Technical due diligence processes for mergers and acquisitions are placing AI, blockchain, and SaaS companies under unprecedented examination in 2026. Investors now routinely probe model governance, data pipelines, and operational scalability rather than treating them as secondary concerns.
Pharos Production released its annual analysis of engineering due diligence findings across AI, FinTech, blockchain, and SaaS transactions. The report identifies that three critical red flags frequently surface together, requiring evaluators to adopt a more integrated approach to risk assessment instead of isolated technical reviews.
This shift reflects the broader maturation of AI-driven businesses. As deal volume in SaaS and AI continues to climb, acquirers want clearer visibility into how these companies manage day-to-day operations, maintain CRM data quality, and scale advertising and sales processes without creating hidden liabilities.
The findings matter because they separate companies that have professionalized operations through automation from those still relying on manual workflows that create compliance and scalability risks during transactions.
What happened
The Pharos Production report details recurring patterns uncovered during technical due diligence. AI-related codebases, model monitoring systems, and SaaS infrastructure now receive the same depth of review traditionally reserved for financial and legal matters. When multiple red flags appear together, deal timelines extend and valuations face downward pressure.
Why this matters now
Investor expectations have changed alongside rapid AI adoption. Buyers want evidence that target companies can sustain growth without operational bottlenecks in lead processing, campaign management, and cross-team reporting. Companies lacking structured automation increasingly stand out during diligence as higher-risk targets.
Business impact for operators
SaaS and AI-enabled businesses that have already deployed AI managers and specialized agents demonstrate stronger operational hygiene. These systems help maintain clean CRM records, consistent reporting, and documented workflows that due diligence teams can review quickly. The result is often faster deal progression and better positioning during negotiations.
AI automation and AI manager use cases
Businesses preparing for potential transactions or seeking operational resilience are adopting targeted AI agents. An AI CRM manager maintains data accuracy across pipelines while an AI advertising manager ensures campaign documentation and performance logs remain audit-ready. Sales agents and operations assistants automate lead qualification and task tracking, reducing manual work that often raises flags in diligence reviews.
Employee reporting agents create consistent, timestamped records of team activity, supporting the kind of transparency investors now expect. Companies using AI agents for business in advertising operations and marketplace management similarly show clearer process documentation, which aligns with the integrated risk approach highlighted in the report.
Risks and opportunities
Companies that continue relying on fragmented manual processes face higher scrutiny and potential valuation discounts. Conversely, organizations implementing AI-driven sales funnel automation, team workflow automation, and employee reporting automation position themselves as lower-risk, more scalable acquisition targets. The opportunity lies in using these tools not only for efficiency but also to build the operational evidence base that modern due diligence demands.