Welcome to a new era of tech investing

The software investment landscape is undergoing a structural reset

Software investing is at a turning point. For years, predictable growth and reliable margins made software the most attractive sector for private equity. That advantage is fading. Annual revenue growth that once sat comfortably around 20% has slipped to about 10%. Net revenue retention has dropped by roughly eight points since 2021. Deal activity has cooled, and aging portfolios have become more common. Investors are discovering that the assumptions powering the SaaS boom, steady expansion and high predictability, no longer apply in the same way.

The reasons go deeper than simple market cycles. Artificial intelligence has altered the physics of software economics. It challenges the core of what made SaaS models powerful: recurring revenues, stable customer relationships, and near‑zero marginal costs. Investors who treat today’s slowdown as temporary risk misunderstanding the structural shift taking place.

Leaders should not see this period as decline, but as recalibration. The fundamentals of building valuable digital companies remain, deep understanding of customer needs, efficiency in scaling, and operational discipline. But what’s changing is how value will be created and measured. Growth will come less from incremental expansion and more from deploying technology that creates measurable outcomes for users.

Executives who accept that reality early can position their portfolios for leadership in the next cycle. The right move now is to reassess risk exposure, reconsider underperforming assets, and build conviction around where the next durable advantages will lie. Every transformation in technology reshapes value creation; this one is reshaping it faster than most.

AI is redefining due diligence in software investments

The traditional due diligence playbook no longer works. The old metrics, ARR (annual recurring revenue), NRR (net revenue retention), and simple growth multiples, were built for a stable SaaS world. That world assumed low marginal costs and predictable demand. AI doesn’t follow those rules. It changes how companies create value, generate revenue, and even defend their moats.

Artificial intelligence brings new risks and opportunities that investors must evaluate with precision. A strong diligence process now starts with two questions: How much can AI enhance or replace the workflows a product supports? And how likely is it that AI could make parts of that product obsolete? The answers shape valuation more than any traditional revenue metric.

Executives need to dig deeper than surface-level claims about “AI strategy.” The real question is proof. Can a company show evidence that its AI initiatives are creating measurable results, better efficiency, stronger customer adoption, or new product traction? Narrative without numbers no longer carries weight.

Leaders should also recognize that AI alters the pace of due diligence itself. Technology cycles are shorter, market winners are emerging faster, and data-driven analysis has become essential. This means the gap between identifying a trend and acting on it has narrowed sharply. Those who stay anchored to outdated signals risk missing compounding opportunities.

To navigate this environment, general partners must combine technological understanding with operational discipline. AI is not just a category of innovation; it’s a new variable in every investment decision. The investors who learn to measure it accurately and act decisively will define the next generation of returns.

Portfolio companies must actively integrate and scale AI

Artificial intelligence is no longer optional, it’s a core requirement for sustained performance and competitiveness. Companies that rely on older operational or product models risk losing relevance faster than ever before. The opportunity now lies in embedding AI at the heart of both internal operations and the product roadmap. That means using AI to automate repetitive workflows, accelerate decision-making, and create solutions that deliver measurable outcomes.

Many incumbents face a tough transition. They must balance maintaining existing revenue streams while shifting significant resources toward AI transformation. The process demands focus and speed. Executives must have the confidence to reallocate budgets, shut down less strategic projects, and redirect talent where AI can generate tangible progress. Success will depend on leadership’s ability to pair vision with disciplined execution.

Zendesk offers a clear case in point. The company recognized that most customer service inquiries could already be resolved using AI-driven systems. Instead of layering new features onto an old structure, Zendesk rebuilt its platform to achieve autonomous, high-accuracy resolution across multiple categories of support requests. To move quickly, it pursued several targeted acquisitions, gaining expertise in AI automation, enterprise search, and analytics. At the same time, it increased its AI-focused workforce from less than 10% to over 50% within just 15 months. By 2025, Zendesk reached $200 million in annual recurring revenue (ARR) from AI-powered products, serving 20,000 AI customers and handling nearly 800 million AI interactions.

Executives should draw a key insight here: progress demands intensity. Partial transitions produce limited results. When the commitment is broad, measurable, and integrated into every part of the company, from engineering to go-to-market strategy, the likelihood of achieving transformational outcomes increases significantly. Artificial intelligence rewards bold action; companies that act early and invest deeply will own the next growth cycle.

Redefining performance metrics for AI-driven value creation

The metrics that defined SaaS success don’t fully explain how AI creates value. ARR, NRR, and seat-based growth worked well when revenue was consistent and marginal costs were close to zero. In AI, the model is more complex. Pricing depends on actual usage and outcomes, while infrastructure and model costs fluctuate based on data intensity. This fundamentally changes how executives should measure efficiency and scalability.

AI impacts both sides of the profit equation, revenue and cost. Leaders need to separate AI-driven revenue from traditional SaaS revenue and track how each performs over time. The text identifies three main revenue categories that should be observed: traditional predictive AI models such as recommendation systems, AI-powered product enhancements like copilots or smart features, and fully agentic systems capable of executing workflows autonomously. Each has its own growth rate, margin profile, and competitive context.

Costs also differ sharply from legacy models. AI products carry significant variable expenses, including hosting and compute infrastructure, third-party model licensing, and the compensation of specialized AI engineering teams. Without separating these costs, it’s impossible to assess true profitability or to identify where to double down on investment.

For decision-makers, the message is straightforward: you can’t manage what you don’t measure. Modern performance tracking requires granular visibility into AI revenue and expenditure streams. The goal is not just understanding immediate returns, but also quantifying how efficiently AI initiatives scale. This allows leaders to build credible, data-backed value narratives for investors and buyers, proof that AI isn’t just expanding capabilities, but driving measurable business outcomes.

Executives who implement this discipline will position their companies for clarity in valuation, resource efficiency, and long-term resilience as AI redefines what operational excellence looks like across the software sector.

The 2026 shift represents a defining moment demanding decisive leadership

The year 2026 is shaping up to be a turning point for the software and investment industries. Artificial intelligence has moved from being an experiment to a structural force that is rewriting how value is created and captured. The companies and investors that move quickly now will define the next era of growth, while those waiting for clarity may find the window closing.

For decision-makers, this transformation is both a challenge and an opportunity. The rules that guided success over the last decade, predictable SaaS growth, high-margin models, and reliable customer retention, are no longer enough. The new game is about adaptability, speed, and evidence-based execution. Leadership teams must evolve their operating playbooks, updating due diligence approaches, retooling investment criteria, and shifting their internal metrics toward AI outcomes that can be proven.

This moment demands conviction. The technology is advancing faster than traditional business cycles, but that should not deter decisive action. The companies already leading this transformation, those investing early in AI research, refining data measurement, and reskilling their workforces, are pulling ahead. Their strategies are built on clear visibility into results. Every milestone provides measurable progress that strengthens credibility with investors and customers alike.

To navigate this market reshaping, leaders must stay close to data and willing to make hard trade-offs. Prioritize adoption that shows quantifiable business impact. Reassess asset valuations through an AI-centric lens. Demand clarity in how each investment connects to actual outcomes. Decisive leadership isn’t about predicting every future scenario; it’s about acting on what is already visible and aligning resources around it.

The coming years will reward focus and evidence-based strategy. Opportunities will be abundant, but time is short. Those ready to execute now, redefining how they measure, build, and scale value, won’t just adapt to the next era of software investing. They will lead it.

Key executive takeaways

• Software’s structural reset is redefining investment value: Software growth is slowing and traditional advantages are eroding. Leaders should reassess portfolio strategies and shift focus toward durable, AI-enhanced assets that deliver measurable performance.
• AI is transforming how due diligence must be done: Legacy SaaS metrics no longer reflect real enterprise value. Decision-makers should adopt AI-specific diligence approaches that evaluate workflow disruption, defensibility, and proven traction supported by measurable data.
• Active AI integration is now a strategic imperative: Companies must embed AI deeply across operations and products to stay competitive. Executives should commit resources to full-scale transformation, realigning teams, making targeted acquisitions, and driving measurable AI revenue growth.
• Performance metrics must evolve to capture AI impact: Traditional KPIs like ARR and NRR misrepresent AI-driven results. Leaders should implement granular tracking of AI-specific revenue and cost structures to understand profitability, guide investments, and enhance valuation accuracy.
• Decisive, data-driven leadership defines the next growth era: The acceleration of AI disruption demands rapid, informed action. Executives who move first, modernizing measurement, restructuring strategies, and proving AI outcomes, will lead as software investing enters its next era.