Farewell To My Readers

I spent a fair amount of time churning over this last article. Should I try to recap some of the multitude of ideas and concepts I have written about since 2010? Or should I just talk about the current state of the mortgage industry and try to project what the future looks like? I think I will leave the latter to the many bright minds in the industry.

What caught my eye recently was a book, Think Like a Lawyer Don’t Act Like One, by Aernoud Bourdrez. The book is filled with 75 sample strategies for conflict resolution. I will take the liberty to extract some of the more noteworthy ones that may be relevant to both you personally and your business.

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#2 Never lose sight: A conflict can arise when two people have opposite – thus clashing – interests. That makes it difficult to keep your focus on the goal. You’ll only achieve your objective if you focus on it.

#4 Fight for your right: What do you believe in? Creative freedom, respect or money? Then fight for it. Not everyone will agree with you. But everyone will respect you for acting consistently with what you believe. Fight!

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#7 Hard on the problem, soft on the people. Be hard when it comes to the issue at hand, but be gentle on the person across from you: this may be the most valuable lesson in conflict management. Being hard means that you stand by your principles, by what is essential for you. You don’t make any unacceptable concessions. No tampering with market access, artistic freedom, payment obligations, and so on. But be gentle on the person. If you’re hard on him, you’ll create a conflict – but if you’re gentle on the issue, you’ll end up regretting the concessions you make. By being hard on the issue and gentle on the person, you’ll be successful in your negotiations and gain what there is to be gained.

#10 Create goodwill: A tight contract and a top lawyer might be effective, but goodwill offers you even better protection. If you’ve got your rival’s goodwill, he’s less likely to take you for a ride. Goodwill prevents conflicts. Develop goodwill by investing in the personal relationship. Friendship first, then down to business.

#14 Say sorry: That little, five-letter word, “sorry”, is priceless. If you really mean it when you say it, you’ll clear up more problems that a team of pricey lawyers.

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#15 Be conscious of your subconscious: Many times, in a conflict you’re not exactly sure what it is that’s bugging you. The real reason for the conflict lies buried deep below the surface, in your subconscious. So, be conscious of your subconscious. Explore your gut feeling. Retrace what happened – step by step. Find out where that unpleasant feeling comes from. Do you feel unappreciated? Excluded? Humiliated? Are they not taking you seriously? Or do they question your professionalism? This decreases your chances of acting impulsively and further worsening your position. Ad added advantage: while you’re exploring, you’re also taking time out for yourself.

# 21 Know your enemy: Knowledge is power – especially at the negotiating table. So, make sure you know all there is to know. Before you start, lay out all the details and circumstances that led to the conflict. Immerse yourself in your opponent. What kind of person is he? What are his weaknesses? Where are his vulnerabilities? Is he vain? What’s he scared of? What gets him excited? Why is he fighting you? What does he want? And then address these same questions regarding yourself. You must know yourself too. Sounds obvious, but very few do this. Know yourself, know your enemy and know the facts – through and through. Because a poorly prepared case is a lost case.

# 22 Under-promise, Over-deliver: Creating great expectations is tempting, but not always wise. If you end up delivering less than you promised, you make a conflict more likely to occur. Promise less than you’ll deliver, and you’ll have a satisfied client.

# 23 Know what you’re not going to say: Knowing what you’re going to say: Useful. Knowing what you’re not going to say: crucial. Saying what you shouldn’t have said: fatal.

# 26 Know your BATNA: You’re in a conflict and you’re about to negotiate. Think of your alternatives if a settlement is not reached. In other terms, know your BATNA, or your Best Alternative To a Negotiated Alternative. What will happen if you end up in court. If your case is strong. Then you can aim high. If it’s weak, chances are you’ll leave the courtroom empty handed. Here’s how to improve your BATNA. Build a solid case file. Hire a good lawyer. Subtly let your opponent that you have the means and the patience to use the lawyer and the file.

#27 Kiss: Keep it short and simple.

# 29 Use the power of silence: Silence is a powerful negotiating weapon. Listen more than you speak, and let the silences be. Your adversary, out of embarrassment or discomfort, will fill in the gaps. He’ll talk away and probably say things he doesn’t want to say. Moreover, what you don’t say can’t be held against you.

# 31 Take one step at a time: Solutions take time. They involve many steps. First, you lay out the problem. Then negotiate. Then put the agreement down on paper. Then you must make sure it is complied with. If not, then you must take follow-up steps. The point is that you have to concentrate on the step in question. It makes sense, because if you’re too concerned about steps later down the line, you’re likely to stumble.

# 36 Walk away for a change: Sometimes, walking out of a negotiation – however difficult – is the only way of getting what you want. If the opponent fears a good result is getting away from him, then he’ll be more disposed to make concessions.

# 69 Agree to disagree: When you’re negotiating it’s easy to slip into “did not, did so” arguments about things that are completely irrelevant. This is obviously counter-productive. It reinforces differences, while your goal is just the opposite. Declare out loud that you think it’s fine to agree to disagree on side issues. Once that’s done, you can focus on the central issue, which is why you came to the table in the first place.

The articles I have written over the years have encompassed a variety of subjects, but in retrospect I think a common theme is apparent. Whether the topic is conflict resolution, artificial intelligence, the Exponential Age, or mortgage technology initiatives, the only thing we can do in an uncertain world is position ourselves for change. Locking ourselves or our organizations into static mindsets guarantees that we will fail to adapt to new situations, that our objectives will go unmet, and that we will watch competitors outpace us.

Thank you to all of you who have taken the Future Trends journey with me. Your feedback and comments over the years have been a gift. I am always reminded that change is not the enemy of success, but as the catalyst for it. I’m ready for the changes that will come when I step away from Future Trends. I hope you embrace your changes as well.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

The Legacy Of Steve Jobs

Steve Jobs and Steve Wozniak founded Apple in 1976, with Jobs playing the part of strategic visionary and businessman, while Wozniak serving as the engineering expert who translated the vision into products. Neither brought any experience running a company into the Apple venture. Mike Markkula, one of Apple’s earliest investors and sources of business expertise, addressed this by bringing in his friend Michael Scott as Apple’s first CEO. Markkula himself became CEO in 1981 after Scott’s departure.

In 1983, Jobs himself recruited John Sculley from PepsiCo for Apple’s next CEO, even though he already saw himself as the right person for the job. It was clear that Apple’s board wasn’t confident in Jobs’s ability to lead. His reputation for managerial callousness and obsession with detail was well known and considered a liability for the CEO’s office.

By 1985, the power struggle came to a head. Jobs had led the initial development of the Lisa, the first computer with a graphical user interface (GUI). While a technical marvel, it was not a commercial success. His follow-up project, the Macintosh, had better sales, but nowhere near enough to shake IBM’s control of the PC market. It was the beginning of the end. Sculley, acting on direction from the Apple board, tried to limit Jobs’s efforts to launch expensive products in untested markets. After a failed boardroom coup attempt by Jobs, he resigned and founded NeXT.

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NeXT’s trajectory followed a similar pattern as Apple: technically impressive products—delivered late, priced beyond what most consumers could pay—failed to find market traction. In its first post-Jobs era, Apple saw remarkable success. The Mac offered color and the first PowerBook laptop was released. But there were flops as well and a costly strategic error in microprocessor technology that kept the price point of Macs out of reach for many potential customers.

Gil Amelio became CEO in 1996 and it was his idea to acquire NeXT and its NeXTSTEP operating system. The move returned Jobs to Apple as an advisor. It would also be Amelio’s undoing. The next year, an anonymous party sold 1.5 million Apple shares in a single transaction. As a result, Apple shares fell to a 12-year low. In the next weeks, Jobs convinced the board to fire Amelio and make him interim CEO. Jobs later confessed that he was the anonymous seller of the Apple stock.

By August 1997, Jobs brought in a new board and mended business fences with long-time rival Bill Gates. Microsoft announced a $150 million investment in Apple at the Macworld conference. In 1998, Apple introduced the iMac, its all-in-one computer, reinforcing the company’s turnaround. In 2000, Apple officially dropped the “interim” from Steve Jobs’ title of CEO. Steve Jobs said, ”Getting fired from Apple was the best thing that could have ever happened to me. The heaviness of being successful was replaced by the lightness of being a beginner again. It freed me to enter one of the most creative periods of my life.”

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In his exclusive biography of Steve Jobs, Walter Isaacson writes about the man and the inevitable and often difficult intersection of personality and inventive success.

Let’s look at some of the traits Walter Isaacson considered the keys to Job’s success:

Focus: Jobs returned to Apple with something to prove, and he would prove it by focusing on the core business as he understood it. Apple’s product line was a mishmash of computers, gaming consoles, cameras, and printers. Jobs dumped products, slashed R&D projects from 50 to 10, and laid off over 2,000 employees. “Deciding what not to do is as important as deciding what to do,” Jobs said. “That’s true for companies, and it’s true for products.” Instead of making more products, he focused Apple on making only four computers, one for highly specific market segments. By getting Apple to focus on making just four computers, he saved the company.

Simplify: When designing the iPod interface, Jobs looked at every angle to reduce extraneous click for decisions that users shouldn’t have to make. One proposed navigation screen required users to specify if they wanted to search by song, album, or artist. “Why do we need that screen?” Jobs demanded. The designers realized they didn’t. As a result, the device does what a human brain will do: search all artificial categories (Is it a song? Is it an artist?) for any matches to the keywords supplied to it.

Take Responsibility End to End: Jobs was a controlling person, and while that made him dig in on decisions that were questionable, it also brought order to what could have been a disjointed product experience. He envisioned an Apple ecosystem that allowed for an intuitive, integrated user experience with connected Apple devices. This was not a matter of making an elegant product; it was about making the suite of products that worked together to create an experience greater than any single component.

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When Behind, Leapfrog: No company, regardless of its innovative leadership, will always be first with a new idea. The trick is to know when you’re behind and then use that position to catapult ahead. In Jobs’s own assessment, he missed the first wave of digital music technology by being blind to user behavior. The original iMac was geared to managing photos and video, but not music. Competitor PCs provided the avenue for downloading and swapping music and then burning personal CDs. The iMac’s slot drive couldn’t burn CDs. He could have simply upgraded the the iMac’s CD drive, but instead he created an integrated system (again, end-to-end ownership) that transformed the music industry. The resulting combination of iTunes, the iTunes Store, and the iPod allowed users to buy, share, manage, store, and play music better than they could with other devices.

According to Google, Steve Jobs is still the most interesting tech CEO. Steve Jobs may be gone, but clearly he’s not forgotten. The mythology around the man is so strong that even five years after his death he still dominates online discussion, more popular than Apple CEO, Tim Cook; the Tesla and SpaceX CEO, Elon Musk; Facebook’s CEO, Mark Zuckerberg; and Microsoft founder Bill Gates.

Steve Jobs said it best in 1995: “Of all the inventions of humans, the computer is going to rank near or at the top as history unfolds and we look back. It is the most awesome tool that we have ever invented. I feel incredibly lucky to be at exactly the right place in Silicon Valley, at exactly the right time, historically, where this invention has taken form.”

This article cannot begin to explore the life of Steve Jobs in detail, but I hope it will give you a snapshot and encourage you to read ”Steve Jobs,” by Walter Isaacson, the official biography published in 2011.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

Listen To Forbes

For those of you who didn’t get a chance to read the centennial issue of Forbes magazine, you missed a collection of thoughts from 100 of the greatest living business minds. I thought I would select some excerpts from the visionaries and early adopters.

Steve Case, Co-founder, AOL & Revolution: The story of American business over the last 100 years is a story of different sectors rising and falling (and often rising again in unanticipated ways) in different regions of the country. When Detroit was an automobile powerhouse and Pittsburgh was the steel city, Silicon Valley was just fruit orchards. As the industrial revolution peaked and the technology revolution accelerated, the role of these places changed. As we enter the internet’s third wave, where entrepreneurs will leverage technology to disrupt major real-world sectors—like health care, education, financial services—startups will increasingly move to cities where industry expertise exists. The opportunity to grow companies that spur job creation and economic growth holds great promise for what I call these “Rise of the Rest” cities. This will lead to a more dispersed innovating economy, where jobs and wealth are created across the country, not just on the coasts.

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Michael Milken: Philanthropist: I came of age and went into business right in the middle of these past 100 years. Two issues of Forbes, the 50th and the 60th, had a particularity significant influence on me. Both issues really made me think about how financial structures changed over time and how leading companies changed. A century ago, the automobile was radically changing transportation and mobility. Ford Motor was the 21st largest company. By the time it went public in 1956 with what was then the largest stock sale in history, it was one of the most valuable companies in the U.S. Today its total market value is less than the annual price variations of Amazon, Facebook, Apple, or Google. In 1917, most of a car’s cost was based on raw materials, the country’s largest company by far was U.S. Steel. Today the American steel industry directly employs fewer than 140,000 workers. Today’s growing challenge: create meaningful lives for the world’s population. We’ve accomplished the greatest achievement of mankind, the extension of life. Since 1900, average life expectancy worldwide has grown from 31 to over 70. Economists estimate that about half of economic growth is tied to the public health and medical research advances that underlie increased longevity.

Bill Gates, Co-founder, Microsoft: In early 1975, when I was in college, my friend Paul Allen showed me an issue of Popular Electronics, featuring the Altair 8800 computer, the first commercially successful personal computer. We both had the same thought: “The revolution is going to happen without us!” We were sure that software was going to change the world, and we worried that if we didn’t join the digital revolution soon, it would pass us by. That conversation marked the end of my college career and the beginning of Microsoft.

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We’ve just began to tap artificial intelligence’s ability to help people be more productive and creative. The pace of innovation is accelerating—and that opens up more ideas for exploration. Big advances in clean energy will make it more affordable and available, which will fight poverty and help us avoid the worst effects of climate change.

The next 100 years will create more opportunities and we need people to keep believing in the power of innovation and to take a risk on a few revolutionary ideas.

Masayoshi Son, Founder, Softbank: When I was 19 years old I saw a photo of a microprocessor in a science magazine. It was just a tiny chip that could fit on a fingertip but represented an entire computer. ‘Oh my God,’ I said to myself, “this is going to change mankind’s life.” This is the biggest invention that man ever created. Those microprocessors were compacted into PCs, then linked together to create the internet and later smartphones. Now they are extending our knowledge and intelligence via artificial intelligence.

Tim-Berners-Lee, Inventor: I published my proposal for the World Wide Web in 1989. From the outset, I imagined it as an open, universal space, where anyone, anywhere could take their ideas and bring them to life without having to ask for permission or pay royalties. I hardwired these factors into the Web’s design and made a conscious decision not to try to copyright or patent it. In 1993, CERN, my employers at the time, agreed to make the code available to anyone royalty-free, forever. But now, as the Web matures, this openness is under attack. For the economic, social and political benefit of all, the Web must be recognized as a public good and locked open through appropriate corporate and government action—including the preservation of net neutrality.

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Marc Benioff, Founder, Salesforce: We are living in the fourth industrial revolution, with advancements in robotics, genetics, stem cells, autonomous vehicles and especially artificial intelligence. All will dramatically change life itself. We need to have a beginner’s mind to think about what is happening. That idea of a beginner’s mind is the core to innovation.

Michael Dell, Founder, Dell Technologies: The Computer Age is just beginning. Most companies today have about a thousand times more data than they actually use to make better decisions. When you overlay the latest in computer science—AI, machine learning, deep learning, unsupervised learning—you will create an explosion of opportunity and a real emergency. Over the next few years, as the cost of making something intelligent approaches zero, companies will succeed and fail based on their ability to translate data, including historical data, into insights and actions and products and services in real time. We like to think of ourselves as a company with big ears: We listen, we learn, we understand—and we create things.

Jeff Bezos, Founder, Amazon: We’re in the midst of a gigantic transition, where customers have incredible power because of transparency and word of mouth. It used to be that if you made a customer happy, they would tell five friends. Now with the megaphone of the internet, whether online customer reviews or social media, they can tell 5,000 friends. In the old days, an inferior product could prevail in the marketplace with superior marketing. Today customers can tell whether a product and service is good because there’s so much transparency. They can compare it to others very easily, and then they can tell all their friends—the customers will do part of the heavy lifting, marketing-wise. Rather than inferior products shouting louder, we have sort of a product meritocracy. It’s very good for customers, it’s very good for the companies that embrace it—and it’s very good for society.

So what can we take away from these various reflections? We don’t have to be the visionaries who start a revolution, but if we want to succeed in an ever-evolving social and economic landscape, we need to be positioned for adaptation. Organizations that make the best use of data will recognize the signs of change first. And the organizations that design their product offering to fit the customer—rather than hoping that customers will change their behavior to fit the products—will have the competitive advantage regardless of the industry in question.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

Hurricanes Harvey, Irma, Etc.

Sunshine, Blue Sky, and Spectacular Sunsets: That’s why I moved to Florida. Oh, lest I forget, I need to mention some other important objectives: unlimited golf and no more snow or wind chills below zero. Then along came Hurricane Irma!

It’s not like my wife Kristy and I weren’t aware of the potential damage from hurricanes. We had just taken possession of a house we had built in Cape Coral when Hurricane Charley ($ 16.3 billion in damages) struck the area in 2004.

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This is my observation of the analysis, preparation, and the response by government agencies, businesses, and the general population to the hurricane threat and what the future holds.

It’s all about the data: Mother Nature can be capricious and even though predicting the path and severity is not an exact science, the U.S. and European models for Hurricane Irma came very close and they were able to make adjustments along the way. The forecasting seems to get better every time and I attribute that to the constant monitoring and analysis of a very complex data model. Every hurricane is different. Harvey, for example, stalled over Houston and dumped an enormous amount of rain. Irma, however, was originally anticipated to head to the east coast of Florida but shifted to the west after touching Cuba as a category 3. It elevated to a Category 5 and hit the west coast at Marco Island and Naples. It then traveled north right up the center of the state. Irma was huge, wider than the state. Along the way it was downgraded to a tropical storm, yet Jacksonville was hit hard with storm surge.

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Hurricane forecaster, Phil Klotzbach, recently commented; “Harvey, as well as the damage that Irma had done in the Caribbean, caused people to take this storm very seriously.” Those that didn’t paid the price.

So, as of 9/12, let’s review what is happening with Hurricane Jose. Two of the most robust computer models meteorologists use to determine the odds of landfall— the GFS, which is the American forecast model, and the ECMWF, the European model—keep Jose over the ocean. But models can have trouble forecasting unusual tracks such as Jose’s expected path. There is generally not a dominant weather feature that is steering the storm, so model forecasts can vary widely between each other and from run to run. The National Hurricane Center recognizes this issue in its early forecasts for Jose, saying “there is a lot of uncertainty in the intensity forecast.” Considering we are just at the peak of the hurricane season, which has been predicted to be a very active season, we have to be diligent and prepared.

The takeaway for the mortgage industry is that it is crucial to have a comprehensive understanding of what data is important to your organization, that this data is well-defined, and you have confidence you are collecting it properly. In addition, your data model must be continuously monitored and you need to be able to adjust it as necessary.

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Proactive, reactive and inactive: Everybody in Florida fits in one of these categories.

Sometimes it is best to consider alternative strategies. Kristy and I were very proactive when looking at our options for Hurricane Irma. This looked like a monster storm and it certainly turned out that way. We closed down the house and left on Wednesday, September 6th. We avoided the I-75 parking lot and took the old way (US-41) north. This took us through lots of little towns, but there was little traffic and gas was readily available. We stopped for two nights in Albany, Georgia, and continued to Columbus, Indiana, where we plan on staying until power is restored. As they did in other areas in Florida, the police went through our neighborhood ordering the few remaining people to leave. They were not going to respond to 911 calls and put their officers in danger.

The reactive ones tried to wait it out because of the earlier forecasts that predicted an East Coast track for Irma. When they finally decided to leave, they encountered problems: finding gas and stop-and-go traffic on I-75. Hotel vacancies were basically nonexistent in northern Florida and across the southern parts of Alabama, Georgia, etc.

The inactive ones decided to bunker down, even as all of southern Florida issued mandatory evacuations. Many in this group did not have the means to leave and some were forced to go to rescue centers.

How would you define your organization, especially, as it pertains to technology? Are you ahead of the herd? Remember, if you are not the lead dog, the view never changes. Are you just a follower? Maybe you are waiting for other organizations to blaze the trail so you can follow their lead. Or are you just maintaining the status quo? If so, you may wake up one day and wonder what happened to your business.

At this point, I can’t say enough about the unbelievable effort and collaboration of the federal, state, and local authorities in managing the preparation for Hurricanes Harvey and Irma and their aftermath. They are getting better with each hurricane. Texas and Florida were the latest benefactors. The aid from other states sending in personnel to get power back and debris cleared enables people to get back to their homes to assess the damage and begin the task of getting back to normal.

“The number of people killed in hurricanes halves about every 25 years, in spite of the fact that coastal populations have been increasing, because of what we’re doing with forecasting,” said Hugh Willoughby, a professor of meteorology at Florida International University in Miami. The modern science of hurricane monitoring and preparation, which has saved countless lives through forecasting, satellite monitoring, and government planning, has dramatically improved in recent decades.

The coverage from the major media stations was extraordinary and allowed the evacuees to monitor the storm from afar. The use of social media like Facebook, Twitter, and Instagram let everyone stay in touch with families and friends.

With estimates of 70 deaths and $180 billion in damage from Harvey, 68 deaths and billions of damage from Irma, two thirds of 21 million Florida residents without power, the road back to recovery will be challenging, but manageable.

The focus on data, the absolute necessity to be proactive, and the need to work collaboratively with customers, partners, and vendors should be top of mind for every mortgage lender today. Integrated technology is a necessity. Think differently.

I will leave you with one final thought. It might be time to go to the moon, retrieve the golf balls, and return the rocks. We have upset the whole balance of nature.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

Augmented Intelligence

One of the first introductions of artificial intelligence to the general population came in 2011 when Watson competed on Jeopardy. Ken Jennings and Brad Rutter were arguably the best players the show had produced over its decades-long lifetime. In total, they had walked away with more than $5 million in prize winnings, a testament not only to the breadth and depth of their knowledge, but their strategic savvy with category selection and wagering. Watson, a computer system developed by IBM, was capable of answering questions posed in natural language. Watson had access to 200 million pages of structured and unstructured content, but was not connected to the Internet. Watson consistently outperformed its human opponents on the game’s signaling device, but had trouble in a few categories, notably those having short clues containing only a few words. The key here was the development of a natural language processor that would become the foundation for numerous future applications like Siri. But while its rapid responses to questions may have struck many as robotic, Watson was not a robot in the traditional sense. Robots are machine built to carry out physical actions and may or may not be designed to approximate the human form. I am sure many of you remember the TV series, ‘The Jetsons’ with Rosie, the humanoid robot maid and housekeeper. Or maybe not, because that series was on in the early 1960s.

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‘In Search of a Robot More Like Us’ was a 2011 New Your Times science article written by John Markoff. He stated that:

The robotics pioneer Rodney Brooks often begins speeches by reaching into his pocket, fiddling with some loose change, finding a quarter, pulling it out and twirling it in his fingers. The task requires hardly any thought.” But as Dr. Brooks points out, training a robot to do it is a vastly harder problem for artificial intelligence researchers than IBM’s celebrated victory on Jeopardy…. Although robots have made great strides in manufacturing, where tasks are repetitive, they are still no match for humans, who can grasp things and move about effortlessly in the physical world. Designing a robot to mimic the basic capabilities of motion and perception would be revolutionary, researchers say, with applications stretching from care for the elderly to returning overseas manufacturing operations to the United States.

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So, let’s leave the discussion about robots for another time. Instead, I’ll focus on defining augmented intelligence and differentiating it from artificial intelligence. It’s more than a question of semantics. Artificial intelligence, perhaps from its popular culture use in general and its science fiction use in particular, can conjure up images of the sentient machines with personal agendas. It suggests a culture where, at least in some part, humans are no longer required to make decisions. Some industry experts believe that the term artificial intelligence can create more negative speculation about the future than hope. defines augmented intelligence as an alternative conceptualization of artificial intelligence that focuses on AI’s assistive role, emphasizing the fact that it is designed to enhance human intelligence rather than replace it. An alternative label for artificial intelligence also reflects the current state of technology and research more accurately.

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According to an article by Athar Afzal, “We’ve transitioned from an agricultural-dominated society to the industrial revolution – and now to a more data-driven economy. What we’ve witnessed during each of these stages is some form of mechanics or machinery developed to augment our performance, thereby improving our outcome…. The world has a lot of opportunity to gain and make our lives better with augmented intelligence – it’ll make our lives far smoother and more enjoyable. I invite everyone to view Ginni Rometty’s speech at the World Economic Forum.”

Researchers and marketers hope the term augmented intelligence, which has a more neutral connotation, will help people understand that AI will simply improve products and services, not supplant the people who use them.

While a sophisticated AI program is certainly capable of making a decision after analyzing patterns in large data sets, that decision is only as good as the data that human beings gave the programming to use. The choice of the word augmented, which means “to improve,” reinforces the role human intelligence plays when using machine learning and deep learning algorithms to discover relationships and solve problems. I’ve summarized some definitions by Jean-Albert Eude below.

Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range. The processes involved in machine learning are similar to that of data mining and predictive modeling. Both require searching through data to look for patterns and adjusting program actions accordingly.

Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. Each algorithm in the hierarchy applies a non-linear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label “deep.” The advantage of deep learning is that the program builds the feature set by itself without supervision. This is not only faster, it is usually more accurate. In order to achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing.

The value of such augmented predictive analytics to a segment of the economy as dependent on data as the mortgage industry is obvious. What is also obvious, unfortunately, is that we may be among the last to seat ourselves at the technology table.

Often, an early title or tag line for a concept or theory evolves over time as others develop their ideas and work toward a solution. In the mortgage industry, the concept of paperless mortgages was proposed in the early 1990s to reduce and/or eliminate what some conceived as unnecessary paper and to improve the overall experience for the consumer. Along the way we started referring to it as an electronic mortgage (e-mortgage) and now it is the digital mortgage, an all-inclusive data and documents packaged in a format for both human and machine consumption. That will certainly achieve the initial objective to eliminate paper and improve the consumer experience. The operational benefits extend from origination all the way through to the secondary market.

But going digital without building the internal architectures to capitalize on data-driven support technology is like going to a 3D movie, but not putting on the 3D glasses to watch it. If we don’t keep moving our own finish line, we risk being trampled by those with a longer view of the race.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

An Interesting Look At The Future

If it is true that the only constant thing in life is change, then the twenty-first century is proving to be a predictably constant time in which to live and make a living. Our industrial revolutions have always been about dramatic changes in the scope and scale of the technology platform supporting societies: water, steam, electricity, electronics, and information technology—all have transformed our standard of living even as they have irrevocably altered the business landscape. And if we look at the timeline of human civilization, we can see that those revolutions are all grouped in the more recent past, with the time between sea changes becoming shorter as we get closer to the present.

And why not? There are mathematical laws about exponential growth that govern our understanding of everything from how an avalanche will cascade down a mountain to how a virus will spread through an unvaccinated population. Change won’t just keep coming; it will keep happening more quickly. Some are even referring to the new industrial revolution as the Exponential Age because of the exponentially accelerating technologies that have the potential to disrupt industries that seem isolated and protected from the trends affecting the more obvious “technology” sectors.

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An interesting look at the future: Singularity University is a Silicon Valley think tank founded in 2008 at the NASA Research Park in California and is supported by NASA and Google. Udo Gollub, a German writer and entrepreneur, documented his thoughts after a Singularity University Summit. Some of his thoughts are included below to provoke thought and discussion within your organization.

Software will disrupt most traditional industries in the next 5-10 years. Every organization, both big and small, in every conceivable line of business face a very daunting task of deciding how much time and effort should be spent on their current marketplace and product line. My previous articles were focused on looking at new opportunities. You need to ask yourself if the business space in which you want to operate will exist in some form in the future. If you think that it will, what can you do to future-proof it from a fate like Kodak’s?

In 1888, George Eastman founded Kodak. In 1998, Kodak had 170,000 employees and sold 85% of all photo paper worldwide. Within just a few years, their business model disappeared and they went bankrupt. What happened to Kodak will happen in a lot of industries in the next 10 years – and most people don’t see it coming. Did you think in 1998 that 3 years later you would never take pictures on paper film again? Yet digital cameras were invented in 1975. The first ones only had 10,000 pixels, but the technology followed Moore’s Law. So as with all exponential technologies, it was a disappointment for a long time, before the technology advanced enough to gain mainstream acceptance in only a few short years.

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Moore’s law is directly related to computing. It is an observation made by Gordon Moore that the number of transistors in a densely integrated circuit doubles approximately every two years. More loosely, Moore’s law here refers to the exponential growth of technology.

What happened to Kodak will happen in a lot of industries: product and service leaders will be blindsided by revolutions that have been hiding in plain sight for some time, but are only now reaching critical mass. Let’s look at some examples and thoughts offered by Gollub.

>>Uber is just a software tool, they don’t own any cars, and are now the biggest taxi company in the world.

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>>Airbnb is now the biggest hotel company in the world, although they don’t own any properties.

>>Bitcoin will become main stream this year and might possibly become the default reserve currency in the future.

>>In 2018 the first self-driving cars will appear for the public. Around 2020, the complete industry will start to be disrupted.

1.) You don’t want to own a car anymore. You will call a car with your phone, it will show up at your location and drive you to your destination. You will not need to park it, you only pay for the driven distance and can be productive while driving. Our kids will never get a driver’s license and will never own a car.

2.) It will change the cities, because we will need 90-95% less cars for that. We can transform former parking space into parks.

3.) Most car companies might become bankrupt. Traditional car companies try the evolutionary approach and just build a better car, while tech companies (Tesla, Apple, Google) will do the revolutionary approach and build a computer on wheels.

4.) 1.2 million people die each year in car accidents worldwide. Insurance companies will have massive trouble because without accidents, the insurance will become 100x cheaper. Their car insurance business model will disappear.

5.) Real estate will change. Because if you can work while you commute, people will move further away to live in a more beautiful neighborhood.

>>The price of the cheapest 3D printer came down from $ 18,000 to $ 400 within 10 years. In the same time, it became 100 times faster.

>>Electricity will become incredibly cheap and clean: Last year, more solar energy was installed worldwide than fossil. Solar production has been on an exponential curve for 30 years, but you can only now see the impact.

1.) This represents a smooth doubling every two years of the amount of solar energy we’re creating, particularly as we’re now applying nanotechnology, a form of information technology, to solar panels.

2.) The price for solar will drop so much that all coal companies may be obsolete by 2025.

>>A generation ago students at MIT all shared one computer that took up a whole building.

1.) The computer in your cellphone today is a million times cheaper, a million times smaller, and a thousand times more powerful.

2.) That’s a billion-fold increase in capability per dollar that we’ve experienced. And we’re going to do it again in the next 25 years.

Computers will become exponentially better in understanding the world. Let’s take a moment to compare linear steps with exponential steps. When we take 10 linear steps (1, 2, 3, etc.), we get to 10. If we take 10 exponential steps (2, 4, 8, etc.), we get to 1024. The difference between the two rates of growth becomes staggering in a relatively short period of time.

The exponential growth of computing predates Gordon Moore and applies to any technology with measurable information properties. People have asked about what happens after Moore’s Law comes to an end. The answer, as always: we will then go to the next paradigm.

In the 1950s, technology was shrinking vacuum tubes, making them smaller and smaller. They finally hit a wall; they couldn’t shrink the vacuum tube anymore and keep the vacuum. And that was the end of the shrinking of vacuum tubes, but it was not the end of the exponential growth of computing. We went to the fourth paradigm, transistors, and finally integrated circuits. When that comes to an end we’ll go to the sixth paradigm: three-dimensional, self-organizing, molecular circuits.

Our current generation of business leadership must be able to navigate these industry evolutions faster and more effectively than any time in the past if their organizations are to survive and thrive.

Next month, we will examine the impact of Artificial Intelligence on technology innovation.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

The Art Of Opportunity, Pt. 2


In my last article, we examined the business strategies described in The Art of Opportunity, by Marc Sniukas, Parker Lee and Matt Morasky.

Michael Porter’s classic book, The Competitive Advantage: Creating and Sustaining Superior Performance, described strategies for achieving competitive advantage by 1) becoming a cost leader, 2) differentiating your offering, or 3) focusing on a niche.

So how does The Art of Opportunity differ from these more traditional approaches championed by Porter? First of all, we see that there is a shift from focusing on achieving competitive advantage by simply being cheaper or different to finding and seizing opportunities by creating value.

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To be clear, the authors don’t suggest that the sort of traditional strategic management approaches described by Porter do not work. For some organizations and in certain industries, they work extremely well, if applied in the right way. And yet, a lot of companies nevertheless struggle when attempting to achieve their growth and innovation targets within these traditional frameworks. The following sections summarize some of the authors’ key points.

Where to look for new growth opportunity? This statement from Professor David Bell says it best, “The first principle of finding new growth is that you’re always better off going after customers who are underserved or neglected.” Why is that? The authors state, “Only by gaining a deep understanding of customers, their true needs and expectations, as well as their satisfaction or dissatisfaction with current offerings, will you gain the insights needed to develop solutions that customers really want to buy. Most companies don’t know why customers do or don’t do business with them in the first place.”

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Understand customer needs, expectations and choices: The basic idea is that customers do not buy products because they want to own the product, but because they have an objective they would like to fulfill with that product…. So, should you just ask existing customers what they want and need? Henry Ford is credited with saying, “If I had asked people what they wanted, they would have said faster horses.” Henry Ford’s customers might have wanted faster horses, but they probably also wanted something that was a bit more comfortable and convenient, needed less maintenance, and possibly was cheaper. Understanding why and when customers buy a certain product opens new ways of segmenting your market. Opportunities are a function of the chosen customer segment, its needs, and expectations toward the solution offering. Once you understand your customers’ needs and the experiences they have trying to fulfill those needs, you can investigate what stands in their way to having a satisfactory customer experience.

Understand your firm: Looking at your customers is an external search approach to uncovering growth opportunities. While looking at your company with an internal slant, the key is still to discover new opportunities for growth from existing and new customers. Once you understand why customers come to you, you will have a good sense of what you excel at doing. This is the underlying capabilities and competencies that make your company special. Think about the strengths, capabilities, and resources of your business that you could leverage to create new businesses. The book lists several questions for you to identify your valuable, rare and costly-to-imitate resources and how to organize to exploit those resources.

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Frame your growth opportunity: For starters, we are not focused on traditional forms of growth like the following:

  1. Selling more of the same: Market penetration occurs when a firm enters the market where its current products already exist or its services are provided, allowing the business to go head-to-head with incumbents in the market.
  2. Growth through mergers and acquisitions: Mergers and acquisitions ae often taken to increase the size of the firm. Some are to add capabilities and some to add product lines outside of their current core business to diversify.

Instead the authors see three types of growth:

  1. Evolutionary growth: This type of growth is closest to your core. You evolve by removing hurdles to satisfaction and barriers to consumption for you existing offerings. This could include making your products more consumer friendly or upgrading your services.
  2. Adjacent growth: This is expanding your offering to cover additional steps in the consumer experience or offering other similar products. It is closely related and complements your core. Adjacent growth bears a little more risk than evolutionary risk, as you are venturing into slightly new territory. Yet, as you are staying close to your core, nevertheless the risk is manageable.
  3. Breakthrough growth: This is the type of growth that goes well beyond the limits of your current business. This entails not only the development and launch of a completely new strategy to market an offering outside of your company’s existing business definition, but also the design of a new business model and/or revenue model as part of the new strategy. Breakthrough growth is obviously not only the most difficult, but also the riskiest type to achieve. Yet it also bears the highest rewards, if successful.

What type of growth are you aiming at? Each growth model is appropriate for specific situations. There is not one prescribed model type and, in fact, you may benefit from combining them in order to adapt your firm’s individual situation. Clarifying your objectives and the type of growth you are aiming at will enable you to focus your subsequent strategy efforts, provide your team with guidance, and avoid pursuing opportunities your company might not be comfortable with at present.

Now that you’ve identified your opportunity, how are you going to seize it? While traditional strategy would have you focus on products and services, strategic innovation means you will focus on the following:

  1. Offering: The mix of products, services and the customer experience.
  2. Business model: The way you operate and the activities to do business.
  3. Revenue model: Where the money will come from, how you set prices and how payment is done.

Although the three parts are presented in a sequential order, innovation can come from each of them. In practice, you are likely to cycle back and forth as each component informs the other. At the end of the day, you need to make sure all three parts are integrated and support each other.

The author’s research has shown that successful strategic innovators go through three phrases.

(1) The inception phase, within which an opportunity for new growth is discovered.

(2) The evolution phase, during which the offering business and revenue model are adapted.

(3) The diffusion phase, during which the focus of activities shifts from designing and crafting the strategy to scaling up the new business.

Summary: It isn’t possible to give adequate emphasis to the depth and breadth of the authors’ material here. There are many examples throughout the book of companies utilizing this process and methodology. The authors have skillfully employed visualizations, diagrams, and templates in support of their concepts. My articles have simply been an overview of this book and hopefully have piqued your interest to explore this further.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

The Art Of Opportunity


The 2016 Progress in Lending Innovation Award winners are presented in this issue. As was the case in the previous six years, this year’s honorees are a mixture of well-established companies and first-time entrants. However, what is consistent in the applications is the detailed responses to the application criteria: significance, originality, positive change, intangible ROI, and hard savings ROI. And the applications get better every year. The panel of judges, comprised of members of the Progress in Lending Executive team, rely heavily on those responses and the scores are weighted based on the category. It has truly been an honor over the years to recognize some outstanding innovative solutions for the financial industry.

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Over the years, I have probably written over 100 articles on the mortgage industry. I certainly don’t consider myself a journalist and sometimes find it very challenging to find something unique to write about that hasn’t been presented many times before. My goal is to simply provide the reader the opportunity to explore ideas that might make a difference in their everyday life. I have primarily focused on how technology can be leveraged. I want the reader to be creative and innovative, to think outside the box, and avoid the limitations of a thought process that beings with, ‘We have always done it this way’. I am an avid reader and many of my story ideas come from a multitude of articles and books that are not necessarily related to our industry. In keeping with the theme Innovation, the focus of this article is the book The Art of Opportunity, by Marc Sniukas, Parker Lee and Matt Morasky. This book lays out a roadmap and a collaborative process supported by visualizations, tools, and templates, as well as many real-world samples, to direct you in developing a business growth plan for your organization. Let’s start with some excerpts from the Foreword:





The difficulty lies not so much in developing new ideas — as in escaping from old ones. John Maynard Keynes

Our industry does not respect tradition, it only respects innovation. Satya Nadella, CEO, Microsoft

When many of today’s leaders joined the workforce, ”innovation” was synonymous with research and development or process deficiencies—the hallmarks of traditional competitive advantage. Little did any us know then, that in our lifetime an entire occupational discipline would emerge to keep companies ”innovative” or continuously inventive….

But it did. And for good reason. The relativity short span of time in which we’ve seen some of the titans of industry displaced by ”innovative” start-ups put the entire business world on notice. And the message is clear: merely maintaining your position is no longer sufficient. New growth, the kind associated with genuine innovation, that will bring value to your customers, your business, and even the world around you is the only way to ensure survival….

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The problem is, finding and capitalizing on new growth opportunities is hard—especially for established organizations that are often hampered by outdated mindsets, legacy business models, or large scale bureaucracies. Core competencies can morph into corporate rigidities if we’re not strategically alert and careful. Under these types of circumstances, the ability to think outside the box and create new growth initiatives is difficult. But with increased urgency comes the need to find a new path to growth—one that isn’t rocket science.

Over the years there have been numerous business books on how to improve your organization’s innovation, strategy and competitive advantage. So, what makes this book stand out? Mainly, it is because the authors focused on two major points: Strategic Innovation that differs from traditional approaches by directing our focus on finding and seizing opportunities by creating value and Business Design Thinking that is defined as a collection of principles to help understand, address and develop solutions to business problems.





Instead of simply addressing cost, pricing, and product/service differentiation with how to win, you focus on creating customer value by solving your customer’s needs better than anyone else.

Executives applying business design thinking to their way of working will develop capabilities and practices that differentiate them from their peers.

Traditional strategic management is fixated on where to play and how to win. You determine where to play in your industry and with a specific market/product offering. You determine how to win by setting your competitive advantage to focus on a niche and by being a cost leader.

Strategic Innovation redefines where to play as finding new growth opportunities. The emphasis is on the customer, their needs, expectations, and experiences rather than the industry or competitors. Instead of simply addressing cost, pricing, and product/service differentiation with how to win, you focus on creating customer value by solving your customer’s needs better than anyone else. You create value for your firm with further opportunities. Inserted between the two is how to play, where you design the business required to seize these opportunities. Let’s examine this further.

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Where to play: This is all about finding your new growth opportunities. Research has shown that organizations develop more successful and innovative offerings by starting with their customers. Opportunities are a function of the chosen customer segment, its needs, expectations toward the solution offering, and current barriers to consumption or hurdles to a satisfactory customer experience.

How to play: This is all about crafting strategy, which includes the mixture of products, services, and the customer experience with the manner in which you operate and the activities necessary to do business. This will define where the money will come from, how you set prices, and how payment is made.

How to win: This is all about creating value for the customer, your organization, and the ecosystem. Instead of competing on low cost and/or differentiation, the winners in today’s economies focus on creating value and benefits for multiple stakeholders.

Finally, the book illustrates how the process for strategy making and execution and for building the new growth businesses is neither entirely linear nor completely iterative. It provides examples of how companies go through an iterative process with phases that favor action over analysis and planning.

What is Business Design Thinking? If strategic innovation focuses on the content of your new growth strategy and the process of crafting that strategy, business design thinking focuses on the practices that enable your team to achieve success more effectively and efficiently. These are the five principles of business design thinking: 1) Keep a human-centered focus, 2) Think visually and tell stories, 3) Work and co-create collaboratively, 4) Evolve through active iteration, and 5) Maintain a holistic perspective.

The Art of Opportunity incorporates each of the five principles to represent how an organization can change its way of working. Executives applying business design thinking to their way of working will develop capabilities and practices that differentiate them from their peers.

I would encourage everyone to read this book. We will continue this exploration next month.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

The AI Era Is Here – Pt. 2


In the latest issue for Fortune, Erin Griffith examines the investment trends in AI (Artificial Intelligence) technology and poses the question: is AI an overhyped fad or a revolution? She writes, “There’s an easy way to tell when the hype around a technology trend has peaked. 1) Are the smartest venture capitalists complaining about valuations? 2) Are big tech companies snapping up start-ups so young they can barely be considered real businesses? 3) Are Fortune 500 executives talking about their [insert trend here] strategy? If the answer to any of these questions is yes, congratulations! You’ve identified a fad.”

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Of course, most revolutions look like fads in their early days—because they are. Distinguishing between those fads that will fade and those that will become the norm in the long term can be difficult, and as in all things, hindsight has a much higher success rate than foresight when it comes to identifying the winners and the losers. So what data might guide such an evaluation?

The research firm CB Insights recently reported that in 2016 there were 658 venture capital deals in the AI sector. In 2016, that amounted to $5 billion in startup funding deals, a significant increase from $589 million in 2012. International Data Corporation projects worldwide revenue from artificial intelligence and cognitive systems to be $47 billion in 2020, up from $8 billion in 2016.

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CB Insights selected 100 of the most promising artificial intelligence startups globally from a pool of 1,650 candidates based on factors like financing history, investor quality, and momentum. A look at the top 50 shows that AI is surging worldwide with 20% located outside the United States. They cover a wide range of market segments: core AI, FinTech, auto, health care, commerce, CRM, cyber-security, robotics, business intelligence, and text analysis and generation.

Interestingly, the fact that AI does not necessarily intersect with established business cases has not proven to be a hurdle to investment. “These are not businesses,” says John Somorjai, executive vice president of corporate development at Salesforce, which has acquired a handful of AI companies. “These [deals] are about technology and talent.”

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The development of artificial intelligence has inspired both fascination and dread.

In 1955, the term AI represented the concept of autonomous systems modeled on the structure of the human brain. At the same time, other researchers were tackling a different problem: finding patterns in what was then considered great volumes of data and making proper selections, or decisions, based on that data. In 1956 William Ross Ashby wrote in his Introduction to Cybernetics that “…what is commonly referred to as ‘intellectual power’ may be equivalent to ‘power of appropriate selection’.” This was not intended to as “artificial intelligence” in the way we typically understand it, and in fact was labeled as the inverse: IA, or Intelligence Augmentation. If this model sounds suspiciously familiar, it is because today’s AI systems are constructed on the IA paradigm. Our real-world applications, including language processing, machine learning, and human-computer interaction are based on IA—data pattern recognition and appropriate decision making—and as such, they augment our capacity to understand what is happening in the complex world around us. While the term “AI” became the label of choice for such technology, it is an ironic misnomer.

Let’s look at the “Why You Should Let Artificial Intelligence Creep Into Your Business” article in the March, 2017 issue of Inc magazine for some definitions:

How AI works: problem solving: Unlike traditional computing, which delivers precise solutions within defined parameters, AI, sometimes referred to as cognitive computing, teaches itself how to solve problems. “Instead of delivering specificity, AI-centric programming generates millions of solutions, evaluating each for efficacy and then choosing the most viable and optimal ones,” says Amir Husain, CEO and founder of SparkCognition.

What it does better: data diving: Manually finding your target customer, by searching and poring through income-level, interest-based, and geographical data, is labor-intensive and time-consuming. AI cuts to the chase. “For example, using a feed of three key pieces of information that the entrepreneur provides; a brief product description text, images and a price range; an AI system can zip through social media and other online outlets, looking for correlations between product and digital conversations,” says Husain. If you give it the green light, AI’s natural language processing technology then writes and sends a sales pitch, notes transmission times, and analyzes feedback. “You can almost hear an AI system going, Aha! I’ve cracked the code.” says Husain, adding that AI constantly optimizes itself by making slight changes to the message.

Where it works: practical apps: One key reason for AI’s upsurge is entrepreneurs’ free or inexpensive access to libraries such as IBM Watson, Goggle TensorFlow, and Microsoft Azure. These application programming interfaces (APIs) allow coders to build AI apps without starting from scratch. Husain expects to see a proliferation of AI-centric marketing, sales and other service startups focused on small and medium-size businesses.

Let’s look at some specific examples from the same article.

Call Centers: The biggest misconception about AI is that it’s robots with human faces sitting at remote desks. “AI is nothing more than an add-on technology, spice and flair, to an otherwise conventional system, such as a traditional travel-reservation site that, because of AI can now converse with a human,” says Bruce W. Porter, an AI researcher and computer science professor at the University of Texas, Austin. Porter emphasizes that future breakthroughs will not be 100 percent AI. “AI will likely provide a 10 percent product or service performance boost,” he says. That is, in fact, huge. Firms that fail to make the leap, he says, may fail to have customers.

Information Retrieval: Not all searches are as simple as typing a few keywords and having Google take over. Entrepreneurs often need more in-depth and complicated excavations for patent and trademark data, for example and that, in turn, involves an often-hefty legal budget to pay a highly-trained human to do. Porter foresees within five years many companies offering services to consumers who have no experience in AI or specific knowledge fields. They’ll be able to conduct their own AI based data retrieval. Count on industry disruption, he says, as this type of AI application will leapfrog current data-retrieval-service providers.

Contract Generation: Because it’s able to generate natural language, AI is an exceptional tool for helping entrepreneurs assemble contracts, as opposed to buying them off the shelf at, say LegalZoom. AI applications will converse with – by text and, ultimately, voice – and tease information out of humans that will become components of formal agreements, such as details about fee payments and product returns. Porter anticipates users will pay to access cloud-based AI computer systems to produce such documents. AI-centric startups, because they don’t require a human in the loop and won’t need to hire staffers, can offer their services at a very low cost, especially given an anticipated large volume of customers and business competition.

AI can displace humans, but it can’t replace them.

Leaders of every industry and institution are sprinting to become digital. Who will win? The answer is clear: It will be the companies and the products that make the best use of data. And the ones that make the best use of data will likely be the ones that use AI to gain efficiencies in data analysis and decision making.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at

The AI Age Is Here


Artificial intelligence has gained prominence recently due, in part, to big data, or the increase in speed, size, and variety of data that businesses are now collecting. Artificial intelligence, or AI, can perform data-related tasks with great efficiency, and it can identify patterns in the data that often eludes human analysis. As organizations strive to gain more insight from their data, it’s not surprising that the business world is looking to AI for a competitive edge.

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It’s not like this is the latest and greatest innovation! The term artificial intelligence—an umbrella concept that encompasses everything from robotic process automation to actual robotics—was coined in 1955 by John McCarthy, an American computer scientist, and it gained traction in the academic community at the Dartmouth Conference the next summer. As Daniel Crevier describes it in his book Ai: The Tumultuous History of the Search for Artificial Intelligence:

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In the summer of 1956, ten young scientists, some barely out of their doctoral studies, sat down to consider the astounding proposition that ”every aspect of learning or any other feature of intelligence can, in principle, be so precisely described that a machine can be made to simulate it.” Armed with their own enthusiasm, the excitement of the idea itself, and an infusion of government money, they predicted that the whole range of human intelligence would be programmable within their own lifetimes. Nearly half a century later, the field has grown exponentially – with mixed results.

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By the early years of the 1980s, a consensus was forming that expert systems were the future of artificial intelligence. An expert system is a computer system that mimics the decision-making skills of a person. It makes sense in theory: feed enough data to the system to create the proficiency of a human expert, and you can theoretically get human-like decisions from it. Unfortunately, such systems are prohibitively expensive to develop and have only proven to be useful in targeted scenarios. In many respects AI has demonstrated a wide scope, but shallow influence: it has touched countless disciplines, but its impact has been limited to the most simple form of call-and-response interactions.

Today’s AI research and development focuses on artificial neural networks: systems duplicating the interconnected process of the human nervous system. AI can combine the reasoning ability of the human mind with the processing power of computers, such as in Apple’s Siri personal assistant and Amazon’s Alexa. A recent article in the Wall Street Journal stated, “Spending on AI technology is expected to grow to $47 billion in 2020 from a projected $8 billion this year, according to market-research firm IDC.”

As a consequence, some business executives are working to become familiar with methods of managing the development of applications and the design of algorithms across multiple lines of business. Brian Uzzi, a professor at Northwestern University’s Kellogg School of Management, has co-developed three AI courses for M.B.A.s. In April 2017, Kellogg plans to introduce Human and Machine Learning, a 10-week elective course. The broader objective, according to Mr. Uzzi, isn’t to create a cadre of engineer-executives, but to introduce future corporate leaders to the idea of making decisions with the help of machines. Artificial intelligence is now on the syllabus at top-tier business schools.

A recent MIT Technology Review looked at a major report from Stanford University, coauthored by more than twenty leaders in the fields of AI, computer science, and robotics and concluded that AI looks certain to upend huge aspects of everyday life, from employment and education to transportation and entertainment. The analysis is significant because public alarm over the impact of AI threatens to shape public policy and corporate decisions.

The report predicts that automated trucks, flying vehicles, and personal robots will be commonplace by 2030, but it cautions that remaining technical obstacles will limit them to certain niches. It also warns that the social and ethical implications of advances in AI, such as the potential of unemployment in certain areas and likely erosions of privacy driven by new forms of surveillance, will need to be open to discussion and debate.

In December 10, 2016, Andrew Tonner published the 9 Artificial Intelligence Stats That Will Blow You Away.

1.) Voice assistant software is the #1 AI app today: Many of these voice-powered AIs still leave something to be desired in terms of accuracy, and it was surprising that voice assistants outnumbered big data in overall popularity with businesses.

2.) AI bots will power 85% of customer service interactions by 2020: Bye-bye, call centers and wait times. According to researcher Gartner, AI bots will power 85% of all customer service interactions by the year 2020.

3.) Digital assistants will “know you” by 2018: Also from Gartner, digital customer assistants will be able to “mimic human conversations, with both listening and speaking, a sense of history, in-the-moment context, timing and tone, and the ability to respond, add to and continue with a thought or purpose at multiple occasions and places over time.”

4.) Amazon, Alphabet, IBM, and Microsoft to host 60% of AI platforms: These 4 tech giants already have significant cloud computing businesses, a trend researcher IDC sees as likely to continue and by the start of the next decade, will control most of the market for AI software applications.

5.) Get excited for self-driving cars: According to a study from leading consultancy McKinsey, the impact of self-driving cars will be tremendous, saving an estimated 300,000 lives per decade by reducing fatal traffic accidents. This is expected to save $190 billion in annual critical care and triage costs.

6.) 20% of business content will come from AIs by 2018: In a potentially apocalyptic turn for members of the media reading (or writing) this, AI-powered software will write as much as 20% of business content in a mere two years’ time according to Gartner.

7.) AI drives a $14-33 trillion economic impact: In a research report to its investors, Bank of America argued that the rise of AI will lead to cost reduction and new forms of growth that could amount to $14-$33 trillion annually, in what it calls “creative disruption impact,” and that’s just the tip of the iceberg in some experts’ view.

8.) Robots will be smarter than humans by 2029? According to Alphabet director of engineering Ray Kurzweil, machines will be smarter than us by 2029. Kruzweil doesn’t necessarily see this as being a negative, though. Among many other “bold” predictions about our AI-laden futures, he believes people will start living forever around the year 2029 as well. Whether that’s the result of some Matrix-like scenario coming to fruition isn’t immediately clear, but obviously leading experts in the field believe major changes to our social fabric are only a little more than a decade away.

9.) Zero people actually know how big an impact AI will have: While it’s certainly easy to get wrapped up in the litany of predictions, it’s perhaps most useful to simply keep in mind that AI should have a major economic impact from which investors can undoubtedly benefit from today.

The one concrete takeaway is that AI will contribute to the rapidly shifting technology landscape for our industry. Organizations that want to get or stay ahead will be flexible adapters who are willing to evolve their operations to take advantage of AI-based tools that enhance the customer experience, streamline internal processes, and feed the business pipeline.

Summary: Artificial intelligence (AI) is all around us – we encounter it in our daily tasks, such as talk-to-text and photo tagging, and it is contributing to cutting-edge innovations such as precision medicine, injury prediction, and autonomous cars. AI is the next big revolution in computing and holds the promise to provide insights previously unavailable while also solving the world’s biggest challenges.

About The Author

Roger Gudobba

Roger Gudobba is passionate about the importance of quality data and its role in improving the mortgage process. He is an industry thought leader and chief executive officer at PROGRESS in Lending Association. Roger has over 30 years of mortgage experience and an active participant in the Mortgage Industry Standards Maintenance Organization (MISMO) for 17 years. He was a Mortgage Banking Technology All-Star in 2005. He was the recipient of Mortgage Technology Magazine’s Steve Fraser Visionary Award in 2004 and the Lasting Impact Award in 2008. Roger can be reached at