If we want to implement AI successfully, we need to completely change how we do businesses

I’ve always thought it was interesting that we’re willing to fight and die to live in a democracy, but everyone is happy to work in a company which is structured like a dictatorship. This thought feels even more pertinent given the rise of AI. As AI continues to transform the world of business, we’re starting to notice a clear gap between those implementing a ‘throw it at the wall and see if it sticks’ approach, and those examining the fundamental changes that need to be made to a business.

While we don’t need to get into the pros and cons of oligarchy, over the past year of leading consultation and training sessions for over 80 organizations, I’ve realized that, if you introduce AI by working from the middle out, you can actually move a lot faster.

I’ve seen organizations try to bolt AI onto their existing workflows, and while there may be initial productivity gains, this generally doesn’t work out in the long term. We often see scattered pilots which don’t go the distance, duplication of tools or inefficient processes. The organizations setting themselves up for success are redesigning how teams experiment with and implement solutions.

We can use the transition from steam to electricity as an example. Paul A. David notes that there was a 40-year lag between the electric dynamo’s introduction and its productivity impact. When factories first adopted electricity, many simply replaced their steam engines with electric motors, while leaving the rest of the factory unchanged. Productivity gains were modest. David argues that the bottleneck was organizational structure. It was only when engineers redesigned factories around small electric motors throughout the factory that we began to see the benefits. General purpose technologies, like electricity — or, in this case, AI — require co-invention and firm restructuring before we can see the benefits.

It’s time to restructure.

AI is developing fast and it’s difficult for companies to keep pace

While most companies are built with a top-down model, this is not an effective way to identify and roll out technology, particularly when it’s moving as quickly as AI is.

We’re already seeing the impact that the speed of AI development is having. Companies are struggling with things like AI sprawl and shadow AI. AI sprawl is when employees are using tools everywhere, without shared norms or strategy. Gartner estimates that by 2028, an average global Fortune 500 enterprise will have over 150,000 agents in use, up from less than 15 in 2025. This creates significant agent sprawl, IT complexity and management challenges. Take a retail company for example, if sales uses one AI chatbot, support uses another and marketing uses a third we could start to see inconsistent customer experiences, where customer-facing AI chatbots give conflicting answers about pricing or return policies.

Shadow AI is the unauthorized use of AI tools by employees without IT or security approval. Common examples include employees pasting code into ChatGPT, uploading customer data to public web apps or using unvetted browser extensions to speed up daily work. Today, over one-third (38%) of employees say they share sensitive work information with AI tools without their employers’ permission. This introduces severe risks like intellectual property leaks, data privacy violations and non-compliance.

Building the assembly line of the AI era

The companies solving these challenges are finding ways to convene subject matter and AI experts from every part of the company to create a center of excellence, steering committee or a power user group. Once assembled, this group should be empowered to experiment, vet and validate new technology for the company. This is what I’m calling the new ‘assembly line’ of the AI era.

This assembly line is a dedicated team with the power to implement new solutions. They can vet any tools being used and compare them to systems already in place. They can then make decisions on whether the identified tools should be rolled out across the company, and the training and processes that need to be in place to make this rollout a success.

Some of the companies I’m working with are already putting this into practice. One water pumping company in Minnesota wanted to figure out how AI could be used to educate, train and inform employees, as well as preventing AI sprawl or shadow AI usage. Together we have mapped out who should be part of their center of excellence, who owns what and how to set up approvals. With the model we’re creating, we are establishing AI as a force for empowerment, education, training, tooling and most importantly change management.

On the flipside, I’m also working with a healthcare company that has an existing center of excellence trying to oversee ALL AI projects at the business unit level. This recreates a hierarchical problem that slows adoption, since it doesn’t empower individual units to move on their own. It makes sense, given HIPAA compliance means healthcare companies have to be cautious, but the focus of a Centre of excellence should be enabling teams through approved AI tools, not taking complete ownership of every project themselves.

By giving these teams authority to make AI specific decisions, you prevent the bottleneck which usually happens at the executive level either due to busy schedules or a less in-depth technical understanding. The center of excellence can redirect sprawl, combat shadow AI usage and escalate things when necessary.

Turning individual experiments into company decisions

A center of excellence gives AI adoption a working rhythm instead of leaving it to Slack threads, scattered pilots or executive guesswork. Each team should have someone close enough to the work to spot where AI is useful and where it’s a distraction. A finance lead might see value in automating invoice checks. A legal lead might reject a tool because it mishandles client data. A customer service manager might test whether an AI assistant actually improves response quality or just produces faster, worse answers.

That group can then turn individual experiments into company decisions. They can test tools, compare them against existing systems, check the security risks and decide what needs training before anything is rolled out. They can also stop bad habits early, like teams uploading sensitive documents into public tools because nobody gave them a safer option.

If companies want AI to work, they need to completely overhaul their processes. The companies that move fastest will be the ones that give people in the middle the authority to test, challenge, approve and teach. That’s where the real work happens: close enough to daily operations to know what’s useful, and connected enough to turn that knowledge into practice across the business.