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When Slack introduced its new Enterprise Grid product in January, it pledged to bring “much of the same day-to-day Slack experience that users have come to know and love” to large organizations. Similarly, CRM giant Salesforce unveiled its new Einstein artificial intelligence service this past fall to great fanfare, touting it as “AI for everyone.” But, as many enterprise leaders already know — and would-be disrupters are quickly learning — the promise of AI and its reality are, for now, two very different things.

While chatbots, predictive analytics, and intelligent search are all the rage these days, AI’s current business value is typically overstated. One analyst recently called Einstein “a great starting point,” while IT departments are “freaking out” over security concerns — such as phishing scams — due to bots’ potential to sound a little too much like real people. And that’s key — most things AI today are just that: potential. While a lot of companies are trumpeting AI as a competitive differentiator, the technologies are still in their infancy and are a lot more speculative than disruptive. That’s no doubt a relief for those frightened of the self-aware, revenge-seeking androids from film and TV.

A reality check: AI is beginning to take on the low-hanging fruit of the modern enterprise, such as handling critical time-saving tasks — like streamlining email inboxes, prioritizing/scheduling meetings, and creating data-driven daily to-do lists. Some solutions already use predictive analytics to mine the rich work graph of data within a company, adding valuable context around workflows.

As the technology improves, it will get much better at anticipating employees’ needs, as well. In the near future, voice recognition technology may even become a type of universal ID, allowing people easier access to information and experts from partner and customer networks, as well as their own companies. But to take AI further along the path from potential to practical, organizations must set aside the hype and get the right systems and processes in place. Here’s how.


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Requirements for successful AI-powered collaboration

1. Overcome fragmentation

Data provides the brainpower for artificial intelligence. With the amount of data set to expand to a mind-boggling 44 zettabytes by 2020, the problem for machine learning systems is no longer a lack of information; it’s the potential for fragmentation. Without unrestricted access to a ton of data, AI can’t possibly live up to its promises — either real or imagined. Unfortunately, companies are adopting more and more disparate systems, and it’s not helping that stack vendors are continually adding more disconnected tools to their productivity suites, and emerging conversational apps are siloing information in ever-narrower message threads. Companies need their technology vendors to provide open APIs and connected hub solutions in order to make sure valuable data won’t get locked inside niche tools and to ensure that the “signal” doesn’t get lost in a clamor of extraneous noise.

2. Leverage work graph analytics

In order for work graph mapping to be effective, it’s important to choose software vendors that not only enable relationships between people, applications, and business processes but also provide visibility for individual interactions. The systems that most successfully leverage workplace AI are those that let you analyze work that’s getting done in one particular tool but also capture all the conversations, content, sentiment, actions, groups, teams, and people across multiple collaboration apps. Only then do companies get insight into dynamic relationships across the full spectrum of work so they can analyze their organizational network and affect positive change for better business outcomes in a repeatable manner. For example, intelligent work graph technology could help leaders figure out how to strategically build diverse project teams with the right experts in order to ensure successful outcomes.  

3. Embrace a collaboration hub solution that brings it all together

Solving the challenges of fragmentation, as well as those surrounding the natural cultural resistance that exists in many organizations when it comes to adopting AI solutions, will require not only revamping current technologies and processes, but a change in mindset. The payoff, at least according to this Accenture report, will be nothing less than “unprecedented opportunities for value creation.” Fortunately, many software vendors are already finding ways to overcome the current and future obstacles to AI. Some collaboration hub solutions do a great job of enabling the transparency and ongoing discussions necessary to overcome cultural resistance, while also seamlessly integrating with the applications and tools companies have already invested in (including Microsoft Office 365, SharePoint, Box, Salesforce and others). In fact, without some type of agnostic and heterogeneous place to capture all of the conversations, content, sentiment, and actions of individuals, groups, and teams (the work graph) where they are accessible and searchable, AI will never be able to live up to its lofty promises.

The original Einstein (Albert) once famously said, “Imagination is more important than knowledge. For knowledge is limited to all we now know and understand, while imagination embraces the entire world, and all there ever will be to know and understand.” Replacing people is not (nor should ever be) the end goal of artificial intelligence. Instead, AI — by dealing with the knowledge side of work — will augment and expand our inherent human capabilities, including our imaginations, allowing both businesses and people to thrive.

By freeing siloed data and letting individuals and teams to do their most creative work today, businesses can ensure that, when the future does come — and it’s coming fast — they’ll be ready. Remember, despite what you’ve heard, AI isn’t the end of the world. I believe it’s just the beginning.

Ofer Ben-David is the Executive Vice President of Engineering at Jive Software,  a provider of communication and collaboration solutions for business.

Above: The Machine Intelligence Landscape. This article is part of our Artificial Intelligence series. You can download a high-resolution version of the landscape featuring 288 companies by clicking the image.

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