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

• Surge in M&A activity: Mid-market enterprise software vendors will face consolidation pressure. Merger and acquisition activity is projected to increase by 30–40% year over year, reaching around $600 billion in 2026.
• Vendor landscape shifts: Many mid-sized ERP and enterprise software providers may be acquired or merge, changing who you buy from and how long-term vendor relationships work.
• New buyer profiles: Buying decisions will shift from primarily IT-led evaluations to functional leaders who can directly interact with AI-driven, conversational interfaces.
• Changing roles and workflows: AI will reduce the need for human intermediation in data analysis and reporting. Users will increasingly query systems in natural language instead of relying on static dashboards and specialist report builders.

For ERP and enterprise technology leaders, this means revisiting vendor strategies, preparing for consolidation risk, and updating evaluation criteria to account for AI capabilities, governance, and trust infrastructure—not just traditional feature lists.

AI is clearly improving parts of the software development process, but those gains are not automatically translating into strategic business outcomes.

Key data points from the research:

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

However, most organizations are not capturing equivalent strategic value. The main reasons:

• The bottleneck has moved: The constraint is no longer engineering capacity; it is strategic clarity—deciding which features and capabilities truly create competitive advantage.
• Weak product strategy: Faster coding without a clear product strategy leads to more output, but not necessarily better outcomes. This can create waste rather than value.
• Misaligned roadmaps: Vendors may struggle to align accelerated development cycles with coherent product roadmaps that reflect customer business priorities.

What ERP and technology leaders should do:

• Evaluate vendors on product strategy maturity and decision frameworks, not just AI-enhanced development speed.
• Ask how vendors prioritize features, validate customer value, and manage their product portfolio—not only how they use AI in engineering.
• Internally, invest in product management, strategic planning, and launch optimization to match the pace of AI-enabled development.

In short, AI is making teams faster, but only organizations with strong product strategy and governance are turning that speed into measurable business value.

AI-driven, conversational interfaces in ERP and enterprise software require organizations to rethink governance and trust from the ground up.

Why this matters:

• 95% of AI pilots are failing, largely due to governance gaps and lack of trust.
• Trust deficits are costing organizations about $670,000 extra per security incident.

Key governance and trust implications:

• Permission models must be redesigned: Conversational AI needs to understand not just what users ask, but what they are allowed to see. Traditional role-based access tied to dashboards is not enough.
• Data governance moves earlier in projects: Governance and security teams must be involved from the start of ERP and AI deployments, not as a late-stage check.
• Role disruption: Many tasks in data manipulation, reporting, and business intelligence will be automated or reshaped, requiring new responsibilities and skills.
• Trust infrastructure becomes foundational: Identity, privacy, and audit capabilities are now core product requirements, not just compliance add-ons.

Recommended actions for ERP and transformation leaders:

• Treat trust infrastructure (verifiable credentials, audit trails, privacy-enhancing technologies) as a primary selection criterion for ERP and enterprise software vendors.
• In regulated industries, recognize that trust-native platforms are already commanding pricing premiums as enforcement tightens.
• Plan to allocate around 20–30% of AI program budgets to trust and governance capabilities by 2027, viewing these investments as enablers of scale rather than overhead.

Organizations that proactively redesign governance and invest in trust infrastructure will be better positioned to move AI from pilot to production and to scale conversational ERP interfaces safely.