Enterprise AI has hit a different kind of wall. Model capability and generation are no longer the limiting factors; the difficulty now is proving that a system works, tying its output to a decision someone will stand behind, and getting business and technology leaders to agree on what defines success. Among enterprises developing AI applications in-house, 85% expect to increase that spending over the next 12 months, and just over half still cannot point to a return on it.
A growing set of technology vendors are responding by placing small data and engineering teams inside customer environments against a defined outcome and a fixed clock. These embedded-engineering programs differ sharply in structure, though: whether a contract pays for hours or a named deliverable, who owns the system and data at exit, whether the vendor will build outside its own stack, and whether the engagement changes the vendor’s own product roadmap. Those four tells separate a program that leaves capability behind from one that quietly becomes another subscription.
In our latest thought leadership brief, The AI Last Mile Problem, completed in partnership with Snowflake, Futurum Research examines why enterprise AI stalls between pilot and production, scores the embedded-engineering models vendors now offer against four buyer tells, and lays out the five requirements a CIO and business-unit owner should set before signing an engagement agreement.
In this report, you will learn:
- Why 53% of enterprises developing AI in-house still cannot point to a return, and why that gap is organizational rather than technical
- The four places the “last mile” actually breaks (evaluation and data, integration, ownership, and value definition) and who should own each one
- How the three embedded-engineering models (permanent teams, staff augmentation, and transfer-and-exit) differ, and the four tells that separate a real exit from a slow subscription
- Five requirements to set before scoping an engagement, from a named business outcome to a capability transfer plan
If you are interested in learning more, be sure to download your copy of The AI Last Mile Problem today.
Author Information
Mitch Ashley is VP and Practice Lead for the CIO & Technology Buyers and Software Lifecycle Engineering practices at The Futurum Group. A multi-time CIO and CTO with 30+ years leading technical organizations, Mitch built and operated production systems spanning cybersecurity for the U.S. Department of Defense, PKI services for the broadband and 5G industries, SaaS platforms, large-scale telecom and banking systems, and a national broadband network. His work with AI began early, developing expert systems that diagnosed and repaired complex mainframe environments. That operator foundation grounds his analysis in operational consequence, covering the technology buyer's world of software engineering, cybersecurity, DevOps, cloud, and AI.
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
