Looking for a Quick AI Solution? A.C.T. Now!

By Anil Kaul On Oct 16, 2018

AI is already changing the world. As consumers, we encounter AI every day, from customer service chatbots to smart Netflix recommendations to spam filters on our inboxes. In the business sector, AI’s disruptive force has convinced many business leaders that they better embrace AI soon to avoid being left behind.

They’re not wrong. Yet many companies are struggling to get near-term results from their AI initiatives, concluding that it will take more time than expected to instill AI into the company’s processes successfully. This is where they may be wrong. It is absolutely possible to implement AI rapidly and see results fast.

The transformative effects of AI are just scratching the surface today. Companies are taking many different approaches to bring AI into the organization: hiring data scientists, buying off-the-shelf point solutions, outsourcing AI projects, launching multi-phased Digital Transformation initiatives, and consulting with large firms to recommend an AI strategy. But layering on an AI strategy, team, or technology isn’t the answer in a competitive landscape.

In addition to the AI tools, talent and techniques — which alone won’t yield big results fast — three elements are needed for efficient implementation that accelerates time to value. Companies that want to rapidly deploy AI need an A.C.T. strategy to make it a success.

Analytical framework: AI can be incredibly effective in solving business problems, but to be effective quickly, the analytical framework applied is an important foundational element. Choosing an analytical framework guides the process of building a custom AI solution and enables businesses to avoid a scenario where they simply trust AI to produce the insights they need without understanding how it works.

To create an analytical framework, think about a small, specific business problem you’re trying to solve, such as predicting when customers will make a purchase. Consider the data you need in that scenario, like information on past buying behavior and data on where prospects are in the customer journey. Map out how you’d gather the necessary data and the ways you would apply AI to drive predictions, and you’ll have a better chance of succeeding on an accelerated timetable. That’s because the analytical framework ensures that you understand exactly what you’re asking the AI solution to do.

Context: The second success factor is to include business context to help your AI solution perform better upon implementation. One common mistake with AI is to assume that it’s all-knowing; it’s not. There are common business rules, and business rules unique to the organization, that you understand but AI doesn’t — until you tell it. For instance, AI needs all the customer budget information you have to make the right decisions on when to issue offers and which products appeal to customers at specific price points.

Other business context factors can come into play, such as the appeal of a new product vs. products that have been popular in the past. AI might continue recommending a past product because it has more sales, more success, a track record of appeal. But AI won’t automatically know that new products typically generate more customer excitement than established products unless you provide that context. These are just a couple of examples, but the bottom line is that it’s important to spell out the business and decision-making context so AI can operate effectively. The sooner you provide context, the sooner your AI solution can start delivering results.

Technology: To stitch the pieces together and deploy AI successfully and on schedule, the company will need the right technology. For many companies, it may be a good idea to avoid point solutions since they are inherently limited in their application and will quickly run into scaling issues. It’s also a best practice to leverage AI-capable tools that are already in use, such as Salesforce and other solutions created by the AI community, instead of trying to build AI capabilities with an in-house team from the ground up.

Scaling is critical because when you deploy an AI solution that successfully solves a business problem, such as predicting buyer behavior in the scenario outlined above, you’ll want to scale it across the organization to maximize the value. If you partner with a company that offers an AI platform, you can rapidly extend your AI capability to other parts of the organization, extending the value of your AI solutions more quickly.

The A.C.T. strategy requires more thought and planning up front than a “plug-and-play” approach, but it gives the level of results companies are expecting from their AI solution sooner. Businesses spend less time trying to make a plug-and-play solution mold to the company’s unique business environment, and they can implement subsequent projects faster, with better results. So, the time-benefit compounds.

In real-world use cases, AI implemented with an A.C.T. plan allowed one large company to build an AI solution to recommend specific SKUs. It achieved a 3 percent to 8 percent higher growth rate across channels within a three- to six-month timeframe. Another company’s product recommendation solution boosted sales by 4 percent in just seven weeks.

Together, these four components combined — AI plus A.C.T. — can yield significant impact in weeks. So, if your goal is to embrace AI quickly so you can transform your business, you’re on the right track. But be sure to A.C.T. now — that’s the best way to gain a lasting competitive edge.

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