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AI technologies are swiftly transforming industries by enabling market differentiation through innovative products, driving operational efficiencies, and unlocking new markets. As AI becomes a core technology enabler for businesses, conducting thorough AI tech due diligence is increasingly important. Overlooking the implications of AI could mean missing out on substantial value or exposing your organisation to risks. In this article, we explore the importance of reviewing the AI landscape in M&A transactions, equipping you with key insights for a successful acquisition.
While the opportunities presented by AI are significant, they come with specific challenges that must be addressed as highlighted below.
- Lack of understanding of AI models’ limitations
Many buyers struggle to understand AI's limitations due to a gap in technical knowledge and the complexity of AI systems. This can lead to overestimations of AI capabilities, especially in customer-facing or regulated industries, leading to inappropriate investments in companies that may not deliver the expected value. - Lack of awareness on AI-driven value creation potential
A private equity firm or a corporate company might overlook key AI-driven value creation opportunities, such as: 1) personalised customer experiences; 2) tailored AI features to differentiate itself in the market; 3) predictive analytics; and 4) intelligent automation - all of which could substantially boost the company’s competitive edge. Acquirers need to carefully assess the art of the possible and how AI can help achieve their objectives. - Inability to clearly articulate technological differentiation
The fact that a company uses AI doesn’t mean its solution is unique or that competitors can’t easily copy it. Buyers need strong evidence that the target company's AI features are unique and innovative. This often requires a thorough understanding of the company's unique processes, the value of its proprietary data, and the research and development efforts invested (i.e., intellectual property). Additionally, the acquirer’s ability to further innovate and the expertise of their internal AI team is important.
Key questions that you need to address
Uncovering what really lies behind AI is complex and involves more than just technological considerations. To achieve success and mitigate AI risks, companies must develop a comprehensive understanding of AI across multiple dimensions. Below are the key areas and questions that need to be addressed:
AI Strategy
- Is the company’s AI strategy clear and well-aligned with the overall M&A and value creation objectives?
- Is the contemplated company at risk of being disrupted by the market, or is it gaining a competitive advantage because of AI?
- Are the appropriate foundational components (e.g., team skills, data maturity, infrastructure) addressed prior to launching AI projects post-acquisition?
- Are initiatives structured such that there is an appropriate evolution in complexity (e.g., gates, validation, and adjustment of AI investment cases) and that value can be confirmed before investing more?
Pitfall:
As AI is a constantly evolving domain, target companies and buyers may lack appreciation for the necessary level of effort and potential requirements, increasing the likelihood that projects go over timeline and/or budget. If management does not carefully coordinate and stage initiatives, they may plan projects inefficiently, resulting in a longer lead time to achieve value and lower returns on investment, loss of competitive edge, or complete write-offs of AI investments.
Team & Talent
- What AI capabilities already exist in-house? Have skills been developed internally, or have resources been hired with existing experience?
- Can the existing resources effectively deliver against the AI initiatives?
- What is the target’s organisational structure, and how does it facilitate cross-functional collaboration?
- What level of educational support is available, and how does this support employee AI upskilling?
Pitfall:
Companies may not appreciate how quickly AI advancements are being made. Those that do not keep pace with learning the latest developments risk spending time on sub-optimal solutions. Additionally, if the organisational structure does not promote cross-functional engagement, solution development risks not being as valuable to users as expected.
Data Assets
- What are the company’s unique data assets, and how are these leveraged within the AI strategy to gain a competitive edge?
- Is it clear whether data are shared or dedicated? Are they accessible and available?
- What data integrations exist, and how can these be used to drive new insights?
- Does the organisation have sufficient data assets to support planned AI initiatives?
Pitfall:
Before buying a company, the buyer needs to understand the value of the data assets to assess the opportunity to utilise them to their fullest extent. AI solutions should incorporate this information to drive competitive differentiation.
Governance & Processes
- What committees (Centres of Excellence, and other structures) exist to enable oversight of AI initiatives and ensure appropriate security, compliance, and privacy considerations are appreciated?
- What Responsible AI processes are in place to ensure large language models (LLMs) used are free from potential biases?
- What model guardrails are in place to avoid prompt injection attacks and potentially damaging responses?
Pitfall:
Organisations incorporating customer-facing AI solutions without appropriate governance and processes risk reputational damage, legal repercussions, and compliance violations.
Technology & Infrastructure
- What are the technological choices made, and how do they impact the cost and scalability profile? (e.g., cloud, on-prem, hybrid, etc.)
- Is the company leveraging appropriate solutions to enhance pipeline/workflow automation?
- Is the data model constructed to facilitate usage by AI?
- Is the company using natural language query (NLQ) tools to perform analytics?
Pitfall:
Companies that do not have a foundational infrastructure for their data will not be able to capitalise on AI solutions that could bring substantial value.
IP & Modelling
- How does the target define and integrate AI into the product architecture? Does it rely on open AI services or its own intellectual property?
- Who owns the intellectual property (IP) rights for any third-party developed software?
- Are there any contractual obligations or restrictions that could impact the transaction?
What processes does the company use for managing machine learning (ML) and large language models (LLMs), such as evaluating models and updating them?
Pitfall:
Buyer companies might overestimate the real value of the AI solutions presented by the seller. Also, target companies that are not leveraging their data to fine-tune foundational LLMs risk competitors easily replicating GenAI solutions and taking market share.
Seeing the results from case studies
To help you understand the tangible impact AI can make in M&A transactions, we present a series of case studies that highlight concrete outcomes achieved through AI tech due diligence. These examples showcase how we helped organisations identify significant cost savings, enhanced productivity, and unlocked new revenue opportunities.
Case Studies
Anticipate and get prepared ahead for your upcoming acquisitions
Given the rapidly evolving AI landscape, with new advancements occurring in months rather than years, firms conducting M&A need to assess where a company’s AI progress stands today and what characteristics provide indications about their ability to adapt in the foreseeable future.
To increase efficiency and ability to capture the AI potential in upcoming acquisitions, key measures can be taken:
- Clarify the strategic objectives of conducting AI tech due diligence
A clear articulation of the strategic objectives for conducting AI tech due diligence is paramount for ensuring that the M&A strategy aligns with the company's broader business goals. Companies should define specific expected outcomes and the impact of AI in the transaction. It’s also essential to assess how the target's AI assets and competencies can integrate with or complement the target company's existing technology stack and business models. - Assess and strenghten your internal AI capabilities
In preparation for potential acquisitions, organisations should conduct a thorough internal assessment of their own AI capabilities, including the buyer’s team ability to understand investment cases based on the target’s AI strategy. Understanding internal strengths and weaknesses can help identify gaps and the ability to incorporate opportunities coming with the transaction. - Early involvement of data and AI leader
Involving a Data and AI leader early in the due diligence phase ensures that AI opportunities can align with strategic business goals intended to be achieved as part of the transaction. Their expertise will allow for better implementation and a quicker realisation of ROI.
These actions will enhance acquirers’ readiness before looking at the AI landscape of a target in a potential acquisition.
In conclusion, AI can no longer be overlooked, as it holds significant potential for value creation while also posing potential major business risks. By evaluating a target's AI maturity, aligning solutions with business goals, and addressing potential risks early, you can separate hype from reality, identify key opportunities, and assess whether the company can remain competitive in the AI era.
While the AI can be assessed during the due diligence phase, it is also relevant for private equity firms to review the AI maturity when assessing their portfolio companies.
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