Market research and strategic planning form the basis of an M&A roadmap even before any specific targets come into focus. This is where an acquirer determines why to pursue a transaction, not where.
AI can help automate market research by complementing human awareness. In analyzing market trends and identifying potential growth areas, it help set clear business objectives and growth strategies.
AI can also enhance strategic planning. If people define various potential scenarios, AI can simulate them to help evaluate courses of action.

Example: A technology giant might use AI to analyze market data and identify a niche market with high growth potential. The result is a defined strategic objective to acquire a startup in that space.

With the opportunity defined, an acquirer moves to identify specific potential targets and use competitive and market intelligence to evaluate them.
AI can bring speed and accuracy to target identification by scanning vast amounts of data and following it to entities that meet defined criteria. This ability far exceeds what humans can do in the same amount of time, but it complements the qualitative assessments that count alongside the quantitative ones.
In a similar way, AI can process large volumes of competitive and market information to vet potential targets for their competitive impact.

Example: AI might sift and process a high volume of corporate or patent data to pinpoint a two-year-old startup with an innovative technology that complements a larger, more mature acquirer’s product line.

With a target in sight, it’s time to dive deep into everything the acquirer can responsibly learn, and AI couples well with human insight in turning over all the right stones.
AI can help to automate the due diligence process by reviewing and reporting on financial, legal, and operational documents—not only summarizing their contents and tallying quantitative elements from within them, but also highlighting potential risks and opportunities that humans may then evaluate.
AI can further contribute to target risk assessment by analyzing historical data, extracting patterns and anomalies, and flagging areas of concern.

Example: That two-year-old startup doesn’t have a century’s worth of financial statements, but what’s there is detailed and merits close analysis. AI reviews the documentation and identifies a handful of potential legal liabilities for the human team to investigate.

Now the sides are joined in talks, and it’s time to see if they will end up joined for real. Coming to a mutually satisfactory arrangement is a human process informed by massive data volumes.
In crafting the structure of a potential deal, AI can simulate potential options and their likely outcomes to illustrate which set of terms may work for both parties.
Valuation is an indispensable element of this step, and AI can help automate the valuation process not only by processing data that’s already ready for analysis, but also by extracting information from unstructured documents and feeding them into the analysis.

Example: Faced with a large volume of legal contracts and financial documents, a human team scans them to find the occasional insight. But AI extracts information from them to turn unstructured input into structured data, ready for analysis that will help shape the valuation and deal.

Once the parties agree to a deal, there’s plenty of work ahead to turn two entities into one. Integration planning and project management require a dual focus on the big picture and the smallest details.
AI can assist integration planning by spotting potential value-creating synergies—and potential risks—where they might be difficult for humans to uncover given the time and scope of their work.
Managing the many individual projects that make up an integration effort is another area where AI can complement people by keeping tasks on-time and on-budget.

Example: AI identifies key areas where the target startup’s technology can be integrated into the acquirer’s product line in ways that not only combine value but create it to form measurable synergies.

Just as M&A doesn’t begin with the “A,” it doesn’t end with the “M,” either. Once the formerly separate companies have merged, integrated, and settled into combined operations, it’s still vital to keep watch on how well expectations are panning out and performance is living up to them.
AI can enhance performance monitoring by continuously reviewing operational data in ways that outperform human capability, flagging areas in which reality is falling short or improvement appears possible.
Real-time data analytics can help identify these opportunities to do better, define them, and suggest steps to carry them out.

Example: With the former startup now fully integrated into the new parent, AI continues to monitor key performance indicators (KPIs) such as revenue growth and client retention. Where adjustments to improve performance appear likely, it calls them out.