Predicting the Future: 3 Ways AI Will Change Your Business
Seasonal trends and historical patterns offer a glimpse into a company’s possible future. That’s good. But what if I told you, that by combining the right data with the right algorithms, you could make detailed predictions about your company’s sales from one quarter to the next? And not just predictions, but suggested next steps and courses of action, so your team can take action before it is too late.
While some companies already forecast well (many don't...see Wall Street for ones that miss their targets), companies are only good at knowing the figure, but not which deals will actually close, which won't and why. This is exactly where AI brings a new perspective, completely revolutionizing the way companies are running their business — from the sales office to the C-suite.
They say no one knows what the future holds, but today AI is changing that
Let’s look at the three key areas where AI is changing the game.
Thousands of data points determine how a quarter is going to turn out, from historical considerations to how the team makes its own predictions. Done right, AI can see past human biases. With the right algorithms and data, AI can forecast sales performance and offer suggestions for how to course correct or stay on track — this is especially important when the system predicts a miss or a bad quarter.
This kind of insight is particularly useful for those businesses that depend on seasonal traffic, the kind of revenue that can make or break a whole year. Some businesses make their money on weekends, like hotels and movie theaters; others, like retail businesses, often live and die based on their holiday sales. While these annual surges often present an opportunity for sales reps, AI is uniquely suited to gather data on those surges — enabling sales teams make the most of any opportunity thrown their way.
Similarly, AI can spot outliers. If a convention comes through town that bumps sales a bit, or bad weather on a holiday weekend cuts into sales, AI will find those anomalies and keep them from influencing the forecast. By using AI to break through the noise, companies are able to find valuable insights, information and recommendations — keeping their earnings on track.
By using tools like lead scoring, the sales team can prioritize those potential clients most likely to close. This kind of triage allows team leaders to keep reps focused on the most promising and highest-value deals. The AI gives sales leaders the ability to control risk: if the forecast predicts goals will be missed, the team can zero in on the best possible deals, or shift resources to make sure a deal in jeopardy gets the attention it needs to close. When better quarters are predicted, the team can shift their attention to prospects that may be more of a longshot, but that could offer big returns — creating an even better quarter.
Where teams make their own predictions, tinged by optimism or pessimism, AI isn’t influenced by those (admittedly human) qualities. The analysis is unbiased and points out those deals most likely to close, those accounts that need attention, and those prospects who should be called — now.
Building a Smarter Business
A forecasting system that didn’t learn — that didn’t teach itself — is not much better than the spreadsheets businesses traditionally use. But not all predictions are created equal. The system that doesn’t adapt to the specificities of a company or a team will offer general predictions that prevent teams from focusing and, therefore, from increasing revenue. Self-teaching AI, then, is essential to sales forecasting.
Beyond sales performance quarter to quarter, AI can become the institutional memory of a business. Historical data at its fingertips, AI never forgets. Whatever lessons came from those predictions are stored and accessible forever. As important as AI’s ability to predict the future, is its ability to recall the past.
Even employees that have been with the company since day 1 can’t recall every holiday sale, every fluke. The CEO can’t claim that kind of recall, either. But we can use AI to learn and remember it all. Better yet, AI is able to continuously learn — making predictions as it gathers more information and continues to refine itself.
While the future is never 100 percent clear, AI is here to help us learn from the past, act in the present and (hopefully) predict — and shape our future.
Why Under Investing In Your Preference Management System Is Costing You Customers
How the Internet of Things can Unearth Innovation in Oil & Gas Industry
Cloud : A Key Player in Welfare of Citizens through IT
Architect Your Business to Engage, Interact and Serve 'Things' as a New Customer Segment
By James Seevers, CIO & GM, Toyoda Gosei
By Bill Krivoshik, SVP & CIO, Time Warner Inc.
By Gregory Morrison, SVP & CIO, Cox Enterprises
By Alberto Ruocco, CIO, American Electric Power
By Bruce. D. Smith, SVP & CIO, Information Systems, Advocate...
By Adrian Mebane, VP-Global Ethics & Compliance, The Hershey...
By Graham Welch, Director-Cisco Security, Cisco
By Michael Watkins, Senior Product Director, Global Knowledge
By Bernd Schlotter, President of Services, Unify
By Patrick Hale, CIO, VITAS Healthcare
By Steve Bein, VP-GIS, Michael Baker International
By Jason Alan Snyder, CTO, Momentum Worldwide
By Mike Morris, CIO, Legends
By Louis Carr, Jr., CIO, Clark County
By Bill Dow, SVP and General Manager of Business Solutions,...
By Jim Whitehurst, CEO, Red Hat
By Darren Cockrel, CIO, Coyote Logistics, a UPS Company...
By Nathan Johnson, SVP and CIO, Werner Enterprises [NASDAQ:...
By David Tamayo, CIO, DCS Corporation
By Neil Hampshire, CIO, ModusLink Global Solutions, Inc....