ADDING ARTIFICIAL INTELLIGENCE TO THE MODERN SALES STACK

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Written by The Modern Sale® editorial team

The promise of artificial intelligence has been widely discussed, with industry analysts like Accenture predicting that annual economic growth for the technology could double in 12 countries by 2035. As a result, enterprise AI has gone from a nice-to-have feature to a competitive necessity. Below are five indispensable tips to help sales leaders make the leap.

1. Adopt and Invest in AI to Be Buyer- and Customer-centric

Many of the world’s largest companies—Procter & Gamble or American Express, for example—embraced artificial intelligence early on, using it to customize Olay products for individual consumers or as a shield against credit fraud. To the victor goes the spoils, so they say, and to the others—the Blockbuster and Borders chains, for instance—well, the rest is history. Indeed, in a rapidly shifting B2B landscape, the winners are fast and customer-centric, and a company’s sales and marketing divisions collaborate seamlessly. If AI can enhance the sales strategy, ensure more efficiency or provide new insights, it’s smart implement immediately, especially when a return on investment could lag behind ponderous executive decision making. “The advantages to adopting some of these new tools are that you can get a competitive advantage over the others you’re dueling within the marketplace,” said Mary Shea, a B2B marketing analyst at Forrester, the research and advisory firm. “You can also arm your sellers so that they don’t get disintermediated by other firms that are more aggressive in pursuing these technologies or get disintermediated by e-commerce.”

2. Deploy AI to Eliminate Legacy Processes: Less Is More, and More Is Less

Too frequently, sales organizations try to add artificial intelligence to existing sales processes rather than reimagine better alternatives. Avoid digital solutions that automate the analysis of data from human sources or lack the data to provide deeper insights. Sales applications that use AI should reduce non-revenue–producing work and provide more insights than simply automating the process of analyzing internal data. More than anything, artificial intelligence should increase productivity and shed light on buyers, allowing for more personalization, productivity and revenue. AI applications that boast shared data networks are preferable to customized technology. When evaluating these options, consider the thought leadership a potential partner brings in addition to their service. “You need to surround yourself with individuals who understand the data at its root source,” said Matt Miller, a vice president of sales operations for ADP. “There are a lot of companies that say they can do things and show numbers—but those are all soft figures. The reality of is that when you look closer, those numbers are not what they appear to be.”

3. Adopt AI to Augment, Not Eliminate, People

Artificial-intelligence–infused applications should make the sales process more efficient and automate reporting so that operations and management can focus on strategy. At their best, they remove the need for manual data entry, generate insights that encourage collaboration and reduce unnecessary meetings. When choosing the ideal application, managers should consider whether it can strengthen the sales team’s natural abilities or improve the quality of work while simultaneously ensuring a more transparent pipeline. “AI is a new frontier, and it’s only natural for humans to fear the unknown,” said Catherine Gutowski, a vice president of General Electric’s commercial digital thread. “Don’t be surprised if your organization doesn’t really ‘get’ AI immediately. New technologies that can enable radical process change take time to marinate, but eventually you will get there.”

4. Make Sure Legal, Procurement and Privacy Teams Understand the Trade-offs

Artificial intelligence is a broad description of technologies that enable machines to learn from data. Less data means less learning, and thus fewer machines necessary to augment human intelligence. The most powerful applications that use artificial intelligence, like Google, Waze, Collective[i] and Facebook, operate shared networks—an exchange between contributors that, in some cases, can conflict with policies created by internal risk managers to evaluate on-premise software or SaaS. It stands to reason, then, that AI requires a paradigm shift in how companies think about data, especially in the legal, procurement and privacy realms.  The key is understanding what value business users and customer service receive in exchange for contributing data. In some cases, the benefit is so critical to compete that failing to share data may be seen as anti-competitive, as was the case in a recent successful challenge to LinkedIn’s attempts to withhold its data from a competitor. Before integrating AI, make sure that risk managers are asking the right questions and that they understand competitive risks to keeping intelligence out of the enterprise. Be sure to update outdated legal forms, privacy policies, and internal corporate policies and contracts so that valuable partnerships don’t get sidelined by outdated assessments of risk.

5. Don’t Forget the People. Devise a Change Management Strategy to Get Everyone On Board

Nearly half of the responsibilities employees are tasked with executing can be automated with technology already on the market, thereby necessitating the development of a plan for vendor relationships, future staffing and information-technology architecture. For executive leadership, a critical role following the integration of artificial intelligence will be managing the inevitable disruption. Help sales managers understand that their time is valuable and to be invested in the most productive activities. Reassure sales professionals that they won’t be penalized for problems that are uncovered, and reward early adopters with recognition and opportunity while providing as much training as necessary to acclimate to the new technology. “Don’t underestimate the importance of implementing a change-management strategy in parallel with your AI strategy,” added Gutowski.