5 Strategies for Successfully Implementing AI into Your Organization

6 min readApr 24, 2023

The use of Artificial Intelligence is gaining traction in businesses each year — strategies to implement it seamlessly into your organization instead of getting left behind

The use of Artificial Intelligence is gaining traction in businesses each year. In fact, a Gartner survey reveals that forty percent of organizations have deployed thousands of AI models within their businesses.

However, implementing AI is not so straightforward. It’s known that only a small percentage of AI initiatives ever make it into production systems and many are shelved or fail completely. For most problems, challenges such as confusion around AI, mismatched expectations, the lack of AI-ready infrastructure, and the inability to articulate ROI are what cause these AI failures.

To increase the chances of AI going into production, let’s look at 5 strategies for implementing AI to maximize success

5 Strategies for Maximizing AI Success

1. Focus on AI-solvable problems

Successful AI initiatives truly start with the right problems. Although many software automation problems will seem like good AI problems on the surface, most will not be. Many of these problems only need straightforward software engineering. Trying to solve these problems with machine learning (a branch of AI), for example, can lead to no tangible benefits.

To spot AI opportunities, look for complex decision-making problems with a high workload. For example, trying to automatically predict if a credit card transaction is fraudulent or not is a complex, high-volume and recurrent task. You’d have to look at hundreds of different data points on a case-by-case basis to render one decision. Plus, you’d have to do this repeatedly. Performing this task manually would demand a large, around-the-clock team. This makes it a good place to leverage AI, as AI systems can make continuous predictions very quickly and can work around the clock.

2. Track the right metrics

When it comes to measuring the success of AI, different people in the organization focus on different things. Technical people often think that an accurate model is what AI success means. Executives on the other hand tend to look for a financial ROI.

However, the success of AI is a combination of model success, business success, and user success. The idea is that, first and foremost models should solve the problem they’re tasked to solve accurately. Next, an AI model is a means for solving a business pain point. So, we need to also track relevant short-term and long-term business metrics.

Finally, the end users of the AI system should be happy with the solution and perceive it to be a viable long-term solution. Otherwise, users would go back to their “old way” of doing things and that defeats the whole purpose of deploying AI. If these three success pillars are strong, then, we can be sure that your AI initiative is on the path to success.

3. Address Foundational Gaps

Technology companies, such as Meta and Google, are inherently AI-driven. Not only do they have massive tech talent, but their infrastructure and way of thinking is already set up for ongoing AI initiatives. From aggressive data collection to model development and deployment, these companies have the necessary resources.

Your organization may be different. Your establishment may be a consumer goods company or a nonprofit organization with a small IT team, or your focus may not relate to building intelligent software. At any rate, you’ll likely have some legwork ahead of you before you’re able to implement or deploy AI.

Say you’re looking to start a series of AI projects, but you realize that the data needed for those projects are currently not being collected. Instead of hiring a team of data scientists, the first step for you would be to put together a team to get data collection started and address storage gaps. There can be many such foundational gaps in your organization. Addressing those gaps takes time and planning, but you can always fill them in stages.

If there are obvious foundational gaps, start filling those first before starting AI initiatives. You can always experiment with AI on a small scale, but initiating the steps to address gaps is critical to the long-term adoption of AI.

4. Invest in AI education

Whether you’re a C-level executive, product manager, or engineering manager, if you’re thinking about using AI in your organization, start with education. You need to know enough that you’re comfortable exploring the possibility of using AI in your company.

This AI education can help you in various ways including closing AI adoption gaps, vendor selection, hiring AI and data science employees, and making strategic investments.

If you’re a leader new to AI, start by building a foundation around understanding AI use cases, what it is, and what makes AI initiatives different from traditional software engineering. Understanding the misconceptions of the field and how to spot opportunities will also significantly help identify high-impact use cases.

You can get some of this information by reading relevant books as well as industry reports from big consulting firms. Attending AI leadership seminars and presentations can also be helpful. I wouldn’t recommend podcasts to build your foundation. The scattered nature of podcasts can be confusing and should be supplemental knowledge once you have a general foundation.

5. Set the right expectations and be committed

AI initiatives require a long-term commitment. You don’t get quick, incredible results from AI. It takes time to acquire the right data, develop and test models, and finally, operationalize them. Maintaining AI models after development also incurs ongoing costs and time commitment.

Further, your first AI model may not be the best. It will give you a good start, but from then on, it’s an iterative process. You keep improving the solution so that it gets better over time. Also, you can’t just rely on your IT or engineering teams to maintain these models. They can certainly monitor models, but if you need to change the inner workings of the solution, you need AI experts.

In short, AI is not quick, nor is it the most cost-effective solution. For a quick and cost-effective solution, start with simple software automation or a manual process. With time, you can replace these “less efficient” or “less accurate” solutions with AI if there’s a clear benefit. Remember that it takes more time and a bigger investment to develop and maintain AI solutions than to introduce better software engineering into an existing workflow.

Summary

The use of Artificial Intelligence is gaining traction in businesses each year. However, to truly reap the benefits of AI, you need to be strategic about its implementation. From laying the right foundation to bringing clarity to metrics to track, all of this can have a greater impact on the success of every initiative.

Kavita Ganesan

Kavita Ganesan is the author of The Business Case for AI, and an AI advisor, strategist, and consultant. With over 15 years of experience, Kavita has scaled and delivered multiple successful AI initiatives for large companies such as eBay, 3M, GitHub, and McMaster-Carr, as well as smaller organizations. She has also helped leaders and practitioners around the world through her blog posts, coaching sessions, and open-source tools.

Article published 3rd February 2023.