Path To Market | 2026 Enterprise Software Technology Predictions Report

AI is reshaping how enterprise software is sold and priced, forcing a fundamental redefinition of what software companies actually deliver, and leaving the go-to-market playbook in a state of significant disruption. Specifically, sales teams are deploying AI tools across marketing, pipeline management, and customer success, while struggling to demonstrate improved win rates amid fragmented solutions and poor underlying data. At the same time, AI-native competitors are disrupting established categories with outcome-based pricing, charging only when customers realize tangible value. All told, the commercial models that powered two decades of SaaS growth are proving incompatible with AI's economics. Companies that can respond to these transitions will capture market advantage. Those clinging to legacy approaches will watch their customers defect.

AI SALES TOOLS

By the end of 2026, AI will fundamentally reshape enterprise software's go-to-market operations. An average of 70% of activities— spanning client acquisition, onboarding, value realization, and expansion—will be AI-enabled.

The GTM transformation

AI will no longer be a differentiator in enterprise software sales—it will be the operational baseline.

AI is transforming enterprise software sales processes, extending far beyond the automated customer support tools in use for years. Comprehensive operational integrations have become the norm, with AI now widely embedded in marketing, sales, pricing, sales operations, and customer success functions. The primary catalyst is availability, with major software makers now putting AI tools directly into their products. With adoption barriers lowered, AI has become normalized as a default functionality, rather than an optional add-on. Key to driving this transformation is that in enterprise software, vendors become early adopters of their own technologies. Everyone eats their own dog food. As first-mover demonstrate measurable improvements in win rates, deal cycle times, and customer engagement, pressure builds for others to follow.

Competition accelerates adoption. By the end of 2026, we expect

• 75-80% of enterprise software companies to deploy AI tools in marketing functions
• 60-70% in sales, sales operations, and customer success;
• 30-40% in pricing teams.

Yet even as AI transforms GTM processes, it is not expected to upend the fundamentally human nature of enterprise software sales. The personal engagement of presentations and negotiations will persist, even as they become more AI-enabled.

The challenge now facing organizations is how effectively they can adapt their workflows and scale.

Problem - The integration gap

Four challenges to adopting AI in sales:

1. Data quality
2. Demonstrating ROI
3. Navigating fragmentation
4. Maintaining the human element

Despite the promise of AI-enabled sales operations, significant obstacles still stand in the way of capturing its transformative effects. Even as 70% of go-to-market activities tap AI tools, capturing their business value requires solving core problems.

The first hurdle is data quality. Many teams lack the clean data AI systems require. Companies of all sizes often cannot provide basic profitability numbers, revenue attribution, or sales productivity metrics. Leaders struggle to explain what their revenue actually is, or how sales productivity should be measured. Without clear visibility into business performance, AI implementations cannot deliver meaningful insights. To respond, we believe organizations must invest in the underlying data infrastructure.

The second challenge is demonstrating the ROI from these new and expensive tools. To move past pilots, boards and CFOs require clear links to productivity metrics. Efficiency alone is not enough. As with software development, there is a real risk of missing the opportunity to direct those gains towards business value realization. New processes demand new scrutiny.

The third challenge is navigating the fragmentation of the first-generation tools. Today's software is spread across conversation intelligence, enablement, pricing, and forecasting. Until these functions consolidate within enterprise platforms, their usefulness will suffer. Even the most powerful AI platforms will struggle to deliver value if they do not fit easily into daily workflows. Expect rapid evolution of existing offerings.

The final challenge is maintaining the human element. Sales recommendations should be contextualized and aligned with brand values, strategy, and customer trust. AI tends to lack wisdom. Without it, even the most successful technical integration will disappoint.

Solution - Building AI-native commercial operations

The strategic imperative is to recognize that the successful adoption of AI tools is not a matter of procurement, but organizational transformation. AI requires new measurements, new tools, and extensive training. It will inevitably have broad impacts on the staffing and operation of marketing, sales, and customer service functions. Success will come from execution, not merely adoption.

That varies starkly by function:

Marketing teams

For marketing, AI enables the scaling of personalization and content in ways that have always been too expensive or slow.

Marketing teams should aim to deploy AI-driven campaign systems capable of generating dynamic and engagement-optimized content at scale. The goal is to ramp engagement rates past what manual processes could match. Most often, that means using AI to create starter content, to be edited and refined.

Pricing functions

For pricing, teams can now analyze massive data sets and uncover new patterns, allowing them to deliver customer-specific recommendations and true personalization (rather than broad segmentation).

Pricing functions can use AI to move away from broad segmentation and towards customer-specific recommendations, thanks to platforms that analyze real-time data to tailor prices individually and prevent margin leakage. AI promises a sensitivity that traditional tools cannot capture—but only if organizations have the data infrastructure in place.

Sales teams

For sales, new tools like AI-generated call-summaries, real-time coaching, and AI-guided content recommendations are bringing a new evidence-driven approach to a process that has always been dominated by intuition. The shift is partly individual; new sellers can be onboarded faster, and playbooks executed more consistently. But it is also organizational, in that the new tools allow for better and far broader visibility into customer interactions.

Sales teams should equip every seller with AI-powered conversation intelligence, objection-handling tools, and deal risk detection. Applications like Gong listen to live calls and provide guidance simultaneously. At the organizational level, AI can surface pipeline risks, enable better visibility into customer interactions, and embed data-driven recommendations directly into seller workflows—shifting execution from intuition-driven processes to evidence-driven ones.

Sales operations

For sales ops, AI is automating routine tasks, optimizing workflows, and unifying data—changing the function from a reactive problem solver to an execution accelerator.

Sales operations should use AI to shift from reactive problem-solving to proactive execution enablement. That means implementing AI-driven lead scoring, pipeline risk detection, and revenue forecasting.

PRICING STRATEGIES

Hybrid SaaS pricing models featuring usage- and outcome-based elements will comprise a majority of enterprise software revenue by 2026, marking a shift from today's per-seat dominance.

The second pricing transformation

The golden age of SaaS is over. AI solutions are driving a wildly disruptive business paradigm: usage- and outcome-based pricing.

Enterprise software is undergoing its second major transformation in the last fifteen years—one that may redefine the notion of software itself. The first shift came when the industry moved from one-time licenses to subscription-based pricing. Untethered from the need to manually install software on servers, developers leveraged the flexibility of the cloud and "Software as a Service" to change how they built, sold, and valued their product. Rather than annual releases, SaaS made it possible to offer continuous innovation. Priced "per seat," customer relationships deepened as procurements scaled. By many measures, the SaaS era has been a golden age for enterprise software.

But now AI-enabled and AI-native solutions are driving a new and wildly disruptive business paradigm: usage- and outcome-based pricing. Per-seat charges are predictable and directly linked to annual revenue. But they are poorly suited to the way that AI software creates value. For an autonomous agent, work is measured in completed tasks and delivered outcomes. The work itself may be overseen by one person or many. Often, that relationship is inverse: the better the AI software performs, the fewer seats required. At scale, the impacts become dramatic. One AI customer service system already performs work equivalent to 700 human agents, while a single AI-augmented security analyst is managing a workload that required 20-30 humans before.

Per-seat pricing norms buckle under this reality. In the face of rapidly evolving agentic capabilities, SaaS buyers will seek comfort in pricing clearly linked to measurable outcomes and productivity gains. While SaaS sellers will embrace pricing disconnected from dwindling seat counts.

These structural changes cannot be ignored. By 2026, we believe hybrid pricing models combining usage- and outcome-based elements will capture a majority of enterprise software revenue, ending per-seat dominance.

Problem - The AI-native pricing advantage

AI-native companies are disrupting established software categories with fundamentally transformative product models built on autonomous agents and outcome-based delivery. They often price based on results, charging only when customers realize tangible value, such as in tickets resolved or customer issues closed. As new as this is, the interest in the commercial model is soaring. For buyers, it offers an implicit promise that the software itself might reduce the need for the seats occupied by humans. ROI is baked in. For established SaaS sellers, the shift represents an existential threat: outcome-based pricing from AI-native competitors directly cannibalizes recurring revenue streams by ending per-seat annual contracts. In an effort to defend these installed bases, legacy vendors are racing to embed AI copilots and agents into existing suites and experimenting with new pricing strategies. Yet these adaptations are inevitably—and irredeemably—reactive, given the enormous structural advantages that AI-native entrants bring.

Not surprisingly, the software stack used by large organizations is rapidly diversifying. Agent ecosystems are likely to emerge as the default architecture because of their ability to integrate and orchestrate across AI capabilities. Legacy vendors cannot miss the urgency. Agents are now present in at least one workflow at 85% of organizations. By 2026, 40% of enterprise applications will include task-specific agents, accelerating the transition to usage- and outcome-based pricing.

We believe established software companies must now consider dismantling the pricing models their businesses were built to deliver. They face a prisoner's dilemma: maintaining seat-based pricing encourages customer defection to consumption-based competitors. But transitioning means accepting immediate initial revenue declines and infrastructure investments. The per-seat era may well be ending. Adapting to what comes next is unfortunately proving considerably more complex than defending against it.

Solution - Implementing hybrid pricing strategies

Enterprise software companies should prepare by fundamentally redefining their role from "vendor" to "partner." That shift requires more than pricing experimentation. Selling the seat license is no longer enough. Customer success should be invested in as a core capability—either built in-house, via partnerships with specialized consulting firms, or acquired through M&A. "Features" should give way to "outcomes." Most often, that means adding implementation specialists who ensure solutions actually address customer problems. We believe the core value proposition must shift. Software is no longer a cost center but a component of how business value is realized.

This transformation need not mean abandoning revenue predictability. Hybrid pricing models can preserve committed contracts while offering flexibility about how the new tools are used. A customer's commitment may still be for $10 million annually, but instead of locking into 1000 seats for three years, they may be committing to a certain volume of resolved conversations or delivered outcomes, changing as their needs evolve. The unit changes, but the recurring revenue remains. Through carefully structured contracting, major enterprise customers can still be counted on to maintain their spend, within the flexibility the new tools afford.

Executed properly, this shift represents an expansion opportunity. Many established vendors already find themselves at the limit of wallet penetration, as they run out of empty seats to chase. Outcome-based pricing unlocks growth by enabling upselling in ways that per-seat pricing cannot. The premiums shift from bundled seats to tailored solutions. Executed correctly, the pitch is mouth-watering for both vendors and customers: If an agentic tool can replace a $75,000-per-year phone operator with a $30,000 solution that delivers equivalent or superior outcomes, the potential value capture on both sides is startling.

Legacy vendors ready to leverage existing customer relationships and implementation expertise will have a strong hand to play. The per-seat era is ending, but recurring revenue doesn't have to disappear with it.

Explore more from the 2026 Enterprise software technology predictions report in our other chapters

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