AI Trends in M&A

Tech M&A is heating up, with 2025 on pace to match or beat previous annual highs for aggregate deal valuation as AI and $100M+ deals drive momentum. At the top, it's an arms race among giants. Meta broke its two-year acquisition drought to grab TWO voice AI startups. NVIDIA bought CentML for $400M. Apple's CEO signaled openness to large M&A for the first time in company history. For others, this isn't just about adding features anymore; it's about survival. With AI threatening to obsolete entire business models, companies are buying their way out of irrelevance. The incumbents have heard the death knell, and they're buying the bell ringers. The numbers tell the story: ↳Public SaaS acquisitions of AI startups will more than double YoY in 2025 ↳By year-end, they'll surpass 2022, 2023, and 2024 combined ↳AI startups are exiting 6 years faster than peers Can incumbents buy their way out of disruption? With deal sizes nearly doubling, companies are betting everything on yes. The other side of the M&A boom sees a PE roll-up renaissance. Private equity is salivating over the flood of companies that raised in 2021. They see complementary products ripe for bundling, overlapping costs ready for elimination, distressed assets, opportunity to create super-platforms, and of course, the opportunity to make everything "AI-first". The punchline: Whether it's strategics buying innovation or PE rolling up the wounded, tech M&A's new wave is just getting started.

The big megatrend in AI right now is the rise of AI teammates & agents - not just augmenting software, but replacing traditional services (10x the software market size). What's surprising is this evolution doesn't just impact tech; it may be opening up a whole new company building playbook well documented in private equity (PE) but previously not leveraged in venture capital (VC) backed companies: growth through acquisition. Here's the VC version of the M&A playbook: 1. Target: Find a traditional, low margin, services-based industry with low revenue multiples. 2. Acquire: Purchase companies at reasonable valuations to gain access to their customer relationships (and expertise). 3. Leverage: Use AI teammates / agents to replace the service layer, increasing gross margins. 4. Unlock: Transform the business model to achieve revenue multiples typically seen in high-growth software companies. The kicker? The hope is with this approach, distribution can be hacked, and companies could grow 4-5x YoY instead of 2-3x. One could envision this strategy in industries like accounting, market research, advertising, recruiting, etc. I'm intrigued. Services markets are 10x the size of software markets. If AI teammates can tap into a fraction of these markets, perhaps an opportunity exists to redefine entire industries and get to scale fast. Lot's of questions remain. It's not easy to pull off acquisitions. The change management required to go from services to AI won't be simple. Further, should equity dollars be used versus more common PE structures like debt? I'm excited to see how this trend evolves. What industries do you think could be ripe for this kind of disruption?

Bain’s latest M&A survey show just 21% of M&A practitioners are using GenAI, with 16% planning to. But those leaders are reaping real benefits: 54% see accelerated timelines, 33% say reduced cost, and 79% report less manual effort. But those are just the real basics of the value generated by using GenAI strategically for complex, high-stakes work. Companies that do more acquisitions are twice as likely to use GenAI. Over 60% of private equity firms are using GenAI in at least one aspect of sourcing, screening, or diligence. Screening and identification of targets is not just faster and easier, it is more likely to surface the best matches. Analyzing internal and external data, how industry landscapes are shifting, and the strategic implications can be done more efficiently and effectively. And the execution of M&A deals can be accelerated on multiple fronts, including drafting integration plans and transition service agreements. “Within the next five years, we expect every single step of the M&A process will be enabled by generative AI... Late followers in adopting generative AI for M&A are going to face an uphill battle,” says Bain. This is just one illustration that some of the greatest value from GenAI will be in strategy and complex strategic tasks. GenAI in strategy is at the core of my work today. I’m finding that suddenly the lights are switching on in boards and executive teams that this is a domain of immense potential value creation.

In M&A deal-making, it is not about man vs. machine. It is now about bankers who use AI vs. those who don’t. That’s the shift we are witnessing in real-time. And the article in AIM yesterday has just validated this. Gone are the days when networks and pitchbooks alone used to win you deals. Today, algorithms can spot exit signals, buyer intent, and synergy fit faster than the sharpest minds in the room, if you know how to train them. ⏺️ Let us face the reality - investment banking, especially in M&A, has long run on three things: - Networks - Nods across tables - And neatly packaged pitchbooks. But times are changing. ⏺️ Most deals today still rely on: - Public databases - Surface-level filters (like revenue, geography, or industry tags) - Executive introductions brokered by bankers But the reality? Startups are increasingly private, operating in stealth mode, or global. And by the time they show up on your radar… They’re already off the market. ⏺️ Enters AI: Your Most Powerful Companion Yet AI is quietly transforming the deal origination and screening process, and I say this not just as a technophile but as someone building in this space. > It can scan millions of data points across social media, customer reviews, hiring patterns, and product launches. > It can predict which companies are gearing up to raise, exit, or acquire, even before they announce anything. > It can spot synergies that don’t show up in CIMs or balance sheets, like culture fits, R&D trajectories, or supplier overlaps. As GrowthPal’s Amaresh Shirsat put it: “We’ve trained algorithms not just on public info, but on four years of proprietary M&A dialogues. That’s where the real signals lie.” ⏺️ What does this mean for firms like ours? > At STIR Advisors, we have already begun integrating AI-based tools and GenAI models for smarter screening, industry mapping, and cross-border deal scouting. ⏺️ We are building a framework where: - Strategic fits aren’t just matched by sector, but by intent, momentum, and complementary strengths - Market heatmaps evolve in real-time - And proprietary + public data feed into AI layers that enhance, not replace, our judgment ⏺️ But a major reality check: - Even the best algorithm can’t negotiate with egos. - Or sense boardroom power plays. - Or convince a founder to let go of their baby. As Deloitte’s Jayakrishnan Pillai puts it, ▶️ “AI works best where data is dense and time is short. Not where emotion and ambiguity dominate.” And as Shirsat sums it up: ▶️ “AI is still a co-pilot. And I hope it stays that way.” I suppose the future of #investmentbanking is shifting from being cold and robotic to more precise and pattern-aware. And more human, thanks to better tools. And while only a few players have started embracing this shift, the writing is clear: "The game hasn’t changed. But the playing field has." #investmentbanking #mergers #acquisitions #deals #AI #future #professionals

AI Won’t Replace Dealmakers, But It Is Becoming An Essential Tool AI is fundamentally reshaping the M&A landscape—not by replacing human judgment, but by enhancing efficiency, accuracy, and strategic insight at every stage of the deal process. From my experience, here’s where AI is making the biggest impact: Target Identification AI rapidly scans vast datasets to surface high-potential acquisition targets that align with strategic, financial, and cultural goals—often revealing opportunities traditional methods would miss. Due Diligence AI automates document review, contract analysis, and risk assessment. Natural Language Processing (NLP) tools quickly flag red flags, hidden liabilities, and key contractual terms—saving time and improving precision. Valuation and Forecasting Predictive analytics models assess historical performance and simulate growth scenarios, helping dealmakers better understand value, risks, and synergies. Deal Execution AI supports negotiation and execution by summarizing diligence findings, drafting memoranda, and even sourcing relevant case law—freeing up professionals to focus on higher-order thinking. Post-Merger Integration AI-powered tools streamline integration with task automation, milestone tracking, and synergy identification—critical for delivering long-term deal value. Continuous Market Monitoring AI keeps a constant pulse on the market, identifying new risks and targets to keep the pipeline fresh and relevant. The Bottom Line Speed. Accuracy. Insight. Efficiency. AI is making M&A faster, smarter, and less risky—ultimately enabling companies to extract more value from their transactions. When using AI always be sure to verify the data being provided. #MergersAndAcquisitions #AIinM&A #Dealmaking #CorporateStrategy #PrivateEquity #LegalTech #Innovation #DueDiligence #PostMergerIntegration #FutureOfWork

AI in M&A is not a pilot anymore. It is the playbook. KPMG’s new Mid-Year M&A Pulse makes it plain. 80% of corporates already use GenAI in deals. PE usage is over 90. And the impact is concentrated where value is won or lost, search and screen, and integration and separation. Focus on cycle time and execution quality. The report shows AI is lifting the front end by widening the target funnel and scoring fit, then protecting the thesis post-close by mapping systems, owners, and handoffs. Diligence use is rising too, which means cleaner inputs and faster IC. Translation for Main Street deals: faster yes or no, fewer surprises, and Day-1 that actually lands.. Ignore shiny tools that promise everything and measure nothing. Ignore long-horizon “transformation” narratives without a 30, 60, 90 plan. Ignore activity metrics. The winners in this data are tracking on-the-ground results like time to first qualified conversation, Day-1 task completion, and defect rates after cutover. Build a simple Deal Radar this week. List your top 25 targets. Write 8 scorecard lines that reflect your thesis, then automate enrichment with a lightweight workflow so each target gets a fit score and a one-page brief. If you do nothing else, this will cut false positives and move the right owners to the top of your call list. I help owner-led buyers in the Midwest do exactly this, search and screen that saves weeks, diligence with human-in-the-loop, and Day-1 control towers that keep the thesis on track. #MergersAndAcquisitions

M&A activity around AI is continuing to intensify. Thoma Bravo’s $2B acquisition of Verint, merging it with Calabrio, signals both a long-term bet on AI and, I believe, the start of a wave of AI-driven take-privates. First off, congrats to my past colleagues at Verint — very excited for you all. Over the past few months I’ve been sharing thoughts on the tectonic changes in AI and customer engagement, from Salesforce’s strategic acquisition of Informatica to strengthen its data layer, to private equity firms doubling down on AI applications. Yesterday’s move is another big marker in that strategic move. As we look at the strategic acquisitions in the AI space one thing is clear, things are moving fast! from AI infrastructure to applications and back again. This specific deal joins other AI-related acquisitions by Thoma Bravo in an exciting wave of consolidation for strategic and growth reasons across the industry. Why is this happening now? - AI is table stakes - Legacy software needs serious AI to stay relevant, and PEs see the opportunity. - Capital needs to be deployed - With fewer IPOs and low interest rates, acquisitions are becoming the strategic play. - Scale matters - Combining CX, VoC, conversational AI, and insights creates strong strategic advantages — improving efficiency, enabling cross-sell, and building durable strategic moats. What to expect next? More M&A. More consolidation. Expect PEs and strategic tech platforms to build integrated AI stacks, creating new market leaders through strategic inorganic growth.

The market reaction to recent AI megadeals is reinforcing a shift we’re seeing. Investors are concentrating capital where return sustainability is demonstrable, challenging previously assumed market positions and moats.   Two dynamics are now unavoidable for anyone making capital decisions within the AI ecosystem.   First: Valuation discipline is returning to AI markets. Despite continued depth of capital – with AI capturing close to half of global VC funding in 2025 and around 80% of dollars flowing into megarounds – access to capital is increasingly being determined by execution. Revenue durability, retention, margin expansion and the quality of growth are now central to how businesses are valued, financed and positioned for the next phase.    Second: Pressure across software is creating opportunities.  Public software stocks fell around 6.5% in 2025, versus a ~17.6% gain in the S&P 500 – and has persisted in 2026, with software ~-17.05% and the S&P ~+1.63% – driving a meaningful valuation gap. As AI reshapes workflows and compresses parts of the tool stack, that gap is prompting sponsors and strategics to reassess portfolios – where to double down, where to divest, and where selective M&A can accelerate AI adoption versus building it organically.

AI is beginning to break the startup acquisition playbook. Startups used to win on speed. Big companies had scale—but moved slow. That balance is gone. I’m seeing it firsthand: one intrapreneur with an AI stack, executive cover, and access to real data can now replicate what used to require a 10–15 person startup. And they can do it faster than the startup can raise its Seed round. So what are acquisitions buying as this unfolds? Often just permission, not capability. Code is no longer scarce. The emerging moats are: • Proprietary data rights • Distribution • Trust and compliance Enterprises already own these assets—so why pay a premium for the commodity? Early prediction: copying is becoming cheaper than integration. M&A shifts from innovation to legal clearance. The most dangerous founder in the next phase won’t be in a garage. It’ll be an intrapreneur with legacy data, AI leverage, and exec cover.

In 2024 I began building a database to track AI partnerships. Here’s what stood out in the past 3 months: ⁣⁣1️⃣ 𝗔𝗜 𝗶𝘀 𝗮𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 This is the first quarter where compute / AI infra is the dominant partnership category. Over a quarter of deals involve chip providers (NVIDIA, AMD, Cerebras, Intel, Samsung, Broadcom, Qualcomm), compute providers (hyperscalers, OCI, neoclouds), governments building sovereign AI infra (Japan, KSA, Germany), and power providers. 👩🍳 Interesting shift: focus on 𝗰𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 vs. off-the-shelf compute (e.g., Mistral × NVIDIA (optimizing latest family of models on NVIDIA hardware), OpenAI × Broadcom). 🏗️ The bigger-faster trend continues: multiple 𝗚𝗪-𝘀𝗰𝗮𝗹𝗲 projects announced (e.g., xAI × NVIDIA “Colossus 2”, Meta × Blue Owl “Hyperion”). 🍽️ Another shift: 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 → 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲. Deals like Fireworks AI × AMD and Baidu × Samsung are explicitly about serving/optimizing inference at scale. NVIDIA × Groq announcement confirms this trend. 2️⃣ 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆: 𝘁𝗵𝗲 𝗺𝗼𝗮𝘁𝘀 𝗮𝗿𝗲 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 We’re moving from setting standards (MCP, A2A) to real commercial interoperability. The headline example is Snowflake and Microsoft agreeing on “zero-copy, bi-directional” data sharing. Unthinkable a couple of years ago. Companies are increasingly willing to 𝘁𝗿𝗮𝗱𝗲 𝗼𝗳𝗳 some 𝗱𝗮𝘁𝗮-𝗴𝗿𝗮𝘃𝗶𝘁𝘆 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 to: • enable customers use AI services (MSFT) • remain the source of truth even when AI tooling lives elsewhere (SNOW) More deals like this: SAP ↔ MSFT & Databricks, SAP ↔ Snowflake, Snowflake ↔ Tableau. 3️⃣ 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 & 𝗜𝗣 𝗹𝗶𝗰𝗲𝗻𝘀𝗶𝗻𝗴 Two trends stand out: • data/IP licensing is 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗼 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗴𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴 (Meta x CNN, Fox News, Reuters, etc.) • OpenAI ↔ Disney is in a category of its own (~$1B), and could set a new 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗽𝗿𝗲𝗰𝗲𝗱𝗲𝗻𝘁 𝗳𝗼𝗿 𝗜𝗣. (See great article by Stratechery in comments.) We also see continuation of trends from previous quarters: • 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗶𝗻𝗴: Anthropic × Deloitte, Accenture, OpenAI × Intuit, Perplexity × Bharti Airtel • 𝗦𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻 𝗔𝗜: “OpenAI for Germany” with SAP, Arabic AI tools via Humain x Adobe x Qualcomm, Google × UK government • 𝗖𝗵𝗮𝘁𝗯𝗼𝘁𝘀 → 𝗲𝗮𝗿𝗹𝘆 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Perplexity × PayPal, Lovable × Atlassian, OpenAI × Instacart & Zillow