This article was co-authored with Andy TuraiVice President and Principal Analyst at Constellation Research, a follow-up to our last post. article With AI’s secret sauce. )
AI, show me the results
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The move from artificial intelligence, which is simply a collection of disparate projects, to adopting the secret sauce of business use cases requires some degree of organizational relaxation and strategic planning, but is difficult and daunting. It doesn’t have to be a simple process.
AI can lead organizations to great success, but success doesn’t just happen randomly. You’ll need to identify the right use cases, get the right support, raise the necessary funding, and ultimately strive for rapid and successful deployment.
Here’s how to elevate your organization’s AI efforts to successful business use cases.
- Don’t advertise “artificial intelligence”. Market your business growth. Use AI to advocate for business advancements that deliver far better results than current practices. The term “artificial intelligence” will certainly catch everyone’s attention, but remember that you are not selling a technology implementation. They sell ways to improve processes, cost efficiencies, new revenue streams, or gain new insights for decision making. If you can’t justify your project in one of these ways, your project is unlikely to succeed in the first place.
- Learn about problems from the field and management. Identifying potential use cases can be very difficult. Business and field users may not be aware of what AI can potentially do for them, especially in organizations where AI has not been widely adopted. Instead of explaining AI, it might be easier to ask them about their struggles and what makes their lives easier. Finding business cases becomes easier when there is common ground between departments, regions, and even partners. If it’s possible, it’s easy to validate with an identifier that suggests what the AI could do in this particular case. Not only does this make life easier for them, but it can also solve a big problem, so it’s a big investment. They obviously help sell this to executives who could consider funding this effort where the issue is being resolved.
- We strive to democratize AI. Where does a promising AI development activity go wrong? First, if it’s tied too closely to the technology itself instead of the business, it’s almost black magic for business users. “AI needs to be in the hands of everyone, not just experts,” said Monachada, category management director for his service at Amazon Web. “AI tools should be easy for line-of-business users to apply and get value from. I have.”
- Identify your supporters. Identify champions within your organization who can sell AI to executives and managers looking for better ways to address business problems and opportunities. These individuals need to understand the scope of her AI needs within their company, rather than the developers and data teams building or embedding AI solutions. They need to speak the language of business and help business leaders understand how AI addresses their biggest pain points.
- Build trust with potential users. Business owners and managers are so enamored with technology itself, especially complex technologies such as AI, that they may be hesitant to bet their business on it. This may be due to the perceived confidence gap between the insights and recommendations provided by AI and what is seen in the field. Here is a solid demonstration of a successful use case from within an organization or from outside an organization that has had a very successful similar implementation.
- Follow new success stories. Successful AI initiatives now exist and are proving their value to businesses. Examples include diagnosing diseases and providing personalized care, eliminating traffic congestion, optimizing supply chain flows, providing proactive inventory tracking, helping protect sensitive data, and using conversational AI to provide personalized including the use of AI for customer engagement, training, coaching, and measuring marketing benefits. Look for areas where your competitors are using AI to solve business problems, or look for successful use cases in adjacent industries to use as a starting point.
- Establish success metrics. You can’t manage or improve what you can’t measure, much less build robust and scalable business use cases. How broadly has this AI project been successful by looking at changes between cost savings, efficiency gains, revenue attainment, or other success metrics established before and after the AI solution was deployed? Prove that you are
A good AI strategy is just the starting point. Without proper execution, it’s just a hallucination.
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