Ignorance Will Not Save You : Why Leadership and Boards Cannot Afford to Sideline AI Consulting

Ignorance Will Not Save You : Why Leadership and Boards Cannot Afford to Sideline AI

In British boardrooms and leadership meeting rooms, the refrain still echoes:

"AI is not really our priority right now."

Quietly, that sentiment reveals something else entirely: systemic inertia. Many leaders, especially those from earlier decades, cling to comfort, confusing safety with strategy. I have lived this in boardrooms myself, unveiling technology no one had seen, armed with prototypes and clear ROI, only to watch the moment slip away. Leaders said it was impressive, but they did not act. They turned the wheel again and again until the wheel fell off.

What these leaders fail to grasp is a fundamental truth: doing nothing is never neutral. In a market accelerating this fast, inaction is a bet against the future and one step closer to irrelevance. Every day of delay compounds the disadvantage.

The signals are not subtle

The evidence of this acceleration surrounds us, impossible to ignore for those willing to look. KPMG's UK survey found investor demand for AI returns jumped from 68 per cent at the end of 2024 to 90 per cent in the first quarter of 2025 (KPMG, 2025). In three months, the patience for "wait and see" evaporated completely.

This shift in investor sentiment reflects deeper market realities. The Stanford AI Index reported global adoption rising from 55 per cent to 78 per cent in a single year (Stanford HAI, 2025). While some executives are still debating whether AI matters, their competitors are embedding it into operations and moving the dial on performance metrics that actually count.

The productivity gains are no longer theoretical - they're measurable and significant. A study across more than 5,000 customer service agents showed that those with access to generative AI delivered 14 per cent more output, with the steepest gains among junior staff (NBER, 2025). These aren't marginal improvements; they're competitive advantages that compound daily.

The contradiction at the top

This creates a profound contradiction that should trouble every shareholder. Boards are meant to safeguard shareholder value. Executives are measured on margins, EBIT and resilience. AI influences each of these levers directly and measurably. Any AI consulting advisor would say that to treat AI as optional, is to step away from the very responsibilities leaders are appointed to uphold.

Yet the gap between pilot programmes and production deployment remains vast. Recently published MIT's Project NANDA suggested only a small fraction of pilots reach production. The underlying sample and access conditions drew criticism (Futuriom, 2025; Everyday AI, 2025) in my view the research methodology was deeply flawed, yet the practical point stands: projects that are not anchored in governance, skills and capital allocation rarely survive the transition from experiment to operation.

This failure pattern isn't accidental - it's predictable. Brynjolfsson and Hitt's productivity J curve (2003) explains the mechanism perfectly. General purpose technologies lag before they lift productivity because firms must build intangible assets first: new skills, redesigned processes and governance frameworks. Leaders who treat AI as a demo never build those foundations, ensuring their pilots remain permanently stranded in proof-of-concept purgatory.

The psychology of denial

The problem runs deeper than strategy - it touches the very psychology of leadership under pressure. Decision makers carry several kinds of pain simultaneously, creating paralysis where action is most needed.

Functionally, they worry about disrupting fragile systems they only partly control. Emotionally, they fear reputational damage if a risk goes wrong. Socially, they fear being seen as reckless in front of peers or shareholders. So they retreat to what feels safer: the familiar rhythm of meetings, reviews, and incremental adjustments that change nothing fundamental.

I've watched this play out many times over the years, in boardrooms across the UK. Leaders nodding at evidence, praising the work, then returning to comfort. They were not protecting the company. They were protecting themselves, often at the company's expense.

The generational divide in this behaviour is stark and telling. The data reveals that CEOs under 45 adopt AI at roughly four times the rate of those over 65 (ArXiv, 2025). In Japan, younger CEOs were 23 per cent more likely to adopt AI, and they delivered measurable gains in cost reduction, revenue growth and innovation (Kikuchi, 2025). Meanwhile Gen Z doubled their AI proficiency in a single year, while older cohorts lagged; many firms now use reverse mentoring, with younger employees coaching senior leaders (EY, 2024).

However, this isn't ultimately about age - it's about mindset and the courage to lead through uncertainty. Leaders without vision are not leading. They have become caretakers of decline and irrelevance, managing the slow fade rather than driving the bold moves that create the future.

The bubble defence

When pressed on their inaction, another escape route appears with predictable frequency: "AI is just a bubble." Valuations look high, venture funding looks exuberant, comparisons with the dot-com era surface in every quarterly review.

This argument misses the fundamental difference between financial bubbles and infrastructure revolutions. Bubbles can distort prices and destroy individual companies, yet they do not erase the underlying infrastructure. The dot-com crash ended many business ventures but not the internet itself. The fibre and server build of that period became the foundation of the modern economy, enabling new business models that emerged to drive economies across the globe.

Today's AI investment follows the same pattern, but at unprecedented scale. Analysts estimate that capital spending on compute and data centres through 2030 will run into the trillions of dollars, the majority tied to AI capacity (McKinsey, 2025; Brookfield, 2025). This is concrete, steel, silicon and energy - the modern equivalent of building railroads or laying fibre cables across countries. The large technology platforms are committing extraordinary budgets because they understand the sequence: rails first, then trains, then traffic.

The infrastructure arms race

It feels like an arms race because that's precisely what it is. Nations and corporations are competing for compute, energy and talent with the intensity of wartime mobilisation. Some will lose in this competition - that's certain, and these casualties will include both companies and investors who bet wrong or moved too slowly.

However, the infrastructure will remain, and once it is in place, adoption accelerates and profitability compounds in ways that make early hesitation look catastrophically expensive. The companies building this infrastructure aren't gambling on hype; they're positioning for the inevitable transformation that follows.

This transformation is already reshaping economic fundamentals in ways that most leaders haven't fully grasped yet.

AI as a new driver of GDP

For decades United States GDP has been fuelled by consumer spending, representing close to 70 per cent of output. That fundamental balance is shifting before our eyes. Media and sell-side analysis now attribute a meaningful share of recent GDP growth to AI-related investment in intellectual property, data centres and power infrastructure. Recent estimates attribute as much as one third of first-half 2025 growth to AI capex, with point estimates of roughly half a percentage point added to annual GDP (Reuters, 2025a; Reuters, 2025b; Business Insider, 2025).

The economic story has moved from consumption to construction, from buying things to building the future. Infrastructure now drives growth in ways that fundamentally alter the competitive landscape for every industry.

This shift carries profound implications that extend far beyond technology companies.

Lessons we should not have to relearn

History offers brutal clarity about what happens to companies that misread technological transitions. Borders outsourced e-commerce to Amazon and collapsed (Time Business, 2011). Debenhams delayed digital transition and fell in 2020 after two centuries on the high street (The Guardian, 2020). Kodak built the first digital camera but never restructured around it, and lost everything (Garvin, 2016).

In each case, technology did not fail these firms; leadership did. They saw the future coming and chose comfortable denial over uncomfortable adaptation. Their shareholders paid the ultimate price for that choice.

The pattern repeats with depressing regularity, yet some companies learn from others' failures and break free from the pack.

The companies that broke from the pack

There are firms that refused to stay stuck in pilot purgatory, and their results illuminate the path forward for those willing to follow.

Klarna, the Swedish fintech, embedded AI directly into operations rather than treating it as a side project. By 2024, its deployment of generative AI matched the output of hundreds of service agents - not through layoffs, but through radical productivity enhancement. That transformation happened because capital allocation, retraining programmes and governance frameworks were planned together from the beginning, rather than as disconnected afterthoughts (MIT, 2024).

Motorola took a different but equally decisive approach, building a measurement framework that compared tasks with and without AI enhancement. By linking AI activity directly to productivity metrics and EBIT impact, it transformed novelty into operating advantage. Again, this demanded deliberate capital investment, structured workforce development and governance integration rather than hoping for organic adoption (MIT, 2024).

In the UK, NatWest Group publicly committed in 2024 to AI-enabled compliance and customer service across its operations. The bank invested heavily in infrastructure and structured comprehensive staff retraining programmes. It anchored the entire programme to governance frameworks and margin improvement rather than waiting for perfect clarity about future developments (NatWest, 2024).

The pattern across these success stories is consistent and instructive. Progress flows from structural commitment and integrated planning, not from token experiments designed to appease group think or satisfy board curiosity without driving real change.

The human capital disruption

While companies debate strategy, workforce transformation accelerates around them. The World Economic Forum projects that 22 per cent of jobs will be disrupted by 2030 and 39 per cent of core skills will be fundamentally transformed (WEF, 2025). Boards that ignore this reality will face workforce stagnation and margin compression simultaneously - a combination that destroys competitive position faster than most leaders anticipate.

The solution isn't to avoid this disruption but to lead it strategically. Age-diverse boards often create more business value, with studies citing uplift in the low single digits when different generational perspectives are properly integrated. Younger leaders bring digital fluency and pace of adoption. Senior leaders bring institutional context and operational resilience. Both capabilities are essential to manage disruption of this scale successfully (WEF, 2025).

The key is combining these strengths rather than allowing generational differences to create decision paralysis.

Trust as currency

Perhaps counterintuitively, speed and caution must work together in AI deployment. MIT found that the overwhelming majority of executives would delay deployment to ensure safety and reliability. That finding reveals where sustainable competitive advantage will ultimately sit: in the ability to move fast while building genuine trust (MIT, 2024).

Trust is not a soft concept in this context - it is the hard prerequisite for adoption and sustained growth. Accenture reports that more than three quarters of executives consider trust the primary determinant of AI's long-term business impact. Without rigorous bias audits, transparency protocols, explainability frameworks, security measures and clear accountability structures, regulators, employees and customers will reject AI systems regardless of their technical capabilities, and even the most promising programmes will stall (Accenture, 2025).

This creates both challenge and opportunity for leaders willing to invest in getting the fundamentals right from the beginning.

Five steps from an AI Consultancy that leaders cannot ignore

When boards ask where to begin, I always start with business impact rather than technological experiments. Most pilots never meaningfully touch the balance sheet. They absorb time and political capital, generate impressive demos, then fade away without changing anything that matters to shareholders.

First, tie every AI investment directly to revenue, cost reduction or risk mitigation from day one. That means mapping your entire value chain systematically and selecting the three specific points where AI can shift EBIT in measurable, trackable ways. If you cannot draw a clear line from AI deployment to financial performance, you are building an expensive science project, not a business capability.

Second, make human capital investment march in lockstep with technology investment. I have seen firms commit millions to sophisticated tooling while simultaneously freezing training budgets. That choice produces predictable results: stalled adoption, rising employee resistance and higher attrition among the people you most need to retain. Workforces do not magically absorb new capabilities. Reskilling budgets need to move in step with technology budgets, with clear pathways for digital literacy, human-AI augmentation and strategic redeployment.

Third, treat governance as the foundation of sustainable trust, not as a compliance box-ticking exercise. Establish comprehensive bias and performance testing protocols, robust model risk management and security frameworks, and crystal-clear lines of accountability that run straight to the C-suite. When these elements arrive late in the process, programmes collapse under internal scepticism and external regulatory scrutiny, wasting years of investment.

Fourth, embrace scenario planning that goes beyond pilot programmes to model fundamental business reconfiguration. Pilots do not prepare you for market transformation. Scenarios do. Model the near-term cases that could reshape your competitive position overnight: a rival halves customer onboarding time through AI; a regulator imposes stricter assurance requirements; a key customer segment begins expecting AI-enabled service as the baseline standard. If these scenarios are not systematically modelled and costed, your exposure is already accumulating silently.

Finally, communicate with clarity and consistency at every level. Silence breeds fear and resistance. Clarity builds trust and engagement. Investors want to understand the strategic journey even if returns are phased over multiple quarters. Employees want to understand how their roles will evolve and what support will be available during the transition. Tell them directly, repeatedly, and with concrete examples. Then tell them again, because transformation messaging requires constant reinforcement to overcome natural resistance to change.

Readiness check and discovery call

To help leaders assess their current position honestly, I use a short diagnostic with both corporate and SME clients. The questions cut through strategic ambiguity to reveal actual readiness:

• Do you have a board-approved AI governance policy that includes specific accountability structures?

• What percentage of your workforce has completed meaningful AI training that goes beyond basic awareness?

• Are your data assets clean, properly catalogued and genuinely suitable for AI applications?

• Do your current AI initiatives tie directly to EBIT impact or are they limited to experimentation and proof-of-concept work?

• Do you run comprehensive disruption scenarios on a regular cadence and refresh action plans against emerging threats and opportunities?

  
  

As part of this article, I am offering a free 30-minute discovery call to discuss your specific situation and immediate next steps. Book directly here: https://calendly.com/mark-733/30minInsert

My perspective

I have been in those rooms where the future gets decided - or where it gets deferred until it's too late. I have seen leaders stall repeatedly, protecting themselves from short-term discomfort rather than protecting their companies from long-term irrelevance. They convinced themselves that waiting was wisdom, that caution was leadership. It was surrender disguised as strategy.

The harsh truth is that the firms which commit early to AI, invest systematically in their people and build robust governance into their core operations will compound returns in ways that make current valuations look conservative. The companies that hesitate, that wait for perfect clarity or complete consensus, will become case studies for future business school students to analyse.

When this technological horizon fully arrives - and it is arriving faster than most leaders acknowledge - there will be no neutral ground left to occupy. No golden parachutes will soften the landing for companies that chose comfort over capability. No second chances will be offered to leaders who mistook analysis paralysis for prudent management.

The market is already sorting winners from losers based on decisions being made right now, in boardrooms across Britain and around the world. The question is not whether AI will transform your industry - it's whether you will lead that transformation or be transformed by it.

Ignorance will not save you. Competence will.

Are you AI Ready? Take the test here: https://e5h6i7cdnkyy.manus.space/

References

• Accenture (2025) Technology Vision 2025. Available at: https://www.accenture.com (Accessed: 27 August 2025).

• ArXiv (2025) AI Investment and Firm Productivity. Available at: https://arxiv.org/html/2508.03757v1 (Accessed: 30 August 2025).

• Brookfield (2025) AI data centre infrastructure spending to exceed $7 trillion over next decade. DataCenterDynamics, 8 August. Available at: https://www.datacenterdynamics.com (Accessed: 29 August 2025).

• Business Insider (2025) AI spending boosted US GDP growth by 0.5 percentage points. Available at: https://www.businessinsider.com (Accessed: 31 August 2025).

• Everyday AI (2025) Do 95 per cent of AI pilots fail? Available at: https://www.youreverydayai.com (Accessed: 30 August 2025).

• EY (2024) Role reversal: Gen Z workers ready to lead AI. The Australian, 10 June. Available at: https://www.theaustralian.com.au (Accessed: 28 August 2025).

• Futuriom (2025) Why we do not believe MIT NANDA’s AI study. Available at: https://www.futuriom.com (Accessed: 28 August 2025).

• Garvin, D.A. (2016) Kodak’s downfall was not about technology. Harvard Business Review. Available at: https://hbr.org (Accessed: 30 August 2025).

• KPMG (2025) AI ROI Pulse Survey Q1 2025. Available at: https://home.kpmg/uk (Accessed: 29 August 2025).

• Kikuchi, T. (2025) AI Investment and Firm Productivity: Evidence from Japanese Enterprises. Available at: https://arxiv.org/abs/2508.03757 (Accessed: 31 August 2025).

• McKinsey (2025) The cost of compute: the $7 trillion race. Available at: https://www.mckinsey.com (Accessed: 28 August 2025).

• MIT (2024) AI Strategy Playbook. MIT Technology Review Insights. Available at: https://www.technologyreview.com (Accessed: 30 August 2025).

• NBER (2025) Generative AI in customer support: productivity impact. Available at: https://www.nber.org/papers/w31161 (Accessed: 29 August 2025).

• NatWest Group (2024) AI investment for compliance and customer service. Available at: https://www.natwestgroup.com (Accessed: 27 August 2025).

• Reuters (2025a) US GDP revised higher on AI investment. Available at: https://www.reuters.com (Accessed: 28 August 2025).

• Reuters (2025b) AI capex accounts for more than one third of GDP growth. Available at: https://www.reuters.com (Accessed: 23 July 2025).

• Stanford HAI (2025) AI Index Report 2025. Available at: https://hai.stanford.edu (Accessed: 31 August 2025).

• Teece, D.J. (2007) Dynamic capabilities and sustainable enterprise performance. Strategic Management Journal, 28, pp. 1319 to 1350.

• The Guardian (2020) Debenhams to close with loss of 12,000 jobs. Available at: https://www.theguardian.com (Accessed: 28 August 2025).

• Time Business (2011) Why Borders failed. Available at: http://content.time.com (Accessed: 30 August 2025).

• WEF (2025) Future of Jobs Report 2025. World Economic Forum. Available at: https://www.weforum.org (Accessed: 29 August 2025).