Endava (DAVA) Morgan Stanley Technology, Media & Telecom Conference summary

Business environment and digital transformation trends

• Transitioning from digital enablement around core systems to AI-driven transformation requiring deep integration with client core systems, increasing complexity and lengthening sales cycles.

• AI projects now demand access to core data and processes, presenting significant engineering and business case challenges.

• Sales cycles for new programs have extended from 3–6 months to 1–2 years, impacting near-term revenue.

• Clients are more cautious post-COVID, raising the bar for business case approval and increasing technical uncertainty.

• Larger, more complex projects are emerging, but with longer routes to revenue realization.

Financial outlook and geographic performance

• Fiscal 2025 guidance was modestly reduced due to elongated sales cycles and geographic slowdowns, especially in the UK and Asia-Pacific.

• UK growth is expected but at a slower rate, with uncertainty driven by fiscal policy, corporate tax expectations, and slow client decision-making.

• Payments vertical remains stable overall, with mixed performance among top clients and a shift in investment from payment processors to banks.

• Banking and Capital Markets are showing stronger growth, partly driven by payments-related projects.

• Pricing environment is stable to slightly improving, supported by higher value-added work and deployment of proprietary IP.

AI project returns and operational challenges

• Returns on AI projects vary widely; core use cases like chat can deliver faster returns, but high processing costs often challenge business cases.

• Recent advances like DeepSeek may reduce processing costs, improving business case viability.

• Backlog-to-revenue conversion is slower for core modernization projects compared to traditional digital transformation.

• Engineering challenges around AI implementation include security, regulatory compliance, processing costs, and reliability (hallucination), with solutions like agentic AI improving outcomes.

• IP development is focused on tools that enhance delivery efficiency and quality, not on harvesting client IP.