How Businesses Can Bridge the GenAI Divide

When I'm working on a new project or a complex problem, I often start with a simple, personal AI tool. Lately, that's been Google Gemini for writing, Gamma for presentations, and Lovable for site design...although I do hear great things about Base44.... Regardless, I mostly use these tools to brainstorm ideas, refine my messaging, and get a solid first draft. It's a fantastic productivity booster. But then comes the real work: taking those ideas and putting them into action. I can't just throw a half-baked draft at a client; I need to integrate the core concepts into a solution that works for them.

95% of generative AI pilots deliver zero return on investment. - MIT Report

The MIT report, "State of AI in Business 2025," uncovered a surprising and a little frustrating result: 95% of organizations are getting zero return on their GenAI investments, even with tens of billions of dollars being poured into them. They call this the GenAI Divide. Tools like ChatGPT and Copilot are extremely popular for individual use, but enterprise-grade systems are failing to deliver real value. The report found that while 60% of organizations evaluated these custom tools, only a tiny 5% actually reached production. This isn't because of a lack of talent or a ton of red tape; it's all about the approach.

So, why do so many pilots just stall out? The MIT study points to a fundamental learning gap. Most GenAI systems just don't learn from feedback, adapt to context, or get better over time. Users will drop them for critical work because they just don't have a memory. The failed pilots are often inflexible and don't fit into daily workflows.

This is where my work at Appian comes in. We don't just throw AI at a problem; we embed it directly into existing business processes. Appian gives our AI agents the data they need from the surrounding process and from other connected systems, it’s like a spider web connecting all of your data. This gives our AI the context it needs to make better decisions and actually improve over time. We focus on building adaptive, embedded systems that learn from feedback to tackle specific, high-value cases.

By integrating Appian AI into its fulfillment process, CenterWell achieved a 70% reduction in costs.

The MIT report supports this as well. It illustrates the companies truly succeeding are the ones demanding process-specific customization and evaluating tools based on actual business outcomes, not just technical benchmarks. They want systems that fit seamlessly into their existing processes and get smarter over time, a concept the report calls learning-capable systems. It also found that external partnerships, like the ones we build at Appian, are twice as likely to reach successful deployment as tools built internally. And the most successful buyers? They treat AI vendors more like business partners than software sellers, demanding deep customization and holding them accountable for real results.

Implementation advantage: External partnerships, like the ones we build at Appian, are twice as likely to reach successful deployment as tools built internally.

By focusing on deep workflow integration and continuous learning, we can help our clients achieve what most companies are missing: a measurable return on their AI investment. Instead of a cool but ultimately useless tool, we deliver a solution that becomes more valuable the longer it’s used. We're helping organizations move beyond the pilot phase and truly cross the GenAI Divide.