Why 95% of AI Initiatives Fail
And How Forward-Deployed Engineering Changes Everything
The GenAI Divide: State of AI in Business 2025
A comprehensive MIT study that shocked the tech and business sectors
Over $30-40 billion was invested in enterprise AI efforts for 2024-2025, with strikingly little ROI for most. The study included data from 300 public AI deployments, interviews with over 150 business leaders, and surveys of more than 350 employeesinvolved with recent AI integrations.
Root Causes of Failure
The study emphasized that the root cause was not simply technical, but stemmed from a lack of alignment between AI technology initiatives and real business workflows, needs, and outcomes.
Lack of Alignment with Business Workflows
Organizations tried to bolt generative AI into existing processes without deeply embedding AI-savvy personnel within the business to understand operations, pain points, and objectives.
Pilots Never Advanced Beyond Experimental Stages
Most pilots never advanced beyond experimental stages—meaning they did not get adapted or scaled to drive measurable business results.
Human Factors and Cultural Resistance
Lack of necessary skills in staff, cultural resistance, and not having cross-functional teams embedded within the business further compounded the disconnect.
Learning Gap in AI Tool Usage
Organizations did not understand how to use AI tools effectively or structure workflows to extract benefits, leading to poor adoption and ROI.
How Forward-Deployed Engineering Solves This
Our approach addresses each failure point identified in the MIT study
Deep Business Process Mapping
Forward-deployed engineers work directly within your business to understand operations, pain points, and objectives before implementing any AI solution.
Embedded Technical Teams
AI-savvy personnel are embedded within your business, creating cross-functional teams that span technical and operational domains.
Workflow Adaptation Expertise
We don't bolt AI onto existing processes—we adapt workflows to leverage AI's strengths, ensuring seamless integration with your business operations.
Scalable Implementation
Our approach ensures pilots advance beyond experimental stages, scaling to drive measurable business results and ROI.
The Bottom Line
The MIT study's central lesson is that successful AI initiatives require more than investment—they demand deep integration with business processes, collaborative teams that span technical and operational domains, and a readiness to adapt workflows to leverage AI's strengths. Most failures occur precisely because these elements are missing.
This is exactly why our forward-deployed engineering approach works. We don't just build AI solutions—we embed ourselves in your business, map your processes, and adapt workflows to ensure success.
Study Citations
References and sources for the MIT study findings
Why 95% of Corporate AI Projects Fail: Lessons from MIT's 2025 Study
ComplexDiscovery • 2025
MIT Study: 95% of AI Initiatives Fail
LinkedIn • 2025
Between 70-85% of GenAI Deployment Efforts Are Failing
NTT Data • 2024
MIT Study on AI Initiative Failures
YouTube • 2025
MIT Study Finds That 95% of AI Initiatives at Enterprises Fail
Reddit - IT Managers • 2025
MIT Report Finds 95% of AI Pilots Fail to Deliver ROI, Exposing GenAI Divide
Legal.io • 2025
MIT Study: Why AI Pilots Fail
Marketing AI Institute • 2025
MIT Says 95% of Enterprise AI Fails—Here's What the 5% Are Doing Right
Forbes • 2025-08-22
Why the $40 Billion AI Failure is Actually a $40 Billion Opportunity
EverWorker.ai • 2025
An MIT Report That 95% of AI Pilots Fail Spooked Investors—But the Reason Why Those Pilots Failed is What Should Make the C-Suite Anxious
Fortune • 2025-08-21
Be Part of the Successful 5%
Don't let your AI initiative become another statistic. Our forward-deployed engineering approach ensures your project succeeds where 95% of others fail.