Keynote: AI Transformation – Competing in the Age of AI #KMWorld


Speaker: Marco Iansiti, Professor of Business Administration & Coauthor, Competing in the Age of AI , Harvard Business School

Session Description: Join Marco Iansiti as he shares insights on the revolutionary impact AI has on operations, strategy, and competition beginning with a look at the core of the new firm, a decision factory he calls the AI factory. All the more relevant in the age of COVID where we have seen digital transformation move at an accelerated pace, the AI factory is where analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide and automate operational workflows. As digital networks and algorithms are woven into the fabric of firms, industries begin to function differently and the lines between them blur. The changes extend well beyond born-digital firms, as more-traditional organizations, confronted by new rivals, move toward AI-based models too,” says our speaker. With insights from the co-author’s revised preface, gather ideas to meet the challenges of a new reset world and find the correct strategies to harness AI for your organization.

[These are my notes from the KMWorld Connect 2020 Conference. Since I’m publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]

Rembrandt and a developer walk into a bar…

AI creates the next Rembrandt

The developers on this project created a 3D-printed canvas that looked a lot like a Rembrandt original. The response was mixed: some people were delighted by the potential of AI to enrich the arts but others thought it was a travesty. Jonathan Jones, one of the leading experts on Rembrandt’s art, described the digital Rembrandt as “a new way to mock art, made by fools.”

Regardless of what you think about this particular work, Iansiti points out that with the help of AI, you no longer have to be a genius to produce something that could possibly pass as a Rembrandt painting.

That is the power of AI.

AI Basics

  • Definitions:
    • Weak AI: “Any activity computers are able to perform that humans once performed”
    • Strong AI: “Machines that can think or act in a way that matches or surpasses human intelligence”
  • Relatively simple AI, coupled with effective algorithms and good data, can do remarkable things.
  • You don’t always need strong AI to make a meaningful difference.
  • Weak AI can improve a wide range of operations across your organization and across the economy. Some examples:
    • Customer intelligence and recommendations
    • Market intelligence and forecasting
    • Diagnosis and treatment systems
    • Fraud analysis and investigation
    • Business process automation and internal bots
    • Predictive maintenance and resource optimization
    • IT automation
    • Adaptive learning
    • Research and discovery
    • Intelligent search
  • As you move labor and management off the critical path of these key operations you change the basic nature of the firm. AI transforms the firm.

Rethinking the firm

  • To take advantage of the real possibilities of AI, we have to think about the firm differently.
  • It no longer makes sense to have a traditional, siloed structure. Rather, it makes more sense to build firms with a “softer core” based on a platform of data analytics, coupled with people creating algorithms and enabling smarter automated processes as quickly as possible.
  • To rethink the firm, you have to look at both its business model (i.e., value creation and value capture) and its operating model (i.e., how they deliver value via scale, scope, and learning).
  • AI-savvy firms are able to offer a broader and richer array of products and services than traditional organizations, and they do it with far fewer people.

Ant Financial Case Study

  • Iansiti says that Ant Financial has about one-tenth the number of employees of Bank of America.
  • The core of Ant Financial is a data lake exploited by a huge range of algorithms. They use their vast data to identify consumer preferences and then create products and services to meet those needs. Then they can scale and personalize these very quickly.
  • At Ant Financial, traditional human-centered “processes are digitized to connect with market opportunities at near zero marginal cost. Operational bottlenecks are digitized.”
  • Firms like Ant Financial use AI to drive digital scale, scope, and learning.
    • As the data accumulate, they drive even faster experimentation, improvement, innovation, and personalization.
    • This enables an even greater number of profitable products and services.

The AI Factory

  • The core of a company like Ant Financial is an AI Factory.
  • The AI Factory feeds data and models systematically into the software-enabled operating layer of the firm.
  • This requires more than a collection of Excel spreadsheets supporting traditional human analytics.
  • You have to “industrialize” the process of data gathering, cleaning, normalizing, integrating, and use.
  • This then feeds the Operating Model Core: data, software components, APIs, and applications.
  • There are humans involved with designing, monitoring, and managing operations but they are not on the critical path. They do their work from the perimeter of the operating core.

The Economics of the AI-Enabled Firms

  • AI-enabled firm create relatively little value until they reach scale. They create more value as they get bigger.
  • By contrast, traditional siloed firms tend to create less value as they get bigger because it is hard to manage large human organizations. They become bogged down by silos, red-tape, complexity, administrative overhead, and other operating inefficiencies of size.
  • This means that AI-enabled firms have the potential for unlimited value creation while traditional firms face diminishing returns.

From Disruption to Collision

  • As more AI-enabled firms emerge, they are colliding with traditional players in their industries. They are fighting for the same customers but their operating models are completely different:
    • Ant Financial vs HSBC, AirBnB vs Hilton, Waymo or Uber vs Ford, Moderna vs Merck.
  • As these collisions occur, they fundamentally change their industry and force traditional firms into digital transformation.

Digital Transformation Creates New Responsibilities

  • With the expanded use of AI comes new ethical concerns
    • Increased data collection triggers
      • cyber security issues
      • Privacy issues
    • New focus on algorithms — how they are created and their unintended consequences:
      • Increased issues of inclusiveness and inequality
      • Algorithmic bias

Thanks to Covid-19, this Change is Accelerating

  • Digital Transformation is no longer an option — the coronavirus pandemic has forced even the most hidebound organization to become digital and distributed.
  • After the pandemic, some companies will see the benefits of the digital virtuous cycle and will build on their learnings and technological gains. Others will jump at the opportunity to “return” to their pre-pandemic status quo.

AI-Enabled Responses to the Pandemic

  • We are the midst of a period of huge uncertainty in science, logistics, and policy. This requires enormous agility.
  • In a period of uncertainty, anyone with a successful model is celebrated.
  • Moderna
    • It was built on a powerful software and data platform. It has one system of record, an enormous data lake that combines all their data ranging from research, to clinical tests, to company financials.
    • It uses a very innovative approach in a very traditional industry
    • Their vaccine was created in 25 days — this is extraordinarily fast
  • Massachusetts General Hospital
    • It is on the other end of the spectrum from Moderna.
    • They had an old-fashioned ERP. [NOTE: members of the audience disputed this point. They say that MGH spent $1.2B (by public admission) on Epic. They don’t believe Epic should be considered a legacy ERP.]
    • They went through years of effort to achieve any digital transformation — and then they stood up telemedicine in a matter of weeks.
  • IKEA.
    • Ikea has an innovative but fairly traditional approach to retail
    • When the pandemic began, they shut down all their stores for three days so they could implement a whole host of planned digital transformation projects.
    • They transformed their web presence and made real progress on digitizing their supply chain.
    • The key to their success was that they were far along their planning process so they primarily faced an implementation challenge. They did not have a standing start.

Amazon Case Study

  • Amazon started a process of digital transformation in the early 2000s after they realized they were almost dying under the weight of complexity.
  • Prior to 2002, Amazon was built like a traditional company. By 2002, they were essentially coming apart at the seams.
  • So Jeff Bezos issued an API mandate that fundamentally changed their course.
  • In response, they re-architected their operating model using service-oriented architecture (SOA), which is a way to make software components reusable via service interfaces. This enabled Amazon to become a Platform.
  • The rest is history.

Microsoft Case Study

  • They are transforming from a traditional approach, where the organization is siloed and then develops its IT within those silos, to an AI-first approach — where the entire organization operates from a shared foundation of a single AI factory.
    • Microsoft now operates from a single data lake.
  • Once the data is consolidated, they then deploy agile teams to build processes across the organization in response to needs.
  • Satya Nadella: There is too much promise in AI to trap it in the IT department. The entire organizational now must be involved.

Call to Action

  • Understand and actively anticipate the transformation of our economic and social environment.
  • Drive business and operating model transformation consistently and from the top down.
  • Invest heavily in data pipeline and architecture: encourage strategies grounded on experimentation, analytics, and digitization
    • Don’t do it on spreadsheets — invest in the technology
  • Use pilots to build internal capabilities. Make sure you have a plan and process to implement and scale.
  • Build inclusion, privacy, transparency, and security from the ground up. Focus on the ethical implications — digital transformation is creating new ethical obligations for organizations and their leaders.
  • Help the growing part of the population facing greater need.

Advice for those beginning the digital transformation journey

Congratulations! The good news is that you have the advantage of the latest technology. From a technology perspective, there is no longer any excuse for not being in the cloud. The bad news is that you will have a LOT of work to do to transform your organizational culture and processes.

Your organization MUST change.

The last few months have been mainly about reacting to changes in the environment. Now, we can take a step back and re-imagine a better, more thoughtful, planned approach for our organizations.

The research has shown that the deployment of AI and KM tools will affect the full-range of jobs. Everyone becomes a knowledge worker to the extent they embrace the new technology and approaches. This makes it more critical, as a policy matter, to make sure the workforce is up to the task. The workforce needs to be able to respond quickly to changes. What does the workforce look like when everyone is able to use these new tools?

We are at an inflection point. The pandemic is accelerating enormous changes. Within 5-10 years, every organization will be run differently. Those who invest in it will do just fine. Those who do not will be left behind. It’s time to really do this!


Keynote: Responsible AI – Ethics and Inclusive Design #KMWorld


Speakers: Jean-Claude Monney, Board of Directors Member, Keeeb; Phaedra Boinodiris, IBM Academy of Technology, Executive Consultant, Trustworthy and Responsible Systems; and Steve Sweetman, Customer & Strategy Lead, Ethics & Society Engineering, Microsoft

Session Description: Join our exploration into the future of AI and other emerging tech as it transforms the knowledge sharing, collaboration and innovation in our organizations. Responsible AI, ethics and knowledge management definitely intersect and are routed in culture change and business transformation. Our experts share a lively discussion with the audience and will leave you thinking about what’s next for AI, KM, and our world in 2021 and beyond!

[These are my notes from the KMWorld Connect 2020 Conference. Since I’m publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]


  • Why are these speakers here today?
    • Phaedra has been interested for a long time in both technology and social justice. Her new role at IBM is to work in the trust and AI practice. She is focused on how to reduce bias and increase trust in tech systems.
    • Steve had an aha moment on March 23, 2016 when Microsoft’s friendly chat bot had been poisoned by hackers and turned into a racist and vicious bot. This taught him that ethics were no longer academic. They needed to make ethics real in their tools so that they can build responsible AI.
    • JCM taught students in the Columbia M.S. in Information & Knowledge Strategy about ethics in connection with digital transformation. The students quickly realized how critical this issue is.


  • KM’s basic concept is to provide relevant information for reuse. When this is enabled by AI, where is the bias in the system? (See below!)
  • For the last 20 years, we’ve been teaching people how to enter data into computers and then work with that data. With the advent of AI, we are teaching computers how to consume data and work with it. But the great dilemma of AI is that we don’t understand how the system reaches a specific conclusion. So how do we trust it?
  • 4 questions to ask before purchasing an AI system
    • What are the intended uses of the system that you’ve built it for and trained it for?
    • What are the unintended uses that you haven’t built it for and trained it for?
    • What makes it perform? What makes perform well?
    • What are its limitations?
  • Other questions you should also be asking:
    • Is it fair? Is it biased?
    • Is it easy to understand and explain to non-technical stakeholders, users or administrators?
    • Is it tamper-proof?
    • Is it accountable? Does it have acceptable governance standards?
  • How can organizations mitigate bias?
    • There are a lot of tools. For example, IBM has donated Fairness 360 to the Linux Foundation.
    • Culture is a big issue. How are teams made up? Consider employing red team vs green team tactics (borrowed from the cybersecurity world).
    • Governance: make sure you have published standards that explain your company standards to the market and your employees. Do you have a diverse, inclusive AI ethics board? Do employees have a way to submit anonymously their ethics concerns?
  • Education is a big challenge
    • Why are we not teaching AI and ethics in high school and even middle school?
    • Current leaders in organizations and government do not seem to understand AI. So they cannot understand the true impact of this technology. All leaders should be at least fluent in AI because it will affect every part of their organizations.


  • Recommended reading: Brad Smith, Tools and Weapons
  • If is not a question of IF we have bias in our systems. It is a fact that we DO have bias in our systems.
    • bias comes from the lack of diversity among developers and executives
  • Do NOT attempt to determine AI ethics this alone. It is not something for data scientists to do by themselves. You must involve different stakeholders who bring different points of view to the discussion.
  • The diversity prediction theorem = the more diverse and inclusive the crowd, the closer you get to ground truth.
  • Warning signal: major lack of diversity leads to diminished fairness in AI systems
  • Forensic technology = the tools you can use to create responsible systems. They help address fairness, explainability, transparency
  • How to find bias in your AI systems?
    • Ask if your models would keep you from offering the same service to people? Do you discriminate on a false basis?
    • Do you have fair representations of the services you are recommending? Do different people get the same outcomes?
    • Are we stereotyping? Are we using labels, for example, that reflect inherent bias?
  • How to mitigate?
    • Correct the existing data
    • Collect more representative data
    • Look at all the models across your systems — work to improve all of them and track your progress
  • How to address bias in AI used for hiring and promoting?
    • It is rare to find a bias-free system. Be hyper aware of hidden bias. There are many types of bias beyond race and gender.
    • Pay attention to the training data set. There may be bias in that set — for example, if successful people in a specific job were historically white and male, then the historic data used to train the AI will be biased in favor of white males.
  • Microsoft has a sensitive use protocol. Not all AI systems have the same impact. When AI systems could have a disproportionate impact on peoples lives, then you need to slow down development to ensure they are fair, safe, and trustworthy. Examples of high impact systems:
    • hiring, lending, admission to school
    • system misfires could result in injury to someone
    • system could diminish a person’s human rights)


  • Microsoft
    • You need to create internal standards that you will live by: put ethics and fairness at the same level as security and innovation.
    • Ensure diversity of teams at every level from ideation to design to development to market delivery sysems
  • At IBM
    • Culture — big focus on diversity and inclusivity; advocacy for ethics in technology
    • Forensic Technology — donating tools to the open source community to tackle fairness, explainability, transparency
    • Governance — shaping global standards on technology governance



Keynote: Revolutionizing KM with AI & Document Understanding #KMWorld


Speaker: Paul Nelson, Innovation Lead, Search & Content Analytics, Accenture

Session Description: Intelligent document understanding is drastically changing the search and knowledge management landscape thanks to AI technologies. As 80% of all enterprise data is unstructured, document understanding delivers tangible benefits across industries and business functions saving time, money and resources. Paul highlights how one of the world’s leading pharmaceutical companies leveraged this solution, along with innovative AI technologies and assets, to automate KM for the purpose of detecting sensitive IP within unstructured enterprise content. He shares how this solution is helping other clients, including Accenture, to improve compliance and risk management, increase operational efficiencies, and enhance business processes.

[These are my notes from the KMWorld Connect 2020 Conference. Since I’m publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]


  • He will focus on the three practical KM scenarios to make abstract AI real
  • Scenario #1: Customer Support — we started with individual customer support representatives who use their knowledge to respond to customer request, then we documented the usual queries and responses (FAQs), then we added some search for efficiency. But as the system scaled, we needed a document management system to organize all then content. Then, we superimposed AI to monitor data streams, summarize the patterns, and determine what topics should be addressed by technical writers and included in the document management system. But, with the advent of a new neural network (GPT-3), which can recognize new tokens, now we can replace a technical writer by a robot writer to create a fully automated customer support system.
  • Scenario #2: Research Reports — initially we relied on individual researchers to run tests and produce research reports. But sometimes, this resulted in duplication. So we interposed a gatekeeper who received research requests, checked to see if the work has already been done, and then either distributed the earlier report or commissioned a new report. Now, a robot can replace the gatekeeper. It can understand the incoming request, search the database, understand the content of the research reports, and send the appropriate responsive report — even if it contains words that are different from those in the initial request.
  • Scenario #3: Proposal Writing — how do you respond to RFPs or RFIs? No one person knows enough to respond to the entire proposal request by themselves. Currently, various experts complete the section of the proposal related to their expertise. To increase efficiency, we have tried to create templates and snippets that can be used as necessary. Now, the proposal writing is happening in collaborative sites. And key sections are tagged with relevant data. Going further, you can slice and dice the sections into Semantic pieces that can be sent into a semantic search of a neural network to find relevant information that can be reused automatically. Further, the search can identify cross-correlations, which will find the pieces that usually go together for a specific kind of proposal. After the proposal has been assembled and sent to the potential customer, the system can use cross-correlation between the proposals and the CRM system to determine which proposals were successful and which were not.
  • Closing Remarks
    • AI is more than just classification and entity extraction
      • It can find new topics
      • It can connect content by meaning — Semantic Search
      • It can evaluate content quality
      • It can summarize and rewrite
    • Successful projects will improve existing knowledge flow
      • [This is similar to the old KM adage: pave the cow paths]
      • Don’t fight the existing process. Instead, work within it.
    • Successful projects will include targeted AI
      • Focus on solving the problems you can solve today via AI
      • Gather the data you need to solve the problems of the future
    • You still need a great search engine
      • The results of AI feed and enhance the search engine
      • Search engines are the only way to scale