The transition of artificial intelligence from science fiction to everyday reality started many years ago, but...
the practical applications still tend to be in their early stages. Nevertheless, AI is making headway in a number of industries, and the sales enablement arena is no exception.
As CRM vendors start to bake AI and machine learning into their platforms and other innovators layer AI integrations on top, the sales engagement process is already beginning to see the benefits of this added layer of intelligence.
AI technologies are helping organizations automate processes, focus sales muscle where it matters most, target marketing to the best leads, and even identify problems that endanger deals or contribute to customer churn. And, as we move forward with tighter AI integrations, sales professionals and technologists hope those enhancements will be more tightly coupled with sales processes throughout the customer lifecycle.
The most obvious use case among today's current crop of AI technology within CRM is that of lead scoring.
"We are in the early stages, but are already seeing how AI integration can make a difference in terms of lead scoring -- AI-enabled lead scoring allows sales representatives to focus on the best leads first," explained Greg Gsell, senior director of Sales Cloud product marketing at Salesforce, which has been making a big push in AI with its Einstein platform. "Sales reps automatically see the best leads and the most crucial information -- including the prospective company's profile, marketing engagement data and the prospect's role -- which helps to accelerate the sales cycle."
Going beyond data with AI integration
However, according to Andy Byrne, CEO of Clari Inc. in Sunnyvale, Calif., truly integrating AI into CRM is about much more than just scoring leads.
"It's about looking beyond data in CRM to take into account historical and current patterns of human behavior and business activity, such as emails, calendaring meetings, webinar attendance, to get an immediate and real-time understanding of pipeline health and forecasting," Byrne said. "Current CRM systems are still predicated on manual entry, and true AI advancement makes this manual data outdated. To really integrate AI into CRM means building a foundation that encompasses the entire opportunity-to-close process, which spans the point from when a lead converts into a sales opportunity to when a deal is signed."
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The breadth and depth of this kind of impact is still largely aspirational for most organizations, but early gains are evidenced. Clari helps a number of B2B technology firms piggyback off of their existing CRM data to build out more accurate sales forecasting models to make better business decisions.
"Down the line, the most impactful sales features that we see AI tackling are advancing predictive forecasting," Gsell said. "Sales forecasting is key for any business -- it tells us where the money is and how much business we can expect to close at company, regional and team levels."
He added that Salesforce is already seeing momentum, as Einstein also has a forecasting model available for customers.
The question, of course, will be whether platform vendors like Salesforce will provide the predominant functionality or if organizations will want to go to third-party firms, like Clari, that specialize in these features and may offer a deeper dive into the intelligence. It's a situation that will likely continue to develop.
AI technologies can't do all the work
One thing that is certain is AI integration is not necessarily a magic wand, and it will not replace smart sales people and processes.
"I foresee AI working as an assistant for sales reps. It's my hope that AI will allow reps to enhance what they are already doing," said Chris Rothstein, CEO of sales automation platform Groove. "AI can point out triggers that something is not going well. Or if a customer stops talking to a rep in the middle of the sales process, AI can alert the rep of the issues to help get the process back on track."
This is the direction in which we're moving, as technologies like sentiment analysis and natural language processing are combined with machine learning to guide the sales team in the right direction. Sales people still have to do the work -- AI is just a tool to boost their efforts. For example, technology from firms like Cogito help companies like Humana detect conversational cues and coach call center agents in real time to improve customer service and interactions with customers over the phone.
"True 'generalized' AI is still far from reality," said Ali Azarbayejani, CTO of Cogito. "Instead, machine learning and other AI technologies are best leveraged as augmentative intelligence. AI is best leveraged, at this point, to augment and support human understanding -- helping to make people their best selves -- instead of trying to replace humans altogether."
In order to get the most out of that augmentation, organizations will also need to invest in effective training of both people and the technology.
"For AI to work properly, the technology needs to be able to learn the task at hand, and communication is one of the most difficult human interactions to learn," Rothstein said. "The technology will need to understand how sentiment, context and other engagements all interact with each other in order for it to generate outcomes that will be beneficial to reps."
So, if AI integration is providing actionable insight from interactions between reps and customers, it'll likely need a solid body of analysis of past interactions to provide it.
"To overcome this hurdle, sales reps will need to be involved in the training of AI to classify interactions and help point out the important information," Rothstein said.
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