03.13.2023 Executive Data Bytes – Beating Your Competition With A Concise AI Strategy

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, we're talking about AI Strategy and how it can be a game changer for your company's competitive advantage.

Focus piece: “How to create an effective AI strategy ”

Executive Summary

AI strategy is an important concept for businesses looking to improve their results, better serve their customers, and differentiate themselves from the competition. And, in their eagerness to disrupt the status quo with machine learning technologies, they often overlook the small details that lay the groundwork for a great AI strategy. Deloitte's piece demonstrates how to build an AI strategy differently by approaching it from a new angle.

Key Takeaways

• While developing an AI strategy is important for any organization, it must be done correctly in order to produce the desired results. One of the mistakes organizations make when developing an AI strategy is doing so in isolation. It bears little or no resemblance to the company's north star, or core business strategy. As a result, the strategy fails. To address this, companies developing an AI strategy should focus on using the company's core business vision and goals as the strategy's foundation.  As mentioned in the article "The strongest AI strategies tend to begin without ever mentioning AI".
• Cost versus value is a constant battle in every business decision, and it is also present in the process of developing an AI strategy. And, while the majority may favor cost cutting, it is best to find a way to balance the cost and value of your AI strategy. Putting cost ahead of value may cause you to miss opportunities and insights on how to impact your current business outcomes, resulting in your AI strategy not reaching its full potential. Always remember to strike a balance.
• Companies' executives should constantly emphasize the AI strategy across the organization, while keeping in mind how it relates to achieving the business core strategy. This helps to ensure focus and that decisions made across the board in the company are consistently aligned with the company's vision.
• Nothing is set in stone, and neither should your AI strategy. As you begin to implement these strategies, be flexible enough to iterate the strategy so that it is always in sync with the company's vision and future plans.

Focus piece: “How to Implement a Successful AI Strategy for Your Company”

Executive Summary

Why create an AI strategy if it will be riddled with flaws? As a result, it's critical to understand the traps and pitfalls to avoid when developing this strategy. This article by phData clearly explains the pitfalls to avoid as well as what to put in place to ensure a successful AI strategy.

Key Takeaways

• AI projects require significant investment due to the computing capacity required to build and deploy these projects. As a result, it is critical to justify all project investments in terms of the business value or return on investment they bring to the company. There are no prizes for being named an AI focused company; only for developing AI-based products that help the organization's vision and core mission advance. As a result, before embarking on a potential project, properly estimate its ROI.
• According to research, only 13% of machine learning models make it into production, which means that 87% of these models are never used. One major reason for this is a lack of a path to commercialization for these models. Unlike software projects, which can be easily deployed, ML projects are more complex. As a result, it is critical to have the right team in place to effectively deploy these solutions.
• Machine learning projects are heavily reliant on research and development, and research is useless unless it can be replicated and recreated. Companies should therefore ensure that the work of data scientists and machine learning engineers is properly documented so that projects can be easily replicated. If a project cannot be replicated, it will take longer to resolve issues and will make previous work appear ineffective. As a result, configure your projects, infrastructure, and team to ensure reproducibility.

Focus piece: “How To Develop An Artificial Intelligence Strategy: 9 Things Every Business Must Include” 

Executive Summary

A common question from business executives about AI strategy is whether it is necessary, if there is a need for it, and if there is already a data strategy in place. That question has a yes answer. A data strategy assists businesses in making effective use of data to drive decisions, whereas an AI strategy assists businesses in developing a process to operationalize solutions that require the use of machine and deep learning technologies. In this article by Bernard Marr & Co., you'll learn about the components of an AI strategy, including data strategy.

Key Takeaways

• When developing your AI strategy, the first thing to consider is your business strategy. Without a proper business strategy, an AI strategy is worthless. As a result, you may need to ask yourself whether your business strategy is still right for you, whether it is in line with current technologies, products, and services, and whether your business priorities have changed recently. In light of current events, answering these questions will make your strategy more useful.
• Another component is a well-defined data strategy, as AI cannot exist without data. You must ask yourself whether you have the right data, and even if you do, whether you have enough of it, because the more data you have, the more the machine learns, resulting in much better accuracy. And if you don't have the data, how will you get it, what method will you use to collect it, or will you rely on a third party?
• Finally, as you move forward with implementing the necessary strategies, consider whether your organization has the necessary skills and capacities to build the required AI projects. You must identify the current skills gap and decide whether it is best to hire new employees, train existing ones, or collaborate with an external AI provider. These elements, along with technology, ethical and legal issues, and your short-term and long-term AI priorities, will constitute your AI strategy.

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