AI Maturity Levels

Let’s look at the potential steps you might take to apply Artificial Intelligence at your workplace and what challenges you might face along the way. Gartner, a research and advisory company, has defined maturity levels for AI adoption as follows:9

Level 1 - Awareness: Conversations about AI are happening, but not in a strategic way, and no pilot projects or experiments are taking place.

Level 2 - Active: AI is appearing in proofs of concept and possibly pilot projects. Meetings about AI focus on knowledge sharing and the beginnings of standardization.

Level 3 - Operational: At least one AI project has moved to production, and best practices, experts, and technology are accessible to the enterprise. AI has an executive sponsor and a dedicated budget.

Level 4 - Systemic: Every new digital project at least consider using AI, and new products and services have embedded AI. Employees in process and application design understand the technology. AI-powered applications interact productively within the organization and across the business ecosystem.

Level 5 - Transformational: AI is a part of business DNA, it goes into every business process, and it is a natural framework to work with. Every worker knows the strengths and weaknesses of AI.

To that, I can add Level 0 - No Awareness, where there’s simply no discussions of AI or AI-related solutions at work, and workers are not aware of what exactly AI means and how it works. Unfortunately, this is the most common stage right now, even among the largest organisations. Following a study by McKinsey10 on Artificial Intelligence adoption, 53% of respondents are at Level 0, even though the studied companies were already more aware than most of what AI can do for them.

Fortunately, these statistics grow each year. Another study by McKinsey shows a 25% growth11 in AI adoption across various industries. Some major criteria why this happens can be tracked to:

• the cost of implementing AI is falling with AutoML solutions (automatic machine learning) and easy to use platforms, which automatically analyse data without a need for a data science team.
• the general awareness of how AI can be used is growing with growing coverage in major magazines and newspapers.
• there are more and more data scientists and machine learning engineers available on the market, thus it is easier to find experts to work with on AI. Especially if a company is willing to hire an external team from a software house to jump into AI.
• the track record and business case studies are more widespread. It’s easier for managers to see the real benefits of using AI in cases similar to theirs.

You can also apply the same levels of AI maturity to society as a whole. If you think about it this way, then we are currently entering Level 3 - Operational:

• There are numerous AI pilots going on around the world in each domain of our life. Most efforts here are made by private and public companies with substantial help from governments as well. Examples: traffic optimization, cashless shops, e-government, big data analytics.
• Many countries have already adopted AI strategies and approved a budget for AI solutions. Publicly administered funds are directed towards startups and R&D. Governments put money into universities creating AI faculties.

To achieve a higher level of maturity as a society, we need to:

• broaden the scope of AI education, starting with primary schools, introducing Python as the main tool for computer science classes.
• include AI and machine learning framework into education at business schools.
• popularise machine learning and data science knowledge among the general public, showing strengths and weaknesses of using AI, overcoming fear, and showing potential benefits.
• incentivise businesses to invest in AI solutions and use AI in their processes.

In general, I’m optimistic it’s just a matter of time when we transition to the highest level of AI maturity as a society. Artificial Intelligence is such a transforming force, with a proven track record already that it’s a question of when it will happen rather than whether.

Nevertheless, you can go into level 5 right now with your organisation by carefully planning and mapping your activities related to AI. There are two main tasks you have to do to start:

• assess the maturity level of your AI adoption,
• follow a list of recommendations to go to a higher level.

The first step is to assess the maturity level of the company by asking what the company is doing when it comes to:

1. team/people
2. data
3. tools for optimization and automation
4. vision and values

For example, let’s say we are concerned with the Marketing Department responsible for Customer Analytics. Data-wise this means we will be working with the following data:

• customers’ feedback
• effectiveness of in-store promotions
• planning and forecasting
• customer segmentation

The first step toward assessment of the AI maturity level of your company is to gather data from the department by asking your coworkers and looking through past projects. You should start by asking the following questions to assess properly how AI is used in your organisation.

Team

1. Is there a dedicated AI team working on machine learning and NLP algorithms: data scientists or machine learning engineers?
2. Is there a team of data analysts who analyses, cleans, and processes data?
3. Do some of the jobs/tasks at your department are tedious and repetitive? What’s the biggest obstacle in automating them via AI?
4. Is there a person responsible for managing the AI team?

Data

1. How is data stored currently? Do you store it in-house or using external services/cloud?
2. What kind of data is currently stored?
3. Do you clean and process data in any way upon reception?

Tools

1. Do you use existing tools for data analysis? What kind of tools are you using? How much automation is in the solutions you’re using?
2. Do you use any Machine Learning platforms to extract insights from your data? Which data do you submit to it?
3. Do you use algorithms built in-house by your data science team to analyse your data?

Vision and values

1. Do you think about applying AI to each new project?
2. Do you continuously try to improve existing processes by using machine learning algorithms and available AI systems?
3. Are there communication channels between the AI team and other teams so that AI can be easily implemented whenever needed?

Based on those questions, you will be able to assess the AI maturity level of the company by measuring it against Gartner’s description. The answers to the above questions would certainly allow you to pinpoint the AI maturity level and moreover might indicate what you should do in order to level up.

We will now go through each level to discuss how you can level up, no matter what’s your current stage. This applies to each department or an organisation as a whole.