Is Your Organisation’s AI Adoption Piecemeal?

Boards face increasing pressure to understand why and how AI technologies matter at the strategic level. Yet much of the current dialogue is dominated by hype, tools, and experimentation rather than governance and strategy. This article explores the challenges boards face, the evidence that oversight is decisive, and how Anekanta® equips leaders to move from superficial adoption to sustainable value.

Why Boards Struggle with AI

Gen AI as ‘Fast-Food’

Artificial intelligence is widely defined as technologies capable of acting autonomously, learning from patterns, making predictions, and generating content without explicit programming. However, in practice the term has become synonymous with generative AI, now viewed as the “go-to” business tool.

This narrow perception often results in superficial adoption. Gen AI can deliver instant productivity gains, drafting reports faster, answering customer queries, or supporting internal knowledge search. But these are equivalent to fast-food: quick fixes that satisfy immediate needs but provide little long-term nourishment.

The real value lies in using AI to transform business performance at scale. AI will not, on its own, set a company’s vision, define its mission, or formulate competitive strategy. Organisations discover quickly that it is not technology holding them back, but the absence of strategy, risk intelligence, governance, and leadership oversight.

Boards must begin to see AI not as a technical tool, but as a system-wide, cross-functional capability that requires direction at the highest level.

Strategy and Governance – Not Technology – Unlock Value

Evidence from leading institutions demonstrates that sustainable value from artificial intelligence is contingent on governance, strategic alignment, and oversight. Here are a range of additional extracts which underpin this thought:

AI impact at scale is most strongly correlated with CEO and board oversight. Without it, more than 80% of firms see no enterprise-level gains.
(McKinsey, 2025)

MIT (2025): Almost 95% of generative AI pilots do not progress because of organisational and governance deficiencies rather than technical limitations.
Deloitte (2024): Risk and regulation have now overtaken cost and technology as the primary barrier to scaling AI.
Gartner (2025): Weak data governance and insufficient enforcement mechanisms prevent organisations from moving beyond experimental pilots.
Stanford HAI (2025): Responsible AI incidents are increasing more rapidly than organisations can respond, exposing governance shortcomings.
KPMG (2025): Employees are frequently deploying AI covertly without training or policy, creating unmanaged risk.
World Economic Forum (2025): Artificial intelligence is amplifying cyber and supply-chain vulnerabilities, placing resilience at risk.

The conclusion: sustainable AI value is only achieved when strategy and governance are owned and directed from the boardroom.

The Anekanta® Framework: Building Strategic Nutritional Value

Five years ago, Anekanta® anticipated these adoption challenges and created the Anekanta® AI Governance Framework featured by the OECD and adopted by the Institute of Directors. Built on 12 core AI governance principles, it equips boards and executives with the fundamentals required for AI strategy and governance success, for example:

Establish clear strategic objectives for AI.
Define accountability and ensure independent oversight.
Align investment with measurable outcomes.
Anticipate regulatory and reputational exposure.

The framework is deliberately high-level, providing boards with strategic direction. Its principles are operationalised through international standards and regulation, including: ISO/IEC 42001 (AI management systems), ISO/IEC 42005 (impact assessment), ISO/IEC 23894 (risk management), the NIST AI RMF, IEEE standards, ETSI specifications, and the EU AI Act. These mechanisms enable functional leaders to execute, while accountability remains firmly at board level.

The framework is also powered by Anekanta® AI Risk Intelligence, applied in our risk evaluation missions. This uniquely identifies inference challenges particularly in high-risk domains under the EU AI Act such as automated decision-making based on biometric categorisation.

From Principles to Practice

Boards often ask how governance principles can be embedded into day-to-day oversight. Anekanta® provides a direct answer through its one-day CPD-certified workshop (6 CPD points), designed as the natural entry point for executives and non-executives.

The workshop:

Introduces AI concepts and terminology linked to familiar board-level use cases.
Explains the origins of AI governance from intergovernmental treaties to OECD AI principles.
Applies the 12 principles in plain terms in a business context.
Connects each principle to real-world evidence and regulation.
Examines high-profile use cases, highlighting both successful and failed approaches.
Illustrates the inconsistency of Gen AI outputs and the need for clear purpose, scope and literacy.
Equips boards to set strategy and distinguish governance from operational delivery.
Provides a clear roadmap to action, whether towards ISO/IEC 42001 certification, EU AI Act compliance, or broader strategic assurance.

Download the CPD workshop brochure.

The 12 Principles Explained

The table below illustrates how each Anekanta® principle translates into practical meaning, the evidence supporting it, and how we enable organisations to act. (Examples given; workshops and scope of work are tailored to each client’s context.)

No.	Principle	What It Means	Evidence (2024–25)	How Anekanta® Delivers
1	Board accountability	Board/CEO formally owns AI strategy and governance	McKinsey, MIT	Board briefings, strategy design, risk intelligence reports
2	Audit & measure	Metrics are essential to track success and risk	Deloitte, Stanford	KPI design, audit protocols, ISO 42001/NIST preparation
3	Independent Review Committee	Neutral oversight and challenge from a multi-disciplinary panel	MIT, McKinsey	IRC setup, purpose setting, reporting structures
4	Set high-level goals	AI tied to strategy, beyond pilots	McKinsey	Strategic facilitation, roadmap design
5	Data sources	Trustworthy, traceable data is non-negotiable	Gartner	Data stewardship frameworks, lineage mapping
6	Impact assessments	Systemic risk evaluation is mandatory	Deloitte	EU AI Act/DPIA playbooks, risk evaluations
7	Training	All roles—not just tech teams—need AI literacy	KPMG, MIT	Board/staff training, policies, continuous learning
8	Privacy & compliance	AI must uphold laws and avoid risk vacuums	Deloitte	Privacy impact reviews, supplier evaluation
9	Monitor regulation	Fast-changing global landscape demands adaptation	Stanford, EU AI Act	Horizon scanning, gap analysis, compliance playbooks
10	Secure by design	Cybersecurity and supply-chain resilience must be embedded	WEF, Gartner	Threat modelling, secure AI Software Development Lifecycle, vendor frameworks
11	Testing	Pre-deployment and runtime evaluation are critical	Gartner	Red-teaming, evaluation protocols, runtime checks
12	Review cycle	Governance must be continuous, not one-off	McKinsey, Stanford	Quarterly reviews, audits, risk refresh cycles

Equipping Boards for Strategic AI

Artificial intelligence is not a panacea. Without board-led strategy and governance, adoption risks becoming fragmented, costly, and unrewarding. Boards that apply the Anekanta® AI Governance Framework lead AI as a strategic capability ensuring alignment with objectives, compliance with regulation, and delivery of measurable business advantages that build resilience and fuel future growth.

To equip your board and senior leadership with the essential knowledge, contact us to arrange your one-day CPD-certified workshop.

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Use of Generative AI: Generative AI tools may be utilised in research and drafting processes. All published materials are subject to substantive human review, professional judgment and oversight prior to release.