The seven AI sins preventing AI adoption

70% of AI projects fail. It’s not because of technology or lack of data. They fail because of people: executives, users, and developers. I call it the human factor of AI. As confirmed again and again, this is the main reason for AI failures.

Each of these three roles influences different areas relevant for success with AI. Over the years, I have identified seven sins that these three groups commit, sometimes intentionally, but most often unintentionally. Evaluating these seven AI challenges and the human factor is key to understanding how ready your organization is for AI and how successful AI adoption will be.

What are the seven sins of AI?

EXECUTIVES are responsible for providing strategic directions, allocating resources, and being a role model. Their sins, or let’s call them challenges if “sins” sounds too harsh, are:

1️⃣ Starting with technology, by implementing IT tools and only then asking what problems they can address (think of all the FOMO-driven ChatGPT implementations without clear business objectives)

2️⃣ Disregarding company culture, by forgetting how changes are embraced (or not) in the organization

USERS are non-technical employees and their management teams who are supposed to use AI solutions. Their challenges are:

3️⃣ Ignoring employees, when people managers assume that their employees will do what they are told to do

4️⃣ Lack of data & AI mindset, sometimes due to limited understanding how data and AI can assist employees in their daily work

DEVELOPERS are data scientists, data engineers, and other data professionals with their management teams who develop AI solutions to be used in the organization. Their sins are:

5️⃣ Project mindset, by delivering a solution and “moving on”

6️⃣ Being oblivious to users, by ignoring if and how AI solutions are used by internal business customers

7️⃣ Focusing on facts, by overlooking the “fight or flight” decision process of the human brain

Leveraging my experience dealing with these sins, I offer the following seven recommendations during engaging keynotes, lunch & learn sessions, and interactive deep dive workshops.

What can executives do to avoid the AI sins?

1️⃣ Always start with business needs

To avoid technology solutions looking for a business problem, define first the business challenges that could potentially be addressed with AI. Second, identify what data is required to address these business needs. Only then evaluate which AI is best to address these business needs. Sometimes you may even realize that AI is not the right solution, a dashboard or a process change may suffice.

2️⃣ Beware of your company culture

Since culture eats AI for breakfast, your company culture will determine if AI will deliver the financial performance you expect. Is your company culture encouraging doing things differently than has been done in the past? Is it supporting employees to take calculated risk? Is it allowing experimentation? All of these are required for success with AI. If you answered “no” to even one of these questions, start the cultural change ASAP.

What can users and people managers do to avoid the AI sins?

3️⃣ Answer “What’s in it for me?”

Some data and AI users in non-technical functions are afraid that AI will replace them. Others trust only the data and insights they have created themselves. And for many, it’s not clear what’s in it for them from using data and AI. Do your employees understand the value that AI will bring for them? If not, answer for every employee the question “What’s in it for me?”.

4️⃣ Improve data & AI mindset

Are your non-technical employees using data & AI products as intended? If you have a dashboard created for 1000 employees, how many are using it on a regular basis? If you have an AI recommendation system, how many of these recommendations are being implemented? If the answers are not satisfactory, develop and launch a multi-year data & AI literacy program to improve the mindset. This program needs to be custom-designed based on today’s level of the data & AI mindset in your organization.

What can developers and data leaders do to avoid the AI sins?

5️⃣ Shift from project to product mindset

People developing data & AI solutions often have a project mindset. They create a dashboard or an AI recommendation system and throw it over the proverbial fence to business users, with the hope it will stick and be used. It seldom does. By shifting from project to product mindset, developers not only develop AI solutions, but are also aware how these solutions will be used by non-technical colleagues.

6️⃣ Create & execute a marketing plan

Your company runs marketing programs to convince your external customers to buy your products. Otherwise, your external customers may not understand the value of your products. Why should that be different for AI products you are providing to internal customers? Most employees don’t understand the value of AI. Leverage the framework of 5 Ps of product marketing to create and execute internal marketing plans for your AI solutions.

7️⃣ Tell data stories

The human brain processes new information first to identify if it’s a threat and second how relevant it is. If the new AI solution is perceived as a threat, it will be resisted. Don’t overwhelm business users and executives with technical features, numbers, and facts, which often are perceived as threats or irrelevant at best. Instead, upskill your technical teams to tell data stories. Turn the facts into an emotional story.

To succeed with AI adoption, change is needed across all three groups: executives, users, and developers. I developed this 7-AI-sins framework as a guideline to assess challenges that companies face across all these stakeholders. Unlike the typical assessments applied by consultants that focus on technology, this assessment focuses on people. After all, people are the biggest driver of AI adoption.