When Arccos Golf launched its first performance tracking system for golfers, it combined the telemetry from sensors and a smartphone app to give players detailed data and feedback about every shot.

Knowing how far and how accurately they could hit the ball under different conditions helped players uncover weaknesses and improve their game. But there was so much more that could be done.

“We had an ‘a-ha moment’ about providing a virtual caddie for every player. Just like a human caddie, ours would know the player, know the course, know the weather and provide the player with a club recommendation,” said Jack Brown, Senior Vice President of Product & Software at Arccos Golf. “So we thought, ‘Why don’t we use AI to create a virtual caddie?’”

At this point, the two-year-old startup faced two questions that a growing number of companies now find themselves asking: Where do we start with AI? And how do we use it to our best business advantage?

“A lot of companies don’t know where to start or how to pick the right use cases,” said Tony Baer, principal analyst for Ovum who leads its big data research. “It’s like suddenly wandering into a store and being surrounded by a lot of shiny new toys. You don’t know where to start but you do have a sense that some of these toys are going to require a lot of knowledge to use. It’s bewildering.”

Microsoft says it has the products to help. The company says its AI offerings — which span tools in Azure Machine Learning for building deeply customized solutions to Azure Cognitive Services that allow developers to add AI capabilities with a few lines of code — are designed to help businesses of any size easily deploy AI solutions.

And, Microsoft says, it also has the expertise to help other companies work through challenges in figuring out how to use AI to build solutions with differentiated advantage.

Microsoft begins those conversations with an assessment of a customer’s “AI maturity,” or an honest look at the organization’s preparedness to embrace, implement and benefit from AI, said Norm Judah, Microsoft’s Chief Technical Officer for Enterprise. Based on the client’s readiness, it then offers solutions that it thinks the client can successfully build and run with their level of expertise.

Only one of the four pillars of competencies in that assessment involves technical depth or how many data scientists a company employs. With decades of experience in helping companies navigate new technologies, Judah said, Microsoft has a deep understanding of how other dimensions of a business — strategy, culture and how an organization makes decisions — can be equally important.

The first iteration of the Arccos Caddie used data about a golfer’s shot history and the layout of the course to give recommendations about how to play a hole straight from the tee. The second incorporated real-time environmental data such as elevation and wind speed, Brown said. The third version released earlier this year is able to readjust after each shot — if a player makes a mistake and winds up in the rough, the AI caddie offers recommendations based on that new reality.

When choosing the technical tools to build the AI platform, Arccos also clearly defined system’s most important performance criteria: speed. It decided to use Azure Machine Learning, Azure Kubernetes Service and the Azure Cosmos DB database service because those tools delivered recommendations faster than anything else, Brown said.

‘AI should go wherever the data is’

Even within an industry or a single company, Microsoft says it is finding that the use of AI today may be inconsistent. One of the most common roadblocks customers cite is poorly organized data that exists in silos across a company. Many find it difficult to divert time and brainpower away from day-to-day business functions. All want assurances that AI tools can be trusted, said Microsoft’s David Carmona, general manager for AI marketing.

“We hear constantly from companies that it’s very difficult for them because they have a very siloed and unstructured data state, and in order to apply AI on top of that they need to put it in order,” Carmona said. “We believe that AI should go wherever the data is — not the other way around.”

To help solve that problem, Microsoft says it is the first to enable cognitive services to be used in containers, which means people can take advantage of AI tools without sending their data to the cloud. Customers can use these pre-built AI services to analyze data wherever it lives — on the intelligent edge, in remote environments or in networks that a company maintains onsite.

Microsoft Azure Cognitive Services uses AI to uncover useful information hiding in this type of unstructured data, with tools that can recognize words in images, extract key phrases and rate whether feedback is positive or negative. They allow developers who aren’t data science experts to take advantage of machine learning with a few lines of code and solve common problems that AI is particularly well suited to handle.

Microsoft also has other offerings that can help companies make sense of their data. With new tools from Microsoft Dynamics 365 AI, companies can also use out-of-the-box solutions to enhance sales, marketing or customer service efforts. They can be up and running — using AI tools to determine which sales leads are likely to be most productive or what products to offer them — within a matter of days or weeks.

“What really becomes powerful is when a company can start bringing AI to every business process and every person within that company,” Carmona said.

 Identify the right AI opportunities

One of the most important responsibilities CEOs will have in charting a company’s AI strategy is aligning the business with a set of beliefs around how it will be developed and managed, Judah said. To lead employees and earn customers’ trust on the use of responsible AI, a company must transparently outline the organization’s position on issues of trust, fairness, transparency, privacy, safety, inclusiveness and more.

“Our advice to other businesses is exactly the same advice that we followed — every company in one way or another needs to develop their own AI and ethics manifesto,” Judah said.

Executives also need to ensure that both the technical and business sides of a company are involved in identifying the right opportunities for AI and scenarios to test — whether that’s enhancing customer service through a conversational agent, improving efficiencies, reducing manufacturing defects or something unique.

Anheuser-Busch InBev (ABI), for instance, undertook a massive digital transformation effort to overhaul its siloed data state and build a worldwide analytics platform on the Microsoft Azure cloud. That’s also enabled and accelerated the use of AI to drive growth and innovation across different dimensions of the global brewer’s business, from sales and sustainability to human resources and supply chain management.

ABI is employing AI tools in more sophisticated and public-facing ways, like using conversational agents to meet consumer needs and driving growth across the brewer’s entire operations, from seed to sip.

“If I think about it, AI was at first a bottom line technology for us — helping us find improvements and efficiencies— and now it’s become much more of a revenue generator,” said Patricio Prini, ABI’s Global Vice President, Innovations. “But we always start with the business case first. That helps you see how AI can enable a huge transformation.”