What is AI adoption?

By Joe Sunderland on December 1, 2023 - 5 Minute Read

We can all admit that AI is pretty cool. But when it comes to AI in the context of work, cool doesn’t cut it. We want AI to make a difference in our organization, for it to automate life’s less glamorous tasks, and to optimize processes and decision-making.  

Put more simply, we want results. To get those results from your organization, you have to implement AI effectively, make sure it’s integrated with your existing tech set up and run change management programmes to make sure you actually use it to its fullest potential. What you need is an AI adoption strategy.

If you’re wondering what AI adoption is, why it matters and how you nail it, we’re here to help. We’re Peak. We’ve worked with organizations of all shapes and sizes to help them get game-changing results through AI-driven personalization, pricing and supply chain optimization.

If you want to know how to adopt AI for your organization, then stop clicking and start scrolling because the answer you’re looking for are right here on this page.

What is AI adoption? 

Trying to define AI adoption shouldn’t be hard, but it is. That’s because the term “AI adoption” is used interchangeably to refer to two different things: AI adoption as a project and AI adoption as an end state.

AI adoption as a project

As a project, AI adoption describes all the steps involved in deploying and getting benefits from an AI solution. It typically involves the implementation of a single use case or application.

AI adoption as an end state

When people use the term “AI adoption” to describe an end state, they’re talking about an organization that has ingrained AI into its decision-making and across its strategy, technology, people and processes.

We use the term “AI maturity” to refer to this end state. You can learn more about AI maturity in our report here.

The key components of an AI adoption plan

Connect with your chief technology officer (CTO)

If you’ve not already got a relationship with your chief technology officer (CTO), now’s the time to reach out. Integration, how your artificial intelligence (AI) works with your existing technical set up, is crucial. It will determine what solution is appropriate for your organization. For instance, you need certain infrastructure (e.g., an AI platform) to build your own AI. They can advise the integration aspects of AI adoption, making them perhaps your most important ally.


You want your first AI project to tackle one of your organization’s most pressing problems, so you’ll need a good understanding of the organization: its strengths, weaknesses, threats and opportunities to know where to get started with AI. 

Set a goal 

One of the main reasons AI projects fail to deliver value is because they fail to set goals. A project can’t fail or succeed if it doesn’t have defined goals. That’s why one of the most important aspects of any AI project is setting an achievable and measurable goal.

Win executive buy-in and budget

Before you can get started when it comes to AI, you’ve got to get buy-in from your organization. That means getting buy-in from key people, whether that’s executives who will sponsor your project through budget or team members who’ll be impacted on the ground. 

Get the experts in

Whether you choose to deliver AI completely in-house, or you choose to partner with an AI company, experts are absolutely vital to delivering any AI solution. Whatever path you choose, you’ll want experts with a proven track record of creating AI solutions that deliver value. 

Deal with your data

AI works by processing and making calculations with data. AI runs on data. The better your data is, the better your AI will be. That doesn’t mean your data has to be perfect, but it’s something you’ll need to put a good amount of thought into when it comes to building your AI solution. 

Assess your tech stack

A tech stack is basically the technical set up that you’ll need to fit your AI solution into. For instance, if someone is looking to build a personalization solution, their tech stack might include a customer data platform (CDP), a data warehouse, an email platform (e.g., Twilio) and a social media management platform (e.g., Sprinklr). You’ll need to make sure any AI personalization  solution you create or buy can sit among and work with these elements of the stack. 

Assemble your A-Team

Building an AI solution can’t just be done by data scientists. Subject matter experts in the areas you want to use AI play an equally vital role in building the solution. For instance, if you’re building an AI solution to improve forecast accuracy, you’ll need people from your supply chain planning teams who use their existing solution to help build the AI solution. This is where you’ll select and train the most important members of your team: your super users.

Find a solution

In tech solutions, there’s this age old question of whether you should build or buy. Buying a solution is said to be cheaper and produce results faster, but the benefit a bought solution can provide is limited by the fact it’s often not customizable to the organization’s data and ways of working. On the flip side, building an AI solution is expensive, complex and can take a long time to deliver results. Finding a balance between these two options should be the goal of anyone considering AI adoption. 

Select your super users

To succeed with AI adoption you need to understand the people your AI project will impact, what their motivations and pain points are. This is where super users come in. Super users (a.k.a. power users or beta testers) are a representative sample of the future user population of a new piece of software — in this case, your AI solution. Super users are recruited into an AI adoption project to help shape the solution and make sure the voices of end users are heard. 

Run business exploration sessions 

Business explorations are vital to planning any AI project. In these four sessions, your technical and non-technical teams will come together and decide what your AI solution should look like. These sessions will look at your solution, data, integration and how you measure and extract value from your AI.

Build your model

Next it’s over to your technical team or AI partner to build the solution. They should quickly develop a minimum viable product (MVP) so that your end users can test and they can then move onto the final version. 

Test and iterate

In this critical step, end users will test your AI solution for functionality, identifying any issues in functionality. They’ll feed this back to your technical team who will then iterate the solution until it’s ready to deploy.

Communicate the change

It’s important to communicate at every stage of the journey. Development times can vary between AI solutions and methods, so you’ll want to keep your end users engaged and excited for their AI.

Roll out training

End users will need support and training to ensure they fully understand how to perform their role using your new AI solution. 

Go live

Going live with your solution is a huge moment on the AI journey. It’s when your end users will move from their old ways of working to their AI solution. 

Monitor and manage

You’ll need to continuously monitor your AI to make sure the solution is being used by your teams and to make sure it’s delivering optimal value.

The Ultimate Guide to AI Adoption

Learn more about AI adoption in this comprehensive ebook. Find out how to secure executive sponsorship, budget & buy-in, and make your AI strategy a success.

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