Talk to any business software executive right now and all they want to talk about is Machine Learning (ML). There is a real belief that the vast data sets that companies are accumulating about their customers, their employees, their partners, their competitors, etc, when combined with 2016 computing power and the rapidly advancing engineering discipline of ML, are going to reinvent how the enterprise runs.

At its simplest level, Machine Learning is an engineering discipline that gives computers the ability to learn based on pattern recognition, without being explicitly programmed. A vivid example of ML’s power lies in the difference between how a computer defeated a champion chess player in 1997, and how a computer bested a champion Go player in 2015. Whereas IBM Deep Blue defeated champion chess player Gary Kasparov in 1997 because it had been painstakingly programmed to evaluate every move via a decision tree methodology, Google DeepMind’s AlphaGo defeated Lee Sedol in Go by observing millions of games and teaching itself to play.

So how does this phenomenon apply to business?

The case goes something like this: businesses are capturing more data about their customers, partners, employees, and competitors than ever before. Just about every business process is getting instrumented and measured, and powerful systems of record now span most corners of the org chart.

ML is progressing at a rate that those who have been paying attention can now see a day where every business process will run better, guided by intelligence from the cloud, and fed by data. In this AI / ML enabled world, every business metric that matters will go up and to the right.

In the sales world, close rates will go up, deal sizes will go up, and deal cycles will shorten as all aspects of the sales funnel are optimized by data driven intelligence. In the world of HR, employee engagement will go up and attrition will plummet as companies, with the help of software, learn to deeply understand the needs of their employees, and predict and address morale issues before they happen. In the engineering world, AI enabled pattern matching will bring development cycles down and drive more effective roadmaps, while improving quality.

Put simply, more data, more deeply understood, will result in better decisions and processes in every corner of the org chart.

Just as a computer can observe millions of Go games en route to becoming the greatest Go player the world has ever known, a computer can also observe millions of sales behaviors, millions of email copy combinations, millions of employee satisfaction surveys, etc. on the path to perfecting those processes and outcomes.

I recently had the opportunity to see Professor Jerry Kaplan, entrepreneur and computer science professor from Stanford, advise a room full of top cloud CEOs to “store every bit of data,” as ML is going to lift the entire cloud industry by augmenting the value and ROI of every signal in the database. He went on to say that there is also an interesting time machine aspect in play. As the disciplines of ML and AI progress, it’s not only current data that will be lifted and illuminated by this intelligence, it will be historical data, too.

So, data captured yesterday will soon be better leveraged to inform tomorrows decisions.

Under this context, the business software giants – Salesforce, Oracle, SAP, IBM, Adobe, Microsoft, Google (and perhaps even PE companies like Vista) – are not only trying to hire the best ML thinkers in the world, but they are in a data arms race looking to augment their own data capture capabilities, and to snap up emerging vendors that are amassing valuable datasets.

Salesforce has spent $4B in acquisitions this year (with another $700M acquisition announced today), and remarkably, is considering a run on Twitter to feed its intelligence engines with more real-time data. Microsoft came out of left field to buy LinkedIn at an extraordinary price, and stunningly just announced a reorg that resulted in a 5000 person AI business unit under one of its most senior leaders. Google also recently reorganized its enterprise business units into Google Cloud, in part to apply its AI initiatives across a dedicated “G Suite.”

Which brings me to the world of live events.

Long segmented from the rest of the marketing and business orgs, live events have run virtually data free for hundreds of years. Despite representing the largest marketing spend in the world, the event ecosystem has been stubbornly immune to the reach of technology and data due primarily to how difficult it has been to automate the extraction of data signals from a physical world environment.

This is changing quickly as live event attendees turn to technology to help them navigate the event, connect and communicate with other attendees, and dive into the content. Each of these attendee workflows is generating data exhaust, and with it massive amounts of signal to feed the ML engines.

On the DoubleDutch network, we see on average 183 data signals per attendee, per event. There are very few online comps for this volume (and quality) of signal, and its still very early days.

We call this practice of designing experiences that combine the best aspects of the physical and digital worlds Live Engagement Marketing (LEM), and we believe LEM has a critical role to play in a world that is informed by data.

Event professionals are world class at designing experiences. By applying the principles of LEM to our events and augmenting attendee experiences with technology, we are illuminating a data set that has historically been dark; the signal that reflects face to face professional interactions.

Combine this firehose of professional human interaction signal with the power of ML, and the sky is the limit.

It’s not farfetched to imagine an era for live events where every attendee has a technology assisted, personalized experience, where valuable introductions are seamless and frictionless, and no leads are left on the table for sponsors. The largest most powerful marketing spend in the world is also its least optimized – no channel of marketing or business unit has as much to gain from ML as live events.

For those that run events, you are sitting on a goldmine of data signals that will only increase in value as ML gains momentum. Your employer’s systems are becoming increasingly thirsty for more inputs. Your path to a seat at the strategy table hinges on how quickly you learn to design hybrid face-to-face/digital experiences.

The time to embrace LEM is now – your ML / AI enabled future will thank you for it.