How AI is Moving Beyond Chatting to Actually Contributing in Business
263OpenAI is shifting its focus from making AI that just chats to developing systems that actually help with real work. Instead of creating a friendly chatbot, the company is training its models to understand and perform specific tasks that people typically do in fields like consulting and finance. This move signals that AI is becoming more of a team player in the workplace, ready to contribute meaningful outputs rather than just generate language.
Training AI with Real-World Work Data
Recently, OpenAI has been working with former professionals from top consulting firms like McKinsey, Bain, and Boston Consulting. Over 150 ex-consultants have been hired by a third-party to teach the AI how to handle entry-level consulting tasks. The goal is for AI to learn the actual work process, not just produce text. Similarly, in finance, more than 100 former investment bankers from big banks like JP Morgan and Goldman Sachs are helping train AI to build financial models. This project aims to reduce the long hours junior bankers spend on routine tasks.
This approach shows a clear shift. Companies no longer want AI that only understands broad topics but instead want it to perform specific, structured tasks. A recent survey found that most decision-makers see AI as a coworker rather than just a cost-saving tool. They’re looking for AI that can integrate into workflows and deliver real results, not just shallow capabilities.
Bridging the Gap Between Insights and Action
However, experts warn that many AI projects still struggle to deliver real value. Craig Le Clair, a top analyst at Forrester, explains that most companies run AI pilots that don’t lead to actual savings or improvements. Often, these AI tools produce reports or insights that no one acts on. This is called the “Action Gap”—the difference between getting an insight and turning it into a business action that creates value.
OpenAI’s new focus on learning from people who have done the work at scale is a step toward closing that gap. Instead of just mimicking language or creating generic responses, the AI is being designed to understand the practice behind tasks. For businesses, this is important because AI that only talks isn’t enough; they need AI that can actually help make decisions and improve processes.
Workplace Disruption and the Need for Structure
Le Clair points out that AI agents in the workplace are becoming as smart as humans within their specific domains. This means the workplace itself will change a lot as AI becomes a real partner. But deploying these systems isn’t just about the technology. It’s about the organization adapting. Companies are spending a lot on services to help them implement AI at scale and train staff to work alongside these new systems.
Greyhound Research’s Sanchit Vir Gogia emphasizes that even well-run AI projects need strong structure. For example, a bank tried using AI to draft credit memos, which initially seemed successful because it cut down the time needed. But when compliance teams checked the outputs, they found gaps and unverified statements. As a result, the bank added review processes and brought legal and risk teams into the mix to oversee AI results.
Another example involves a consulting firm that integrated AI into its analyst workflows. While productivity improved, staff felt unsure about their roles and how much the machine was doing. To fix this, the firm shifted its training to focus on supervising AI rather than just using it. The lesson is that success depends on how well organizations structure and manage these new AI tools.
Gogia sees OpenAI’s efforts as a sign that the company is deeply embedding AI into core workflows. By training models with inputs from experienced professionals, OpenAI aims to create systems that don’t just be accurate—they incorporate judgment and practical thinking. This signals a future where AI is an active contributor, not just a chatbot.
In the end, the journey toward effective AI in business isn’t just about smarter algorithms. It’s about building the right processes, training people to work with AI, and ensuring that these tools deliver real value. As AI continues to evolve, organizations that focus on structure and practical application will be the ones to truly benefit.
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