▼ Summary
– MIT research shows 95% of enterprises fail to see measurable revenue or growth benefits from generative AI implementation.
– Organizations with a formal AI strategy are twice as likely to experience revenue growth and 81% more likely to benefit from AI, yet only 22% have such strategies.
– Thomson Reuters created Open Arena, an internal AI platform providing secure access to multiple leading LLMs for testing and development purposes.
– The company developed about 200 AI use cases through experimentation, implementing approximately 70 live applications focused on improving sales, content development, and call center processes.
– Business leaders should embrace emerging technologies like agentic AI and deep research while maintaining human oversight and continuously reimagining processes for maximum impact.
Business leaders seeking to harness artificial intelligence effectively must move beyond experimentation and develop a clear, actionable strategy. While many companies are exploring AI, a recent MIT study indicates that a staggering 95% of enterprises fail to see measurable revenue or growth benefits from generative AI initiatives. Despite this, pressure from boards to leverage emerging technologies continues to intensify. The solution lies not in scattered pilot projects, but in a structured approach that aligns technology with business objectives.
Thomson Reuters, a global business information services provider, offers a compelling case study. Their research reveals that organizations with a formal AI strategy are twice as likely to achieve revenue growth and 81% more likely to realize tangible AI benefits. Yet only 22% of companies have such a plan in place. According to Kirsty Roth, Chief Operations and Technology Officer at Thomson Reuters, clarity from leadership is non-negotiable. Companies must define why AI matters and how it creates opportunity, a message that resonates across industries.
One foundational step is establishing a dedicated platform for AI experimentation. Thomson Reuters developed Open Arena, an internal environment where employees can securely access leading large language models and company data. This isn’t about offering every available tool, it’s about strategic selection. “If you don’t develop software, you might only need one or two models,” Roth notes. But for product-driven firms, testing multiple LLMs is essential. The platform also supports internal foundation models and specialized acquisitions, like Safe Sign Technologies, which is helping build custom legal AI models. This blend of external access and in-house development creates a robust innovation engine.
A clear destination is equally critical. Rather than adopting AI for its own sake, organizations should identify high-impact use cases. At Thomson Reuters, teams evaluated around 200 potential applications, from sales improvements to content development and call center efficiency. About 70 were implemented. This experimental, use-case-driven approach avoids theoretical planning and focuses on real-world value. Roth emphasizes that encouraging smart risk-taking means maintaining a human-in-the-loop mindset. Professionals must still apply judgment and verify AI-generated outputs, treating the technology as an assistant rather than a replacement.
Looking ahead, the most forward-thinking strategies involve reimagining entire processes. Instead of incremental improvements, leaders should ask how AI can fundamentally transform operations. Agentic AI and deep research capabilities are shifting the landscape rapidly. Thomson Reuters is already embedding these technologies into products like CoCounsel Legal, which automates complex legal research and generates citation-backed reports. Deep research tools allow professionals to interact with AI as a trusted teammate, producing comprehensive insights in a single interaction rather than through fragmented queries.
The pace of AI innovation demands continuous market awareness. What seemed cutting-edge six months ago may already be outdated. Staying agile means monitoring new developments while focusing on scalable, impactful implementations. For those willing to invest in a clear strategy, a test-friendly platform, and a culture of measured experimentation, AI offers not just efficiency, but a significant competitive edge.
(Source: ZDNET)
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