What Early Adopters Can Teach Us About AI

 Some good thoughts.  

What Early Adopters Can Teach Us About AI

Interview with Thomas H. Davenport   in APQC

ChatGPT burst upon the AI landscape in November 2022 with a media and market frenzy not seen since Steve Jobs introduced the iPhone in 2007. Over one million people immediately signed up to test if OpenAI’s large language learning algorithm could perform as well or better than a human at answering text-based research questions, writing a poem in the style of Shakespeare or songs like Bob Dylan, completing high school homework assignments (you can imagine the handwringing here), and much more. In many cases, the answer was yes.  

Only three months later, there have already been three major upgrades to the search and auto correct algorithm. ChatGPT4 gets the answers wrong and “hallucinates” (makes up stuff) less frequently. Competitors from Google and many others have joined the race to use AI to empower everything we do online and virtually every process we use inside organizations.

As we all know, the promise of AI won’t be actualized unless we develop a strategy, explore use cases, and operationalize the technology in our organizations. This takes organizational savvy and change management skills, which no algorithm can give you. 

Fortunately, we can learn from multifaceted experts like Tom Davenport and a decade of early adopters who have used AI to dramatically accelerate their businesses. In addition to dozens of books and articles on topics like knowledge management and analytics, Tom is co-author (with Nitin Mittal) of All in On AI: How Smart Companies Win Big with Artificial Intelligence. In this interview excerpt, Tom speaks with me about what it means to be “All in” on AI, shares some key lessons from early adopters, and provides insight into what AI will mean for the workplaces of the future.  

What does it mean to be “all in” on AI?

Organizations that are “all in” on AI are highly invested in a variety of different ways. One is that they have different types of AI spread throughout the company quite broadly. We’re talking about dozens of use cases at a minimum, but more commonly it’s hundreds or even thousands of use cases because AI is a narrow technology. It tends to support or automate tasks, not entire jobs—and certainly not entire processes. So for example, if you want to automate all customer service or all order management, you will need to assemble many different pieces of AI to have a high level of impact. 

Being all in means you’re not just using AI for optimization at the margins of your business but really changing something dramatically. In the book, we talk in terms of organizations that use AI to transform their strategy, business model, products, services, operations, or even customer behaviors—not just making small tweaks for operational improvement. 

You’re probably going to get into trouble unless you’re ethical about the ways in which you use AI. Organizations that are all-in also have a framework for ethical and trustworthy AI in place that includes guidelines, policy approaches, and governance structures. 

What led you to focus on early adopters of AI in your book?

I wrote a book called Competing on Analytics in 2007. My readers found it quite helpful to look at companies that were aggressive early adopters of analytics, and I thought that the same thinking probably applies with regard to AI. The people I was working with at Deloitte were starting to talk about the ways in which AI could help transform these big legacy companies, a lot of whom were their clients. I think the nice thing about those examples is that even if people can’t or don’t want to go all in, they respond to reading about what it’s like to be all in and what companies can accomplish if they’re really aggressive in their adoption of the technology.  ... ' 

State of AI in the Enterprise

Useful view from recent surveys Deloitte in 2017 and 2018 by Deloitte.

Irving Wladawsky-Berger reports on The State of AI in the Enterprise

A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy.  His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018.  Davenport was a co-author of both reports.

The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies.  The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries.  All of these respondents were early AI adopters compared with their counterparts in an average company, - 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.

Davenport started his talk by summarizing the key findings in the Deloitte surveys:

20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;

Many projects are in pilots but some are already in production;
Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;

Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
Further AI growth is inevitable.

Overall, the 2018 survey found that “Early adopters are ramping up their AI investments, launching more initiatives, and getting positive returns.”  Compared to executives in average companies, early adopters have been implementing key AI technologies at a growing rate, including machine and deep learning, natural language processing and computer vision.  63% of respondents had adopted machine learning, an increase of 5% over the 2017 survey and 50% were using deep learning.  62% had adopted natural language processing, compared to 53% in 2017, while 57% were using computer vision.  .... "

Catching Up with AI Development

Interesting view,  personally think the catch up will be provided eventually by more automated AI systems.   I also like the mentioned of the 'knowledge systems' of times past, ultimately a complete AI systems will need them to operate, and those systems will require maintenance and delivery details - FAD

Why Companies That Wait to Adopt AI May Never Catch Up   by Vikram Mahidhar, Thomas H. Davenport in HBR

While some companies — most large banks, Ford and GM, Pfizer, and virtually all tech firms — are aggressively adopting artificial intelligence, many are not. Instead they are waiting for the technology to mature and for expertise in AI to become more widely available. They are planning to be “fast followers” — a strategy that has worked with most information technologies.

We think this is a bad idea. It’s true that some technologies need further development, but some (like traditional machine learning) are quite mature and have been available in some form for decades. Even more recent technologies like deep learning are based on research that took place in the 1980s. New research is being conducted all the time, but the mathematical and statistical foundations of current AI are well established.

System Development Time

Beyond the technical maturity issue, there are several other problems with the idea that companies will be able to adopt quickly once technologies are more capable. First, there is the time required to develop AI systems. Such systems will probably add little value to your business if they are completely generic, so time is required to tailor and configure them to your business and the specific knowledge domain within it. If the AI you are adopting employs machine learning, you will have to round up a substantial amount of training data. If it manipulates language — as in natural language processing applications — it can be even more difficult to get systems up and running. There is a lot of taxonomy and local knowledge that needs to be incorporated into the AI system —similar to the old “knowledge engineering” activity for expert systems. AI of this type is not just a software coding problem; it is a knowledge coding problem. It takes time to discover, disambiguate, and deploy knowledge.

Particularly if your knowledge domain has not already been modeled by your vendor or consultant, it will typically require many months to architect. This is particularly true for complex knowledge domains. For example, Memorial Sloan Kettering Cancer Center has been working with IBM to use Watson to treat certain forms of cancer for over six years, and the system still isn’t ready for broad use despite availability of high-quality talent in cancer care and AI. There are several domains and business problems for which the requisite knowledge engineering is available. However, it still needs to be manipulated to a company’s specific business context. . .... "

Augmentarians

And on the same topic.  How much is it augmenting, diminishing, replacing jobs and how much is the replacement of component skills?

On AI and Jobs, We Are All Augmentarians Now:

Tom Davenport writes in Forbes:

For a couple of days this week, I attended the EmTech NEXT conference at MIT, which is organized by MIT Technology Review. The focus of the event was that fabled idea “The Future of Work,” and if you are on the side of the humans, the future seems pretty bright. Virtually every speaker (MIT folks, AI and robotics leaders) came out in favor of augmentation over automation. They say that AI and robots won’t take our jobs, but rather augment them by doing the things we humans don’t do so well..

I must say that I was a bit surprised that augmentation has become the consensus view among experts. That wasn’t the case three years ago, when Julia Kirby and I were writing the book that became Only Humans Need Apply. At that time, most of the bets were on automation eliminating lots of human jobs. The Oxford researchers Carl Benedikt Frey and Michael Osborne had just published their study on “The Future of Employment,” which predicted that “47% of total U.S. employment is at risk.” Martin Ford had published the book Rise of the Robots, which basically suggested that human workers were toast. The McKinsey Global institute did a similar analysis to the Oxford researchers, and concluded that the “number”—the percentage of automatable jobs —was 45%. This was the heyday of automation fearmongering, and it all received a lot of publicity  .... "

How to Outflank the Competition with Analytics

Brought to my attention:

How to Outflank the Competition with Analytics  from iiAnalytics Blog   By Thomas H. Davenport

CIOs can help drive business value by following the lead of high-performing companies that use advanced analytical techniques and data-driven insights to rise above their competitors.

Data analytics has helped rewrite the rules of business competition, creating a growing number of analytical competitors—businesses that have gained an advantage by competing not only on products or services but also on advanced analytical capabilities.

Many companies use analytics to drive decision-making and better understand their businesses, markets, and customers; analytical competitors go a step beyond by leveraging analytics extensively and systematically to help outthink and outmaneuver the competition. Many of today’s most advanced analytical competitors are Silicon Valley innovators that pioneered applications—such as search engine algorithms, network graphs, recommendation systems, and advertising algorithms—and have since moved on to analytics for employee performance, digital marketing attribution, venture capital investment decisions, and many others. .... " 

International Institute of Analytics

Brought back to my attention, the IIA, International Institute of Analytics

See also:

Bill Franks
Analytics & big data focused speaker, blogger, consultant, and author.   Chief Analytics Officer for The International Institute For Analytics (IIA), where he provides perspective on trends in the analytics and big data space and helps clients understand how IIA can support their efforts to improve analytic performance. In his role, he helps guide IIA’s global community of analytics practitioners in determining the best strategy and path forward for their particular analytics journey.  .... 

IIA is the authority on analytics maturity and best practices.

Founded in 2010 by Jack Phillips and Thomas H. Davenport, the International Institute for Analytics is an independent research firm that works with organizations to build strong and competitive analytics programs.

IIA offers unbiased advice in an industry dominated by hardware and software vendors, consultants and system integrators. With a vast network of analytics experts, academics and leaders at successful companies, we guide our clients as they build and grow successful analytics programs.

Since its inception, IIA has worked with more than 200 organizations, sharing the keys to analytics maturity so that our clients gain an edge in an economy increasingly driven by data. Through our in-depth research library, moderated phone calls, webinars and events, our clients get the guidance and expertise needed to compete on analytics and win. ... " 

Radiologist Case Study of AI and Professional Jobs

Useful thoughts about how professional expertise might or might not be replaced.  Augmented, but not replaced?  With good detail.    We examined similar professional applications in healthcare in previous AI efforts.

AI Will Change Radiology, but It Won’t Replace Radiologists
Thomas H. Davenport, Keith J. Dreyer, DO   in HBR.

Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings.

That said, deep learning is rapidly advancing, and it’s a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology. Radiological practice would certainly benefit from systems that can read and interpret multiple images quickly, because the number of images has increased much faster over the last decade than the number of radiologists. Hundreds of images can be taken for one patient’s disease or injury. Imaging and radiology are expensive, and any solution that could reduce human labor, lower costs, and improve diagnostic accuracy would benefit patients and physicians alike. ... "

What does this mean for radiologists? Some medical students have reportedly decided not to specialize in radiology because they fear the job will cease to exist. We’re confident, however, that the great majority of radiologists will continue to have jobs in the decades to come — jobs that will be altered and enhanced by AI. One of us (Keith) is a radiologist and artificial intelligence researcher, and the other (Thomas) has researched the impact of AI on jobs for several years. We see several reasons why radiologists won’t be disappearing from the labor force, which we describe below. We also believe that several of these factors will inhibit the large-scale automation of other jobs supposedly threatened by AI. ... "

Kroger and Embedded Machine Learning Analytics

Good to see how much work Kroger is doing in advanced analytics, congrats to my friends there, we worked with them in the innovation area, always following them for leading tech.  This is a leading direction, good to see Kroger leading the way in making the business needs known and investing in solutions.

84.51° Builds a Machine Learning Machine for Kroger
By Tom Davenport , Forbes Contributor

Opinions expressed by Forbes Contributors are their own.

Machine learning is a great way to extract maximum predictive or categorization value from a large volume of structured data. The idea (at least for “supervised learning,” by far the most common type in business) is to train a model on a one set of labeled data and then use the resulting models to make predictions or classifications on data where we don’t know the outcome. The approach works well in concept, but it can be labor-intensive to develop and deploy the models.

One company, however, is rapidly developing a “machine learning machine” that can build and deploy very large numbers of models with relatively little human intervention. You may have heard of dunnhumby, a UK-based analytics company that’s owned by the big retailer Tesco. dunnhumby had a US joint venture with Kroger named dunnhumbyUSA. In 2015 Kroger purchased dunnhumbyUSA and named it 84.51°. The name coincides with the location of its Cincinnati headquarters and is a tribute to the longitudinal analytics the company employs. But 84.51° also analyzes data like a finely-tuned machine.  .... "   

 (read the rest for details of the embedding)

How P&G and American Express Are Approaching AI

I am quoted in the Harvard Business Review about how P&G successfully used AI in the past to improve systems, including estimates of actual value.     This HBR article has just been reposted, and the complete article is for sale if you don't have a subscription .... Ask me for more about these efforts.  Much supporting information has also been posted here.   More details were also published in the Cognitive Systems Institute archives.

How P&G and American Express Are Approaching AI
By Thomas H. Davenport, Randy Bean

Published March 31, 2017

There is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel. That impression is widespread with cognitive technologies — which comprises a range of approaches in artificial intelligence (AI), machine learning, and deep learning. Some have argued for the creation of “chief cognitive officer” roles, and certainly many firms are rushing to hire experts with deep learning expertise. “New and different” is the ethos of the day. .... 

Two good examples of combining well-established practices with cognitive technology to achieve business success are American Express and Procter & Gamble. Both firms are actively undertaking cognitive technology initiatives.  Both are well into their second centuries; they wouldn’t still be here if they weren’t able to accommodate change well and introduce new technology effectively. We spoke with top executives at each of these firms about the rise of cognitive in their organizations. Ash Gupta is President of Global Credit Risk and Information Management at American Express, and Guy Peri is Chief Data Officer and Vice President of Information Technology at P&G. Both executives have longstanding track records of success at their respective organizations, having seen business and technology change come and go for 20 years or more.

How it will impact business, industry, and society.

Both organizations have a considerable history with artificial intelligence. Gupta at American Express reminded us of the Authorizer’s Assistant, which was one of the more successful rule-based expert systems of the late 1980s. As described in a popular Harvard Business Review article on that generation of technology, the system made recommendations to human authorizers whether to approve large purchase transactions by cardholders.

P&G also built and employed a number of rule-based expert systems. In addition to Peri, the current CDO, we also spoke with Franz Dill, a retired P&G IT manager who focused on AI during the 80s and 90s. He said that the most well-known expert system they developed was one that blended Folgers coffee (no longer a P&G brand). This system, Dill noted, saved P&G in excess of $20 million dollars a year in green coffee costs. The company also built an expert system that helped advertisers at P&G to use, modify, and reuse the company’s advertising assets.

Both American Express and P&G are companies that have explored artificial intelligence over the years, and while the technology may have changed, the established yet innovative approaches that these firms take to incorporating new technologies and capabilities continues to evolve. Their fundamentally sound innovation practices provide a foundation for evolution. The attributes of their respective approaches to cognitive technology include .... " 

Just How Smart are Smart Machines?

Very good piece from some of our communications with MT Sloan.  Read the whole thing.  May require some registration.   Again, very non technical, management  approach to the question.

Just How Smart Are Smart Machines? 
Thomas H. Davenport and Julia Kirby

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities.

Smart Machines
If popular culture is an accurate gauge of what’s on the public’s mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don’t expect to see machines displacing knowledge workers anytime soon — they expect computing technology to augment rather than replace the work of humans. In the face of a sprawling and fast-evolving set of opportunities, their challenge is figuring out what forms the augmentation should take. Given the kinds of work managers oversee, what cognitive technologies should they be applying now, monitoring closely, or helping to build? ... " 

P&G and Amex, Building AI Past and Present

Tom Davenport mentioned some of our past AI work at P&G in his recent HBR article.  Below an introductory excerpt, much more detail at the link.   While considerable improvements have been made in available technology since then, some of the same key challenges exist.

How can advanced technical methods (Logic-based Expert Reasoning back then,  Deep Learning neural nets now)  be integrated with business process and applied to provide intelligent, but adaptive reasoning to real business systems?  Then how can these systems be effectively tested, maintained and reapplied in new contexts.  How can decision makers and the consumer understand their implications and risks? Many of us are working on that now.

How P&G and American Express Are Approaching AI
Thomas H. Davenport and Randy Bean

" ... P&G also built and employed a number of rule-based expert systems. In addition to Peri, the current CDO, we also spoke with Franz Dill, a retired P&G IT manager who focused on AI during the 80s and 90s. He said that the most well-known expert system they developed was one that blended Folgers coffee (no longer a P&G brand). This system, Dill noted, saved P&G in excess of $20 million dollars a year in green coffee costs. The company also built an expert system that helped advertisers at P&G to use, modify, and reuse the company’s advertising assets.

Both American Express and P&G are companies that have explored artificial intelligence over the years, and while the technology may have changed, the established yet innovative approaches that these firms take to incorporating new technologies and capabilities continues to evolve. Their fundamentally sound innovation practices provide a foundation for evolution. ... " 

Tom Davenport Writings

Had a chat with author Tom Davenport, we have long been in touch in the Analytics and AI spaces.  Here is a link to his recent writing.

Tom Davenport on the Four Eras of Analytics

Tom Davenport gave this talk to our Columbia University Adjunct group for the School of Professional Studies, recording below.  He also mentions me and some of our work at P&G.  I verified the link works, but not sure  how long the link will be active.  He has insightful and non technical thoughts on how analytics and AI will work in real world conditions.  Worth understanding.

Talk :  http://columbia.adobeconnect.com/p3frrrujhp9/

" ... The talk is called "Four Eras of Analytics". He traces a bit of the history of how the field develops. The focus of his new book is the age of "machine learning". In particular, he focuses on the paradigm of augmentation and discusses how managers design work processes to capture the relative strengths of humans and machines. All very relevant to our program.  ... " 

Only Humans Need Apply

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines Hardcover – May 24, 2016 by Thomas H. Davenport (Author), Julia Kirby (Author).  They write:

An invigorating, thought-provoking, and positive look at the rise of automation that explores how professionals across industries can find sustainable careers in the near future.

Nearly half of all working Americans could risk losing their jobs because of technology. It’s not only blue-collar jobs at stake. Millions of educated knowledge workers—writers, paralegals, assistants, medical technicians—are threatened by accelerating advances in artificial intelligence.

The industrial revolution shifted workers from farms to factories. In the first era of automation, machines relieved humans of manually exhausting work. Today, Era Two of automation continues to wash across the entire services-based economy that has replaced jobs in agriculture and manufacturing. Era Three, and the rise of AI, is dawning. Smart computers are demonstrating they are capable of making better decisions than humans. Brilliant technologies can now decide, learn, predict, and even comprehend much faster and more accurately than the human brain, and their progress is accelerating. Where will this leave lawyers, nurses, teachers, and editors? ... " 

Nicely done, reminds me of some books at the end of the last AI era that turned out not to be very relevant.

Tom Davenport Presentation

Virtual Session With Tom Davenport

Sep 21, 2016 -  7:00 p.m. to 8:00 p.m.      Online

Tom Davenport will be the virtual presenter for the Columbia School of Professional Studies MS in Information and Knowledge Strategy and MS in Applied Analytics programs. He will share his recent research on the human implications of cognitive technologies, and discuss what they mean for contemporary knowledge work. He'll also explore their impact on analytics and knowledge professionals' jobs.

What new responsibilities and skill-sets will emerge in the corporate, non-profit and public sectors? Please join us for this lively and interactive virtual session, which kicks off our 2016-2017 community presentation series.

At the time of the event, join the AdobeConnect session with this link:
http://columbia.adobeconnect.com/seminar_1000.

Helping Humans Work Better with Smart Machines

 Useful thoughts, because we will have to deal with increasingly smarter machines.

How to Help Humans work Better with Smart Machines
by Thomas H Davenport,  

It’s pretty clear that smart machines—computers and robots that can digest information, make recommendations and decisions, and take informed actions—are going to be a significant factor in the workplace of the future. In some areas, like insurance underwriting, credit decisions, and financial trading, they’re already in wide use. In other areas such as medical diagnosis and treatment, document analysis in commercial litigation, and digital marketing, they’re taking hold rapidly.

But the prospect of a lonely intelligent machine in a lights-out office is not very likely. Smart machines take over individual tasks rather than entire jobs. We humans will be working alongside them for the foreseeable future. Leaders, then, need to prepare their employees and organizations to collaborate effectively with cognitive technologies. ... " 

Who Does the Analytics?

This came to light in a recent project.   ....  and no one was willing to consider both alternatives.

Human or Machine: The Most Important Question in Analytics   by Tom Davenport

Arguably the most important questions in analytics these days is, “Who (or what) is going to make the decision?” There are two fundamental answers: a human or a machine. How the question is answered has all sorts of implications for what kind of people will do the analysis, what kinds of tools will be used, the process for the analysis, and so forth. ... " 

Just How Smart are Smart Machines?

Very good piece from some of our communications with MT Sloan.  Read the whole thing.  May require some registration.   Again, very non technical, management  approach to the question.

Just How Smart Are Smart Machines? 
Thomas H. Davenport and Julia Kirby

The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities.

Smart Machines
If popular culture is an accurate gauge of what’s on the public’s mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don’t expect to see machines displacing knowledge workers anytime soon — they expect computing technology to augment rather than replace the work of humans. In the face of a sprawling and fast-evolving set of opportunities, their challenge is figuring out what forms the augmentation should take. Given the kinds of work managers oversee, what cognitive technologies should they be applying now, monitoring closely, or helping to build? ... " 

Beyond Automation

Talk today by Tom Davenport:  In particular how can we look at the current influence of cognitive/AI in the workplace? ... “Beyond Automation: Smart Machines + Smart People”, where he talks about some of the material in his forthcoming book,  which I will further review here.   See also his HBR article " Beyond Automation".  

Slides here.    Talk recording here.

Tom Davenport: Beyond Automation CSI Talk

" ... Cognitive Systems Institute Group Speaker Series call this week, on Thursday, February 11, 2016 at 10:30 am ET (7:30 am PT).  Our presenter on Thursday will be Tom Davenport, Distinguished Professor at Babson College and author, will be presenting:  “Beyond Automation: Smart Machines + Smart People.”     I hope you will join the call.

Please point your web browser to https://apps.na.collabserv.com/meetings/join?id=2894-8491password=cognitive.   Use audio on computer or dial 855-233-7153 in the US (other countries numbershere) PIN Code: 43179788   Non-IBMers, please use the "guest" option rather than entering your email on the opening page.   ....  

We encourage those who join the calls to add questions and comments to the https://www.linkedin.com/groups/Cognitive-Systems-Institute-6729452 on LinkedIn and we ask that you ask questions at the end of the call. ....  "