AI is no longer the exclusive domain of large enterprises. Itâs entering the business mainstreamâbut itâs 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 <a href="https://scale.com/events/transformx" target="_blank" rel="noopener noreferrer">Scale TransformX 2021 conference</a>, 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. <br />
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Here's a summary of the seven key takeaways from Taylorâs presentation. For more details, watch the session video above.
Business today is all about the data
âIf you don't have a digital business, you don't have a business,â 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, âSoftware is eating the world.â Today, however, itâs AI that has become central to everything that a company such as Salesforce does.
Build a data culture
AI and deep learning canât 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 donât.
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 companyâs 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 arenât data scientists to help shape the responsible use of these technologies.
Focus on strategy, not AI for AIâs 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. âDonât start with the technology; start with customers,â Taylor says. Focus on their experience and what your customers need and want. Clarify the projectâs scope and look for the easy wins first. âIf AI becomes the purpose of the project, it becomes a bottomless pit of investment,â he says.
One shortcut to good AI: Donât build It; buy It
Salesforceâs AI platform Einstein is a prime example of Salesforce focusing on building AI technology that is customer-focused and that âcan be turned on with clicks, not code,â 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 Orvisâ 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, Orvisâ 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.
Donât forget the quick wins
Getting value from AI isnât science fiction, Taylor says. Simply step back and focus on the low-hanging fruit to achieve tangible business outcomes.
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