We're in the midst of an AI revolution, especially with generative AI, which has seen explosive acceleration in the past year and a half. AI is now the hot topic in boardrooms everywhere.
Businesses are exploring ways to gain a competitive edge, reduce costs with more automation, and boost customer lifetime value by creating personalised and predictive customer experiences. However, the race for all things AI will be won by those who can map and accelerate their journey to digital maturity. Those who think beyond use cases in silos. Those who take a strategic approach and give equal importance to culture and ethics as to work systems and products.
AI maturity isn't a one-size-fits-all journey. It has various stages, each with its own set of opportunities and challenges. Here's a look at the five key stages of AI maturity, helping you determine where your business stands and what your next steps could be.
Stage 1 - Initiation
Your business is aware of AI and some potential benefits it might bring. Discussions about AI solutions are happening, but not yet widespread.
Goals for AI are undefined, and understanding of how AI could benefit the business is limited.
Customer experience is disjointed, and data remains scattered across platforms.
AI understanding isn't widespread across the company, and communication about AI from leadership is minimal.
No concrete steps have been taken toward governance of AI.
Stage 2 – Foundation
AI use cases are now being identified and explored. The organisation is starting to understand the importance of data and how it can enhance services.
Future AI implementation objectives are forming, but resources and budget allocation for this purpose are not yet established.
Core customer experience touchpoints are being audited, and initial plans for AI implementations are forming. The importance of collating data onto a single platform is recognized.
Discussions around use cases and product optimisations have begun.
AI governance issues are being recognised but are not yet a key focus.
Stage 3 - Piloting
The business is piloting AI solutions for selected use cases. These projects are usually contained within a particular department.
Proof-of-Concepts (POCs) are being explored with a focus on productivity, automation, personalization, and improving customer experience.
Specific solutions targeting customer experience improvements are being developed.
The business now has a data strategy that is moving towards predictive analytics.
Partnerships with APIs and SaaS providers are being formed to trial AI implementations.
The ethics of AI implementation is acknowledged but not prioritised.
Stage 4 - Scaling
AI has become part of the company strategy, with data considered a core competency. The AI focus has moved from optimisation to market-oriented strategies.
AI transformation is a strategic focus across the entire business.
A roadmap and data requirement with AI at its core is created, focusing on improving customer experiences.
The data strategy is now a core competence, and it's influencing new products and developments.
AI integration is recognised company-wide, and data science and AI resources have been ramped up and are totally connected.
The ethics of data use and its impact on customers are considered, with measures for responsibility and transparency in place.
Stage 5 - Mastery
AI is woven into the fabric of the business. The data strategy is widely understood and is adding significant value.
AI is integral to the business, playing a critical role in digital transformation and competitive positioning.
AI is at the heart of omnichannel customer experiences. The business thrives on efficiency and intelligence in creating new customer experiences.
The business is reaping benefits from its cumulative data advantage.
The company has a unified view of customers, data, and efficiency, which drives strategic direction.
AI development is a recognised core competency and is seen as a product in its own right, creating opportunities for monetization.
The business encourages a learning mindset, promoting design thinking, experimentation, and innovation.
AI project governance is well-defined, with an ethics committee in place to enforce guidelines.
Remember, reaching AI maturity is a journey with unique challenges and rewards at each stage. By understanding these stages, businesses can better navigate their transition, ensuring they harness the full potential of this transformative technology.
So, whether you're just beginning with Initiation or already feeling the value from Scaling, keep going! Your journey towards AI maturity is shaping the future of your business.