As we look forward into 2017, intelligence augmentation (IA) will begin to take its rightful place alongside artificial intelligence (AI).
Here’s one of my talks about this. Summary below.
“This year, our priority is customer experience. Everything we do must connect to that.”
“We’ll upgrade the network in a neighborhood when the bandwidth utilization exceeds 80%.”
“I’m going to get rich, then I’ll be happy.”
Good ideas, on the surface. Problem is: they’re often wrong.
And they share a common thought pattern: the use of a proxy—a substitute—for what we actually care about.
Thank you, Einstein, for your subscription.
So this is pretty basic. But it’s huge.
Problem: You are working at a bank and have been tasked with developing a new customer care program. You’re making good progress, and reach out one day to a colleague for their ideas. Word of the meeting gets around, and an executive walks into your office one day, assuming you’re floundering, and tells you what to do.
Problem: Your second grader is in trouble at school. Teachers and other parents call you on the phone, asking you to fix the problem. You are starting to develop some good ideas and making plans. But one day, the school principal makes the decision to move your child to a special classroom.
Problem: You work on an automobile assembly line. Your bonus depends on the quality of the cars you help to build. You see a problem with a welding machine which would cost $100 to fix. But management won’t approve the repair.
What’s the common pattern here? It’s responsibility without authority: a good recipe for insanity.
Please enjoy this interview broadcast today with me and Daniel G. Faggella of TechEmergence. I touch on intelligence augmentation (IA), machine learning in vision, text, and other domains, the emerging decision intelligence ecosystem, the limits of data, and how to hire a machine learning consultant.
Are we getting dumber? Or is stuff just harder?
Take this “invisible art” artist. She sold a few.*
Real decisions are made in the heart, the gut, based on a good story. So we’re vulnerable to master wizards: good story tellers. And, often, we’ll do what they say.
It’s impossible to assemble hundreds of graphs and data visualizations in our heads to make good decisions. It’s a fiction that we can. So we’re overwhelmed, take short-cuts, but it’s hard to admit.
The good news: we have new superpowers.
Last month I received an intriguing email inviting me to an event at Kimberly Wiefling’s house. I’d met Kimberly before through Jonathan Trent, as part of the work I’ve been doing to help out the Omega Global Initiative. I knew she was an international consultant, but it was great to also learn that she was passionate about systems thinking and visualization. Jonathan and I drove up to Kimberly’s house together, where she and Peter Meisen explained their initiative to bring a Buckminster Fuller-inspired Sim Center, based on a similar center in San Diego, to Silicon Valley.
My team and I offer decision intelligence and decision engineering to solve your complex problems. Simply put, data is backwards-facing. It’s like watching an elephant’s footprints, rather than understanding the elephant itself. Systems are forward-looking. There’s no coincidence in the fact that your rear-view mirror is smaller than your windshield. Yet…Most organizations look backwards, assuming future equals past. This is no longer true. Click To Tweet
So let’s roll up our sleeves together, and take your organization to the next level.
It doesn’t have to be hard.
You still need your data. It’s always going to be an important tool in validating where you’ve been, and of course, operational data (like a customer’s address) is critical.
But when it comes to making decisions, data is only part of the picture. Once you make a decision and execute on it, this creates a cascade of events within your organization. Understanding how the decision leads to an impact…and another…and another…until you’ve reached your objective (or not) may sound like a daunting proposition. But here’s the rub: you’re doing it already, in your head, every time you think through the consequences of a decision. Why not get some computer help with that? Even a little can go a long way. Because otherwise, you’re facing the fog shown below:
Here’s what you can expect when working with me to improve your organizational decision making. Think of this as an “a la carte” menu; some organizations choose the whole bundle, some do one part at a time. Each has considerable value on its own.
Add-ons to the above include:
So please drop me a line, and let’s explore together whether supercharging your organization’s decision intelligence makes sense for you.
Before Martin Luther King had a dream, E.D. Nixon had a plan. It was a good plan, too. Nixon thought he could make huge strides in the struggle for racial equality in his hometown of Montgomery, Alabama by orchestrating a boycott of the city bus system–both a bastion of segregation and a huge money-maker for the city.
He had a sound strategy and a thorough plan. But it was proving difficult to get the masses of African-American bus riders to go along with the change plan, and actually stop riding the buses. Just like countless team leaders and project managers in today’s organizations, Nixon had all the dominoes lined up, but he couldn’t get them to start falling.
Then one morning in the early spring of 1955, a courageous young woman took a seat on a city bus. After the bus filled up, the driver ordered her to move so that a white woman could take her seat. When she refused, the irritated bus driver then flagged down two police officers who grabbed the young lady and hauled her off to jail. And the rest of the story is history.
Today, everyone knows that Rosa Parks’ decision to stay seated on that bus inspired a domino effect of change that tore down the walls of racial segregation in America.There is just one problem with that story.
Rosa Parks wasn’t there.