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This article distils key insights from Dartmouth’s recent roundtable with senior Corporate Development and M&A leaders, examining how AI is beginning to shape deal origination, execution, and decision-making. While adoption remains early, teams are actively experimenting with tools like ChatGPT, Claude and AlphaSense to streamline research, drafting and diligence. Five themes emerged, highlighting that AI will augment rather than replace human expertise, with structured adoption expected to accelerate over the next 12–18 months.
It was a pleasure to bring together senior corporate development and M&A leaders for an open discussion on how artificial intelligence is beginning to influence the dealmaking process. While AI adoption across in-house corporate development remains in its early stages, the conversation revealed a strong sense of experimentation and a growing appetite for practical integration.
Many organisations are now experimenting with tools such as ChatGPT, Claude and other LLMs (large language models) to support drafting, research and internal communication – yet very few have fully integrated these technologies into core processes.
The discussion set out to explore not only where AI is adding value today, but also what needs to change for it to evolve from an experimental tool into a reliable capability that supports origination, execution, and strategic decision-making. This summary captures the key themes and reflections from our recent conversation, an opportunity to look back on the insights shared and consider how AI is beginning to reshape corporate development and M&A.
Across the discussion, five key themes emerged that captured both the current reality and the future potential of AI in corporate development and M&A. Participants spoke candidly about the opportunities AI presents, as well as the challenges around data quality, trust and security. There was a shared belief that AI will augment rather than replace human capability, reshaping workflows and expectations while reinforcing the importance of judgment, context and collaboration.
Participants agreed that AI adoption within corporate development and M&A remains in an exploratory phase. Most organisations are running pilots or using AI for isolated, task-specific applications rather than implementing enterprise-wide systems. The most common uses today include drafting investment memoranda, preparing committee materials, and producing internal summaries. Despite this increasing experimentation, there is currently no clear market leader or definitive platform in the space.
Many expect the ecosystem to consolidate around a small number of major hyperscalers, with “above-the-line” SaaS tools that sit on top of LLMs unlikely to provide a long-term solution. Many investment banks are adopting a cautious “wait and see” stance, trialling systems such as Rogo and Mosaic.pe without yet committing to full deployment. One challenge consistently raised was how to audit AI-automated workflows when manual documentation disappears, as well as concerns about hallucination and factual reliability that continue to limit confidence in AI outputs.
Deal origination emerged as the area where AI is already demonstrating clear value. Many participants described using ChatGPT and other LLMs as a powerful research companion – effectively a “new Google” that can accelerate market mapping, summarise investment memoranda, and produce initial target lists far more efficiently than traditional methods.
Beyond the mainstream tools, platforms such as AlphaSense and proSapient were highlighted for their ability to synthesise expert calls and analyst research into actionable insights. Others, such as Inven, help teams discover less visible targets through intelligent web scraping, while Clay enables the automation of outreach workflows. Together, these tools are reshaping how origination teams identify and qualify opportunities, especially in the context of smaller “bolt-on” acquisitions where information can be fragmented or hard to source.
Despite the efficiency gains, there was strong agreement that human oversight remains essential. AI can streamline research and summarisation, but validation, context, and judgment must continue to come from experienced professionals.
AI is increasingly taking on a more structured role in due diligence and deal execution, particularly as external providers integrate these tools into their offerings. Participants described growing use of AI for reviewing documents, analysing data rooms, and identifying potential red flags earlier in the process. AI is also increasingly used to summarise expert calls and consolidate research outputs, though privacy and confidentiality concerns were acknowledged as constraints on how and when these tools can be applied.
Tools such as Fireflies and Granola, originally developed in the venture and technology sectors, are beginning to enter the M&A diligence workflow – but most participants agreed they are only suitable for later-stage processes once NDAs and data safeguards are in place.
The conversation also touched on the rise of custom GPTs built within enterprise ChatGPT environments to automate specific internal tasks, such as reconciling PAYE data or consolidating disparate financial records. These models are already saving time and improving consistency, while tools like Notebook LM are being explored for synthesising large document sets and even generating interactive or audio summaries.
Overall, these developments point to a future where AI enhances analytical depth and efficiency, provided governance, accuracy, and security are rigorously maintained.
A consistent message across the group was that AI will not replace corporate development professionals but will fundamentally reshape their workflows. The technology is increasingly viewed as an enabler that automates repetitive administrative tasks, freeing teams to focus on strategic activities such as origination, market development, and relationship management.
There was no expectation of team downsizing; instead, participants foresaw a gradual rebalancing of skills, with more emphasis on analysis, interpretation, and data fluency. While AI literacy is not yet a formal hiring requirement, many expect it soon will be. The conversation also highlighted that the effectiveness of tools like ChatGPT or Claude depends on user engagement – specifically, on learning how to craft effective prompts, use agents, and train models. Those who invest time to develop these skills are likely to derive the greatest benefit.
Embedding AI successfully, it was agreed, will rely as much on culture as on technology: encouraging curiosity, rewarding experimentation, and integrating new tools seamlessly into existing workflows rather than layering them on top.
Trust, governance, and data integrity were seen as essential prerequisites for any meaningful AI deployment. Participants emphasised that without reliable, auditable data and clear confidentiality frameworks, organisations cannot safely operationalise AI outputs. The ability to trace and verify how AI-generated content is produced was seen as critical for both regulatory and reputational reasons.
Many participants indicated that they are waiting for their existing CRM systems to incorporate AI functionality rather than migrating to newer, untested platforms. This approach reflects a preference for stability, integration, and control. Ultimately, responsible adoption will depend on thoughtful vendor evaluation, careful attention to data security, and a strong internal culture of compliance.
The discussion closed with a consensus that AI’s immediate impact will lie in enhancing productivity and decision support, not replacing human expertise. By automating routine and administrative tasks, AI can enable teams to focus on higher-value strategic work, make better-informed decisions, and lay the groundwork for more data-driven innovation in the future.
“The real opportunity isn’t in the tool itself, but in how much time we’re willing to spend learning to use it properly – in training it, refining it, and embedding it where it genuinely makes a difference.”
For now, AI in corporate development and M&A remains in a formative phase, but its potential is undeniable. The next 12 to 18 months are expected to bring a shift from experimentation to structured adoption, as teams build confidence, establish governance frameworks, and begin to scale their use of AI in daily practice.
| Use case | Examples |
| Research and drafting | ChatGPT, Claude |
| Market intelligence | AlphaSense, ProSapient |
| Target discovery | Inven |
| Workflow automation | Clay |
| Data synthesis | Notebook LM |
| DD and conversation analysis | Fireflies, Granola |
| Banking/CRM pilots | Rogo, Mosaic.pe |
Our AI in Corporate Development and M&A roundtable highlighted that while AI will not replace human judgment, it is already reshaping how information is sourced, synthesised, and applied. The technology’s near-term promise lies in supporting smarter, faster, and more informed decision-making – freeing professionals to focus on what truly matters: strategy, relationships, and value creation.
We look forward to continuing the conversation as adoption accelerates and best practices evolve.
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