Opinions on the GenAI Divide report have been broad and varied in the AI community, viewed as validation by AI contrarians, rebuffed by AI investors and activists, and a confusing aggregation of assumptions about how enterprises operate. The major highlight focused on is the 95% of organizations achieving zero return in their AI approach and investment.
With so many schools of thought about what is right or wrong with the report, there’s a historical optic to consider. Enterprise companies are generational economic clusters of innovation that continually need to adapt to a variety of market inputs and conditions. The term “ecosystem” is commonly used to describe how companies work, which in some ways is synonymous with how people function within their corporate roles. When viewed through the lens of a brief point in time, the health and well-being of the investments, business models and long-term thinking of any company can create myopic perspectives on what the value of any change, including AI, might bring.
Let’s consider the story of incandescent light bulbs. Early scientific experimentation into incandescent lighting by researchers such as Sir Joseph Swan and Thomas Edison were long endeavors, with development spanning over 30 years, from 1848 to 1880, with thousands of iterations of filaments tested to find the tolerance and burn-out rate. Their research was built on decades of earlier research and implementation of arc-light systems, which led to future development by Edison of power stations to enable commercialized electrical distribution.
Viewed solely through the perspective of any six-month window, one might consider the collective work of Swan and Edison to have zero return, when in fact it set the foundation for much greater success in the future, culminating in massive returns. Similarly, AI investments today are being narrowly viewed through the lens of expectations which have much longer return windows than six months. The report even suggests the emergence of MCP and A2A protocols are examples of AI communications continuing to evolve, such that the report’s perspective on AI adoption and disruption passes over what the long-term value might be.
At the same time, it’s important to acknowledge that companies are already seeing meaningful returns in multiple areas today. Many organizations are quietly achieving production‑level value by focusing on narrow, well‑scoped problems that do not require a tremendous amount of context or deep business logic, such as automating high‑volume customer inquiries, summarizing documents, or personalizing sales outreach. Indeed, a Harvard Business Review analysis found that 93.7% of Fortune 1000 companies reported seeing some business value from their AI efforts in 2024.
At Gruve, we see the same pattern with our customers. We are already helping enterprises realize tangible ROI with these kinds of use cases today. These use cases don’t depend on documenting 30‑step workflows or solving complex context challenges upfront. They deliver tangible, measurable business value today and set up a solid foundation for broader transformations over time.
With corporations investing in AI and finding the right ways for their businesses to incorporate GenAI use cases, it’s important to remember that innovation isn’t always a sprint. Sometimes the greatest discoveries appear from deeper thinking about how the company operates and analyzing problems through the lens of their customers and partners, as well as uncovering the technical and operational debt built through years of internal evolution. That debt can be viewed as an inhibitor of growth but can also be viewed as a massive industry disruptor. When companies have a deep understanding of their true business objectives and can leverage their wealth of available data to benefit customers, they have a greater opportunity for successful AI disruption.
The report’s snapshot of the current GenAI divide is exactly that — a tiny view compared to the larger picture of how companies evolve, grow, and disrupt industry through their adoption of any recent technology. Any technology journey must consider each organization’s culture, adaptability and motivations for change, and the services to support that journey must align to the company’s market objectives, including competitive differentiation and economic conditions for success. Whether building or buying, investing in custom systems or singular agentic outputs, simply starting down the AI path is the right first step for any company, and a longer view of time is needed to fully understand what will or won’t be successful.
