AI adoption: a dynamic journey in 3 phases
Artificial Intelligence (AI) can boost to innovation and growth enormously. For many companies, however, it continues to be a serious challenge to successfully implement it. After all, unlike traditional IT projects, AI projects are dynamic and often unpredictable. AI implementation requires a new approach because it is more akin to R&D processes, than conventional software development.
Typically, companies go through three phases in the adoption of AI technology: exploration, growth, and transformation. In each phase, the focus of the approach is slightly different.
The exploration phase
The vast majority of companies today are in the exploration or 'proof-of-concept' phase: they are starting to experiment cautiously with AI or have already developed simple applications such as chatbots or data analysis tools.
However, real value creation is yet to begin. The biggest challenge in this phase? The lack of relevant, structured and qualitative data. Organisations often do not know how to get the best out of their own data for AI-driven solutions (yet). With few resources and little talent devoted on it, AI initiatives often get stuck in the experimental phase.
That is why cooperation with external expert partners is crucial in this phase. Organisations benefit from getting everyone in the company on board. It is essential to start working on intrinsic motivation and acceptance. And above all: to cultivate a culture that encourages experimentation and learning from mistakes.
The growth phase
Only a handful of companies have made the leap to the growth phase where AI moves out of the exciting experiment phase and becomes an essential part of business operations. Companies that have already celebrated their first successes, are now expanding AI applications, balancing innovation and integration, striving to seamlessly weave AI into existing systems and processes.
Management now fully embraces AI and are demanding concrete results. KPIs are more sharply defined and the impact of AI on the bottom line is closely monitored. Data management has also matured, with a data team and/or chief data scientist making AI an everyday tool for everyone in the company.
AI is ingrained in commercial products and services, leading to more refined and personalised experiences. The customer perspective therefore also becomes a compass that helps determine the future of AI. As AI becomes more embedded in the company's DNA, its value becomes more and more tangible for everyone.
The transformation phase
In the final phase of AI adoption, the transformation or deployment phase, artificial intelligence becomes an essential part of how companies operate. AI will no longer just be a tool, but something that supports the organisation’s progress. Large organisations and public companies are often at the forefront. They make advanced AI systems themselves that inspire other companies. But not everyone in the company welcomes these changes.
Companies in this phase have to pay close attention to whether they are using AI in a safe and responsible way. They have rules and systems in place to ensure that AI is only ever used responsibly. AI literacy is now organisation-wide. All employees, not just the experts, now work with AI every day.
Regardless of what stage your business is in, the journey to AI maturity requires patience, a solid strategy, and constant investment. Companies need to think carefully about their approach and continue to invest. They need to understand the issues at every stage in order to use AI properly and learn and adapt quickly. This keeps the risks small and maximises the potential benefits.
After all, the goal is not just to use AI, but to benefit from it.
Regardless of what stage your business is in, the journey to AI maturity requires patience, good strategy and constant investment.
Understanding the challenges of each stage will help you take the right steps to grow and make the most of AI.
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