. In healthcare IT alone, we're watching platform vendors like Epic absorb capabilities that used to justify standalone companies. A board that can't evaluate AI-driven disruption risk is approving valuations built on eroding moats. AI literacy at the board level doesn't mean every director needs to write Python. It means they need enough fluency to ask the right questions, challenge management's assumptions, and distinguish signal from noise in the fastest-moving area of enterprise technology. The boards that build this muscle now will make better decisions for the next decade. The ones that don't will wonder why their portfolio got disrupted by companies whose boards did. #leadership #PE

The New Governance Issues: AI Execution & Digital Readiness In more and more boardrooms today, I’m seeing a shift: AI execution, data governance, and digital readiness are no longer viewed as “technology matters.” Today they are increasingly viewed as governance matters. And activists have noticed. In recent months, activists have begun to probe companies on the gap between their stated digital ambitions and their actual ability to deliver measurable AI-driven value. They’re scrutinizing everything from data architecture and model-risk controls to adoption rates and ROI. In some campaigns, the pressure point isn’t compensation, it’s audit and risk oversight. For Boards and executive teams, the message is clear: If AI is truly “core” to strategy, there must be clear ownership, clear metrics, and clear evidence of execution. What I’m advising leaders today: • Treat digital and data readiness as part of enterprise risk—and review it with the same rigor you apply to financial controls. • Define 12–24-month AI value roadmaps tied to accountable owners, budgets, and hurdle rates. • Track results using business KPIs, not vanity metrics. • Strengthen model-risk governance: inventory, testing, monitoring, and documentation. • Educate the Board, especially Audit and Risk committees, so oversight keeps pace with velocity. • Ensure disclosures match reality, activists are reading between the lines. This isn’t about building flashy innovation labs. It’s about alignment, accountability, and execution, exactly the areas where governance matters most. The companies that get this right won’t just avoid activist pressure, they will accelerate value creation. #CorporateGovernance #Boards #ExecutiveLeadership #AI #DataStrategy #RiskManagement #DigitalTransformation

Most boards are asking the wrong question about AI. They are asking: "What technology are we deploying?" The better question is: "Are we governing the human side of that deployment with the same rigor we bring to financial oversight?" These questions and our experience are what drove my colleague Mary Lee Sharp, global CHRO, attorney, and board member, and me to write for the Silicon Valley Directors' Exchange on what active board governance of AI workforce transformation actually looks like. Enterprise AI adoption has reached 88%. Only about 6% of organizations are capturing measurable economic value from it. Digitally and AI-savvy boards outperform their peers by 10.9 percentage points in return on equity, while those without that fluency trail their industry average. That gap is not explained by the sophistication of the technology. It is explained by governance quality, change management depth, and investment in people. Boston Consulting Group (BCG)'s 10-20-70 rule cuts to the heart of it: successful AI transformations allocate 70% of their resources to people and processes, and just 30% to technology and algorithms. Most organizations invert that ratio. Most boards are not tracking whether their company is one of them. I have seen this from both sides. Architecting AI-enabled leadership programs across Fortune 100 enterprise operations taught me that the resistance is never about the tool. It is about trust, workflow disruption, and the fear that AI measures performance rather than supports it. In markets from the U.S. to across Asia, the organizations that move fastest are the ones where leadership makes adoption visible and safe, not mandatory and opaque. The right board questions are not about technology: Is management investing in structured adoption and training, not just tools? Are leaders equipped to manage human-and-AI workflows? Is change management embedded into the transformation plan? Are we measuring behavioral adoption, not just deployment milestones? As workplaces increasingly include humans, AI agents, and robotics operating in parallel, the governance of that transformation cannot be delegated to IT or innovation teams. It must begin in the boardroom. I speak on this topic and work with boards and senior leadership teams on the organizational readiness, change management, and cultural intelligence required to close the gap between AI ambition and AI impact. If your board or executive team is navigating this, I would welcome the conversation. https://lnkd.in/gtA4VVaH #BoardGovernance #AILeadership #OrganizationalTransformation #HumanCenteredAI #CorporateGovernance