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Carl Ryden (Founder, PrecisionLender) - Build Iron Man Suits Not Terminators

Business of Software Conference
Business of Software Conference
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This video for this talk from Business of Software Conference Europe 2018 will be published here soon: http://businessofsoftware.org/2016/07/all-talks-from-business-of-software-conferences-in-one-place-saas-software-talks/ Carl founded his second business in 2009 with one goal: to let computers do what computers do best, so humans are free to do what they do best. This approach eventually led to Andi®, the AI platform developed by PrecisionLender, which is used within the PrecisionLender application to offer commercial bankers actionable insights at the exact moment they need it. When PrecisionLender developed Andi, they thought about Intelligence Augmentation, rather than AI. They focused on UX, customer jobs-to- be-done, and the incredible power of personification to build their most successful product. Carl will share how he came to realise as he grew PrecisionLender that a relentless focus on people, employees, and customers was the key to profitable growth. And he’s watched as the company itself has grown to over 100 people with staff turnover of just 3%.

Carl Ryden (Founder, PrecisionLender) - Build Iron Man Suits Not Terminators

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Build Iron Man Suits
Not Terminators
Carl Ryden
CEO/Co-founder
PrecisionLender
@caryden
This guy…
Carl Ryden (Founder, PrecisionLender) - Build Iron Man Suits Not Terminators
“If in your office, you, as an intellectual
worker, were supplied with a
computer display backed up by a
computer that was alive for you all
day, and was instantly responsive to
every action you have—how much
value could you derive from that?”
Carl Ryden (Founder, PrecisionLender) - Build Iron Man Suits Not Terminators
oN-Line System
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Carl Ryden (Founder, PrecisionLender) - Build Iron Man Suits Not Terminators

  • 1. Build Iron Man Suits Not Terminators Carl Ryden CEO/Co-founder PrecisionLender @caryden
  • 4. “If in your office, you, as an intellectual worker, were supplied with a computer display backed up by a computer that was alive for you all day, and was instantly responsive to every action you have—how much value could you derive from that?”
  • 8. “I don’t know what Silicon Valley will do when it runs out of Doug’s ideas.” -Alan Kay
  • 15. …every PrecisionLender user …now has their own pricing analyst
  • 16. Her name is Andi. She is new on job, but she learns very quickly. She works 24x7. She never sleeps. She sees every deal within your bank. She learns what is working and what is not. She helps you to price each new opportunity, offering suggestions on structure and how to compete. She monitors your relationships and alerts you to threats and opportunities. ..and a completely extensible “Andi Skills Framework” for adding, deploying and managing new skills
  • 18. Context is King • Narrow the context • User Interface & User Experience matter immensely
  • 19. Not All Data is Created Equal • Labelled behavioral data is most valuable. • Most of the existing data is not this. • Build this data organically to drive results.
  • 20. Nobody actually wants Artificial Intelligence. They just want Intelligence • Simple, heuristic-driven insights: • Drive early value • Establish delivery pathways • Establish success metrics/set the bar • Amplify the human in the loop • Build Iron Man suits not Terminators.
  • 21. The impact of the personification of “Andi” has amazed me. • It is relatable and human. • It extracts the “job to be done” instead of “put a button here.” • It humanizes the machine instead of mechanizing the human.
  • 22. There is an intelligence augmentation value chain • To win, you must implement the entire value chain. • Most die at the “last mile” of AI: Translating insight into action.
  • 23. Ajay Agrawal’s Anatomy of a Task Human Judgement ActionPrediction OutcomeData Feedback Data
  • 25. SoR, SoI, SoE…oh my Systems of Record (SoR) Systems of Intelligence (SoI) Systems of Engagement (SoE) What is it? Captures data around what happened, when it happened and who was involved. Uses machine learning algorithms to transform facts from systems of record into “beliefs” about the business/ world (predictions are just beliefs about the future). Rides “shotgun” with the end user while they “do what they do” and translates facts and beliefs into discrete, measurable actions to coach or nudge the user to a better outcome. Deals in: Facts Beliefs/ Predictions Actions Examples: CRM, ERP, SCP, etc. Data Robot, Tensorflow, etc. PrecisionLender, Hemingwayapp, Chorus.ai
  • 26. Many ways of saying the same thing… Systems of Record (SoR) Systems of Intelligence (SoI) Systems of Engagement (SoE) Perception Cognition Action Greylock’s Jerry Chen Microsoft’s Satya Nadella: Gathering Comparing Coaching Emergence’s Gordon Ritter: Data Prediction Action U of Toronto’s Ajay Agrawal:
  • 27. Centaur or Freestyle Chess When you create a Human + AI team, the hard part isn’t the “AI”. It isn’t even the “Human”. It’s the “+”.
  • 28. Again, many ways of saying the same thing… Cognitive Expert Advisors Citizen AI Gartner Accenture Coaching Networks Emergence’s Gordon Ritter: Cognitive CollaborationDeloitte
  • 30. “If in your office, you, as an intellectual worker, were supplied with a computer display backed up by a computer that was alive for you all day, and was instantly responsive to every action you have—how much value could you derive from that?”

Editor's Notes

  1. [My background and BoS Journey]
  2. Who has two thumbs and is my hero?
  3. It was December 1968. Nearly 50 years ago. Douglas Engelbart was preparing to give a demo to over 1,000 engineers in a packed auditorium in San Francisco. Doug was no Steve Jobs, He was a shy engineer who had grown up on a rural farm in Oregon during the great depression. He had no marketing background. His goal was simple: to show the other engineers in the audience that they could use computers as more than just powerful calculators. He wanted them to see that you could use computers to augment human intelligence, to enhance human communication and collaboration and that this was the path to solve many of the world’s most pressing problems. Exactly 18 years earlier, Engelbart had had an epiphany that had changed his life and set him on this course. He had returned from world war II and completed his Electrical Engineering degree, He had a steady job and he had just become engaged. He realized that he had no career goals other than “a steady job, getting married and living happily ever after.” He decided: 1. He would focus his career on making the world a better place 2. Any serious effort to make the world better would require some kind of organized effort that harnessed the collective human intellect of all people to contribute to effective solutions. 3. If you could dramatically improve how we do that, you'd be boosting every effort on the planet to solve important problems – the sooner the better 4. Computers would be the vehicle for dramatically improving this capability
  4. Doug opened his demo with a question: “If in your office, you, as an intellectual worker, were supplied with a computer display backed up by a computer that was alive for you all day, and was instantly responsive to every action you have—how much value could you derive from that?”
  5. He then proceeded to conduct what is widely known as “the mother of all demos” – in it Engelbart demonstrated for the first time ever: The Keyboard The Mouse The Graphical User Interface Windows Hypertext (the precursor to the HTML that serves as the basis of the modern web) File Systems Document Sharing Word Processing Video Conferencing Version Control Remote/Collaborative Editing …and he even hinted at the origin of the Internet Itself as he was also working on the ARPANET project. Please bear in mind that this was 1968. Computers then were the size of room. They were only used by large corporations and governments as massive calculators. They were programmed with punch cards. This was 16 years before the apple mcintosh, 35 years before skype and 40 years before google docs.
  6. He called this system the On-Line System…Remember that I said that he was no Steve Jobs… The audience was awestruck. The 1,000 engineers in attendance all gave him a standing ovation. Engelbart expected hundreds to line up to want to join him to transform computers from merely huge calculators into tools that could truly augment human intelligence, solve some of the world’s most pressing problems and improve the human condition.
  7. After their standing ovation, most simply filed out of the room quietly. Years later a reporter tracked down many who were in that room and asked them about this. They were all awestruck, but they felt it was too big of a leap. They had a mountain a punch-cards that needed attention.
  8. Alan Kay who later was to lead XEROX PARC and a silicon valley legend, was in the room for the mother of all demos. For him, it was life-changing. Alan Kay has since remarked, “I don’t know what Silicon Valley will do when it runs out of Doug’s ideas.”
  9. Engelbart once taped a brick to a pencil and tried to write with it. In 1962, Doug Engelbart was investigating how our tools shape our thoughts. At the time, most of Doug’s peers just saw computers as a way to crunch numbers faster. However, he saw something deeper: he saw a way to augment human intelligence. Not that humans augmenting their own abilities is anything new. We don’t have claws or fangs, so our ancestors augmented their physical abilities with spears and arrows. We don’t have large working memories, so our ancestors augmented their cognitive abilities with abacuses and writing. And these tools didn’t just make human lives easier — they completely changed how humans lived. Writing especially: it wasn’t “just” a way to record things, it led to the creation of mathematics, science, history, literary arts, and other pillars of modern civilization. That’s why Doug tied that brick to a pencil — to prove a point. Of all the tools we’ve created to augment our intelligence, writing may be the most important. But when he “de-augmented” the pencil, by tying a brick to it, it became much, much harder to even write a single word. And when you make it hard to do the low-level parts of writing, it becomes near impossible to do the higher-level parts of writing: organizing your thoughts, exploring new ideas and expressions, cutting it all down to what’s essential. That was Doug’s message: a tool doesn’t “just” make something easier — it allows for new, previously-impossible ways of thinking, of living, of being. You may laugh at the brick tied to the pencil, but of the past few I have spoken to many banks where a commercial RM has to touch hundreds of different systems just to get a loan booked. If you ask the RMs, almost none of these systems actually help them to win a better deal, have a better conversation with their client or to build a stronger relationship. These systems are not tools they are tasks. They are not the pencil, they are the brick.
  10. [Story of maps] Engelbart’s ABC processes for improvement. Activity A – How you do what you do – current core processes Activity B – The human systems and tools that you use to improve A – how do we get better? Activity C – The human systems and tools that you use to improve B - how do we get better at getting better? Something happened with the transformation from turn-by-turn navigation to Waze. We moved from Engelbart’s Process B to Process C. Waze improved Process C, “getting better at getting better” and created a system that grows more valuable with each user with each interaction.
  11. Engelbart's Law is the observation that the intrinsic rate of human performance is exponential. Doug’s work in augmenting human performance was explicitly based on the realization that although we make use of technology, the ability to improve on improvements (Doug called this Bootstrapping, "getting better at getting better") resides entirely within the human sphere.
  12. Calculator – no value Calculator connected to DB that generates reports – little improvement We should do what machines do well well so that humans can do what they do well even better. We would build a system such that every interaction makes us better equipped for the next one.
  13. Engelbart’s Process B & C data – data that helps you get better and get better at getting better to drive exponential improvements
  14. Enterprise SaaS will win Horizontal -> vertical -> personal
  15. Garry Kasparov resigned, in less than 20 moves. On May 11th, 1997, IBM’s Deep Blue became the first AI to beat a human World Chess Champion. You can now download a chess-playing AI better than Deep Blue on your laptop. Your iPhone X is 30x more powerful than Deep Blue. Garry demanded a rematch. IBM said no. However, Garry couldn’t help but imagine: what if a human did work together with an AI? The next year, in 1998, Garry Kasparov held the world’s first game of “Centaur Chess”. Not surprisingly, a Human+AI Centaur beats the solo human. But — amazingly — a Human+AI Centaur also beats the solo computer. At first, Garry wasn’t surprised when a human grandmaster with a weak laptop could beat a world-class supercomputer. But what stunned Garry was who won at the end of the tournament — not a human grandmaster with a powerful computer, but rather, a team of two amateur humans and three weak computers! The three computers were running three different chess-playing AIs, and when they disagreed on the next move, the humans “coached” the computers to investigate those moves further. As Garry put it: “Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.” When you create a Human+AI team, the hard part isn’t the “AI”. It isn’t even the “Human”. It’s the “+”.