Charting the Course for Pragmatic AI Adoption: A Strategic Guide

We know that AI is currently experiencing its own transformative moment, poised to impact various facets of business and society. Based on research conducted by Certinia, global spending on AI software is projected to surge from $124 billion in 2022 to $297 billion by 2027. It’s clear that integrating AI into your business strategy is crucial for survival and growth. This means moving beyond superficial AI applications and focusing on practical, results-driven deployments.

Certinia has started to advocate for a pragmatic approach to AI adoption that’s grounded in three core principles (and we here at Cloudteam Company couldn’t agree more – haven’t you already read our latest blog post “Let’s get pragmatic about AI”?).

These three principles include:

Okay, so we now understand that we simply need to ensure that when we adopt AI into our organisations that they must be:

1) Deployable 2) Actionable and 3) Close the loop.

But to be honest… this still doesn’t make the challenge of implementing AI into organisations less daunting.

Certinia themselves admits that adapting to AI is expected to be the top business challenge in the coming years. So, naturally as Certinia does.. they’ve suggested a straightforward model we can use to 1) apply the guiding principles and 2) actually assess our current AI maturing levels and guide our investments based on that.

Not only is this going to save unnecessary spend on the part of organisations trying to get the shiniest AI solution out there, but it will also ensure that when the organisation is in fact ready to adopt AI, that it will be done mindfully and the solution will be working at its best.

Navigating AI Adoption: The Pragmatic AI Maturity Model

It’s not about the destination but about the journey. Very philosophical we know.. but there is some truth to it. To successfully implement AI in our organisations we really need to plan the process and take small steps. The Pragmatic AI Maturity Model offers the structured approach we need to assess and advancing our organization’s AI capabilities through five distinct stages:

1. Initial: At this stage, AI applications are fragmented and often limited to simple tasks, such as using ChatGPT for basic content creation. Data is typically disorganized, relying heavily on spreadsheets and siloed tools. Most organizations are currently operating at this level. AI is easy and straightforward at this stage, and thats where many get comfortable.

2. Repeatable: Next, teams start to discover more about AI and how it can actually help day to day processes. So, they start integrating AI into stand-alone solutions and begin to improve data practices. However, data is still dispersed across various systems without a centralized repository. Organizations at this stage are seeing initial successes with AI but are still developing their data infrastructure.

3. Controlled: Finally, organisations are starting to pull together in a more uniform way and make use of a unified data strategy. They are consolidating transactional and operational data into a single repository. This allows for more advanced AI applications, such as predictive analytics and basic generative AI. Many believe they are at this stage, but few have fully achieved it.

4. Optimized: For those that have managed to reach the controlled stage, its time for them to think about deploying advanced AI models for complex predictions and insights. AI is embedded in business processes, enhancing decision-making and operational efficiency. These organizations have dedicated significant effort over several years to reach this level. The pioneers is what we like to call them.

5. Continuous Improvement: Next, the pioneers become experts as they have managed to “close the loop”. The pinnacle of AI maturity features a closed-loop system where real-time data continuously refines AI models. AI tools drive substantial business outcomes and efficiencies, operating within a seamless, real-time platform. Fewer than 20 organizations globally are at this advanced stage.

So now that you know what the path is ahead of you, it’s time to take the first steps. Don’t worry, advancing through the model is achievable. You just need to be PRAGMATIC. Begin by assessing your AI readiness and mapping out a plan to move through each stage. Focus on establishing a foundation of clean, real-time data to support future AI implementations.

As you progress, consistently evaluate your AI initiatives against the pragmatic principles: Are they deployable, actionable, and part of a closed-loop system? If not, it may be time to reassess your approach. For more insights on leveraging AI for business growth, explore our resources and stay connected with Cloudteam. In fact, why don’t you fill in a form and we will get in contact with you on how you can start your AI journey.