Jake Cantu
Partner & Managing Director, Global Leader, Technology, Media, and Telecommunications
Chicago
AI is transforming how software is built and what it can do. But the business impacts are proving harder to capture than many have expected. Productivity gains from AI-accelerated coding are real, yet they vanish without strategic direction. Conversational interfaces are reshaping how users interact with business data, yet require fundamental changes to product architecture. And trust infrastructure—the ability to verify, govern, and audit AI systems—persists as the critical barrier to scaling adoption.
Together, these three dynamics reveal that technological innovation is racing ahead—but the business innovation required to harness it is lagging behind. Companies that can close this gap will define the next era.
Across our client engagements, we see companies using AI to improve productivity but failing to realize commensurate gains in profitability. We believe this is because AI-accelerated development creates a vacuum of excess capacity, solving the problem of productivity while creating new challenges of evaluation and prioritization.
We believe software development teams must treat AI-enabled productivity gains strategically—as capital to be invested or harvested. They will have to increase attention to the “why are we doing this?” questions on the front end of the development process, and the launch and feedback questions on the back end. Smart organizations are already preparing for this transformation, recognizing that value creation has tilted away from engineering. Crucially, the point on the labor curve seeing the lowest productivity gains from AI is also where the most value is potentially created.
Interface evolutions are rare. In the half-century of business computing, there have only been a handful. Enterprise software is now undertaking the next fundamental transition: the shift to conversational interfaces driven by generative AI. Since ChatGPT’s 2022 launch reset consumer expectations, corporate users are following a familiar pattern in their eagerness for new interactions paradigms.
The enterprise transition to conversational interfaces is accelerating. Major platforms are already shipping conversational features as defaults. As their use catches on and becomes core to business software functionality, the implications for business analytics and data access will be profound. Actionable interaction with business data will no longer require specialized infrastructure and technical expertise. Conversational interfaces will eliminate the need for human intermediation in data analysis and inference generation.
Software has always been deterministic: engineers write code that executes consistently. AI fundamentally changes this equation. The non-deterministic nature of generative and agentic systems creates risks that scale in unpredictable ways—categorically beyond what traditional controls were designed to contain. In this context, "trust" is not a soft concept but an operational requirement: the ability to verify, govern, and safely operate AI systems end-to-end.
Trust—not capability—is the hurdle separating stalled pilots from scaled deployments. The absence of trust infrastructure imposes a quantifiable "Trust Tax" on organizations, that includes delays, manual oversight, shadow deployments, compliance failures, and opportunity costs. Companies that treat trust as foundational innovation—not a compliance afterthought—will separate themselves from competitors and capture premium market positioning.
Explore more from the 2026 Enterprise software technology predictions report in our other chapters
2026 Enterprise software technology predictions report
In this 2026 Enterprise software predictions report, we examine seven critical dynamics reshaping the enterprise software landscape and explain the specific transformations companies should execute to respond effectively.
These predictions reflect the inflection points we clearly observe across the industry, as AI forces fundamental changes across software development, interfaces, trust architectures, go-to-market operations, pricing models, valuation frameworks, and business structure itself.
We believe that companies mastering these transitions will define the next era of enterprise software leadership, and we predict that those unable to adapt will be sidelined as the industry landscape redraws itself.
