Beyond AI Experiments: Chad West on What It Takes to Adopt AI

AI is easy to experiment with. Turning those experiments into real business value is much harder, though. Data readiness, organizational resistance, scaling capabilities, and operating change all determine whether an AI initiative moves beyond the pilot stage.

Some companies successfully take AI to production and integrate it into their everyday workflows. Others remain stuck testing tools and ideas without seeing meaningful results.

In a new video on Instinctools’ YouTube channel, Chad West answers 10 tough questions about AI adoption, and explains why success often depends less on access to technology than on knowing where and how to apply it.

If companies can already experiment with ChatGPT, Claude, and countless other AI tools, why invest in an AI adoption workshop?

Chad’s answer is simple:

No single tool creates business outcomes by itself.

He also highlights the difference between experimenting with AI and choosing AI for a real business use case:

When thinking about AI in terms of a new product or capability, the availability of LLM resources doesn’t matter if you choose the wrong use case.

And the biggest risk may come after the technology is implemented, if people never adopt it:

The greatest risk is internal adoption: when after all the meetings, the energy, and the investment is spent, and no one uses it.

That is where the AI Adoption Workshop comes in. It helps companies determine where AI can create value while taking ROI, risk, data, and technology into account.

AI initiatives often expose problems that existed long before AI entered the picture. Poor data quality and technical debt can quickly become barriers to implementation.

As Chad puts it:

Poor data and less-than-ideal infrastructures are like a ‘credit card’ we all use — technical debt.

Rather than uncovering these limitations halfway through implementation, the workshop takes them into account when prioritizing potential use cases.

Our workshop identifies key aspects of technology debt early in the process and factors them into use case prioritization.

That means a promising AI use case is assessed not only for its potential value, but also for what it will realistically take to deliver.

One of Chad’s strongest points is that successful AI adoption requires more than introducing another piece of technology.

AI adoption fails when it is treated as a tool rollout instead of an operating change.

That operating change involves different things:

The workshop surfaces resistance early: unclear ownership, fear of job impact, weak data accountability, or lack of executive alignment.

Chad also challenges the idea that AI should simply be managed as another project:

AI gets treated as a project — maybe ‘a big, important one’ with several milestones, but a project nonetheless — instead of the development of a new and powerful capability.

For companies moving beyond experimentation, this shift in perspective can be critical. Building the ability to use AI repeatedly, responsibly, and to tangible business effect takes a much broader organizational effort, than launching a tool or completing a project.

Watch the full conversation with Chad West and subscribe for more technology- and AI-related expert insights and news.