AI Readiness: Are You Truly Prepared?

Many business innovation leaders are all too eager to adopt AI. 

They have problems to solve, 

data to analyze, and 

people with deep domain and process knowledge. 

They're already experimenting with tools like ChatGPT, Gemini, and Claude, and feel confident they're ready for more. 

But there's a critical distinction to be made.

While it’s easy to play around with "out-of-the-box" AI models, true AI innovation lies in customized, cost-effective models fueled by your organization’s unique data. Success depends on embedding AI models deep into your business processes or customer experiences.

But, before you do that, it’s important to evaluate where your business currently stands. So, how do you gauge your AI readiness?

Avoid the Trap of Overly Complex Strategies

Some large consulting firms propose multi-month or even multi-year AI strategy engagements costing millions of dollars. While these might sound impressive, the AI landscape evolves rapidly. By the time AI implementation begins, your capital-intensive strategy will likely already be out of date!

Embrace an Agile Approach

Learn from Agile methodologies and adopt an iterative approach to AI strategy that starts small and expands over time:

• Week 1: Plot out your business goals and objectives over the next 12–18 months
• Weeks 2-3: Conduct a rapid assessment of your people, processes, technology, and data
• Week 4: Derive a roadmap of AI initiatives that ladder up to your goals and objectives
• Week 5: Carefully select and invest in one or more AI initiatives to execute and build momentum

From this point forward, set up a quarterly check-in to measure and communicate your position on the roadmap and rapidly run through each step above, adjusting your execution plan accordingly.

For large organizations, this approach can easily scale up with multiple functional groups or multidisciplinary teams performing these steps independently, then rolling up plans to an enterprise level. Alternatively, you can simply pilot it in small groups first and build momentum before extending it to others.

The toughest part of any strategy is anchoring it firmly to reality. So, make sure this effort includes analyzing your data that will fuel AI for real-world relevance.

Stepping Back to Move Forward

In the early 2020s, we worked with a company eager to create a custom AI model that would proactively recommend its content to customers. They were optimistic that the data they had would power a successful recommender out of the gates. We agreed to start with a rapid strategy and feasibility study engagement. 

We executed the steps above for the strategy components and generated a 12-month roadmap. In parallel, we dug into their existing data as an experiment to determine whether we could create a viable AI recommender. A month or two into data spelunking, and it became abundantly clear that their data was lacking in quality, depth, and connectivity. It was a difficult message to deliver to the client but their data wasn’t ready to power a recommender. As the project drew to a close, the data quality challenges opened their eyes to the criticality of data governance. 

Instead of spending millions of dollars attempting to create a viable AI recommender, they heeded our advice and redirected their next quarter investments into improving their underlying data. More specifically, they started a “data-first” company initiative driven by their CEO, hired an experienced data leader, and set off to improve their data quality for future AI solutions. 

A little over a year later, ChatGPT launched. Thanks to the strategy we co-devised and their timely investments in data governance, they were poised to quickly leverage generative AI technologies. 

As a result, they launched a self-service virtual assistant last year for their customers, powered by their unique organizational data.

The Bottom Line

A glacial, monolithic approach to AI innovation is risky in a rapidly evolving market. On the flip side, an overly optimistic approach—like skipping foundational work or immediately investing in risky AI innovation—may waste a lot of time and money. Determining the right AI approach for your organization requires a thoughtful strategy devised swiftly and rooted firmly in reality. 

Embark on a rapid, iterative strategy where you closely assess your underlying data and progress regularly. Don’t fall into the trap of 1–3 year strategy cycles, or you’ll risk being outpaced by more agile competitors or thwarted by a rapidly evolving technology landscape.

Let’s Chat. Direct message me if you’re interested in exploring how a rapid, iterative AI strategy can unlock your unique organization’s potential.