While artificial intelligence captures headlines with futuristic promises, a silent crisis plagues the industry: deployment failure. Studies reveal 70% of enterprise AI projects never reach real users, distinguishing rising companies from those fading into obscurity. Yet in Bengaluru, two young entrepreneurs have cracked this code.
The Deployment Crisis Reshaping AI Business
MIT’s NANDA Initiative confirms the widespread struggle. Despite massive investments, only 5% of enterprise AI projects achieve rapid revenue acceleration. The remaining 95% stall before delivering measurable impact. This quiet crisis now defines which companies survive and which disappear in the AI revolution.
Two 22-year-old founders, Manas Bhasin and Aditya Singhal, launched Echidna AI Foundry to tackle this exact problem. Their platform manages over one million AI operations daily across 50+ companies, achieving a remarkable 94% deployment success rate. This performance directly challenges industry norms of failure.
Strategic Advantage: Infrastructure Over Innovation
While competitors chase algorithmic breakthroughs, Echidna AI focuses on robust infrastructure. This shift reveals a crucial business insight: AI’s future belongs not just to those designing smart models, but to those ensuring these models work reliably at scale.
The transformation becomes clear through client metrics. Before adopting the platform, engineering teams spent 65% of their time maintaining basic AI systems. Post-implementation, innovation efforts surged from 35% to 78%. In competitive markets where iteration speed determines survival, this operational efficiency creates existential advantage.
Global Market Impact: 10 Million Users Affected
Echidna AI’s influence extends worldwide. Companies using their platform serve nearly 10 million global end-users. When clients upgrade fraud detection systems, millions experience safer banking instantly. Infrastructure improvements at the base create massive benefits at the top, demonstrating how operational excellence scales business impact.
Historical parallels support this trend. Cloud computing achieved dominance not through flashy technology, but by solving scale and reliability challenges. Amazon Web Services enabled others to innovate freely by handling the “boring” infrastructure. AI is witnessing identical evolution patterns.
Why 95% of AI Projects Still Fail
WorkOS research identifies critical failure patterns plaguing enterprise AI. According to S&P Global Market Intelligence, 42% of companies abandoned most AI initiatives in 2025, up from just 17% in 2024. The average organization scrapped 46% of AI proof-of-concepts before production.
Model fetishism compounds problems. Engineering teams spend quarters optimizing performance scores while integration tasks accumulate in backlogs. When initiatives finally surface for business review, compliance requirements appear insurmountable and business cases remain theoretical.
Disconnected organizational tribes create additional friction. Product teams chase features, infrastructure teams harden security, data teams clean pipelines, and compliance officers draft policies—often without shared success metrics or coordinated timelines.
Proven Success Patterns for Business Leaders
Successful organizations follow four distinct patterns. First, they start with business pain rather than technical capability. Instead of asking “Which models should we deploy?”, winning teams identify process bottlenecks costing real money.
Second, they invest disproportionately in data readiness—50-70% of timeline and budget for extraction, normalization, governance metadata, and quality dashboards. Bad training data produces inaccurate reports that analysts must debug manually.
Third, they prototype human-machine collaboration early. Microsoft’s sales teams using Copilot achieved 9.4% higher revenue per seller by designing explicit handoffs where AI suggests responses but humans retain control.
Fourth, they operate AI results as living products with uptime requirements, drift monitoring, and user satisfaction metrics tied to financial outcomes.
Infrastructure as the New AI Profit Engine
Echidna AI’s blueprint demonstrates that “boring” infrastructure holds immense business value. MIT research emphasizes empowering managers and ensuring deep toolkit integration for sustainable success. Companies investing in foundational strength unlock long-term competitive advantages.
Lumen Technologies exemplifies this approach, projecting $50 million in annual savings from AI tools that save sales teams four hours weekly. Air India’s AI virtual assistant handles 97% of 4 million customer queries with full automation, avoiding millions in support costs.
What Business Leaders Should Know Now
The key takeaway for executives is clear: prioritize infrastructure and robust deployment mechanisms over algorithmic innovation alone. As organizations worldwide pursue AI-enabled growth, foundational strength ensures projects thrive beyond proof-of-concept stages.
With enterprise AI failure rates exceeding 80%—twice that of non-AI technology projects—the companies mastering deployment are positioning themselves as tomorrow’s industry leaders. The winners won’t necessarily build the smartest models, but they’ll ensure those models work reliably for millions of users daily.
The AI revolution’s next phase belongs to those solving the deployment puzzle. Which approach will your organization choose—chasing algorithmic breakthroughs or building the infrastructure that makes AI actually work?
