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November 8, 2021

7 Elements of a Well-Thought-Out AI Strategy

7 Elements of a Well-Thought-Out AI Strategy

Salesforce COO Bret Taylor says effective AI depends on good data, with clearly defined business outcomes focused on customers.

Janet Rae-Dupree
Janet Rae-Dupree
7 Elements of a Well-Thought-Out AI Strategy

AI is no longer the exclusive domain of large enterprises. It’s entering the business mainstream—but it’s successful only sometimes, says Salesforce President and Chief Operating Officer Bret Taylor. A thoughtful AI strategy should be customer-focused, with clearly defined business outcomes, and the key to all of this is data.

In his talk at the Scale TransformX 2021 conference, Taylor discussed the key considerations that businesses of all sizes should keep in mind to ensure that they delight their customers and achieve sustainable business outcomes.



Here's a summary of the seven key takeaways from Taylor’s presentation. For more details, watch the session video above.

Business today is all about the data

“If you don't have a digital business, you don't have a business,” says Taylor.

Enterprises need to maintain constant engagement with customers, supply chains, partners, and employees, in granular detail. Each of these interactions produces data that businesses can use to create more intelligent and personalized relationships. This growing tsunami of data requires intelligent algorithms and automation to make sense of it all. As Marc Andreessen, co-founder of venture capital firm Andreessen Horowitz, famously said, “Software is eating the world.”

Today, however, it’s AI that has become central to everything that a company such as Salesforce does.

Build a data culture

AI and deep learning can’t be the exclusive domain of technology companies. To get there, every company must strive to develop a data culture that allows employees to see and understand the flow of information moving through the business. A chief data officer (CDO) should act as a steward for all that information, without trapping data in internal silos. Instead, everyone needs to be empowered to make data-informed decisions based on objective views of customers, supply chains, and the flow of resources.

Ultimately, companies that create open data cultures will easily outcompete those that don’t.

Ethical AI builds trust

Shifting toward a data culture requires building in ethics by design, in much the same way that enterprises have incorporated security by design.

To deliver an ethical deployment of AI, the number one value must be trust. With that in mind, Salesforce created the position of chief ethical and humane use officer to guide the company’s adoption of AI.

Salesforce wanted to ensure that all of its AI is responsible, accountable, transparent, empowering, and inclusive. Taylor contends that failing to take this into account from the start inevitably leads to unintended negative consequences and general degradation of trust in technology.

Society is now holding software and AI to a higher standard. Companies that develop AI today must embrace conversations about ethics and trust, and they must embed those conversations into their development processes. Building trust also requires bringing in stakeholders who aren’t data scientists to help shape the responsible use of these technologies.

Focus on strategy, not AI for AI’s sake

While every company needs to become an AI company to remain competitive, too many organizations are succumbing to the hype about digital transformation without having clear goals in mind. According to the 2021 Gartner CIO Agenda survey, while 94% of CIOs are working to deploy AI, only a quarter have done so successfully.

“Don’t start with the technology; start with customers,” Taylor says. Focus on their experience and what your customers need and want. Clarify the project’s scope and look for the easy wins first. “If AI becomes the purpose of the project, it becomes a bottomless pit of investment,” he says.

One shortcut to good AI: Don’t build It; buy It

Salesforce’s AI platform Einstein is a prime example of Salesforce focusing on building AI technology that is customer-focused and that “can be turned on with clicks, not code,” says Taylor. Salesforce customer Orvis uses Einstein to do simple analyses of the potential engagement of its marketing messages prior to their publication.

That approach increased Orvis’ click-through rate by 22% and boosted traffic by 30%. This is one way an enterprise can deploy customer-focused experiences with the help of AI, without having to invest in the substantial skills and technology normally associated with developing it.

Align your AI investment with business priorities

A common key strategy, especially for B2C enterprises, starts with identifying use cases that increase personalization and automation, thus streamlining the customer experience. Then, identify the data and AI tools that could help create those use cases.

For its part, Orvis’ goal is to keep its support calls short, and Einstein can help by transcribing and analyzing customer questions and suggesting answers to call center personnel in real time.

The lesson here: Bring in just a few machine learning experts and combine them with off-the-shelf SaaS platforms to discover unique value in AI that can be customized around your business.

Don’t forget the quick wins

Getting value from AI isn’t science fiction, Taylor says. Simply step back and focus on the low-hanging fruit to achieve tangible business outcomes.

Learn more

To learn more about how Salesforce built its AI strategy and lessons learned, watch Taylor’s talk at TransformX, "Enterprise AI Strategy: Bret Taylor on Lessons Learned at Salesforce" and view the full transcript here.

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Enterprise AI Strategy: Bret Taylor on Lessons Learned at Salesforce
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