Welcome to Part 5 of our comprehensive 6-part series, Beyond Buzzwords: Finding your Purpose for AI, written together with data science expert Noelle Saldana.

AI is not one-size fits all. Consider this spectrum of organisation types with varying levels of AI integration, which starts with the least (AI-boosted) and progresses to the most (AI-led). Each stage requires different levels of investment, both from a technical and a personnel standpoint. If you want to jump to the cheat sheet, skip to the table at the bottom.

The Necessary Team

For many organisations, the biggest challenge is addressing the gap between where they currently are and what they are trying to achieve and hire for. In general, organisations vastly underestimate the size of the team they need.

Becoming an AI-led organisation involves commitment to significant organisational transformation. If your goal is to have an internal AI practice that drives innovation for all your products, it’s unrealistic to expect you can get there simply by hiring only one or two people or having another function cover this area of expertise. This is especially true if you’re trying to cut costs by hiring junior people who don’t have the experience of leading a team or a function.

How big are your AI needs?

An organisation’s current AI capability is directly related to its data maturity. (We recommend checking out this classic by Monica Rogati: The AI Hierarchy of Needs.)

If you have no or low data and do not currently use data to perform any analysis, AI does not fit into your core product. You could, however, use AI to boost efficiency internally and find value from using AI tools.

When you have a data strategy, you’re well-positioned to leverage data to create products that have AI capabilities. The extent to which you can do this depends on your data organisation.

AI explorers are investing in a seasoned leader to develop their data strategy and capability (pioneers). There is existing data within the organisation, but there is likely foundational work still to be done. AI-powered organisations have had time to build upon their data foundations and developed essential data analytics capabilities. These organisations are ready to take the next step to integrating (more) AI into their products.

AI-led organisations have either built their foundations on AI strategy or they have invested significantly in transforming their organisation to use AI to differentiate their business.

We have put together this simple table to summarise our recommendations for developing a level of AI capability based on an organisation’s current data maturity and AI goals.

AI Investment Cheat Sheet

AI-Boosted

AI Explorer

AI-Powered

AI-Led

Needs

AI does not fit into the core product.

AI could boost efficiency.

AI poses viable opportunities for the business downstream

Start with a data-driven product that has a feedback loop

Clear and valuable applications for AI in the business that aren’t being used yet

AI differentiates the business

Data Maturity

no/low

Operational data only. Not using for any analysis or trends. Not centralised

Developing

Product instrumentation, more diverse sets of data, some analysis, some centralisation

Developed

Has a data and analytics strategy, centralised data, analysis/DS work being done

Industry leading

Strong data strategy, DS/ML production pipelines, etc.

Approach for AI

Use AI tools

Hire 1-2 seasoned people (pioneers)

Hire teams and build out capability

Transform organisation to become AI-led.

When you evaluate this chart, consider if your AI needs align with where you currently are with your data maturity and what you are currently able to invest in for hiring. If not, it may be time to realign internal expectations or revisit budget conversations.

And, of course, you need to make sure your organisation is ready for the transformation that will need to take place. More on that in the next post.

This is part 5 in the series. Read on:

Part 1: Beyond Buzzwords: Finding your Purpose for AI

Part 2: AI Hype is New, Our Reaction To It Is Not

Part 3: Is the product you’re building a good candidate for AI?

Part 4: You are inspired to use AI: Now what?

Part 5: Your AI investment = (AI needs) + (the necessary team)

Part 6: Strategies for successfully integrating an AI practice into your org