7 ways to develop a strong and scalable AI business case

This article was written together Andy TuraiHe is the vice president and chief analyst at Constellation Research and our past follow-up Text On AI’s secret soup.)

Moving from artificial intelligence being a collection of disparate projects to discrete business use cases requires some organizational moxie and strategic planning, but it doesn’t have to be a tedious or daunting process.

While AI can make organizations very successful, success does not happen by accident. Efforts need to be made to identify the right use case, get the right support, get the necessary funding, and ultimately successfully deploy quickly.

The following are ways to leverage your organization’s AI efforts into successful business use cases.

• Don’t give it “artificial intelligence”. Pitch business development. Make the case for business growth using AI that can deliver results far beyond current methods. While the term “artificial intelligence” may catch everyone’s attention, remember that you’re not selling a technology implementation—you’re selling a way to achieve process improvements, cost savings, a new revenue stream, or new insights for decisions. If you can’t explain the project in those ways, the project has no chance of being successful from the start.
• Learn the pain points from the field and from executives. It can be very difficult to identify the issue that you can use. Especially in organizations where AI is not widely adopted, business and field users may not know what AI can do for them. Instead of explaining AI to them, a simpler alternative might be to ask about their struggles and what makes their lives easier. If there are commonalities across units, geos, or partners, business cases are easy to explore. If there is potential, it can be easily verified with the accounts by suggesting what AI can do for them in this particular case. This makes it easy for them, but they are very invested because this could solve their main pain point. They can clearly help sell this exercise to their executives who can finance it as their problem is solved.
• Strive to democratize AI. Where will promising AI development activities lead? For starters, if it’s too closely tied to the technology, not the business – to the extent that it’s black art for business users. “AI should be in the hands of everyone, not just experts,” said Mona Chadha, director of category management at Amazon Web Services. “AI tools need to be easy to implement and provide value to line-of-business users. There is a shortage of AI professionals and data scientists who can leverage sophisticated AI frameworks and infrastructure.”
• Identify advocates. Identify supporters who can sell AI to executives and managers looking for better ways to solve problems or opportunities within the organization. These individuals need to understand the scope of their company’s AI needs more than the developers or data teams who build or integrate AI solutions. They need to speak the language of business, and help business leaders understand how AI can solve their worst pain points.
• Build trust with potential users. Executives and managers may be enamored with the technology itself, especially something as complex as AI, and may be hesitant to bet their businesses on it. That could be due to a confidence gap between the insights or recommendations that AI can provide compared to what they see in the field. It is a strong demonstration of successful use cases of similar implementations from both inside and outside the organization.
• Follow emerging examples of success. There are already working examples of successful AI initiatives with proven value for businesses. Examples include using AI to diagnose diseases and provide personalized treatment, resolve traffic congestion, streamline supply chain flows, provide proactive inventory tracking, help protect sensitive data, personalized customer engagement through conversational AI, coaching, training and measuring marketing returns. Look for areas where competitors have used AI to successfully solve business cases, or look for successful use cases from adjacent industries and use that as a starting point.
• Set success metrics. You can’t manage or improve what you can’t measure, let alone build a strong and comprehensive business case. See cost savings, efficiency improvements, revenue gains, or any other measure of success for post-deployment AI solutions versus pre-deployment to determine how effective this AI project is at scale.

A good AI strategy is just the starting point. Without proper performance, it is just an illusion.

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