What is the company’s AI maturity analysis? | AI in business #59

Introduction to AI maturity analysis

An AI maturity analysis is a diagnostic tool that helps assess the extent to which an organization is using the capabilities of artificial intelligence. The methodology of the analysis takes into account several factors, including:

• leveraging AI in everyday business processes — for example, using a customer service chatbot or an e-commerce recommendation system,
• technology infrastructure — using modern solutions like cloud computing and machine learning,
• data architecture employed in a company — verifying that the structure and quality of the data allow for advanced analytics,
• employees’ AI skillset — checking whether employees have received the appropriate training and understand how they can use artificial intelligence for professional purposes,
• AI strategy and business objectives— verifying that AI is part of the company’s long-term plans.

Conducting an AI maturity analysis is especially important for small and medium-sized businesses that are embarking on a digital transformation using AI or want to improve their current implementations. This is because it helps identify specific areas for improvement and develop a strategy for further AI-related development. It also provides a holistic understanding of where the organization is in its AI implementation.

How to identify the level of AI maturity in your company?

There are several models for assessing the maturity of an organization’s use of AI. One of the most popular is a five-point scale developed by the consulting firm BCG:

1. Initial. At this stage, the company is aware of the existence of AI solutions but lacks AI implementations. It is potentially interested in implementing new solutions.
2. Managed. The company has already conducted its first concept tests using AI.
3. Integrated. AI is used in the company in selected areas, for example, in the marketing department.
4. Optimizing. AI is present in many key business processes, but they are not interconnected.
5. Transforming. Artificial intelligence is a key part of the company’s strategy and is deeply embedded in the way it operates.

For example, an e-commerce company at maturity level 1 may attend AI conferences but is not yet testing any specific solutions. On the other hand, a company at stage 3 may have deployed a customer service chatbot but is not using AI capabilities in other areas.

By conducting an AI maturity analysis test, such as the one available on the website of the Polish Development Fund (https://pfrsa.pl/siecfirmprzyszloscipfr/test-dojrzalosci-cyfrowej/formularz-badania.html), you can determine exactly where your company stands. This will help you identify specific bottlenecks and areas for improvement related to AI.

Understanding an organization’s AI maturity is especially important when applying for additional resources and funding for business transformation.

Key areas for AI maturity analysis – technology and data

For AI to deliver real business value, the right technology solutions are needed. Key elements include cloud computing, dedicated architecture, and analytics platforms that enable the processing and analysis of collected data.

For example, a small marketing agency testing AI for the first time may rely on the cloud. On the other hand, a large manufacturing company planning a broad deployment of AI across many areas will need purpose-built solutions that operate locally (on-premise) or specialized cloud solutions like data warehouse or data mart.

The second important area of analysis is access to high-quality, structured data. This is essential for training algorithms and building AI models.

Examples of companies that have been most successful in using their data to teach algorithms include:

• Facebook, Facebook, which targets ads based on user activity data and manages the suggestions that appear,
• Ryanair, whose pricing algorithms analyze historical ticket sales data,
• Netflix, which generates personalized movie recommendations by analyzing data on viewed content.

Here are the questions to help you analyze your company’s maturity in the areas of technology and data:

1. What is the company’s IT architecture?
2. Is cloud technology being used?
3. What kind of data is collected?
4. Is it well-organized and labeled?

Take care of your team – how do employee skills affect AI integration?

Another important area of AI maturity analysis is assessing employee skills and awareness of AI. According to the survey, up to 56% of companies cite a lack of talent as a key barrier to greater AI adoption. The high cost of AI specialists is also an important factor.

In such a situation, the simplest solution is to properly train existing employees through:

• online courses — here you can take advantage of the wide range of courses offered by Coursera (https://coursera.org/), Udemy (https://www.udemy.com/), deeplearning.ai (https://www.deeplearning.ai/) or Google (https://www.cloudskillsboost.google/paths/118),
• workshops and training conducted by professionals,
• personal coaching – within your company or during a session with a professional.

Organizational strategy and culture as a foundation for AI adoption

For AI implementation to be successful, a company’s business strategy and culture must support the process. According to BCG analysis, as many as 90% of digital transformations (including those based on AI) fail because the organization’s strategy and culture are not aligned.

So it is worth answering the questions:

1. Is the adoption of AI part of the company’s strategy and roadmap?
2. What business goals is AI supposed to help achieve? What problems is it supposed to solve?
3. Are employees open to testing and experimenting with AI? Are they rewarded for innovative ideas?

A good strategy and a culture of innovation increase the chances that AI will actually begin to deliver tangible business benefits.

What should I do when I already know my company’s AI maturity?

Once you have analyzed your company’s AI maturity, you can set specific goals and initiatives to help you move to the next level. For example, if your manufacturing company is at level 2, you can plan a multi-month project to implement a predictive maintenance system for equipment. At that time, you can also start building the company’s AI team by hiring your first data analyst.

At the same time, you need to keep in mind that the higher the level of AI maturity you want to achieve, the more effort and investment (human, financial, time) it will require. On the other hand, the potential benefits and competitive advantages that AI can provide are enormous.

A high level of AI maturity in an organization means first and foremost:

• revenue growth thanks to data-driven customization of offerings and more relevant audience targeting,
• lower operating costs through process automation and AI decision support,
• faster time-to-market for new products by using AI in research and development,
• higher supply chain efficiency achieved with predictive analytics,
• better customer service and greater consumer satisfaction thanks to AI chatbots,
• reputation as a leader in implementing AI–based innovations.

Therefore, it is worth the effort and expense to move to a higher level of AI maturity. This will optimize many aspects of the company’s operations.

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