Technology executives are often consumed by daily operations, leaving little time to explore how AI can drive genuine business value. We’ve observed this disconnect repeatedly in our advisory work with ambitious technology firms. While AI promises competitive advantage, most companies struggle to translate its theoretical potential into tangible outcomes that improve ARR, profit margins, or company valuation.
This challenge is particularly acute for Chief Strategy Officers (CSOs). According to recent findings, 54% of CSOs report holding only a supporting role in AI strategy development despite being ideally positioned to align AI initiatives with business objectives. This strategic disconnect represents a missed opportunity that technology companies cannot afford as AI capabilities accelerate.
Value Gaps: The Starting Point for AI Investment
Our approach to AI strategy begins with a fundamental principle drawn from our M&A experience: identifying value gaps. Just as acquisitions are made to bridge capability gaps, AI investments should address specific business needs rather than being driven by technological fascination.
“An acquisition is made to bridge a value gap—something the acquiring firm wants to do but isn’t, the target firm fulfils this,” we explain to our clients. “AI investments should follow the same logic.”
However, AI presents a unique challenge because it evolves so rapidly that new capabilities often emerge before companies can identify relevant value gaps. This creates a tendency for bottom-up adoption rather than strategic implementation. We’ve observed AI tools that create internal efficiencies spreading organically through organisations via word of mouth, eventually becoming formalised into company policy.
Consider AI transcription services in virtual meetings. Once team members recognised the value of having meeting actions automatically summarised, the practice quickly became standard across many organisations. While this organic adoption creates value, it often happens without strategic oversight, leading to fragmented implementation and missed opportunities for systematic value creation.
Why CSOs Remain Sidelined in AI Strategy
The question remains: why are CSOs, who typically drive strategic value creation, frequently marginalised in AI initiatives? Our experience points to a problematic pattern where technology-focused teams—often R&D—identify AI capabilities and implement them independently, bypassing strategic oversight.
“This is dangerous,” we caution our clients. “Companies need to be value-driven, not solely technology-driven.”
When technically savvy teams lead AI initiatives without strategic input, implementations often optimise for interesting technological possibilities rather than business outcomes. These initiatives might demonstrate impressive capabilities but fail to address the organisation’s most pressing value gaps.
This disconnect also creates measurement challenges. Without clearly defined value gaps to address, organisations struggle to evaluate AI’s business impact, leading to continued investment without proportional returns.
Applying M&A Evaluation Frameworks to AI Investments
Through our work with technology executives, we’ve developed an approach that applies the rigour of M&A evaluation to AI investment decisions. This framework helps organisations assess AI opportunities with the same strategic discipline they would apply to potential acquisitions.
The process begins with strategy. We help clients systematically identify their value gaps—operational inefficiencies, market opportunities, or competitive vulnerabilities that could be addressed through strategic investments. This forms the foundation for targeted AI exploration rather than chasing capabilities without clear business purpose.
This work extends beyond initial implementation. Just as we examine post-acquisition integration when advising on M&A, we help clients consider how AI initiatives will integrate into their target operating model. This often reveals additional challenges, particularly regarding human resources.
“It’s not always possible to use the people you have, as most often they’re too busy to accept new responsibilities,” we find. “Identifying value gaps as part of this process is vital, and the work links back to a company’s target operating model.”
By starting with value gaps rather than technologies, organisations can evaluate AI investments based on their potential to close specific gaps. This approach transforms the discussion from vague notions of “AI value” to concrete business metrics tied to identified needs.
Governance in a Fast-Moving Space
The governance of AI initiatives presents unique challenges due to the technology’s rapid evolution. Traditional governance models often struggle to keep pace with AI’s capabilities and applications, creating a situation where oversight frequently plays catch-up to implementation.
We’ve observed that effective governance begins with executive-level understanding of AI capabilities. When leadership teams develop shared knowledge about AI’s potential, it “levels the playing field and allows strategic choices” rather than leaving decisions to technical specialists alone.
This inclusive approach ensures that strategic considerations guide AI implementations rather than technical fascination. It also creates space for CSOs to contribute their strategic perspective to AI initiatives, helping to align these projects with broader business objectives.
Critical Addition: AI Ethics and Risk Management Governance must now encompass AI ethics, bias mitigation, and regulatory compliance. With increasing scrutiny from regulators worldwide, including the EU AI Act and evolving UK AI regulations, organisations must embed ethical considerations into their AI governance frameworks from the outset. This includes establishing clear accountability structures, implementing bias testing protocols, and ensuring transparency in AI decision-making processes.
Measuring What Matters: The Value Gap Approach
A common pitfall in AI implementation is attempting to create AI-specific metrics rather than measuring the technology’s impact on business outcomes. Drawing on our extensive executive strategy experience, we advocate for a simpler, more effective approach: measure against the value gap.
“It’s not AI’s value—AI should be used to fill a gap, and once that gap is identified, it can be measured,” we explain to clients struggling with measurement frameworks.
This perspective shifts the evaluation from abstract notions of AI effectiveness to concrete business improvements. Even for AI implementations that weren’t strategically planned, reflection can reveal the value gaps they’ve addressed.
For example, when companies adopt AI meeting transcription services, they often discover that they had an unrecognised value gap in tracking action items between meetings. By measuring improved follow-through on commitments rather than the technical performance of the transcription service, they quantify business value rather than technical capability.
This approach connects AI investments directly to business metrics that matter: ARR, profit margins, and ultimately company valuation. It transforms AI from a technological curiosity to a strategic tool for value creation.
AI Vulnerability: A Forward-Looking Strategic Lens
Perhaps the most provocative aspect of our approach is examining AI vulnerability as a starting point for strategic planning. When onboarding new clients, one of our first questions is stark but necessary: “Are you at risk from AI making any of your business obsolete?”
This question reflects a fundamental truth about AI that many technology executives prefer to ignore: it democratises capabilities. Functions that once required specialised expertise can suddenly become accessible to competitors or customers through AI tools.
“AI democratises capabilities, so a company’s superpower becomes accessible to all,” we caution. “What if that capability you uniquely have was suddenly not so unique?”
Companies that anticipate this vulnerability can transform potential threats into strategic advantages. We’ve seen this pattern play out across industries, from legal research to financial analysis. When AI tools made basic analysis widely accessible, companies that saw the change coming redesigned their business models—embracing the technology themselves, increasing throughput, adjusting pricing strategies, and adding human expertise where it creates distinctive value.
Emerging AI Threats and Opportunities The landscape has shifted dramatically with the advent of multimodal AI models and autonomous agents. Key threats now include:
- AI-powered competitors entering markets with dramatically lower cost structures
- Customer self-service through AI reducing demand for traditional services
- AI-native startups bypassing traditional industry barriers
Conversely, opportunities include:
- Using AI to create new revenue streams through productisation of expertise
- Implementing AI-driven personalisation at scale
- Leveraging proprietary data with AI to create defensible competitive advantages
This forward-looking assessment helps companies not only protect existing business but also evaluate potential new ventures. In some cases, our analysis reveals that proposed initiatives face unacceptable AI vulnerability risk, helping clients avoid investments in areas likely to face AI-driven disruption.
Due Diligence Challenges in AI Acquisitions
For companies considering acquiring AI capabilities through M&A, we highlight a critical challenge: distinguishing genuine AI capabilities from marketing hype. The term “AI” has become increasingly diluted, applied to technologies ranging from sophisticated neural networks to basic rule-based systems.
“The biggest challenge with regard to AI and M&A is, does the target genuinely have AI capabilities?” we ask clients considering acquisitions in this space.
This verification requires specialised technical due diligence beyond typical M&A processes. Key considerations now include:
- Assessing the quality and uniqueness of training data
- Evaluating model architecture and performance benchmarks
- Understanding intellectual property ownership, particularly regarding training data
- Reviewing AI talent retention strategies
However, our experience suggests that once technical capabilities are verified, cultural integration challenges for AI firms aren’t substantially different from other technology acquisitions: “Tech companies are similar whether it’s a regular SaaS firm or AI firm.”
This insight helps acquirers focus their due diligence on validating technical capabilities while applying proven integration approaches for the organisational aspects of the transaction.
Practical Steps for CSOs to Lead AI Strategy
For CSOs seeking to move from supporting roles to central positions in AI strategy development, we recommend a proactive approach that leverages their strategic perspective while expanding their technical understanding.
“CSOs must take the lead and look to the market, work with product managers,” we advise. “The landscape is changing fast and in ways that can be truly disruptive, so CSOs must expand their skills beyond just understanding their markets and competitors to also include AI, the opportunities it brings and the threats.”
This requires CSOs to develop sufficient AI literacy to engage meaningfully with technical teams while maintaining their strategic focus on business outcomes. By building these bridges, CSOs can ensure that AI initiatives address genuine value gaps rather than pursuing technological capabilities for their own sake.
We encourage CSOs to apply their business strategy acumen to help transform AI ambition into quantifiable value. This includes:
- Systematically identifying organisational value gaps that AI might address
- Evaluating potential AI investments with the same rigour applied to acquisitions
- Developing governance models that balance innovation with strategic alignment
- Creating measurement frameworks based on closing identified value gaps
- Assessing AI vulnerability across the organisation’s product and service portfolio
- Building cross-functional AI steering committees that include legal, HR, and finance perspectives
- Establishing AI centres of excellence that bridge technical and business domains
By taking these steps, CSOs can move from peripheral roles in AI initiatives to central positions guiding these investments toward measurable business outcomes.
A Value-First Approach to AI
Looking across our decades of experience in strategy and M&A, the most successful organisations approach AI with the same discipline they apply to acquisitions—starting with clearly defined value gaps and measuring success based on closing those gaps.
This value-first approach differs markedly from technology-driven AI implementations that often create impressive capabilities without clear business purpose. By focusing on value gaps, organisations ensure that AI investments address genuine business needs rather than technological fascination.
As AI continues to evolve at a remarkable pace, this disciplined approach becomes increasingly important. Organisations that align their AI investments with strategic objectives will create measurable business value, while those pursuing AI capabilities without clear business purpose will likely generate impressive demonstrations but limited returns.
For CSOs specifically, this represents both a challenge and an opportunity. By applying their strategic perspective to AI initiatives and expanding their technical understanding, they can move from supporting roles to central positions guiding these investments toward measurable business impact.
The path from AI ambition to measurable business value isn’t built on technological sophistication but on strategic discipline. By identifying value gaps, applying rigorous evaluation frameworks, developing appropriate governance models, and measuring outcomes based on closing identified gaps, organisations can transform AI from a fascinating technological capability to a powerful driver of business value.
About Lighthouse Advisory Partners
Lighthouse Advisory Partners helps guide technology executives toward value-driven AI implementations. Drawing upon decades of experience in strategic M&A advisory, we empower VC, Private Equity, corporate development teams and C-Suite leaders to apply the same rigorous discipline to AI investments as they would to acquisitions – focusing relentlessly on value gaps and measurable outcomes. Unlike larger, less hands-on firms, our partner-led approach brings real-world expertise directly to your challenges, ensuring your AI ambitions translate into tangible improvements in ARR, profit margins and company valuation. Is your organisation poised to truly unlock the business value of AI? Contact Lighthouse Advisory Partners today for a consultation and let us help you transform technological potential into strategic advantage, starting with identifying your most critical value gaps.