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AI is on the minds of business leaders in 2017:  everyone’s trying to get in on the action. There’s something sexy about all of the potential that AI has to shape the modern enterprise, but often companies get so fixated on bringing it in the door that they are missing the bigger picture.

The greatest benefit that AI can deliver to today’s companies is simplification: AI allows people to see situations – and opportunities within them – differently. By bringing data together, companies can better visualize a consumer as an individual, with a deep understanding of that person’s every interaction with the brand.

More importantly, AI helps solve the outdated way of thinking about the customer experience in silos: with the backdrop of “channels” as the predominant option to organize initiatives, make decisions, and set goals.

The channel mindset is restrictive: it’s company-out thinking rather than consumer-in. When putting an emphasis on leveraging AI in the right ways in your organization, you must first commit to thinking differently about channels.

Easier said than done?

Channels are deeply rooted in the modern enterprise: from the platforms used to organizational structure, budgets, and incentives, marketers in particular break down their priorities channel by channel. Adding sales and operational data to the mix only complicates things further.

So, how can companies overcome this channel mindset to free themselves for better results in the future? Here are three things to keep in mind.

  • Clarify business objectives from the start.

Shared goals help unify teams across the organization, bridging the gap between channels that interfere with improved data decision-making. Personalization won’t bear much fruit unless goals are clear from the beginning, and cross-team alignment exists to remove buffers that stall progress.

  • Make strategic purchases so that your technology stack can automate at scale.

Once shared goals are established, leaders will be forced to focus on the end result rather than the toys they have to show how great their individual efforts are performing in isolation of other campaigns and activities reaching the same consumers. Companies must get serious about the “build versus buy” debate, understanding the difficulty to build AI at scale to train the models that connect the dots between all data at once.

  • Let AI do its thing.

What if I were to tell you that you’re overthinking AI? A recent TechCrunch article summarized it well: “The beauty of predictive algorithms is that they don’t need to understand the cause and effect behind statistical relationships in order to work incredibly well in practice. For an enterprise to glean the benefits of prediction, it must first give up trying to deduce why things are a certain way, and start trusting the lines of code which tell us that they are.”

The best machine learning makes it so that you don’t have to hesitate about omni-channel decisions at all. With improved automation, personalization can happen sight-on-scene, so that people see what makes the most sense based on their behavior in the moment – and the context of what brought them there to begin with.

AI is most powerful when marketers get out of their own way, breaking down the barriers between consumer interactions with a brand so that what the consumer experiences is informed by their history and relationship with the business over time, rather than by a string of engagements on any single channel. When companies get this right, consumers will feel the benefits of AI in action, showing the greatest impact for the investment in the short- and long-run.