6. Disruptive Strategy – Terms to Know


  • Asymmetric Motivation

    This is a term that describes when an incumbent gladly gives up a lower-end part of the market because the company is motivated to move up market. The motivation to flee is most often fueled by higher gross margins. This is often called “flying under the radar”. For more on this term, read here.

  • Autonomous Unit

    An organization that for all intents and purposes operates as a separate company with different resources, processes and profit formula(we use “profit formula”) than the parent company. This includes support functions (HR, finance, operations, marketing) and most importantly sales teams.


  • Bad Money

    Whether money is “good” or “bad” depends upon the circumstances in which a business finds itself. Emergent Strategy: conversely to good money, bad money is impatient for growth and patient for profit. This type of money will likely get you into a self-reinforcing spiral of inadequate growth because you haven’t tested the profitability of the market.
    Deliberate Strategy: conversely to good money, bad money here is patient for growth, but impatient for profit. In this case, the laser focus on target margins will prevent the business from achieving its growth targets. 

  • Business Unit

    An organization or organizational subset that is independent with regard to one or more accounting or operational functions.


  • Celeron Processor

    A product that Intel Corporation first introduced in 1998 as the processor at its very low-end of the product line. It has lower performance, and lower margins, than the rest of Intel’s product portfolio, yet has achieved the purpose of defending the low-end market from AMD.

  • Commoditization

    The process by which goods that have economic value and are distinguishable in terms of attributes (uniqueness or brand) end up becoming simple commodities in the eyes of the market and/or consumers.

  • Compensating Behaviors

    Behaviors people exhibit because no existing solution adequately solves their problems. Customers stretch a product to do something it was not designed for, or “hack” together several products to produce a less than optimal solution. (see Scott Anthony’s book, The Innovator’s Guide to Growth)

  • Construct

    An Idea of theory containing various conceptual elements, typically considered to be subjective and not based on empirical evidence.

  • Core Competence

    The main strengths or strategic advantages of a business. A combination of pooled knowledge and technical capacities that allow a business to be competitive in the marketplace. These tend to be difficult for competitors to replicate.

  • Correlation vs Causality

    Correlation: the extent to which two datasets are related to each other. Statistics such as “Millennials are 2.5x more likely to make New Year’s resolutions” rely on a high correlation factor between the dataset “Millennials” and “New Year’s resolutions”. Being a millennial doesn’t cause you to make a new year’s resolution. 
    Causation: the extent to which one dataset or event causes another. Understanding what causes what and why is the focus of the entire physical science world, yet in business we often rely heavily on correlation to understand our customers (to our own detriment). 

  • Cost of Goods Sold (COGS)

    Costs include all costs of purchase, costs of conversion and other costs incurred in bringing the inventories to their present location and condition. Costs of goods made by the business include material, labor, and allocated overhead.

  • Culture

    The processes and values embedded into an organization by assumption, rather than by conscious decision.

  • Customer Segmentation

    This is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing. Using the lens of disruptive strategy, it is critical to segment customers by circumstances rather than by demographics.


  • De-coupling Point (Interdependence vs Modularity)

    The hand-off point between subsystems (products) or customers and suppliers (supply chain). As a product/service/industry becomes more modularized, many de-coupling points arise as the interfaces between subsystems or the supply chain become well defined. If a product/service/industry has an interdependent architecture, and subsequently a business adopts an integrated strategy, de-coupling points will be few and far between.

  • Deliberate Strategy

    A deliberate strategy is one that arises from conscious and thoughtful organized action. It’s generated from rigorous analysis of data on market growth, segment size, customer needs, competitors’ strengths and weaknesses, and technology trajectories.

  • Disintegrate

    Shifting out of parts of the value/supply chain to focus on a specialized strategy. Companies should disintegrate in a world with a modular architecture.

  • Disruption

    The classic definition of disruption is a disturbance or problem that interrupts an event, activity, or process. With a traditionally negative connotation, this term has taken on a life of its own in the business world to basically mean anything that is counter to the norm. As it pertains to D-Strat, we will define it more precisely.

  • Disruptive Technology

    see Disruption


  • Efficiency Phase

    When companies sell mature products or services to the same customers at lower prices. Companies accomplish this by developing a business model that can still make money at lower prices-per-unit sold in order to increase profitability.

  • Emergent Strategy

    An emergent strategy is one that arises from unplanned actions from initiatives that bubble up from within the organization. It is the product of spontaneous innovation and day-to-day prioritization and investment decisions made by middle managers, engineers, salespeople and financial staff.


  • Getting the Categories Right

    It is part of human nature to put things into categories; it helps us make sense of the universe in which we live. The concept of “getting the categories right” with regards to disruptive strategy is understanding the circumstances underlying a phenomenon, rather than surface-level attributes, and creating categories based on these. For example, if you are attempting to predict adoption rates of a new in-home IOT device, it would be unwise to use the adoption rates of the iPhone as a comparison because “hardware” is an attribute-based category and not a circumstance-based category.

  • Good Money

    Whether money is “good” or “bad” depends upon the circumstances in which a business finds itself. Emergent Strategy: during the nascent years of business, good money is patient for growth but impatient for profit. Money needs to be impatient for profit to accelerate a disruptive venture’s initial emergent strategy process. It forces management to test as quickly as possible the assumption that customers will pay a profitable price for a product. 
    Deliberate Strategy: upon switching to a deliberate strategy (after exiting the emergent strategy process), good money is now money that is impatient for growth, but patient for profit. This is because the profitability of the market has been fully tested and now the business needs to invest for growth. 

  • Gross Margin

    Gross Margin = (Revenue – COGS) / Revenue

  • Guideposts

    This refers to setting up check-points while executing a strategy plan to ensure you are translating strategic concepts into tangible, practical plans of action correctly.


  • Incentives

    Simply put, an incentive is a thing, typically money in business context, that motivates or encourages one to do something. The sales team incentive structure is of huge importance in determining whether your company’s intended strategy will indeed be your actual strategy.

  • Integrate

    Adding parts of the value/supply chain to deliver on an integrated strategy. Companies should integrate in a world with an interdependent architecture.

  • Integrated Strategy

    This strategy focuses on the entire system to deliver an end product/service to meet the minimal satisfactory performance requirements for the customer. An integrated strategy is best suited for industries with an interdependent architecture, i.e. each component in the product/service is design dependent on each other and cannot be separated.

  • Interdependent Architecture

    The structure of a product or service if one part cannot be created independently of the other part – if the way one is designed and made depends on the way the other is being designed and made. Optimize performance. (Innovator’s Solution, Page 127, Getting the Scope of the Business Right)

  • Interface

    The place where any two components fit together. Exists within a product, as well as between stages in the value-added chain, i.e. Design -> Manufacturing -> Distribution.


  • Job to be Done

    A job-to-be-done is defined as the progress an entity, customer or business, is trying to make during the course of day-to-day life. A job-to-be-done is a circumstances-based description of understanding your customers’ desires, competitive set, anxieties, habits and timeline of purchase. 
    Integrating around the job-to-be-done is how a company organizes itself and product/services offerings to deliver on a set of experiences that perfectly “nail” the JTBD. This means instead of organizing around traditional categories, i.e. marketing, product development, sales, etc., companies should organize and integrate in order to deliver a product/service perfectly centered around the JTBD. 


  • Low-End Disruption

    A product, service or business model that enters the market at a lower performance and price point than an existing offering. A low-end disruptor has the following characteristics:
    1) Initially, target “overserved” customers who are unattractive to incumbents due to low margins
    2) Product/service performance is simple, yet deemed “good enough” compared to existing solution
    3) Incumbents either ignore or cede the market easily to the new entrant. This is called asymmetric motivation.


  • Market Creating Phase

    The early stages of a new product or service when a company is focused on the development of the product or service to meet the customer’s job-to-be-done.

  • Modular Architecture

    The structure of a product or service where the fit and function of all elements are so completely defined that it doesn’t matter who makes the components or subsystems, as long as they meet the specifications. Optimize flexibility. (Innovator’s Solution, Page 128, Getting the Scope of the Business Right)


  • Net Margin

    Net Margin = (Revenue – COGS – OpEx – Interest & Taxes) / Revenue

  • New-Market Disruption

    A product, service or business model that creates an entirely new market by targeting non-consumption and offering inferior performance according to traditional metrics, but superior performance according to new metrics. These new metrics often focus on simplicity and convenience. A new-market disruptor typically has the following characteristics:
    1) Targets traditional non-consumption by focusing on the underlying Job-To-Be-Done, Since we don’t give examples for the other ones I think we should keep it consistent. If we want to keep this example then I think we should add examples to the others. But as this is a glossary I don’t think it’s necessary.
    2) Incumbents don’t see the entrant as competition because of different performance metrics and/or product/service characteristics. 
    3) As the new-market disruptor gains market share, incumbents are unable to respond, even if they want to, because they cannot compete on the new performance metrics. 

  • Non-consumption

    This term describes how we traditionally think about people who aren’t buying a product in a category. 
    To compete against non-consumption, introduce a product/service that is focused on the Job-To-Be-Done. 


  • Performance and “Good Enough”

    Performance: the criteria your customers determine to be important in product use and adoption. Performance is usually defined by metrics relating to the end-user experience, but also may be defined by inherent product metrics as a proxy for the end-user. Understanding how your customers define performance is critical to success and understanding your true competitors.
    Good-Enough Performance: this is the performance point at which your customer adopts your product. Understanding what performance is “good enough” enables you to know whether to adopt an integrated or specialized strategy for your industry, as well as understand potential entrants.

  • Performance Defining Component

    The component in the value stack that provides the functionality that customers care most about. This is typically where the most profit can be made. It is IMPERATIVE to understand this can change. For example, the performance-defining component for Intel Microprocessors in the early 2000s was speed, or cycles per second (hertz). With the advent of Wifi and the laptop form-factor, the customer’s performance defining component shifted to battery life. This shift caught Intel flat-footed, even though Intel was the one who developed Wifi in the first place, and they lost a substantial amount of market share to rival AMD. Intel eventually was able to weather the storm and shift its product line to this new performance-defining component, but not without a lot of organizational and financial pain.

  • Permission to Grow

    Any business needs the implicit permission of society, as manifested in laws, regulations and taxes, to grow, operate and simply exist. In the new sharing economy, laws/regulations have changed to grant or refuse permission for this new business model. See this article by HBS Professor Derek van Bever on Uber and the permission to grow.

  • Processes

    The patterns of interaction, coordination, communication and decision making through which the transformation of resources into products are accomplished. They include the ways that products are developed and made and the methods by which procurement, market research, budgeting, employee development and compensation, and resource allocation are accomplished. (Innovator’s Solution Page 183)

  • Product Architecture

    How a product’s components and systems interact – fit and work together – to achieve the targeted functionality.

  • Profitability vs. Profit Formula (Charles Schwab Case)

    Profitability: measured with income and expenses, using both real numbers as well as ratios, i.e. net profit, net income, net margin, etc. 
    Profit Formula: the profit formula is how organizations internally determine which projects to select. Often consists of specific ratios targets, i.e. X% gross margin, IRR of Y, etc. 

  • Purpose Brand

    A purpose brand links customers’ awareness that they need to do a job with products that have been designed to do it well. The highest level in the job architecture.


  • Resource Allocation Process

    The process by which resources are deployed to drive initiatives within a business. There is always a process, regardless of whether it is explicitly stated or otherwise. A resource allocation process alone isn’t “good” or “bad”; it’s just the process. The question should be, does the process prioritize the initiatives we’ve strategically said we want to take on? Does it create the right incentives that align with our strategy?

  • Resources

    People, equipment, technology, product designs, brands, information, cash, and relationships with suppliers, distributors, and customers. Usually people or things – they can be hired and fired, bought and sold, depreciated or built. (Innovator’s solution, page 178)

  • Return on Net Assets (RONA)

    RONA = Net Income / (Fixed Assets + Working Capital), where working capital = current assets – current liabilities


  • Skate to where the "money/puck" is

    A phrase popularized by Wayne Gretzky, a famous Ice Hockey player, when discussing the success of his career. The analogy to business is that instead of focusing on where the money is today, use business theory to understand where the money will be tomorrow and orient your company towards that future.

  • Specialized Strategy

    This strategy focuses on one piece of a system and doing that piece superbly. A specialized strategy is best suited for industries with a modular architecture, i.e. the inputs/outputs and clearly defined and standardized.

  • Sustaining Innovation

    A product, service or business model that provides performance improvements in attributes most valued by the industry’s most demanding customers. The improvements may be incremental or breakthrough. A sustaining innovation typically has the following characteristics:
    1) For new entrants, the incumbent has every motivation to fight your entrance to the market and sees you as a direct threat.
    2) A product/service improves upon traditional performance metrics and charges more money for the improved performance.
    3) For incumbents, uses the existing company’s resources, processes and profit formula to develop and execute the offering.

  • Sustaining Phase

    After the product or service has been defined and deployed into the marketplace, the company tries to evolve the product or service to meet the needs of the best customers in the market in order to beat the competition.


  • The Innovator's Dilemma

    In The Innovator's Dilemma Professor Christensen introduces the concept of disruptive innovation.

  • The Innovator's Solution

    In The Innovator's Solution Professor Christensen offers frameworks to help business leaders face disruption. These ideas may be applied to companies wanting to become a disruptor or to incumbents who are trying to avoid disruption.

  • The Role of the CEO

    The CEO, or other similarly high ranking executive, is critical at the beginning of the creation of a disruptive growth engine. Because the processes and values of the mainstream business by their very nature are geared to manage sustaining innovation, there is no alternative at the outset to the CEO or someone with comparable power assuming oversight responsibility for disruptive growth.


  • Value Stack

    The structure of how value is created and captured in a product/service/industry.

  • Values

    The standards by which employees make prioritization decisions – those by which they judge whether an order is attractive or unattractive, whether a particular customer is more important or less important than another, whether an idea for a new product is attractive or marginal, etc. (Innovator’s Solution page 185). Values and prioritization decisions in for-profit businesses are almost always dictated by the profit formula.

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5. Disruptive Strategy – Sources of Strategy – Notes

  • Two sources of Strategy
    • Emergent
    • Deliberate
  • Strategy is process, not an event. Understanding the process is a key to success. Understand the difference between "good money" and "bad money."
  • By the end of this module you will be able to:
    • Understand the strategy development process within your own company
    • More effectively manage the strategy development process
    • Determine which "type" of money will be optimal to grow your business
  • Resource allocation: Everyday the team has to decide what to focus on, and what not to focus on. We need to look at what the company needs to prioritize financially, then determines what get’s implemented and what does not, this becomes the strategy. The allocation of resources is not an event, followed by implementation. It is actively happening.
  • The resource allocation process will determine which deliberate and emergent initiatives get funded and implemented, and which are denied resources.
  • If you want to see the real strategy of a company. Don’t listen to what they say, watch what they do.
  • Below is the entire resource allocation process. A useful exercise would be to diagram this out for your own organization
  • As companies grow, it makes it hard to see small opportunities. DO not become a monolithic company. Have small business units that add up to a large company, then small opportunities look bigger to the smaller business units.
  • The resource allocation process will automatically prioritize initiatives based on the profit formula for the company, whether good or bad.
  • Intel had a deliberate strategy that was to be the leaders in smart phone processors, however the profit formula/resource allocation process just wouldn’t let it happen.
  • Need to set a new criteria (profit formula) for the resource allocation process.
  • Profit formula controls the resource allocation process.
    • process can prevent innovation. Thus we need a new business model to go after the new business.
    • good, emergent ideas often come from lower-level employees. Thus, implementing processes to surface these ideas is critical.
  • The HR process that promotes based on individual $$ contribution, is based on an ideal that will reward showing value every 18 to 24 months. If an innovation will take longer than 18 to 24 months, they will will probably be passed up.
  • There are three phases of business growth. Here are a few bullet points to crystalize these stages in your mind:
    • Market Creating Phase (job creation) – A deliberate strategy, but know that innovation will come about via an Emergent Strategy, as well.
      • The early stages of a new product or service when a company is focused on the development of the product or service to meet the customer’s job to be done.
    • Sustaining Phase (no net new job growth) – Deliberate strategy, top down approach to grow the business.
      • After the product or service has been defined and deployed into the market place, the company tries to evolve the product or service to meet the needs of the best customers in the market in order to beat the competition.
    • Efficiency Phase (job loss)
      • When companies sell mature products or services to the same customers at lower prices. Companies accomplish this by developing a business model that can still make money at lower prices-per-unit sold in order to increase profitability.
  • IBM/Sears introduced Prodigy
    • Invested $500m each ($1b)
    • started with a focus on shopping.
    • saw people emailing, and decided to charge them.
      • Instead of seeing email as an emergent opportunity, leaders at Prodigy focused on their deliberate strategy to facilitate an online portal where users could access information and shop.
    • AOL focused on email, and a purpose brand "You’ve got mail"
  • It’s hard to get a customer to "change the job", but if you help them do their job , but better, you win.
  • You can;t beat the resource allocation process, you need to manage it.
  • Lesson from AOL
    • Never believe that the strategy that helped us to be successful, will not always be the strategy that keeps us successful.
    • Change in the strategy is not an event, it’s a process
    • Continue to pursue the existing strategy, but build a new BU to be the next business.
    • Blockbuster: Acquire movies for all 5000 locations, rent as many as possible in 3 wks, then clear. This required late fees as a tax for not being able to rent again.
    • Netflix went after the DVD market, at a flat rate.
      • as DVD became more and more available. they switch to rental and late fees.
      • then moved to unlimited rentals for a flat fee.
      • Implemented a recommendation system, this spread out demand. The movie had to be in stock to be recommended.
    • Blockbuster tried to add an online business, but the resources, processes, and profit formula were deployed differently not allow for them to work together long term. The business was built to support stores.
      • Blockbuster merge what was the best in their traditional business with their online business.
    • Netflix created a separate business around streaming.
    • Now they continue to move up to content.
    • "Plans are useless, but planning is indispensable." Dwight D. Eisenhower
  • Good Money and Bad Money
    • The basic idea of good money and bad money is that the type of money a manager accepts carries specific expectations that must be met. These expectations heavily influence the types of markets and channels that a venture can and cannot target. The very process of securing funding forces many potentially disruptive ideas to get shaped instead as sustaining innovations that target large and obvious markets. Thus, the funding received can send great ideas on a march towards failure.As emergent ideas are being nurtured during nascent years, money must be patient for growth but impatient for profits.When winning strategies become clear and deliberate ideas need to be carried out then money should be impatient for growth but patient for profit.
    • Company good money, bad money lifecycle.
      1. Successful companies
      2. company faces growth gap (shareholder expectations must be exceeded)
      3. Good money becomes impatient for growth
      4. executive temporarily tolerate losses
      5. mounting losses precipitate retrenchment.
      6. End up back where you started, with a growth gap.
    • Again, focus on the "Jobs to be done." If you focus on this, then the chances of good money and bad money tripping you up is minimized.
  • OnStar
    • Project Beacon, leveraging car deployments, Hughes electronics, and EDS
    • Very emergent strategy
    • Approaching the market with a broad strategy is actually consistent with the emergent strategy process. During the market creating phase, OnStar didn’t know what services would resonate most with customer jobs to be done. Chet was effective at allowing new ideas to surface and be tested in the marketplace. However, as Chet pointed out, the "swiss army knife" approach isn’t viable long-term. Chet needed to find the winning strategy that would allow OnStar to rally around a deliberate strategy.
    • safety and security emerged as the key value proposition.
    • Gen 2 hardware was 1/3 the cost of Gen 1.
    • GM was focused on RONA (return on net assets) as the profit formula
    • OnStar settled on the broad job to be done of "help me have safety, security, and peace of mind"
    • "The purpose brand gives you license."
    • Advice:
      • The company (GM) was patient with results.
      • Active engagement of most senior people in the company. Would have been great to have split RPP (Resource, Processes, and profit formula.)
  • Managing Your Strategy Development Process
    • Senior management must simultaneously, yet separately manage the strategy development process.
    • Leaders must seek Good Money based on your current situation.

Managing the Strategy Development Process.pdf

Works Cited Managing the Strategy Development Process.pdf

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4. Disruptive Strategy – The natural process of modularity – Notes

  • By the end of this module, you will be able to:

    1. Build a stronger intuition for where the profitability will be in your industry

    2. Determine which operations are critical to be done in-house and which operations can be out-sourced

    3. Better predict the direction of your industry and company

  • Things managers get wrong:

    • do core competence inside

    • let other stuff happen outside

  • “skate to where the puck is going, not to where it is.” gretsky

    • Skate to where the money will be

  • Interdependence and Modularity

    • Interdependence: Design of one piece, depends on the design of the other pieces (unpredictable interdependencies)

    • Modularity: How pieces fit together a re well defined, and theoretically easier to modify. (no unpredictable interdependencies)

    • Modular architectures, lend to disintegration

    • In the early stages of any industry, products and services tend to be very interdependent. Over time, as products and services become more defined, these same products and services become modular.

    • as you mature you have to become more modular to become faster, flexible, and responsive.

What is the right level of integration

  • If you have a superior product, that is not modular to just “plug and play,” then you must integrate backward/forward into the product until you reach modularity. this can be costly.

  • DuPont found that the interface between kevlar and tires was unpredictable and not-good-enough. To be successful in the tire industry, DuPont would have had to integrate forward to become a tire designer. Only then could DuPont manage all of the interdependencies between kevlar and tires. DuPont’s only other option was to wait and hope that an external tire designer would figure out the interdependency.

    • they found applications where they could simply pull out the old, and plug in the new.

  • RCA had color TV, but no color content.. RCA and NBC merged

  • Three types of interdependence:

    1. Functional or Technological Interdependence

      • The interdependence between two components of a product or service.

      • Example: The way kevlar interacts with a tire is unknown and thus, interdependent.

    2. Profit Formula Interdependence

      • The interdependence between a product or service and the way a company makes its money.

      • Example: Organizations that make fighter pilot jets are forced to serve the high-end of the market where they can earn larger margins to cover their high fixed costs. Because of this interdependency, high-end jet organizations can’t target low-end drones because their high-fixed costs don’t allow it.

    3. Marketing or Brand Interdependence

      • The interdependence between a product or service and how it is marketed.

      • Example: Consumers didn’t connect with “kevlar belted radials” because the word “kevlar” didn’t mean anything to them. However, consumers embraced “steel belted radials” because the word “steel” connotes strength and durability. (Note: The interface between kevlar and tires is an example of both functional and brand interdependence.)

Knowing when to disintegrate/become more specialized

  • IBM: heads&disks>drives>computers>software>services (decoupling point)

    • > data > cognitive?

  • If you do not integrate forward to the decoupling point, then you have to sit and wait for someone else to come along. This typically does not happen.

  • Once the industry became “good enough” IBM needed to disintegrate; this was very costly and timely, and IBM’s profitability took a nose dive, and they got out of the less profitable “heads&disks>drives>computers” businesses.

  • It is interesting how your greatest strength can become your greatest weakness if you fail to switch your strategy from integrated to specialized.

Avoiding Commoditization & Finding the Performance-Defining Component

  • As products and services become good enough, their architecture becomes more modular. This allows companies with a specialized strategy to do one piece of the system very well. Over time, as a growing number of new competitors enter the market, commoditization occurs.Essentially, what happens is that barriers to entry are lowered as the various interfaces within the system become more defined (or more modular).

  • As commoditization develops within an industry, profits decline. Companies are forced to find profitability in new ways. In other words, companies must seek the performance-defining component.

  • The performance-defining component (or subsystem) is defined as the component in the value stack that provides the functionality that customers care most about. This is typically where the most profit can be made.The performance-defining component itself tends to be not yet good enough for customer needs. As a result, it is usually more interdependent than modular. Thus, the barriers to entry are high and attractive profits are available for few competitors.

  • Need to go to where the money is. this will shift over time. We are successful cause we are “here” at the right time.

  • Continually “skating” to the performance-defining component is critical to remain viable long-term. Another principle that will help you stay focused on the performance-defining component is the principle of the job to be done. If you can maintain a discipline to build products and services around the job to be done, your chance of continuing to produce the performance-defining component as commoditization occurs is greatly increased.

    • Integrate company around job to be done. (ruggedness?) If you do this differentiation is natural.

    • what we need to integrate and how we integrate to deliver an experience is hard for an attacking company to surmount.

  • MediaTek

    • est. 1997, based in Taiwan

    • chips, chips in optical disc reader, chipsets in mobile, on chip solutions

    • MediaTek integrated forward to the decoupling point. Turnkey solution

      • Low entry barrier

      • faster time to market (3 months to six months to market.)

      • allowed customer to add differentiation to the phone.

    • Leveraged “reference design”, could tweak, qualified components for verified vendor list

    • What if you were able to design a phone per a customers specific job to be done; service and support them all the same.

    • $200m to $7B

    • More business model disruption vs technology disruption.

Discovering the scope for your organization

  • Here are some takeaways to keep in mind as you try to maintain the disruptive scope of your organization:

    1. Always consider your strategy as being “temporarily successful.”

    2. “Skate” to the “performance-defining component.”

    3. Defend your competitive position by organizing around a “job to be done.”

    4. Frequently refer back to “these theories to guide your strategy like a compass.”

Maintaining a Disruptive Scope.pdf

Works Cited Maintaining a Disruptive Scope.pdf

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3. Disruptive Strategy – Organizing for innovation – Notes

  • Questions we will explore:
    • Individuals: Are the people good enough to succeed?
    • Organization: Will our organization have the ability to succeed at this task?
  • Goals of this module, to understand:
    • What can the organization do, and what it can not do.
    • Identify the resources, processes, and profit formula required to succeed.
    • Build the capabilities you need to grow.
  • Three factors (RPP)
    • Resources (tangibles, visible, can be hired and fired, bought and sold, depreciated or built – flexible and transferable – Resources are the things that are available.)
      • Technology, People, Products, Facilities, Equipment, Brands, Information, Distributors.
    • Processes (how resources work together to get the outcomes you need. To understand what a company can and can not do you need to understand processes. decision making, communication – inflexible; not meant to change – Processes tells you how to do it.)
      • Product development, Procurement, Market research, Budgeting, Employee development.
      • Process creation
        1. Task emerges and people need to work together to get it done.
          1. If fails, they re imagine the task
        2. Try task 1.1 execution again, if it passes, use it again… This creates a processes.
      • Processes that people don’t even think about it how to do it, they just do it, becomes the culture.
      • When people follow a process to do a task for which it was designed, it usually works efficiently.  But when the same efficient process is employed to tackle a different task, it often doesn’t work. In other words a process that becomes a capability in executing a certain task can be a disability in executing other tasks.
    • Profit Formula (any person in the company who is going to prioritize this over that. e.g. today I’m going to call customer A, instead of customer B…. based on profit formula. Very diffused, decentralized.. Criteria is critical, if asked to do something is not a lined with the cireteria you can see that the company can not do it. – Criteria in Profit formula tells you what a company can not do.)
      • Gross margin targets, ROI/ROA, thresholds, utilization goals, type pf orders or customers.
  • What your organization cannot do
    • Early in a company, all capabilities exists in the resources.
    • When a company grows, the ability shifts from resources to  to processes.
    • Do we have the resources to succeed, will processes enable us, can we prioritize?
    • Find the ability to do something is rooted, will tell you what a company can and can not do.
    • Good people, working in an organization that is not capable, are destined to fail.
  • NYPRO Healthcare
    • Management that has been around for 30+ years
    • Founded in 1955 Frank Kirk and Gordan Stoddard (50% to 100%)
    • Customers want “Lowest cost per cycle.” They pushed “high cavitation” within a tool, so they are able to extrude more parts in less time.
    • Gordan was not a formally trained engineer. His thing was understanding people and winning them over.
    • netstal machines were used.
    • Reources:
      • Technology: Nypro invested in and developed top-of-the-line equipment in injection-molded plastic manufacturing
      • People:
        • CEO Gordon Lankton, who had an innovative mind-set and attracted great talent
        • Strong general managers of each plant
        • World-class engineers
      • Facilities: Global plant network that allowed Nypro to be close to customers
      • Brand: Recognized name for high-quality injection-molded plastics
    • Processes:
      • Benchmarked plants against each other via a daily and weekly reports to spot areas for improvement (e.g. tool turnover time)
      • Strong base of central talent, but allowed innovation to come in through different plants
      • GM: Good to go out and see what others are doing, to “steal” and make your plant better.
      • Gordon’s weekly visits and person-to-person conversations to find ways to improve
      • Yearly General Manager meeting to gather and share best practices
    • Profit formula:
      • Criteria
        • Fewer, large customer accounts or small accounts with the potential to grow
        • High-volume, long-run orders that maximize machine utilization
        • Complex, technologically challenging orders with better margins.
      • Each machine was like a hotel room. If you don’t use it one night, you lost that revenue forever.
      • Sales team was off volume, engineering was off of profitability.
    • Here are some of Nypro’s strongest capabilities and disabilities (everything they did was geared towards the high-volume market):
      • Nypro organization can:
        • Deliver industry-leading efficient manufacturing
        • Complete high-volume programs with very sophisticated molding machines
        • Solve challenging manufacturing problems
      • Nypro organization can’t:
        • Deliver simple manufacturing with quick change-overs
        • Complete low-volume, short runs
        • Conduct fast prototyping
    • Nypro Dilemma
      • Gordan saw new operation in Japan
        • Operation in japan, built around small machines… 60% were running.
        • The machine caught his attention. it was a simple machine
        • Gordan thought there was a operunityu to gain new customers small run, contaminates, grindings and oils.
        • Easy switch over.
        • faster cycles
        • Less parts per hour
      • They created a novaplast (short run, simple products). After they decided to adopt the NovaPlast machine, Nypro’s leaders had three options:
        1. Nypro initial decision: Integrate 1-2 NovaPlast machines into several different plants
        2. Concentrate all NovaPlast machines into 1-2 existing plants
        3. Second choice: Build a new plant dedicated to the NovaPlast machines
      • They had problems with staff. they wanted to make it more complicated.
      • Sales got paid on volume, so it went against existing model.
        • Needed to create a new sales group for NovaPlast machines
      • Seems an odd way to impart a new tech. Not sure I saw the pain they were solving
      • Ultimately NovaPlast was killed
  • Charles Schwab and introducing an on-line trading platform eSchwab
    • Clay went from Merrill Lynch to Charles Schwab, because the latter cared for the type of client clay was.
    • Schwab was a low-end disruptor
    • Schwab decided to disrupt itself charging $79/trade for traditional, and $29/trade for on-line. They set the latter up as a separate business unit.
  • EMC – Merging competing products through an acquisition.
    • background
      • Core: information storage. 55%
        • Mid-tier (SMB/SOHO) 6%
      • Acquired data general clariion for mid-tier, two server model.
    • data general 20% direct, 80% channel, EMC 80% direct, 20% channel
    • They were going to move data general to EMC model… oh, oh.
    • Formed a separate sales force for Clariion 6% to 35%, with dell as channel partner.
    • Went from $400m to $4b product line.
    • VMWare was treated completely differently. $60m to $6b
    • Big company can kill it, or hug it so tightly it takes the breath away.
    • Focus on the people in acquisitions. Let the small company use the big companies resources when needed, but do not let the large company just use the little company.
    • What teaching like Clays are…Guide posts, warning posts, a way of thinking. Remember that values are non-negotiables, but culture should be flexible.
  • You can not disrupt yourself
    • You need to set up a different business unit, under the umbrella of the existing business
    • If sustaining then fold the innovation/conpany in.
    • if disruptive, allow it to operate separate.. If you fold it in, you will ultimately kill the innovation.
  • When companies succeed, sometime they need to develop new capabilities.. They need to be built independently of the old.
  • This can help us to predict when we need a new business unit, and when we need to craete a new one.
    • If a manager see that they need new resources, proceesses, and profit formula… then the game will be over.
    • You need to be looking out into the future.
  • Summary
    1. We need to think deeply to understand what an organization can and can not do. It tells use where we need to build abilites. telling use where to create new, of leverage old
      1. resources – flexible
      2. processes – inflexible
      3. profit model – dictates criteria to prioritize “this” over “that.”

Organizing for Innovation.pdfWorks Cited Organizing for Innovation.pdf

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2. Disruptive Strategy – Discovering Customer Jobs to be Done – Notes

  • Jobs to be Done:
    • Understand the job the customer is trying to do, and the develop a product that nails this job perfectly. The probability that your innovation will be successful will improve in dramatic ways.
    • Marketing: Casual reason why customers buy your product.
  • 75 – 85% of new products fail, and do not succeed financially. because they do not target a job
  • Companies focus on correlation vs causation.
  • The goals for this module are:
    1. To see that many products fail because they are focused on correlation, not causality
    2. To find important “jobs” in the market for which your products might be “hired”
    3. To understand your products’ true competitors from the customer’s perspective
    4. To figure out what and how you must integrate in order to get a “job” done perfectly
  • If you do this right, it makes it harder for others to disrupt you.
  • a major key to discovering customer jobs to be done is to observe and interview consumers.
  • customer segmentation:
    • I have a job to done, and I look around and pull it in to my life.
  • Include substitutes in the size of the market.
  • Focusing on the job to be done helps you answer some critical questions:
    • What “job” does the customer hire your product to do?
    • How big is the market, and is it growing?
    • Who is competing to help the customer do this “job”?
  • A problem or an opportunity that someone is trying to solve. Job = needs to be done. We hire products are services to solve the problem.
    • Help me…
    • I need to…
    • Help me avoid…
  • Not job to be done (more a part of experience need to provide to customer gets the job perfectly)
    • Low cost
    • convenience
    • Cheap
    • happy
  • Once you know the job, you can clearly see who the true competitors are, how large the market is, what opportunities there are for innovation, and how to market the product.
  • If you frame the business in products, you will come and go. If you frame it around the job, it frees you to change the way you deliver on that.
  • Two dimensions of “Job to be done”
    • Functional: Tangible and measurable
    • Emotional and social: How it makes me feel
  • Disney does the “Job to be done” well.
    • Why Disney: Fun with family and friends, Escape reality, Kid again, Meet characters, Rides & attractions
  • Jobs to be done stay focused over time. Were a focus on products can leave thing unpredictable.
  • When you figure out a better way to get the job done. It always results in financial and market success.
  • Job architecture 4 layers
    1. Purpose Brand: Word that pops in to customers mind, when they have a job to do. Mind share.
    2. Integration: What needs to integrate to provide the experiences
    3. Experiences: What are the purchase and use experiences that will sum up to nailing the job perfectly.
    4. Job to be done: understand of what the job is to be done. Functionally and emotionally
  • You can identify jobs to be done by observing:
    • Yourself: Why do you do what you do?
    • Current customers: Why do they buy your product?
    • Non-customers: Why do they not buy your product?
    • Former customers: Why do they no longer buy your product?
    • Compensating behaviors: Inconvenient workarounds people use because there’s no product that fulfills their job to be done well
    • Entertain, Inform, Educate in a utilitarian way… Always there.
    • Not about a demographic, it’s that we have a job to do….
  • How do you find jobs to be done?
    • Think in geographic terms (e.g. someone out there has a job to be done. Why would they hire us)
    • Reflect deeply on personal experiences
    • Observe current customers (why are they buying our product, and when they do not what are they using to get the job done?) – When a customer leaves, you must find out why…
    • Identify compensating factors (are people having to do work arounds)
    • Why are they buying products? if they dont, what are they doing to get the job done.
  • Minute clinic
    • Yes/No decision
    • No wait for unpredictable wait of time. Every 5 minute response.
    • Job to be done:  “help me to quickly and conveniently, get the healthcare I need without seeing the doctor.”
  • The  Godrej Group – understanding refrigeration needs in India.
    • 1897 making locks – safe, furniture, refrigerators, etc, etc
    • 80% of indias population does not have refrigerators.
    • “Chotukool”
  • There is no job that is created. They are doing the job.
  • Technology exist, job t be done exist, but they have not been brought together in an economic way.
  • Data is not real. It’s a proxy. Data has it’s value, but you need to dive in. Get outside.
  • Chotukool is new-market disruption, that is competing for non-consumption.

Jobs to be Done.pdf

  • Bose – QC20 Noise Cancelling Headphones.
    • Job to be done: Help me to experience air travel in a more tranquil way.
    • Experiences: Product tests that simulate airline travel and other annoying noises. Flexible return policy, and superior customer service
    • Integrations: Placement of stores, where I shop. Advertising with NFL, engineering bent.
    • Name: Bose <Tagline>
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1. Disruptive Strategy – Introduction: Lenses on the World – Notes

  • Two views of God: 1) on top of the universe and with a stroke of the finger and effect change, or 2) inside the universe with a uncanny understand of all of it’s levers and because of this knows what’s possible and how to effect change.
    • Power comes from understanding of constraints, forces, vectors, and processes. If understood perfectly, you can harness those things.
  • When someone comes in the bottom of the market and moves up. “It’s not what to think, it’s how to think.”
  • Align with Amazon
  • Optimize supply chain around you Commerce
  • Hire a bunch of technology and data scientist become a technology company
  • Spend a bunch of money and stores and try to create some new special experience
  • Leverage the vast real-estate become a reit
  • Line with other retailers and become effectively a large-scale department store
  • Creative technology
  • 3 Types of innovation
    • Sustaining Innovation (incumbents usually always win)
      • Characteristics
        • Makes good products into better products
        • Target high-end of market, more profitable customers
        • Brings more profit margins
      • Definition
        • Offering ever better products, to sell at ever better margins, to your very best customers.
        • Incumbents fight new entrants
        • Innovation aligns with existing business model
    • Low-end disruptive
      • Causal mechanism is the pursuit of profit. coming in to the low-end, low margin, part of a business will typically lend itself to less competition as the incumbent is willing to let the lower-margin business go, this can continue up the “stack” until the incumbent is no more. leaving behind low-profit businesses can sometimes open the door to disruptors.  Low-end disruptive innovations take advantage of markets in which existing products offer more performance than many customers want or need. Technology advancements outpace customer need, this opens opportunity for “good enough” products
      • gain market share against old
      • low-end disruptive innovation takes advantage of the performance surplus, which occurs when product performance overshoots what customers can use.
      • Characteristics
        • Offers “good enough” but not much more
        • targets “over-served” customers
        • figured out a fundamental different business model
      • Definition
        • Incumbents flee from new entrants, moving up market in pursuit of more valuable customers.  these disruptive innovation can not be adopted by incumbents.
    • New-market disruptive
      • Make product more accessible. Mainframe – $2m, PC $2k, Mobile $200. What will be $20, what will be $2?
      • Hard for incumbents to move into new markets for the new-market disruption. Because there is no motivation from a profit formula standpoint, it’s not their business model.
      • creates new markets and will not find incumbents there. then sucks customers out.
      • Honda Cub entering market where there was not consumption.
        • High margin dealers did not want to  sell, but lawnmower companies did..
        • Attacking a sustaining market did not work, but New market innovation did.
        • This opened up a new market, where only HarleyDavidson still exists as the domestic player.
        • Need to have an application that would make money, vs going into a market to find money.
      • new-market disruptive innovation targets non-consumers because existing products are too expensive or complicated to use.
      • Characteristics
        • Targets “non-consumption.” people who did not have ability or access to incumbent product
        • Make profit for lower price-per-unit sold than incumbent tech. Think dollar vs percentages.
        • Product provides lower performance for the existing market but higher performance for non-consumers.
      • Definition
        • Incumbents flee from new entrants, these disruptive innovation can not be adopted by incumbents.
  • No technology is intrinsically sustaining or disruptive, that is given based on deployment/implementation.
  • Sustaining or disruptive is relative to existing business model or products
  • If an innovation can deploy into an existing structures (business model/products), it’s sustaining. If the innovation can not be deployed into existing structures, then it is disruptive.
  • Many innovations can be considered both low-end disruptions and new-market disruptions.
  • You can not disrupt yourself. Because the core company can not make money on the disruptive innovation.
    • They will take technology and implement it in the way that serves the company, making it a sustaining innovation.
    • In order to do it you need to set up a company with a different business model.
    • If you try to move into the old business you will ruin what you just built/acquired.
    • Set up a completely different business unit and let them disrupt the core business.
  • Retail: 1960 316 traditional department stores, 1962 discounters entered the market. Only 8 traditional department survived. Only one successfully transitioned into a discounter, it was Dayton-Hudson, that created the separate business unit “Target.”
  • Circle UP (consumer and retail funding)
    • investor
      • Dealflow
      • transparency
      • comparables for analysis
    • retailer
      • Capital raise 8-12 months to 61 days,
      • Charge less money, and
      • Partnerships (distribution, product placement) and services (advisor network)
  • Disruption is typically an opportunity well before it’s a threat. and if you go after low-end disruptive innovation, then you can take share. It’s only a threat when incumbents start to fight back.
  • You need to start innovating now, while the core is strong. The core will classically be strong, while the disruption grows. Do not wait until Core atrophies.
  • Sustaining innovation can be merged in to the core.
  • Here are some guidelines to use when trying to get disruption to work in your favor:
    • Disruption is typically an opportunity long before it’s a threat
    • You must begin to innovate while your core business is still strong
    • Allow disruptive businesses to run independently of the core business
    • Spot disruption by observing customers at the bottom of the market
    • Protect your business by focusing on and integrating around the job to be done (a topic introduced in another module)

Aligning with Innovation and Disruption.pdfWorks Cited Innovation and Disruption.pdf

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“…most of all: nothing without skin in the game.” ~ Nassim Nicholas Taleb


“No muscles without strength, friendship without trust, opinion without consequence, change without aesthetics, age without values, life without effort, water without thirst, food without nourishment, love without sacrifice, power without fairness, facts without rigor, statistics without logic, mathematics without proof, teaching without experience, politeness without warmth, values without embodiment, degrees without erudition, militarism without fortitude, progress without civilization, friendship without investment, virtue without risk, probability without ergodicity, wealth without exposure, complication without depth, fluency without content, decision without asymmetry, science without skepticism, religion without tolerance, and, most of all: nothing without skin in the game.” ~ Nassim Nicholas Taleb

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Innovation Strategy Questions

When seeking to develop an innovation strategy, here are some questions you should get answered.

  1. How do I see emerging trends before they become problematic?

  2. How do I generate a robust pipeline of new growth ideas to consider?

  3. How do I identify and focus on the highest-potential opportunities in areas like blockchain and AI?

  4. How can I motivate traditional company management to realize the need for digital transformation?

  5. How do I evaluate competitive signals in a noisy, buzzword-filled market?

  6. How do I get the middle layer of my company to embrace change?

  7. How do I bring outside ideas into my organization?

  8. When does it make sense to be a fast-follower? And when does it not?

  9. How do I decide whether to build, buy or partner?

  10. Should I start a venture capital fund?

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4: AI for Everyone – AI and Society – Notes


  • Hype

  • Limitations

    • Bias

    • Adversarial attacks

  • Impact on developing economics and jobs

A realistic view

  • Goldilocks rule for AI:

    • Too optimistic: Sentient/AGI, killer robots

    • Too pessimistic: AI cannot do everything, so an AI winter is coming

      • as opposed to the past, AI is creating value today.

    • Just right: Can't do everything, but will transform industries

  • Limitations of AI

    • performance limitations. (limited data issues)

    • Explainability is hard (instructible)

    • Biased AI through biased data

    • Adversarial attacks



  • Biases

    • Bias against women and minorities in hiring

    • Bias against dark skinned people

    • banks offering hiring interest rates to minorities

    • reinforcing unhealthy stereotypes

  • Technical solutions

    • "Zero out" the bias in words

    • Use more inclusive data

    • More transparency and auditing processes

    • More Diverse workforce

Adversarial attacks

  • Minor perturbation to pixels can lead and AI to have a different B output.

  • Adversarial defenses

    • Defenses exist; incur some performance cost

    • There are some applications that will remain in an arms race.

Adverse uses of AI

  • DeepFakes, fakes can move faster than the truth can catch up

  • Undermining of democracy and privacy, oppressive surveillance

  • Generating fake comments

  • spam vs. anti-spam, fraud vs. anti fraud

AI and developing economies

  • AI will eliminate lower rung opportunities. The development of leapfrog opportunities will be required. Think how countries jumped to mobile phones, mobile payments, online education, etc.

  • US and china leading, but still a very immature space.

  • Use AI to strengthen country's vertical industries.

  • More public-private partnerships

  • invest in education

AI and Jobs

  • AI is automation on steroids.

  • Solutions

    • Conditional basic income: provide a safety net but incentivize learning

    • Lifelong learning society

    • Political solutions


  • What is AI?

  • Building AI projects

  • Building AI in your company

  • AI and society

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3: AI for Everyone – Building an AI company – Notes


Case Study: Smart Speaker

  • “Hey Device, tell me a joke”
    • Steps (AI Pipeline):
      1. Trigger work/wakeword detection A) Hey device”? -> B) 0/1
      2. Speech recognition A) Audio -> B) “tell me a joke”
      3. Intent recognition A) Joke? vs, B) time?, music?, call?, weather?
      4. Execute joke
    • These could be 4 different teams
  • “Hey device, set timer for 10 minutes”
    • Steps (AI Pipeline):
      1. Trigger work/wakeword detection A) Hey device”? -> B) 0/1
      2. Speech recognition A) Audio -> B) “Set timer for 10 minutes”
      3. Intent recognition A) “set timer for 10 minutes -> B) Timer
      4. Execute
        1. Extract duration 
          1. “Set timer for 10 minutes”
          2. “Let me know when 10 minutes is up”
        2. Start Timer with set duration
  • Challenge:
    • Each function is a specialized piece of software.
    • This requires companies to train users on what the speaker can, and can not do.

Case study: Self driving car

  •     Steps for deciding how to drive
    1. Image/Radar/Lidar
      • Car detection
      • Pedestrian detection
    2. Motion planning 
      • Steer/acceleration/Brake
  • Key Steps
    1. Car detection (supervised learning)
    2. Pedestrian detection (supervised learning)
    3. Motion Planning (SLAM – Simultaneous localization and mapping)
  • Challenge:
    • Each function is a specialized piece of software.

Roles in AI teams

  • Software Engineers (30% +)
  • Machine Learning Engineer. focused on A -> B mapping
  • Applied ML Scientist: Using State of the art to today’s problems
  • Machine Learning Researcher. Extend the state-of-the-art in ML
  • Data Scientist. Examine data and provide insights. Make presentation to team/executives. Some may be Machine Learning Engineer.
  • Data Engineers. Organize data. Make sure data is saved in an easily accessible, secure, and in a cost effective way
  • AI Product Manager. Help define what to build. What feasible and valuable

AI Transformation Playbook

  1. Execute pilot projects to gain momentum
    • Success is more important than value
      • Need to get the flywheel moving
    • Show traction within 6 to 12 months (quiz said 6 to 10)
    • Can be in-house or outsourced
  2. Build an in-house AI team
    • Can be under: CTO, CIO, CDO, or CAIO
    • Have a central AI center of excellence. Matrix them in to start, untill understanding of AI is throughout the org
    • CEO should provide funding to start, not from BU.
  3. Provide broad AI training
  4. Develop an AI strategy
    • You do this at step 4 to gain concrete experience, vs starting with an academic strategic approach to something so new.
    • Leverage AI to create and advantage specific to your industry sector
    • Design strategy aligned with the “Virtuous Cycle of AI”
      • Better product -> More users -> More data -> [Repeat]
    • Create a data strategy
      • Strategic data acquisition
      • Unified data warehouse/lake
    • Create network effects and platform advantages
      • In industries with “winner take all/most” dynamics, AI can be an accelerator
    • Leverage classic frameworks as well. Low cost/ focus
    • Consider humanity.
  5. Develop internal and external communication
    • AI can change a company and its products
    • Investor relations. to properly value your company
    • Government relations. to align on regulations.
    • Consumer/use education
    • Talent/recruitment
    • Internal communications. to address questions and concerns.

AI pitfalls to avoid

  • Don’t
    • Expect it to do everything
    • Hire 2-3 ML engineers and expect then to come up with use cases.
    • expect it to work the first time\
    • Don’t expect Traditional planning process to apply yo AI
    • Don’t wait for a superstar, get going with what you have today.
  • Do
    • Be realistic
    • Beginners should be linked with business
    • Work with  the AI team to develop new timelines, KPIs, etc

Taking your first steps

  • Get friends to learn about AI
    • Courses
    • Reading group
  • Start brainstorming projects (no project too small)
  • Hire a few ML/DS people to help
  • Hire or appoint an AI leader
  • Discuss with CEO/Board possibilities of AI transformation
    • Will the company be more valuable, or more effective if we are good at AI.

Supervised learning

Unsupervised learning

Transfer learning


Knowledge graphs

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2: AI for Everyone – Building AI Projects – Notes


  • Starting an AI project

    • Workflow of projects

    • Selecting AI projects

    • Organizing data and team for the projects

Workflow of a machine learning project

How do you build, say a speech recognition engine

  • Key Steps:

    1. Collect Data: people saying “Alexa”, and other words

    2. Train model: learns A to B mapping… audio clip to “word”

      • many iterations

    3. Deploy the model: implement in to a smart speaker

      • Will collect new data (get data back), to  maintain /update the model

How do you build, say a self driving car

  • Key steps:

    1. Collect Data: images – > positions of other cars, draw rectangles around cars

    2. Train model: need to iterate and precisely identify cars

    3. Deploy model: may learn that golf carts are identified and positions well. keep iterating.

Workflow of a data science project

output: actionable insights

Optimize a sales funnel

  • Key steps:

    1. Collect Data: where are people coming from, time of day, machines type, etsc…

    2. Analyze the data: Iterate many time to get good insights insights from the data collected.

    3. Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.

Optimizing a manufacturing line

  • Key steps:

    1. Collect Data: clay supplier, mixing time, ingredients, lead times, relative humidity, temperature, kiln duration, etc…

    2. Analyze the data: Iterate many time to get good insights insights from the data collected. 

    3. Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.

Every job function needs to learn how to use data

  • Use data to optimize workflows through data science based analysis, and to take on tasks with machine learning (remember less than a second), Inputs (A) to Output (B).

  • From Sales, recruiting, marketing, to agriculture, and beyond DS and ML are having huge impacts

How to choose an AI project

  • Bring together a cross-functional team knowledgeable in AI, plus domain experts.

  • Brainstorming framework:

    • Think about automating “tasks,” vs automating “jobs.”

    • what are the main drivers of business values?

    • What are the main pain point in your business?

    • Note: you can make progress without big data

      • Having more data almost never hurts

      • Data makes some business [Google, Facebook, Netflix, Amazon] defensible.

      • With small datasets, you can still make progress. The amount of data you need is problem dependant.

  • Due diligence on project

    • What AI can do + Valuable for your business

      • Technical diligence

        • Can AI system meet desired performance (e.g. accuracy, speed, etc)

        • How much data is need to meet performance goals

        • Engineering timline

      • Business diligence

        • Current business: Lower costs

        • Current business: Increase revenue ( getting more people to check out)

        • New business: New product or business

      • *Ethical diligence*

        • money vs impact on society

  • Build vs. buy

    • ML projects can be in-house or outsourced

    • DS projects are more commonly in-house

    • Some things will be industry standard, avoid building those.

  • “Don’t sprint in front of a train.”

    • some times it makes sense to adopt another’s platform or approach than to build your own. resource constraints, capability constraints…

Working with an AI team

  • Specify your acceptance criteria

    • Goal: defects with 95% accuracy…How do you measure accuracy

      • Test Set (n1000): labelled training dataset to measure performance. 

    • Training Set: Pictures with labels

      • Learn mapping from A to B

    • Test Set: Another data set to test the mappings. Often more than 1 test set will be requested.

  • Pitfall of expecting 100% accuracy. Discuss with AI engineers what’s reasonable.

    • Limitations of ML

    • Insufficient data

    • Mislabeled data

    • Ambiguous labels

Technical Tools for AI teams

  • CPU vs. GPU [Great for deep Learning/Neural Networks] Nvidia

  • Cloud vs. On-prem, ….Edge [Processor, where data is collected.]

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1: AI for Everyone – Introduction to Artificial Intelligence – Notes

What is AI?

What is Machine Learning?

  • Supervise Learning… Input (A) to output (B) learning

What is Data?

  • Need to determine what is A (Input – Features) and what is B (Output) on the inputs
  • Acquiring data via manual labeling, observation, available downloads
  • Once you start collecting data, start feeding it to a AI team for a feedback loop to start. Start this early. 
  • You can not assume value in data, because you have a lot of it. Becareful, have an AI with to asses
  • Garbage in garbage out exists here too

The Terminilogy of AI

  • AI(ML, et al (DL/NN, et al))…Data Science cuts across all of these.

What makes an AI company?

What machine learning can and cannot do

  • Technical diligence: looking at the data, look at the input, and output A and B, and just thinking through if this is something AI can really do.
    • Can NOT do market research and write an extended market report
  • Complex interactions require lots of examples. the system would either remain vague in response, or return incoherent responses.
  • What makes an ML problem easier
    1. Learning s “simple” concept <= 1 second of human thought…Cause we have to formulate? why?
    2. Lots of data available
  • AI has a hard time of inferring intention of action in context A= hand gesture, B= stop, hitchhiker, left turn on a bike, hello, etc…

Deep Learning

  • Deep Learning and Neural Network are used interchangeably
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SAS Network Design Cheat Sheet

Transportation Problem

Step 1: Write proc optmodel;

proc optmodel;

That was easy.

Step 2: Create all your “assets”.

I call them assets, but you can call them whatever you want. Think of them as anything in the problem that we can assign attributes to. In this case, we have the Plants and the Regions. So we will create two sets, one for each. That’s why we use the command “set”.

set Plant={“Plant 1″,”Plant 2”};

set Region={“Region 1″,”Region 2”, “Region 3”};

I like calling things by their name, so I can remember them. Make sure to use {} for the sets, and to put each name inside quotes.

Step 3: Input all your data

This is all the information that in being shared in the problem. They will generally be attributes for each of the assets, or they can be constants. For all of these we use the command number:

number demand{Region} = [25 95 80];

number capacity{Plant} = [100 125];

number cost{Plant, Region} = [

250 325 445

275 260 460


The general nomenclature is that you give a name to the attribute, put what asset it describes inside {} and then put the values inside []. When writing tables in, make sure to write {rows,columns}. In the case of the cost, the plants would be the rows, and the regions would be the columns. Make sure that the order of the numbers you input matches the order of the items you defined in your set; SAS will assign the first number to the first item in the set, and so on. It also helps to space out tables to better read them and catch mistakes.

Step 4: Establish your variables

In this case, variables are the flows from the Plants to the Regions.

var flow{Plant, Region} >= 0;

Putting Plant,Region inside the {} means we will have a flow for each combination of Plant and Region. This means six variables will be created flow(Plant 1, Region 1), flow(Plant 1, Region 2), flow(Plant 1, Region 3), flow (Plant 2, Region 1), flow(Plant 2, Region 2), and flow(Plant 2, Region 3). Always remember to define what type of variable it is. In this case, we want our variables to be non-negative.

Step 5: Define your objective function

I called it z here, but you can call it anything you want, like TotalCost if you want to be more descriptive

minimize z = sum{i in Plant, j in Region}flow[i,j]*cost[i,j];

When you write sum {i in Plant, j in Region }, it’s the equivalent of doing a sumproduct in Excel. SAS will take every combination of Plant and Region and do the described calculation of flow times cost, and then add them all up. So in this case, it would take flow[Plant 1, Region 1]*cost[Plant 1, Region 1], then flow[Plant 1, Region 2]*cost[Plant 1, Region 2], do that for all the combinations, and then compute the sum.

I could’ve used p instead of i, and r intead of j, like sum {p in Plant, r in Region}flow[p,r]*cost[p,r]), or any other letter for that matter as long as I remain consistent and I haven’t used that letter to describe something else already.

Step 6: Establish your Constraints

In this case there are only two general contraints: capacity contraints for the plants, and demand constraints for the regions:

con capacitycon{i in Plant}: sum{j in Region}flow[i,j] <= capacity[i];

con demandcon{j in Region}: sum{i in Plant}flow[i,j] >= demand[j];

Let’s take as an example the first constraint. On the left side of the :, con capacitycon{i in Plant}, establishes the name of the constraint as capacitycon. The {I in Plant} means this is a constraint that applies individually to every Plant, so the constraint will be calculated individually for every item in the set Plant. This means this constraint really works as two constraints in this case, one using values for Plant 1, and another using values for Plant 2.

After the “:” we define the constraint itself. Here, SAS will take the flow and change the value j using every  region, and add them up, and it will compare that to the capacity of the Plant. Since we established before the “:” that the constraint would be calculated separately for each Plant, the Plant remains constant for each iteration of the constraint calculation. In essence, it would take the flow(P1, R1) + flow(P1, R2) + flow(P1,R3) and make sure that is less that or equal to the capacity of (P1). Again notice that the Plant doesn’t change; instead, the constraint will do the calculations for Plant 2 as a separate comparison, because we established the {i in Plant} on the left of the “:”.

Step 7: Wrap it up

solve; print z flow; expand; quit;

Don’t forget to check the log for any error (the middle tab between you code and the results). SAS will highlight any mistakes you would’ve made there. Just scroll up to see where the first red line of text is, and that was your first mistake. You can follow these steps any time you are using SAS with this notation.

Transhipment Problem

Network Facility Location Problem

    “transcost” was not used in the sas files, as it is not required when the transportation cost is $1. If it is any other value, will need to add that in.

Network Facility Location Problem w/ LOS

    copy and paste matrix from spreadsheet…

to calculate the binary table dynamically, see below from page  12


For the demand constraint “demandcon“, use an equality instead of inequality as otherwise the solution will create non-integer demand, just to meet the LOS constraint. 

Advanced Supply Chain Network Design

    Supply Chain Network Design Problem

Multi-Commidity Flow Problem:

    Data tables come before “proc optmodel;”


 INFILE Statement Options

DELIMITER= option—Specifies what character (other than the blank default character) to         use as the delimiter in files that are being read. Common delimiters include comma (,), vertical pipe (|), semi-colon (;) , and the tab. For example, to specify a vertical pipe as the delimiter, the syntax is DLM=’|’, as shown here: infile ‘C:\mydata\test.dat’ dsd dlm=’|’ lrecl=1024;

A tab is specified by its hexadecimal value. For ASCII systems (UNIX, Windows, and Linux), the value is ’09’x. For EBCDIC systems (z/OS and MVS), the value is ‘05’x. As an example, the syntax to specify a tab delimiter on an ASCII system is DLM=’09’x. Note: The positioning of the quotation marks and the x in hexadecimal values is critical. No space is allowed between the x and the quotation marks, as shown in this example: infile ‘C:\mydata\test.txt’ dsd dlm=’09’x truncover;

In my case I have troubles with the ASCII so I used dlm=’,’; and I separated the data with , and it run.    

Reading Delimited Text Files.pdf

/* Inputing values of multi-dimensional matrix incost in a table form first*/

Data incost;

infile datalines dsd delimiter=’09’x;

input Product $ Plant $ DC $ incost;


P1 Chicago Atlanta 6

P1 Chicago Boston 5

P1 Dallas Atlanta 4

P1 Dallas Boston 7

P1 Miami Atlanta 6

P1 Miami Boston 9

P2 Chicago Atlanta 6

P2 Chicago Boston 5

P2 Dallas Atlanta 4

P2 Dallas Boston 7

P2 Miami Atlanta 6

P2 Miami Boston 9

P3 Chicago Atlanta 6

P3 Chicago Boston 5

P3 Dallas Atlanta 4

P3 Dallas Boston 7

P3 Miami Atlanta 4

P3 Miami Boston 7



/* Inputing values of multi-dimensional matrix outcost in a table form first*/

Data outcost;

infile datalines dsd delimiter=’09’x;

input Product $ DC $ Region $ outcost;


P1 Atlanta NY 8

P1 Atlanta VA 5

P1 Atlanta PA 6

P1 Boston NY 9

P1 Boston VA 7

P1 Boston PA 6

P2 Atlanta NY 7

P2 Atlanta VA 8

P2 Atlanta PA 5

P2 Boston NY 3

P2 Boston VA 8

P2 Boston PA 6

P3 Atlanta NY 7

P3 Atlanta VA 4

P3 Atlanta PA 4

P3 Boston NY 4

P3 Boston VA 5

P3 Boston PA 4



Fixed Planning Horizon Problem

Aggregate Planning And Distribution Channel Strategies

Aggregate Planning (including numerous factors like hiring and firing, production levels, etc)

Aggregate Planning with Demand Elasticity (including factors like hiring and firing, production levels, discounts, etc)

Omni-channel Network Design

Reverse Logistics…for Batteries

Optimization Based Procurement

Simple Auctions

Capacity by Lane Constraint

Level of Service Constraint

Supplier Capacity Constraint

Minimum $$ Volume Constraint

Combinatorial Bids

Combinatorial Bids (Min 2 carriers) – Add the following constraint to the previous model.

SAS Files


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Three Quick Thoughts: Amazon Go 3000 #OPINION

  1. Mechanical Turk at Work: Given the complexity involved in training “AI’s” to identify people, identify products, and associate and disassociate the two, I believe that humans are in the loop. This integration of humans and AI is referred to as collective intelligence, and I believe it’s behind the assuredness of the system…and that is O.K. This would be a smart use of humans to accelerate the deployment of the platform, which allows for more data to be collected. This type of system requires a TON of data to build a corpus that would allow it to make high confidence predictions autonomously. I’m sure that system has learned a lot since launching to just employees; it’s still a baby. MTurk crowd can review video leveling judgement calls, in low confidence situations, and continue to label data to drive increased “understanding” for the system. Again, this would be smart, creating more labeled training data, never hurts. With Chicago being added to Seattle…Mo’ data, Mo’ data! Everyone always gets caught up on the number of cameras, and “…look, there are still employees.” It’s truly about the data, not the current quantity of infrastructure and staff. As more data is collected….less and less of both will be needed; on an asymptote of a curve.

  2. Ready for Americas Next Killer Franchise: I don’t think Amazon is going to stray from what has worked with extending their capabilities as a service. We’ve now seen it with AWS infrastructure service, FBA, Amazon Delivery Service Partner Program, and other marketplace services. However, I believe the business model will be franchise based, helping that entrepreneur own their own business – “Amazon Go Store Partner Program.” That is how I think the “just walk out” technology will become a profit center,  and not just a capital cost of doing business for Amazon.

  3. Real-Estate Discovery, Check: Amazon has considered the implementation of physical stores for a very long time. I would bet a majority of the real-estate discovery work was completed as a part of the Amazon Book Store planning process. This pre-work, will allow them to move more quickly with targeting specific locations for prospective franchisees.


  • I predict that “Just Walk Out” technology will be in a Whole Foods within 18 months. Instead of aligning with the naysayers who say it can’t be done in a bigger box store, Amazon will do it!
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What is your customer data share?

By the year 2020, it is estimated that the average person will generate 1.5 GB worth of data per day. At that point, it won’t be just about your firm’s share of customer wallet; instead, you be asked… what’s your firm’s customer data share? Oh, and consider today who owns the majority of insight in to “your” customer.

[Infographic Credit: Domo]

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#010: This Game is Not One-Dimensional, You Have to Face the Customer with Shawn Harris, Global Innovation Strategy Lead, Zebra Technologies.

In this episode of The IoClothes Podcast, we speak with Shawn Harris, Global Innovation Strategy Lead for Zebra Technologies. The reality is, innovative products don’t just sell themselves and companies aren’t composed of just designers, developers and engineers. Someone has to interface with the customer, and keep the ship sailing along a strategic path, which includes profitability (that’s if you want to stay in business). Today, we shift gears and talk a bit about the struggles of retail, the importance of differentiating yourself in the marketplace and how are current relationship with MS Excel may be a sign of the future!

For more information on Zebra Technologies, check out their website at, on Twitter @ZebraTechnology and on Facebook @ZebraTechnologiesGlobal. You can also follow Shawn on Twitter @SmarterRetailer

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AI and the Final Judgement.

I believe that for the foreseeable future, human machine collaboration, which will lead to a new type of collective intelligence will be a requirement. Today, we are seeing many advances in the ability of machines to make high confidence predictions; in narrow use cases. This will certainly lead to significant shifts in tasks within job roles. However, where I think machines will lack for some time is in judgement. Judgement being the ability to consider multiple predictions, and come to sensible conclusions in context. This is where humans will need to remain in the loop, playing a key role in judgement. This human machine partnership will lead to a collective intelligence that will results in even higher confidence outcomes, than either could realize individually. However, this prescribed union could quickly find the machine taking more and more of the judgement role as well. Just consider Google’s search results; at this point, how many of us ever get to the second page of results. What’s happening here is that we are trusting Google algorithm’s judgement. We could debate the consequences of this for search results; as we endeavor to use AI in medicine, autonomous cars, and other decisioning based on potentially biased data, we will need to take a more deliberate approach to the policies and practices around final judgement.

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Startups at the Intersection of AI and Blockchain

  • Decentralized Intelligence: TraneAI (training AI in a decentralized way); Neureal (peer-to-peer AI supercomputing); SingularityNET (AI marketplace); Neuromation (synthetic datasets generation and algorithm training platform); AI Blockchain (multi-application intelligence); BurstIQ (healthcare data marketplace); AtMatrix (decentralized bots); OpenMined project (data marketplace to train machine learning locally);
  • Conversational Platform: Green Running (home energy virtual assistant); Talla (chatbot); (quantified biology and healthcare insights);
  • Prediction Platform: Augur (collective intelligence); Sharpe Capital (crowd-source sentiment predictions);
  • Intellectual Property: (IP discovery and mining);
  • Data provenance: KapeIQ (fraud detection on healthcare entities); Data Quarka (facts checking); Priops (data compliance); Signzy (KYC)
  • Trading: Euklid (bitcoin investments); EthVentures (investments on digital tokens). For other (theoretical) applications in finance, see Lipton (2017);
  • Insurance: (P2P insurance), Inari (general);
  • Miscellaneous: Social Coin (citizens’ reward systems); HealthyTail (pet analytics); Crowdz (e-commerce); DeepSee (media platform); ChainMind (cybersecurity).

Read the full article: The convergence of AI and Blockchain: what’s the deal? by Francesco Corea

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