The AI Maturity Model: How to Move the Needle of Digital Transformation Towards an AI-Driven Company?

To properly implement AI, businesses need to have a structured process to ensure that they can fully take advantage of it. The AI Maturity Model is a framework that can help any company understand at which stage of organizational maturity they are and what steps they need to take to become an “AI-driven” business. The first step in becoming an AI-driven business is understanding what it takes to become one.

In this post, we’ll explore the five stages of the AI maturity framework and how you can implement them for your own success. We will also look at AI problems and ways to adopt AI to mature your AI strategy and application.

The Reasons to Implement AI

Organizations are investigating AI as a way to enhance existing applications and processes. For example, artificial intelligence can automate decisions, bring actionable insights, or classify complex data. And while human intervention and investment would still be needed, enterprises can accelerate their processes using AI.

To establish a strategy, you first need to measure how your organization performs on the AI maturity framework. This model (aka framework) can help you identify where the organization is on the potential growth curve. This gives you the much-needed data to then speak to the management and decide what steps to take.

No matter where your organization is, ensure that strategies are highly adaptive, with ample room for experimentation.

The Diverse AI Capabilities and Use Cases

No matter the industry, almost every organization can adopt AI to some extent. AI capabilities can be applied to sectors like logistics, pharma and life sciences, chemicals, telecommunications, and many others. Look at the below use cases just from our portfolio to get an idea of the everyday use of AI technologies.

• AI-powered chatbots can bring intelligent automation and answer consumers’ questions or direct the customer to the right person if no response is found in the database. They can also be used in healthcare products e.g. an AI-powered chatbot supporting mental health.
• Predictive models can forecast when stocks might run low, or highlight and respond to patterns in supply chains,
• AI can be used to identify fraud loan applications based on a business’s loan repayment behavior and credit usage.
• AI tools can aid customs control in advanced image analysis for the detection of prohibited items.  Image classification can be also used in the travel sector by booking engine providers to classify and tag hotel images for the best user experience.
• AI capabilities can help segment data and make proper recommendations to customers. For example, a corporate traveler can benefit from a dedicated, personalized offer for travel inventory. On a social media platform, through AI tools get a personalized stream of content to browse.
• In last-mile logistics, AI plays a crucial role in route and schedule optimization.

No business is too small to utilize readily available AI-powered tools or develop its own AI strategies. If you’re curious about the difference between off-the-shelf AI products and custom AI models development we recommend our recent article. For general advice on artificial intelligence, projects head over to our guide on AI for Business Leaders and Executives for more.

What Is the AI Maturity Model?

In layman’s terms, an AI maturity model provides an organization with a framework for assessing its current AI readiness and capabilities. The data and analytics maturity model gives the organization valuable and actionable data to prioritize investments toward AI technologies, skills, and processes needed to develop, manage, and maintain AI-based systems, including mitigating any potential risks associated with their development and use.

An organization needs to understand AI before successfully harnessing its potential and gain a solid understanding of its AI readiness and capabilities before investing big bucks.

The reasons for not exploring AI initiatives include badly chosen pilot projects, wrong assumptions about the availability of data and readiness of teams to deploy AI-based systems, and the results the technology was expected to deliver.

The AI maturity model can help you avoid these failures that may lead to the potential loss of confidence in AI from the organization’s leadership and other key internal stakeholders, leaving the program dead in the water.

Why You Need an AI Maturity Model?

When considering the use of AI in the organization, it’s important to remember two things: not every task seems to need to be automated, and automation becomes valuable in the long term. Without automation in place, the only option for growth is hiring more team members.

Machine learning algorithms help process data in ways impossible to achieve before. Of course, mathematics has been there for years, but the lack of relevant data and computing power rendered it viable.

Barriers to AI Adoption

Demand for AI and market growth is closely tied to organizational AI maturity levels. Organizations continue to demonstrate a strong interest in AI, with 48% of CIOs in the 2022 Gartner CIO and Technology Executive Survey responding that they have already deployed or plan to deploy AI and machine learning technologies within the next 12 months.

The reality of AI deployment is much more limited. Gartner’s research found that organizations commonly experiment with AI but struggle to make the technology a part of their standard operations. Organizational maturity must be at a certain level to enable proper AI adoption across the whole business.

Gartner also estimates that it will take until 2025 for half of the organizations worldwide to reach what Gartner’s AI maturity model describes as the “stabilization stage”.

Advances in AI maturity will boost AI software revenue due to increased spending, particularly in data analytics. However, a lag in maturity caused by reluctance to embrace AI, lack of trust in AI, and difficulties delivering business value from AI will have a corresponding deceleration effect on spending and revenue.

According to Gartner, nearly two of three organizations cited “finding a starting point” as a concern.

CIOs should definitely set realistic expectations, identify suitable use cases and create new organizational structures. Organizations should thus set realistic timelines for AI projects and ensure the desire to follow trends doesn’t overrule realistic drawbacks and planning. The unnecessary push for AI can be a problem in itself, resulting in other strategic challenges.

AI projects face specific obstacles due to their scope, misperceptions about their value, and the nature of the data they touch.

Time to Market

Many organizations plan to launch and finish an AI project within two years. However, once past the initial planning process estimate, the time usually extends to four years.

Being first to market with products, whether it’s a digital MRI machine, an autonomous cab, or an automated trucking fleet, is important to gain a competitive advantage in AI. The faster you can train your model, the faster you’ll be able to get to market and have a better chance of becoming number one. This means that every second, minute, hour, and day is significant. Because training models for autonomous vehicles can take years, cutting it down to days has a significant financial impact and competitive advantage. This will necessitate infrastructure with the best performance and lowest latency (the secret time killer of machine learning projects).

The size of the training model data set is also important because more data means stronger models and thus a faster time to production. ‘It’s not who has the best algorithm that wins,’ said Andrew Ng, VP and Chief Scientist of Baidu, Co-Chairman and Co-Founder of Coursera, Adjunct Professor at Stanford University, and online learning pioneer. It all comes down to who has the most information.’ The greater the training data set, the more accurate the training model will be and the faster it will be able to reach the market. Large data sets necessitate a shared storage system with enormously high bandwidth and low latency.

ROI

It is hard to determine an AI project’s return on investment because most organizations are too early in the process to see any return. Most ROI will result from cost reduction and increased efficiency, as that’s how AI is currently used. However, as organizations evolve their AI expectations and projects, AI will eventually have a more transformative and strategic impact.

To understand the metrics of the ROI of AI and to find out how to reduce the risk of investment in artificial intelligence projects, head over to one of our in-depth articles on AI investments.

What Maturity Models Are There?

There are many artificial intelligence maturity frameworks, but they share many similarities. For example, most of them have five stages, and some limit them to just four maturity levels.

• The first stage covers undefined and unpredictable processes with no analytics in place
• The second level describes repeatable and reactive processes and analytics
• The third level covers defined and proactive processes and analytics maturity
• The fourth level describes managed processes that are measured and controlled, the analytics maturity is at an advanced predictive level
• The highest level strives for continuous improvement and a prescriptive approach

While the models use different names to refer to capability, there is overlap in the actual capabilities they cover. About half the models provide a detailed description of each maturity level per capability, which can significantly improve the homogeneity across organizations and assessors. The other half provides a general description of the maturity levels based on CMMI and lets assessors define their definition per capability. Finally, all but one model provides specific recommendations for the defined (sub) capabilities.

Almost every model provides specific recommendations, while only Gartner’s model provides general recommendations. An overview of all variables regarding model structure can be found below.

The Dimensions of the AI Maturity Framework

AI maturity can be achieved in several dimensions or areas and stages expressed as a chart. Let’s look at them briefly.

Data

Data is the foundation for the successful application and scaling of AI technology in a company. High-quality organizational data and a dedicated data warehouse are required to realize AI solutions’ full potential in an organization. Therefore, a holistic view of the organizational data is essential during AI maturity identification. Due to overwhelming data accumulation, bad data quality, and frequency of data input, data analytics maturity is the most significant operational challenge for many organizations instead of being considered an important strategic asset.

Use Cases

The ability to identify and develop potential value-adding use cases for artificial intelligence is crucial. It is important to be aware of the limitations and possibilities of AI and transfer that knowledge to practical business problems. Processes for identifying and prioritizing AI use cases based on feasibility and success criteria are important in the scaling phases when various departments are utilizing AI for their business problems.

Team and Skills

A successful build-up of AI capabilities in a company is highly dependent on internal AI know-how and related skills. It is thus important to have the right AI talents available before implementing AI. Building maturity in this dimension is vast and can range from meaningful job advertisements to suitable training concepts for upskilling internal employees.

ML Infrastructure

IT infrastructure forms the technical foundation for the development of AI applications. AI experts need to have the right tools to develop their applications and make them accessible within the organization. Cloud technology adaptation is also becoming an important indicator in determining AI maturity. AI projects can not only be deployed in the cloud. Many large cloud providers also offer AI microservices.

Governance

Various factors play a role in the governance of AI projects. The risks connected with AI must be identified, and compliance with regulations as well as internal policies must be monitored in the solutions. After KPIs and metrics have been agreed upon, frequent reports should be made to ensure the successful steering of AI initiatives. Other important factors in this context are AI ethics and the explainability of AI.

Organization

The benefits that AI teams generate often depend on how well they are integrated at the organizational level and if the organizational conditions are adapted to it. The adoption of an agile, AI-oriented approach and the change in the culture’s mindset also contribute to the initiative’s success.

The Levels of AI Maturity Framework

In each dimension, a company can be in a different maturity stage.

According to Gartner, these stages are:

1. Awareness

Companies in the awareness stage know about AI but haven’t really done anything about it yet. They may talk about it. They have ideas, but not strategies, for using AI in their businesses. Because the company is aware of the existence of AI and its potential. They analyze raw data manually without any intelligent tools and data storing methods but are aware of the potential to use AI to support these processes.

2. Active

Most organizations are at this level of AI maturity. They are just playing around with AI. For example, there is little knowledge and awareness of AI technology, and raw data is mainly stored in fragmented systems. Planning and decision-making are rarely data-based, along with simple training initiatives.

Organizations enter the Active (also known as Exploring) stage of the AI journey when they shift from general awareness to targeted questions about what problems or opportunities they could potentially help address.

What these companies share is a lack of experience in judging a good AI opportunity from a bad one. For example, data scientists can run Tensorflow with GPU acceleration using a toolkit but don’t know where to go next after tutorials. Likewise, business leaders aren’t sure how to help.

Techniques for closing this gap vary for different teams and organizational structures. For example, business and technical leaders usually need help separating hype from reality for AI techniques like deep learning, reinforcement learning, and transfer learning. On the other hand, data science teams might need less help understanding these techniques at a technical level, but still, need to know what cooperation is needed to deliver models in production.

Progress in the Active stage tends to be driven by ambitious individuals or teams who focus on building informed interest and buy-in.

3. Operational

Companies in the operational stage have adopted machine learning as part of their day-to-day functions. They have a team of ML engineers and could maintain models, create data pipelines, or version data. They have the ML infrastructure set up, and they are using ML to assist with some information processing tasks – hence the Artificial Information Processing approach to measuring value.

However, a central platform for AI in the organization does not exist here. Tools and know-how are only available at an operational level to a certain extent. Nevertheless, a few stakeholders push for AI initiatives, and in some cases, AI models have made it to the near-production stages at a departmental level.

4. Systemic

Companies are using machine learning in a novel way to disrupt business models. So, hype at the awareness stage can say that they are disruptive, but the difference between a Level 1 and a Level 4 company is that the Level 4 company has feet on the ground, with the ML infrastructure in place.

At this level, AI is applied in most company areas and integrated into existing products, services, or processes. As a result, AI has become a standard technology within the organization, and rules for the governance of AI models are defined and followed. At the same time, AI serves as a basis for decision-making and is centrally managed.

5. Transformational

It’s the last stage of AI maturity. Unfortunately, few organizations have reached this stage, and few deliver on their full potential.

Organizations that are at this level use ML intentionally regularly. In many cases, it’s the distinguishing value offered to their customers. They rely on AI to do the significant heavy lifting for the business.

In this stage, AI is part of the company’s business model and is firmly embedded into the organization and corporate strategy. For example, companies like Google, Amazon, and Netflix use machine learning to tweak their algorithms, adjust their product offering, and optimize their systems infrastructure (i.e., Netflix experiences low latency in specific time zones).

• AI competencies and training are commonly and systematically cultivated.
• Data is given a high priority and is seen as both raw material and product. AI is also fully exploited in compliance with regulatory and ethical standards.
• Teams have central access to data and AI solutions are managed in the organization’s own AI platform.

This stage of AI maturity is where the promise of AI for business and society meets the biggest impact.

At the transformational stage, companies have transformed to work smarter with AI. As a result, they operate more safely and cleanly than before while creating more value for society and tackling problems that seemed overwhelming before.

Data scientists and data analysis experts and researchers are still debating what this looks like in practice. For example, organizations that transform with AI are expected to leverage intelligence systems to augment human intelligence and unleash collective intelligence between humans and machines — but each of these terms is still being actively defined.

Next Steps?

Once the organization’s AI maturity level has been identified, the next question is: how to increase it? It takes three stages to implement the outcome to boost your AI maturity.

Today, most companies fall under the Awareness stage – their businesses only benefit a bit from artificial intelligence. At the same time, few companies are in stage five, and few are both ready and can integrate AI through every one of their processes.

How Adopting AI Can Increase Business Value

Adopting artificial intelligence shouldn’t be a move organizations take on just because of the hype. It should rather provide strategic, measured value to the business. There are some important questions to ask:

• What decisions does my company make?
• What data does my company collect?

AI is useful when making data-driven decisions. However, adopting AI is an ongoing two-step process consisting of exploring current practices and brainstorming new decisions to boost the company value.

It works best when the data is already collected and a decision needs to be made. If you have the data, ask yourself a question: what decision exactly can be made from it? And then, if you make decisions, what data is necessary to make that decision? Finally, how can you start considering every daily activity as a data-driven decision?

Explore Current Business Practices

Start exploring the business areas which can use AI. Even if a task seems so easy that it doesn’t need to be automated, automation may turn out to be more economical in the long term.

Brainstorm Decisions That Make Your Company More Valuable

After examining current business processes, think about all the ways the organization can conduct business. The center point of attention – all your attention – should revolve around data-driven decision-making.

AI can help any organization make decisions. By looking at your existing teams and processes, you might pinpoint the areas which lend well for AI implementation. For example, look at your sales team. Sales teams can also use AI to segment customers into types. Then, a sales agent can negotiate a deal better or offer a more appropriate service. AI must be fed with data like customer age, email, location, purchasing habits, and user habits to do this. This frees the sales team to move their attention to more urgent matters.

• Rank a list of items in an eCommerce store. For example, a particular color is better liked than another.

• Recommend items. If a customer likes X, they will also like Y.

• Discover anomalies and regularities. Detect inefficient supply chains and customers who are most likely to come back and most likely to buy another product.

• Sort populations into types. Segment your population of customers into top users, occasional users, and one-time users. Alternatively, divide your population into risk-averse and risk-seeking personalities.

  Artificial intelligence can also be used to detect e-commerce fraud.

Conclusions

As you use AI in applications, goals around the maturity of the AI are critical to:

• ensure that you deliver value to your clients,
• use the appropriate level of technological sophistication,
• ensure that the AI is trustworthy and easy to use by your targeted line-of-business users,
• have an AI operating model in place to manage the AI you’ve deployed in the field along with strong data management and data governance practices.

AI is still in its infancy, but numerous studies have shown that companies who are adopting AI are reducing costs, improving efficiency, and delivering bottom-line profit to the company. AI can help with problems as basic as setting a maintenance schedule for a factory floor, all the way to targeting the right product to potential buyers and improving sales closure rates."

If you are ready to discuss how we can support your organization, assess its current AI maturity and support your business through its AI journey, feel free to contact us.