Houlihan Lokey How AI Is Reshaping Digital Engineering 2025

Keypoints

• This type of annual digital engineering report typically begins with an overview of the firm, leadership, and market positioning, then moves into the macro backdrop, industry evolution, key trends, delivery models, end-market analysis, strategic buyer demand, and M&A activity. It often ends with company spotlights, case studies, and forward-looking commentary on where the sector is heading.
• The report’s core theme is that AI is shifting digital engineering from labor-heavy execution to higher-value consulting, orchestration, and product strategy, while still preserving the need for human oversight in architecture, governance, compliance, and complex problem-solving.
• The report estimates the AI-related IT services market will reach $200B by 2029, with about 30% profitability gains expected from new AI stack solutions, underscoring how large the commercial opportunity has become.
• It presents a long-term evolution of software delivery: from modern software development and mobile apps to DevOps, cloud-native, low-code, foundational AI, and ultimately AI-native transformation beyond 2025.
• A recurring finding is that AI is expected to automate repetitive tasks such as basic coding, refactoring, documentation, QA, and bug detection, but not eliminate engineers; instead, it elevates the importance of AI architects, prompt engineers, MLOps professionals, and senior engineering leaders.
• The report highlights a “talent pyramid” shift toward a diamond-shaped structure, with stronger demand for senior and mid-level talent and weaker demand for entry-level roles due to automation at the base of the delivery model.
• Global talent dynamics remain important: India leads STEM graduate output with about 2.55M graduates, while the U.S. produces a smaller domestic pipeline, reinforcing reliance on offshore and nearshore delivery or AI-driven productivity gains.
• Nearshore and offshore models are being redefined by AI. Delivery is moving away from pure labor arbitrage toward value creation, with senior-heavy teams, AI integration, prompt engineering, and model evaluation becoming more important than 24/7 manual coverage.
• Pricing is also changing: commoditized tasks like basic development, QA, and documentation are expected to see compressed rates, while AI-enabled roles and consulting-led engagements should command premiums and support outcome-based or milestone-based pricing.
• The report identifies strong end-market demand in automotive, healthcare, life sciences/pharma, financial services, real estate, consumer, entertainment, and industrials, where digital initiatives such as AI drug discovery, autonomous driving, smart factories, and next-gen banking are central to growth.
• Automotive stands out as a major opportunity, with 83% of leaders saying digital services will be key differentiators by 2040, and McKinsey projecting the global automotive software market to reach $462B by 2030.
• Life sciences and pharma show a large maturity gap, but also major upside: digital transformation could create about $160B-$190B in value, supported by AI, digital twins, clinical trial innovation, and better regulatory and pharmacovigilance workflows.
• The report emphasizes that strategic and sponsor-backed M&A remains strong, with digital engineering and AI ranking as top priorities for buyers in 2025, especially for assets with premium talent, AI capabilities, and exposure to Europe and North America.
• Recent transactions such as Alpha FMC’s acquisition of Auxo Solutions, Sunstone Partners’ investment in KMS Technology, and other deals show how strongly the market values scale, AI depth, and specialized digital engineering capabilities.
• Across company interviews, a common theme is that clients now want measurable ROI, tighter scoping, faster delivery, and stronger alignment with business outcomes rather than “transformation for transformation’s sake.”
• Several firms note that AI is pushing development teams toward business-minded engineering, while low-code/no-code, agentic AI, and modular architectures are changing how products are designed, built, tested, and maintained.
• Governance and security are increasingly non-negotiable: organizations are demanding AI guardrails, auditability, FinOps transparency, and secure-by-design development because AI introduces new risks around compliance, bias, data access, and runaway costs.
• The report also suggests that the most successful providers will be those that combine domain specialization, strong IP, and AI-enabled delivery with consultative relationships and flexible engagement models.