The Real Reason Most AI Projects Fail in Enterprises - [The Strategy Failure]

2026-03-17 · Source: AIM Network · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Consulting & Professional Services · Depth: Intermediate, extended

Summary

Strive, led by CEO Ankur Ry, positions itself as a one-stop shop for operationalizing data analytics and AI, focusing on delivering business outcomes like cost reduction, speed increase, and new offerings rather than just technology. The company emphasizes a unique "AI operationalization" category, combining cutting-edge AI development with the operational muscle to ensure continuous learning and improvement in client environments. Strive differentiates itself by aiming for rapid impact, targeting 7-14 days for an AI proof of concept and 8-10 weeks for deployed AI. While not building foundational LLMs or being a product company, Strive leverages a substantial talent base, including Masters and PhDs, across six industry groups, with a significant presence in India for R&D and global talent connectivity.

Key takeaway

For Directors of AI/ML evaluating AI implementation strategies, prioritize vendors like Strive that offer a holistic approach to AI operationalization, combining rapid development with continuous, expert-in-loop support. Your focus should be on clearly defining business outcomes to accelerate adoption and overcome internal resistance, ensuring AI initiatives deliver tangible value and speed rather than just experimental learning.

Key insights

Operationalizing AI requires a dual focus on advanced development and dynamic, expert-in-loop operations for continuous improvement.

Principles

• Clarity of business outcome collapses organizational silos.
• AI expertise is demonstrated by integrating it into daily life.
• Deepest waters run still; real impact is often quiet.

Method

Strive's method involves packaging AI capabilities into two sets: developing and deploying cutting-edge AI, and providing operational muscle for AI to run and improve dynamically, often with experts in the loop, to achieve rapid impact.

In practice

• Use AI to build music quizzes for family.
• Integrate AI into personal hobbies like drumming.
• Focus on clear business outcomes to overcome legacy system resistance.

Topics

• AI Operationalization
• Data Analytics
• AI Deployment Strategy
• Business Impact of AI
• Global AI Talent

Best for: Director of AI/ML, CTO, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by AIM Network.