From SaaS to Agents: The Coming Shake-up in Enterprise Deals 

June 2025 

We are in the early innings of a profound dislocation in enterprise technology—one catalysed by artificial intelligence and the swift verticalisation of its capabilities. This is no ordinary platform shift. AI is not merely a technological upgrade; it is a strategic rupture rewiring business models, hollowing out legacy moats, and redrawing the economic boundaries of software itself.

Among the most exposed are private equity firms holding portfolios of second- and third-tier SaaS companies. These firms gorged on the predictable cash flows and modest innovation cycles of the cloud era. Many of their assets were not market leaders but modest beneficiaries of cheap capital, recurring revenues, and a roll-up playbook that rewarded scale over substance. That playbook is now in tatters. As AI agents replace not just augment entire workflows, legacy SaaS firms face a trilemma: erosion, compression, or reinvention. For some, irrelevance may come sooner than expected.

Venture capital is also facing a reckoning. Funds that deployed capital at the zenith of the SaaS valuation bubble now find themselves holding stakes in companies that may never justify their last-round prices. These startups were built on assumptions about software economics land-and-expand sales models, incremental product improvements, human-intensive services that AI is rapidly dismantling. In sector after sector, agentic systems are unbundling once-defensible verticals and recomposing them into more fluid, intelligent stacks. The options for VCs are unpalatable: restructure, liquidate, or swallow the markdowns.

The result is an M&A environment increasingly shaped by distress, not exuberance. The next wave of dealmaking will be defined less by synergies and more by salvage. Expect carve-outs of underperforming units, sales of stranded assets, and transformation-led takeovers where acquirers bet on their ability to inject AI capabilities at speed. Yet this moment also demands precision. The AI stack its foundational models, orchestration layers, and interface conventions is still in flux. Missteps now could mean backing the wrong abstraction layer or the wrong technical architecture. Strategic boldness must be tempered with timing.

Compounding the challenge is the limited capacity of many mid-market PE firms to execute such pivots. Their historic advantage lay in leverage and consolidation, not deep operational transformation. Few have partners fluent in AI architectures, let alone the teams required to rewire product roadmaps or build intelligent workflows. For them, scooping up discounted SaaS firms risks turning into a slow-motion value trap rather than a bargain.

Still, this moment offers rare opportunity. As legacy models crumble and AI-native insurgents rise, the software industry is undergoing a generational reshuffle. The spoils will not go to the cautious, nor to the nostalgic, but to those who can tell apart what is broken from what is merely bruised and who are willing to act decisively.

AI-Driven Market Dislocation Opportunities

From SaaS to Agentic Automation: A Structural Shift

The enterprise software stack is fracturing. For a decade or so, SaaS reigned supreme cloud-based, workflow-centric tools that captured billions in revenue and defined operational norms across sectors. Now, a silent upheaval is underway. The rise of agentic AI autonomous, context-aware systems that perform tasks rather than merely manage them is beginning to unseat the very foundations of the SaaS model. For dealmakers and strategists, this dislocation signals less a moment of panic than one of possibility.

At its core, the shift is architectural. Traditional SaaS applications be they CRMs, ERPs, or project trackers are increasingly viewed as inert repositories. Their value lies not in action but in organisation, demanding constant human input and navigation. In contrast, AI agents promise to disintermediate the user altogether. Rather than waiting for instructions via clicks and keystrokes, they observe, decide, and act pulling data, initiating processes, and closing feedback loops with minimal friction. It is not a better dashboard; it is the end of dashboards altogether.

This transition from interface to intelligence has begun to reverberate through the M&A market. The earliest signs are visible in private equity portfolios burdened with second-tier SaaS assets built for a different era businesses optimised for linear workflows, not intelligent orchestration. The emerging playbook replaces these with platforms that embed agents capable of collapsing multi-step processes into autonomous operations.

Strategic buyers are already on the move. Legal AI startups such as Harvey, which compress hours of paralegal labour into seconds of agentic output, are natural complements to enterprise CRMs looking to deepen their vertical stack. Healthcare-focused agents like Abridge offer similar adjacencies in regulated industries where contextual precision is at a premium. Atlassian, long a staple of developer collaboration, may see its future not in ticketing systems but in acquiring agentic DevOps tools like Devin or Codium replacing backlog with execution.

Meanwhile, incumbent giants such as SAP and Workday face a strategic fork. They can either layer thin AI features atop existing dashboards or leap into a different category entirely by acquiring orchestration-layer startups that reimagine HR, finance, and procurement as agent-driven domains. The latter offers more risk, but also more defensibility.

The reshaping of enterprise software is far from complete. But the direction of travel is clear: less interface, more intelligence; fewer dashboards, more decisions. As this wave accelerates, those who mistake the old playbook for a lasting foundation may find themselves outpaced not by hype, but by autonomy.

Agentic Infrastructure Bottlenecks and Strategic Buyouts

The explosive growth of AI agents has brought underlying infrastructure constraints into sharp focus. As demand for compute surges, limitations in data center capacity, energy efficiency, and orchestration capabilities have become significant bottlenecks. Hyperscalers once thought to have near-infinite scaling power are now approaching the outer bounds of their capital expenditure plans and energy availability. This dislocation is creating fertile ground for both strategic and financial buyers to step in.

In response, we are likely to see a wave of infrastructure-focused M&A. Companies like Equinix and Digital Realty may pursue acquisitions of energy-efficient, AI-optimized data center startups such as Lambda Labs to meet the growing demand for GPU-dense hosting environments. Nvidia, aiming to reduce its reliance on external hyperscalers and strengthen control over model deployment workflows, could acquire orchestration startups that specialize in scalable AI infrastructure management. Meanwhile, Schneider Electric, which already plays a key role in power and thermal systems, may look to acquire companies developing next-generation liquid cooling technologies or GPU-optimized power management systems to maintain its strategic relevance in an increasingly AI-centric infrastructure landscape.

“Shovels to the Miners”: Arms Dealers of the AI Era

The reshaping of the AI value chain extends far beyond applications, reaching deep into the foundational tooling layers that enable intelligent systems to operate at scale. Technologies such as vector databases, retrieval-augmented generation (RAG) pipelines, and agentic orchestration platforms have emerged as critical infrastructure serving as the modern-day "picks and shovels" of the AI revolution. These components do not merely support AI applications; they define the speed, accuracy, and contextual intelligence with which agents function.

This shift presents compelling opportunities for strategic M&A. Companies like Snowflake and Databricks are well-positioned to acquire vector database startups such as Pinecone or Weaviate, allowing them to extend their data platforms into the AI-native infrastructure layer. Adobe, intent on staying at the forefront of digital creativity, may target multi-modal content generation startups to power the next wave of AI-driven design tools. Meanwhile, Twilio and Segment could seek to embed agentic intelligence into their customer data stacks by acquiring platforms that enable hyper-personalized, real-time engagement thereby transforming static data pipelines into autonomous, adaptive experience engines.

Sector-Specific AI as Strategic Hedge

Vertical AI startups are increasingly being viewed as strategic hedges by incumbents facing the threat of disruption from more generalized AI platforms. These specialized startups benefit from proprietary access to domain-specific data, tighter alignment with regulatory frameworks, and the ability to deliver rapid, measurable ROI advantages that horizontal AI platforms often struggle to match. This makes them especially attractive acquisition targets in sectors such as legal, healthcare, cybersecurity, and finance.

A recent example is Thomson Reuters’ acquisition of Casetext, a legal AI company. The deal reflects a broader trend: incumbents are racing to acquire embedded intelligence rather than risk being overtaken by nimble, AI-native challengers. Similar moves are expected across other data-intensive and regulation-heavy industries such as financial services, manufacturing, and education, where internal AI capabilities remain limited and external disruption looms.

This environment is also prompting a fundamental rethinking of SaaS consolidation strategies. For years, SaaS acquirers particularly private equity firms built value by stitching together portfolios through adjacent logos and incremental feature sets. That playbook is rapidly becoming outdated. The emergence of agentic AI has shifted the strategic focus: rather than layering features, acquirers now aim to embed AI-native automation into legacy workflows. The goal is no longer just product expansion but full-scale operational leverage delivering cost savings through labor replacement and intelligent workflow execution.

In this context, a large swath of mid-market SaaS firms is now ripe for agentic overhaul. These are companies where growth has plateaued, customer churn is rising due to stagnant innovation, and large datasets remain underutilized. They are particularly vulnerable to disruption, yet uniquely positioned for transformation. By taking such firms private, recapitalizing them, and embedding AI agents into their core workflows, acquirers can reduce operational expenses and relaunch them as “agent-native” platforms. High-potential targets include customer support SaaS companies with outdated architectures, ERP providers serving industrial sectors with legacy systems, and CRM platforms that control sizable but inert user data each offering the raw ingredients for intelligent automation, but lacking the execution layer that agentic AI can now deliver.

Target Underutilized Data Assets in Industrial and Retail Firms

Traditional industrial, retail, and service-delivery businesses are sitting on vast reserves of both structured and unstructured data, yet few have the in-house capabilities to convert those assets into sustained competitive advantage. Logistics providers, for example, still route fleets largely on historical heuristics; retailers warehouse CRM and ERP data in silos that never inform real-time merchandising decisions; and many manufacturers rely on manual quality checks instead of AI-driven anomaly detection. A pragmatic route to unlocking this dormant value is for investors to partner with vertical-AI specialists, layer intelligent platforms onto existing systems, and recast the equity thesis around “AI-enabled operational leverage.” By demonstrating faster cycle times, reduced waste, and predictive maintenance, even a mature industrial asset can be rerated as a tech-enabled growth story.

Reinvent BPO/KPO Firms with Agentic Delivery

Back-office outsourcing vendors particularly mid-sized BPO and KPO providers in areas such as QA testing, HR administration, and customer service face an even more acute threat: the rise of agents-as-a-service platforms that deliver comparable outputs at a fraction of the cost. An acquisition strategy here begins by securing the sticky enterprise contracts these firms already hold, then rebuilding the delivery stack around agentic workflows. Once repetitive tasks are automated, the acquirer can expand margins and reposition the business as an AI-powered managed-services provider rather than a labor-arbitrage shop.

Infrastructure-Adjacent SaaS: Buy, Pivot, Rebuild

Developer-tooling companies in API gateways, CI/CD, observability, and testing are also vulnerable to obsolescence as “self-healing” agents become standard within engineering teams. Many of these firms trade at depressed valuations after growth slowed in the post-SaaS correction. Buying them now, embedding an agent orchestration layer, and relaunching as intelligent developer platforms can restore relevance and unlock cross-sell opportunities throughout the software-development lifecycle.

Agentic Turnarounds of Over-Levered SaaS

Finally, a large cohort of late-stage or private-equity-backed SaaS vendors generating roughly $20 million to $100 million in annual recurring revenue, but struggling with churn and bloated go-to-market costs are ripe for AI-driven turnarounds. By replacing sales-development, onboarding, and quality-assurance teams with domain-specific agents, an acquirer can cut operating expenses by 30 to 50 percent. Intelligent product-led onboarding reignites growth, improved margins reset valuation multiples, and the transformed asset becomes an attractive exit within a three-to-five-year horizon.

For investors and acquirers, the implications are seismic. The market is fragmenting and rebundling at once. Static dashboards and siloed applications hallmarks of the traditional SaaS era are being rendered obsolete by agents that traverse functions, systems, and datasets. Entire categories of enterprise software are now vulnerable to substitution.

The spoils will accrue to those who move with urgency scanning for legacy vendors ripe for reinvention, surfacing underleveraged troves of enterprise data, and backing or buying the next generation of AI-native challengers.

Crucially, it is not enough to build agents. The differentiator lies in the speed and precision with which they are operationalised integrated into workflows, scaled across departments, and turned into defensible margin expansion. As ever, disruption creates dislocation and dislocation creates deals.

Those who read the moment right will not merely survive the reshuffle. They will define the next software epoch.