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Artificial intelligence: 6 tips to get started

Blog: The Enterprise Project - Enterprise Technology

Artificial intelligence: 6 tips to get started
Carlos M. Meléndez
February 22, 2023 – 3:00am

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Every business leader knows their organization will eventually need AI to compete, but as a practical technology, it’s still rather nebulous. Are other companies saying they are driven by AI because all the cool kids are using it – or are they really using it?

Regardless of the perceptions or misperceptions, it’s clear that when done right, AI will enable organizations of all stripes and sizes to optimize marketing, sales, operations, and customer experience. If there’s any doubt, just ask your competitors: According to IDC, worldwide spending by governments and businesses on AI technology will top $500 billion in 2023.

In 2023, companies will explore many types of AI for various practical applications. For example, chatbots can help address customer questions 24/7; predictive analytics can help determine customer eligibility for loans; computer vision solutions will help doctors interpret x-ray images; and machine learning will help to automate tedious processes in manufacturing.

How can your organization secure executive buy-in and take the first crucial steps to embark on an AI journey successfully? Consider the following six steps:

1. Identify the problem

Some business leaders may not yet understand why their company needs AI – they just don’t want to be left behind. AI, as with any type of digital transformation, begins with a problem: You first need to identify your business problem and then determine if AI is the solution. You can accomplish this in two or three weeks with an AI sprint that can let you know if the solution is what you need before you spend costly time in deeper development.

[ Also read Artificial intelligence: 3 trends to watch in 2023. ]

2. Secure buy-in for a 3-year AI initiative

AI is not something that happens overnight. Unlike other forms of digital transformation, ROI can take some time to be realized as an algorithm becomes increasingly more intelligent as it trains on the job. When approaching the executive team to secure buy-in, it’s important to clarify that the AI project should be seen as a three-year initiative. Only after this period should the expected ROI be reviewed.

3. Kill zombie AI projects

Zombie AI projects are those that are dead on arrival. Different departments within an organization may have begun their own AI projects in siloes, and they may even be spending huge amounts of dollars and resources on them. But these projects are often too aggressive, unrealistic, and unprepared for prime time (take Metaverse apps, for example). Leaders should take a unified, strategic approach to AI, starting with practical, proven applications such as predictive analytics.

4. Standardize your data

AI is all about the data. As part of the AI initiative, assign your chief data officer (or CIO if you don’t have a CDO) to begin the multiyear project of standardizing data across divisions and functions. Start by taking a data audit, centralizing the data storage and usage to see what you may be missing, and then cleaning and classifying it. Once that’s accomplished, you can then look to supplement the data with synthetic data if that is what is needed to train your algorithm effectively.

[ Related read Data scientist: A day in the life ]

5. Create a Center of Excellence (CoE)

CoEs are cropping up in organizations everywhere, serving as the in-house experts dedicated to spearheading key initiatives such as AI adoption. It’s important to centralize governance of AI into a center of excellence that is focused on the following:

  • AI diversity and transparency to ensure that your models are explainable, were created using a diverse dataset, and do not discriminate.
  • ModelOps: A CoE can help your organization set standards, processes, and methodologies to operationalize AI models at scale, monitor them for performance decay, and strive for continuous improvement.
  • AI governance to ensure that each business unit’s use of AI adheres to strict governance standards across the AI lifecycle. For example, these governance standards could be that they adhere to data privacy, are created to ensure ethical use, and provide transparency.

6. Train your team and executives on AI

It’s important that everyone, from the top down, understands the role of AI, including what it can and cannot do. You can ensure this by training all team members on the basics of AI and analytics in all its forms. You don’t need to be able to code, but exposure to the technology will give you a better sense of how AI can improve your business and, most importantly, how to make better decisions based on the output it provides.

For example, a leading national credit union sent its executive team to a university class on the basics of AI and analytics to create a culture focused on data literacy. As a result, when the organization rolled out a predictive analytics solution, the team clearly understood and recognized its significance and benefits.

For all the early AI adopters, more companies are just teetering on the edge. If you’re in the latter group, you’re not late to the game. By starting small, taking a more strategic approach, and understanding that AI requires an enterprise-wide commitment, you can reach the real finish line faster – true and sustainable ROI from realistic AI.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

Ready to start developing an AI strategy for your organization? Check out these practical steps
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