Whilst AI has be around for many years in the tech environment, arguably it has only gained a profile with the public, organisations and investors over the last couple of years. Today, the first question for executives and boards in many businesses is “how can AI help us with…”. Whether that is for market and competitor information, redesigning internal processes, improving customer facing interactions, reducing costs and/or driving performance and efficiency.
This is understandable given the profile and acceleration of AI generally, ease of access and use of platforms providing large language models (LLM) and the multiple agentic use cases that organisations, tech firms and consultancies can identity; and increasingly deliver.
All organisations, including consultancies, will and should investigate how AI can support business operations given the speed and cost-saving opportunities that the technology can potentially provide.
This article isn’t a review of AI pros and cons, or a summary of the multiple AI applications organisations can and do adopt at pace – as there is much written about that already.
Rather it is observation and a potential concern that businesses may become over reliant on AI, without challenging the accuracy or quality of the outputs, or interpreting and assessing the implications for their business strategy.
The Speed of AI Adoption Is Outpacing Governance
Boards should assume that over reliance on AI will erode organisational knowledge, which can be a particular concern given the level of AI ‘hallucinations’, increasing the risk of flawed decision making.
It may also lead to skill erosion as specialists transfer from ‘originators’ to ‘checkers’ thereby reducing critical thinking, competence and expertise.
A 2026 BCG global survey of C-suite leaders and senior executives found that half were already observing de-skilling within their organisations. The report concluded that, unless companies deliberately preserve human judgement and critical thinking, overreliance on AI could weaken the quality of strategic decision-making. This will be a recurring theme in this article.
In addition to poor decision making and skills reduction, there is also a sensitivity and risk regarding confidential company information being shared in insecure environments.
And, if businesses are accessing the same or similar sources via LLMs, they may receive the same or similar outputs.
With these points in mind, and particularly the last point, we believe that there is considerable value in organisations truly understanding and considering their unique view of the world; their market, their customer’s need and competitors.
AI may not be able to accommodate or acknowledge these critical nuisances, which can be very valuable and lead to potential competitive advantage. So, if every competitor has access to the same AI capabilities, where does competitive advantage come from?
We, and others like BCG and McKinsey, believe that the answer is to be found in excellent executive judgement and decision making driven by excellent analysis and interpretation of complex and nuanced situations, such that AI may not reduce the importance of business strategy but will in fact increase it.
Further, effectively analysing and contextualising internal and external data and insights, at pace, combined with effective integration to delivery and performance will significantly contribute to achieving advantage.
The Hidden Risk of Trusting AI Too Much
Given the ‘first question’ noted above, there is a risk that the focus on, combined with the volume of AI-enabled opportunities highlighting what a business could do may distract or deflect from what the business should do.
The business strategy being pursued by the organisation is a function of the growth ambitions, risk appetite and financial capability of the company, reflected by the executive team attitudes and behaviours. And set in the context of a dynamic market environment.
Any AI outputs – i.e. market research and/or AI use cases, need to be sense checked filtered through these various lenses and perspectives, combined with efficient human judgement, to ensure that prioritisation and adoption are in line with and are enhancing the design and delivery of the business strategy.
Some businesses may not yet be interrogating AI outputs, use cases or applications with sufficient rigour and applied judgement. As a result, it would be far too easy for the “tail to be wagging the dog” if organisations are not careful, with potential strategic advantage being lost.
Building Confidence in AI-Enabled Decisions
To avoid this, boards and CEOs should work together to establish clear AI governance, supported by education for board members on AI’s capabilities and limitations, so that decisions are guided by strategy rather than hype.
Specifically, boards and executive teams should assess AI-driven recommendations as potential strategic issues rather than IT or operational processes. To ensure these recommendations align with business strategy, leadership teams should define clear AI policies, link investments to measurable ROI and enterprise value and ensure human-decision making is prioritised to assess and validate AI-generated recommendations prior to execution.
Actionable steps boards can take to ensure AI-business strategy alignment include:
- Fundamentals: is the strategy documented, up-to-date and is there clear focus clarity and alignment regarding the strategic ambitions across the board and executive team.
- Define an AI Posture: For example, determine if the organisation aspires to be an AI leader, follower, or considered adopter. Review this regularly to respond to technological, regulatory, market trends and competitive developments.
- Establish a Governance Framework: Adopt or adapt structured frameworks to govern AI output evaluation and development, ensuring recommendations fit business, customer and regulatory requirements, facilitated via Risk and/or Audit committees.
- Link initiatives to Enterprise Value: Avoid isolated and micro pilots and develop clear KPIs, such as customer acquisition, satisfaction and retention, revenue growth, or time-to-market to measure the ROI of AI-driven recommendations, and connection to business value drivers.
- Ensure Data Quality: AI outputs are only as reliable as the underlying data – internal and external. Boards and executive teams should review data governance policies to ensure the inputs are accurate, unbiased, and compliant.
As we help clients analyse, develop and implement growth and performance optimisation strategies across multiple service sectors, we are well placed to help businesses appraise, validate and challenge AI outputs, with a critical analysis and particular interpretation for the firm, and then helping facilitate and support the strategy-fit assessment and executive decision-making process.
These are interesting times and new approaches will be needed to help assess opportunities effectively.
If you’d like to discuss this blog post or share your own perspective on the issues covered, please get in touch or comment via LinkedIn.
Established in 2012, Whitecap Consulting is a regional strategy consultancy headquartered in Leeds, with offices in Manchester, Milton Keynes, and a presence in Birmingham, Bristol and Newcastle. We typically work with boards, executives and investors of predominantly mid-sized organisations with a turnover of c£10m-£300m, helping clients analyse, develop and implement growth strategies. Also, we work with clients across a range of sectors including Financial Services, Technology, FinTech, Outsourcing, Consumer and Retail, Property, Education, and Professional Services, including Corporate Finance and PE.