How to Implement AI in Your Organization (with the Least Amount of Risk)

In today’s rapidly evolving business landscape, integrating Artificial Intelligence (AI) isn’t just a competitive advantage, it’s becoming a necessity. AI empowers organizations to streamline operations, enhance decision-making, and unlock innovative solutions tailored to their unique challenges. By embracing AI, businesses position themselves at the forefront of efficiency and innovation, ensuring they remain relevant and resilient in an ever-changing digital world.

However, adopting AI into any organization isn’t as straightforward as it might seem, regardless of how readily accessible and available AI has become. Sure, your employees might already be using various GenAI tools to help them with different aspects of their jobs or to perform simple tasks; however, adopting AI organization-wide is an initiative that requires thorough analysis and planning to ensure a low-risk and successful implementation.  

In this article, we’ll shine some light on implementing AI into your organization’s strategy

What is Generative AI?

First, let’s begin with some basics. Before understanding how to implement AI, let’s briefly define the two types of AI: Generative and prescriptive. 

Generative versus Prescriptive

Generative, or GenAI, is a type of artificial intelligence that generates new content. This can include text, code, scripts, musical pieces, emails, letters, and even other types of data. GenAI models are trained on massive amounts of data, and they can learn patterns in the data and use those patterns to generate new content and even refine existing content.

Prescriptive is a more advanced form of artificial intelligence that goes beyond analyzing data and making predictions. It can offer actionable recommendations and strategies for achieving specific outcomes. It helps decision-makers determine the best course of action to solve a problem, optimize a process, or achieve a goal based on available data and contextual factors. Implementing prescriptive AI requires much planning in advance, with very specific parameters. 

What You Need to Know Before You Start Using AI

Regardless of the type of AI you want to implement in your organization, there are three elements you must consider:

  1. Data
  2. People
  3. Process

Each of these plays an important role in ensuring any AI implementation is a success and must be considered to mitigate risks. However, before you email a memo to your team or fire off a chat in Microsoft Teams, claiming you will all begin using ChatGPT, effective immediately, hold off on hitting the “send” button. Here are some things you must consider before you and your team begin using AI: 

Ask WHY.

What are you trying to achieve by implementing AI? Implementing AI for the sake of implementing AI or because it’s the “cool thing to do” will only result in elevated costs and risks of failure. Additionally, if you don’t implement AI responsibly in your organization, you increase the risks for legal and compliance repercussions. Case in point: Ask yourself and your team WHY you want to implement AI. What are your organizational goals? What problem(s) are you trying to solve? Why is AI the answer?     

Define AI. 

After determining your WHY, the next step is to define what AI means for your organization and be clear about what you want to achieve. For example, AI can build an organization’s capabilities and scalability or perform risk assessments and analyses. It can also be used as a problem-solving tool for improving processes. However, many misunderstandings exist about what AI is and how it can help organizations. If AI already feels like a complicated, overwhelming, and complex feat, then let’s attempt to unravel that by breaking it down. According to the Project Management Institute (PMI), there are three ways to think about AI:

  1. Artificial intelligence: Utilizing “smart” computer applications to make decisions or perform tasks that historically have been performed by humans
  2. Automated intelligence: Automating manual processes to save time, improve quality and accuracy, and increase speed
  3. Augmented intelligence: Interconnecting fragmented processes via the implementation of tools

One way to think about the difference between automated intelligence and augmented intelligence is the human brain. Our brains constantly perform “automated” processes—they are called habits. Habits are things we do without conscious thinking or decision-making. Our brains have “programmed” habits as automatic processes and behaviors. In order to change our habits, we have to “reprogram” our brains. AI works much the same way.

All in all, AI isn’t a magic silver bullet. Rather, it’s about education and recognizing small wins. It isn’t designed to do the things you just don’t want to do. AI should be tied to specific organizational objectives, such as developing a product, enhancing capabilities, or process improvement and optimization. If you can’t define AI for your organization, you shouldn’t implement it. Sometimes, AI accelerates bad decisions or solves the wrong problems. 

4 Must-do Steps to Implement AI (and Reduce Risk)

1. Don’t adopt AI for the sake of AI.

With so much hype around AI, it’s tempting to think you must have it. I see it more than ever today. Organizations think AI will solve all their problems, but that’s unrealistic. Adopting AI for the sake of AI is a waste of time and resources if you don’t have a full grasp and understanding of the problem you’re trying to solve with it or if you don’t have a clear strategy. Also, AI isn’t designed to just perform the tasks you don’t want to do. Its capabilities are much more than that. As mentioned above, AI should be tied to specific organizational objectives and goals.

My recommendation? Understand the problem you’re trying to solve. Decide what functions or capabilities you need. Then, if AI is a possible solution to those problems, find an AI tool that delivers. Then, develop a strategy. 

Sometimes, organizational challenges appear to be process or technology problems on the surface. However, there is a cultural problem rooted deep beneath those problems. This means that throwing time, money, and technology at a problem, thinking AI will solve it, becomes a waste; it merely is a “band-aid”. This is why defining AI for your organization is so important. Don’t skip this step.

2. You can’t DO AI without data. 

Data plays a consistent role throughout AI implementation. If your organization doesn’t have a reliable data architecture or doesn’t make data-driven decisions, then, as mentioned above, you will throw money and technology at a problem that won’t truly solve it. Data helps an organization clearly define the problem AI will solve.

In addition to using data to identify a problem, let’s say you get to the point in implementation where you determine that implementing an AI tool will solve the problem. Remember that GenAI tools require high-quality data to “teach” it. The more data you can provide your GenAI tool, the higher the output quality will be to its end users. This helps to reduce a slew of organizational risks. 

3. Take an incremental approach to implementation.

Going all-in on AI right out of the gate can be a mistake. Although it may be tempting to fire off that email to your team telling them to all use ChatGPT or Copilot, avoid completely upheaving your entire workflow and exposing potential shortcomings in the solution only after it’s been fully implemented. As with any new solution, a managed approach to adoption and implementation is essential. 

Remember that implementing any type of AI tool or solution is change. Don’t forget about the importance of change management. Piling on TOO much change all at once not only introduces the risk of reduced productivity and morale but also makes it difficult for any new habit to stick. Ease into the process to minimize these risks, assure user confidence, and implement periodic feedback loops to understand what’s working and what’s not.

Assemble an AI task force.

Assemble an AI task force to ensure experiments, pilots, and investments are aligned with strategic business objectives and invested in the right way and solving the right problem. How? By using data. Additionally, the more people you can involve in the process, the less friction you will experience. This goes hand-in-hand with the “human-in-the-loop” approach and change management mentioned below. 

Focus on small wins.

When first implementing AI tools and getting project teams accustomed to using them in their everyday tasks or following specific processes, you must develop and cultivate the habit. Focus on baby steps and fostering small wins. Small wins help reinforce good habits and also allow project teams to achieve larger achievements and successful outcomes. 

All in all, you simultaneously decrease risk by increasing morale, confidence, and productivity. 

4. Don’t overlook the “human in the loop”.

Remember, AI is designed to help humans, not replace them. That’s why many AI tools are called AI assistants. In fact, organizations that think AI and technology replace humans, such as in customer service, are always mistaken. A key to successful AI implementation is not forgetting about the “human in the loop”. At the end of the day, we are still humans, and we want to speak with other humans. Humans also bring a level of emotional intelligence (EQ) to the table that AI tools simply cannot. Remember that reassurance, empathy, reasoning, and compromise are all crucial “soft skills” to have in any organization and cannot be replaced by a machine—no matter how much we work on programming it to emulate human emotions. 

A Low-risk Strategic Approach to Implementing AI

Successfully implementing AI in an organization is not just about adopting the latest technology—it’s about strategic alignment, responsible execution, and ongoing adaptation. By clearly defining your AI objectives, ensuring data readiness, taking a phased approach, and prioritizing change management, you set the foundation for AI to become a true asset rather than a costly experiment.

Ultimately, AI should complement human intelligence, not replace it. Organizations that approach AI with a clear strategy, a data-driven mindset, and an understanding of the human element will be best positioned to leverage its power effectively. By focusing on incremental wins and fostering a culture of continuous learning, businesses can maximize the potential of AI while mitigating risks, ensuring long-term success in an increasingly AI-driven world.