Generative AI is one of the most disruptive and transformative technologies ever created. Yet study after study shows that enterprise deployments of AI are slow to gain traction and rarely deliver the promised ROI. At least so far.
The problem is not that generative AI does not work. It clearly does. The problem is not hallucinations, security, or concerns about training LLMs on company data. Those issues can be addressed confidently today. The problem is that companies are taking tools built for individuals and trying to use them as business solutions. Let me explain.
1. AI-Unfriendly Data
Start with the data. Popular LLMs are trained on well-curated, searchable, accessible, and richly contextualized public web content. Companies spend millions of dollars making their websites exactly that. Enterprise content, on the other hand, is a different species.
It hides behind permissions, access controls, and compliance policies, as it should. It lacks clean metadata and a consistent structure that would add context. It lives across systems that do not always integrate. And then there is the inconvenient truth: corporate content is usually a mess. Redundant files, outdated documents, and trivial content that should never have been kept in the first place. ROT is everywhere.
Cleaning that up is not glamorous, but without it, AI is working with garbage. And garbage in still means garbage out.
2. Tools for Individuals, Not Teams
The second issue is the tools themselves. Most AI products today are designed for a single user, treating them as productivity assistants. That is fine for drafting an email, summarizing a report, or creating a campaign plan. It is not fine for a team of architects designing a building, a group of writers collaborating on a new publication, or an M&A team evaluating a complex acquisition. In business, people work in teams.
Businesses need collaborative AI that produces consistent output, regardless of who uses it, like a team of writers trained in the company’s tone of voice. They need systems that understand company terminology, standards, and constraints. Instead, what they often get is a very smart assistant that is unaware that a colleague is using it for a similar task with a slightly different prompt. That may increase personal productivity, but it does not improve the team's productivity.
3. AI Processes Are a Mess
Then there is the process problem. For all the talk of disruption, copy-and-paste is not exactly a transformed business process. Teams often juggle multiple AI tools that do not talk to each other. One tool for writing, another for analysis, another for images, perhaps another for coding. Context does not flow between them, and outputs have to be manually collated.
As for the outputs, when AI-generated slides or graphics cannot be easily edited, they are not very useful in business. Embedding AI directly into tools like Excel sounds like a promising first step, but it is only a start. Real impact will come when AI is woven directly into business processes instead of hovering awkwardly outside them.
4. Just Because You Built It, They Will Not Come
Finally, there is the issue of adoption. One of the unintended consequences of the Web 2.0 era and the consumerization of the enterprise is the belief that business software should be as intuitive as Facebook or TikTok. In theory, that sounds great. In practice, it doesn’t work.
Business solutions applied to complex business processes require users to be trained on the tool and on the process. AI tools are not an exception. Business users need to be taught not only how to use AI tools, but what to use them for. Adoption is the primary stumbling block for enterprise AI deployments, and yet most enterprises don't include it in their roll-out plans.
The Real Gap
AI is unquestionably reshaping business. But most of the AI tools that knowledge workers use today are consumer or prosumer products built for individuals. Businesses are networks of teams, systems, permissions, and shared standards. They require consistency, collaboration, and integration.
Until AI tools are designed with that reality in mind, many projects will continue to under-deliver. We need AI built for corporate use cases.
No comments:
Post a Comment