By 2026, AI is expected to reshape how enterprise software is developed, sold, and valued, with several concrete market shifts:

• Surge in M&A activity: Mid-market enterprise software vendors will face consolidation pressure, with merger and acquisition activity projected to increase 30–40% year over year, reaching about $600 billion in 2026.
• Vendor landscape changes: Many mid-sized ERP and enterprise software providers may be acquired or merged, altering existing vendor relationships and long-term roadmaps.
• Shift in value drivers: The focus will move from pure engineering capacity to product strategy, governance, and trust infrastructure as key differentiators.

For ERP and enterprise technology leaders, this means:

• Expect more vendor consolidation and potential changes in ownership or product direction.
• Build flexibility into contracts and roadmaps to accommodate vendor transitions.
• Evaluate vendors not just on features, but on their ability to manage AI responsibly, govern data, and maintain strategic clarity in their product portfolios.

AI is delivering measurable productivity gains in software development, but those gains are not automatically translating into strategic business value.

Key data points from the research:

• 20–30% productivity gains from AI-accelerated coding tools across software development.
• Up to 50% productivity improvement specifically in the build and test stages of the lifecycle.

However, organizations are struggling because:

• The bottleneck has moved from engineering capacity to product strategy and planning.
• Teams can build more, faster, but often lack clarity on which features actually create competitive advantage.
• Roadmaps become harder to manage as development speed increases without a matching upgrade in strategic decision-making.

Implications for ERP and enterprise software leaders:

• Prioritize product strategy maturity: When evaluating vendors, look for clear frameworks for prioritizing development investments, not just claims of AI-enhanced coding speed.
• Rebalance skills: Expect product management roles to become less about technical detail and more about business strategy, launch planning, and value realization.
• Align AI initiatives with business outcomes: Tie AI-enabled development work to specific business metrics (e.g., margin, cycle time, customer retention) rather than feature counts.

In short, AI is making it easier to build software; the new challenge is deciding what to build and why.

The move from dashboards to conversational, natural language interfaces in ERP and enterprise software is forcing organizations to rethink governance and trust.

Several trends stand out:

• Governance gaps are costly: Around 95% of AI pilots fail due to governance and trust issues, and trust deficits are adding about $670,000 per security incident.
• Budget shift to trust: By 2027, global systems integrators (GSIs) and transformation leaders are advised to allocate 20–30% of AI program budgets to trust capabilities (identity, privacy, audit).
• Access control must be redesigned: Conversational AI needs to understand not only what users ask, but also what they are allowed to see, which requires new permission and data governance models.

For ERP and enterprise software programs, this means:

• Rebuilding permission frameworks: Traditional role-based access designed for static dashboards must be adapted for free-form AI queries.
• Earlier involvement of governance and security teams: They need to be engaged from the start of ERP and AI deployments, not just at go-live.
• Role disruption: Conversational AI reduces the need for manual data manipulation and report-building, impacting business analyst and BI roles.
• New buyer profile: Purchasing decisions will shift from IT-centric evaluations to functional leaders who directly use conversational interfaces, changing procurement criteria.

When assessing ERP vendors, look for:

• Trust-native platforms with verifiable credentials, audit trails, and privacy-enhancing technologies.
• Clear evidence that trust and governance are treated as core product capabilities, not just compliance checkboxes.

Organizations that invest early in trust infrastructure and governance will be better positioned to scale AI and conversational ERP safely and effectively.