Cognitive Computing Systems: Want to build a great MVP? Pick your ingredients carefully

Over the last five articles I have hopefully presented a very strong business case for the value that a cognitive computing system like Herbie can bring to startup founder like Keith and his team. They have successfully collaborated up to this point and have developed a very strong product vision:

We believe that we can build a cognitive computing system that acts both as an early warning system for students “at-risk” of dropping out of high school and as a virtual education “doctor.” The system would be capable of diagnosing in real-time the reasons why a student is likely to drop out and then be able to specifically tell all relevant stakeholders what actions they could and should take to help the student qualify for graduation. Student Lifecycle Management is a key component to solving the graduation problem and it will be powered by a national cognitive computing system.

Now that we know what we want for dinner we need to talk about the recipe we want to use. The recipe I’m referring to in this case is the specific product features and benefits that will bring our solution to life. By leveraging all of the complex analytics completed up to this point and including the lessons learned from our direct customer interaction, Herbie is ready to help Keith develop and finalize one the last important steps before delivering the Minimum Viable Product (MVP): Documenting the product features list.

The recipe for your MVP is a list of the specific product features and benefits to your solution Click To Tweet

The product features list is a one-page document consisting of one or two sentence summaries of the top 10 (or fewer) features of the complete product vision. Essentially, this is the product development team’s contract with the rest of the company. The biggest challenge will be deciding what features will ship in what order. Developing the MVP will start the prioritization process.

The product features list is a 1 page document of the top features in your product vision Click To Tweet

Customers will be critical in guiding the process as they begin interacting with the earliest version of the MVP. You can think of features as the things that the engineering team is building, and the benefits as the problem you are solving for the customer. Your goal is to describe the specific product benefits as seen through the customer eyes (Something new? Better? Faster? Cheaper?) to then develop a “user story.”

A user story is a short narrative that explains what job the product will do. How will it solve a problem that customers are eager to fix, or fulfil a need they have? Ideally, the product solves a mission-critical problem, delivers a compelling, exciting customer benefit, or addresses an unspoken need:

  • Unfortunately, by the time a high-risk student reaches high school as a freshman, many lack both the academic and social skills necessary to see the long-term value of staying in school;
  • We lose as many as 55% by the time their class becomes seniors;
  • Educated workers are the basis of economic growth. They are especially critical as sources of innovation and productivity given the pace and nature of technological progress;
  • Studies show that the typical high school graduate will obtain higher employment and earnings — an astonishing 50% to 100% increase in lifetime income — and will be less likely to draw on public money for health care and welfare. They will also be less likely to be involved in the criminal justice system. Further, because of their increased income potential, the typical graduate will contribute more in tax revenue over their lifetime than if they had dropped out;
  • When the costs of investment to produce a new graduate are taken into account, there is a return of $1.45 to $3.55 for every dollar of investment, depending upon the educational intervention strategy. Under this estimate, each new graduate confers a net benefit to taxpayers of about $127,000 over their lifetime. This is a benefit to the public of nearly $90 billion for each year of success in reducing the number of high school dropouts by 700,000 — or something close to $1 trillion after 11 years;
  • All stakeholders who significantly impact an individual student’s decision to stay in school until graduation will receive real-time advice on how to effectively collaborate to minimize or eliminate the negative environmental elements that influence many to dropout prior to graduation; and
  • This transaction based omni-channel student lifecycle management cognitive computing system will be the tip of the spear that drives behaviors that will enable the US to achieve a national graduation rate of 90%+ by the year 2020.
A user story is a short narrative that explains what job the product will do Click To Tweet

Remember that at this early stage we are still in the process of developing hypotheses that we must test and validate with real customers. All that is required is to follow the customer discovery process with ideas that are reasonable educated guesses. The goal is to quickly move into building an effective MVP that you can put in front of your prospective market for immediate feedback and iteration.

Creating an MVP can increase development productivity between 14 – 95% while reducing costs by 7 – 29%. Being able to quickly bring a product to market, gather crucial feedback, and iterate on it in real-time is a formidable competitive advantage.

Creating an MVP can increase development productivity between 14-95% and reduce costs by 7-29% Click To Tweet

Do you feel comfortable with the process of defining your product features and benefits? Do you believe that Keith and Herbie have collaborated well up this point and come up with a great product vision? With the help of a cognitive computing system like Herbie, do you think that it is possible to achieve a national graduation rate of 90%+ by 2020?

Let me know what you think.

Cognitive Computing Systems: Want to build a great MVP? It’s all about the prescription…

As I discussed in my last article, with Herbie’s assistance Keith and his team determined that being able to proactively predict the likelihood that an at-risk student is going to dropout of high school would be a key component of their product vision. The other half of the equation involves the actions that an educator and the relevant stakeholders should take once a specific problem with an at-risk student has been identified. This involves a brief discussion about the science of prescriptive analytics and how Keith and his team plan to leverage this capability to help complete their product vision.

As a cognitive computing system, Herbie was instrumental in developing the analytical models (called the High School Dropout Propensity Score) needed for early identification of at-risk students. He also took the next step by leveraging prescriptive analytics to provide educators and relevant stakeholders an interactive voice that would offer them iterative advice on how to set the student back on track. This article will define prescriptive analytics, as well as the role it will play as part of our Minimum Viable Product.

Prescriptive analytics builds upon descriptive analytics and predictive analytics; it predicts what will happen, when it will happen, why it will happen and what the best course of action is to optimize outcomes and reduce risk. These solutions combine predictive models, deployment options, localized rules, scoring and optimization techniques to form a powerful foundation for decision management.

Prescriptive analytics predicts what, when & why something will happen and the best course of action Click To Tweet

As a follow-on to the extensive hypothesis development and testing being continually performed by Herbie, he will begin to use those simulated outcomes to develop a prescriptive list of actions that should be taken when those “conditions” occur again in the real world. Machine learning will allow Herbie to build a comprehensive library of real-time actionable “prescriptions” that will directly impact how effectively and efficiently educators and relevant stakeholders serve their at-risk students.

Machine learning can build a comprehensive library of real-time actionable “prescriptions” Click To Tweet

In the process of building this prescriptive library, Herbie and the team will focus on learning the characteristics, attributes and behaviors of the following demographics: 

  1. Low income students: The link between low income and low academic performance is strong, but research shows it is solvable. Among non-low income students, 40 states are above the national average graduation rate of 80%. However, among low-income students, 41 states are below the national average. The good news is states with narrow achievement gaps between low-income and non-low income students appear to be those with the most robust interventions in place to counteract the effects of poverty;
  2. Big city dwellers: While there are nearly 200 fewer dropout factories in urban areas in 2012 than in 2002, more than half of those remaining are located in large urban areas. Most big cities with high concentrations of low-income students still have graduation rates in the 60s, with a few even in the 50s;
  3. Students with disabilities: The national average graduation rate for students with disabilities is 20 percentage points lower than the overall national average. While graduation rates for these students varies greatly by state, these students represent 13% of all students. Without gains nationwide, a 90% graduation rate cannot be reached;
  4. The state of California: As the most populous and most diverse state, California needs to be the focus of national attention and work. With the highest poverty rate in the country, a median household income 20% higher than the nation’s, and a population that is 61% non-Anglo, California is key to reaching the 90% graduation rate nationally, but it also remains a laboratory of innovation in education reform. California has 14% of the nation’s total students, and 20% of the country’s low-income student cohort. The school age population is 52% Latino and 12% Asian/Pacific Islander, with a poverty rate among school age children of 63%; and
  5. Young men of color: Despite gains made by all students of color over the past six years, young men of color continue to lag behind other subgroups of students. In a sub-set of Midwestern and Southern states, which educate a large percentage of African American students, graduation rates for African American males remain in the upper 50s and low 60s.

It is important to think of prescriptive analytics as one of the many successful outcomes possible with a well-designed cognitive computing system. I believe that the real value that Herbie offers the founding team is a single-minded focus on finding better approaches to solving the designated problem based on real data received from real-life attempts to solve the problem with real customers. The only way to reduce bias is to minimize the programmer’s pre-conceived notions of the problem from the equation.

Cognitive systems offer founders a way to solve their problem based on real data from real customers Click To Tweet

The ideal process should look like this:

  • The founding team presents Herbie a problem in the form of a question;
  • Herbie understands the context of the question and performs the necessary analytics to formulate an answer;
  • The founding team considers the answer and makes a decision as a team;
  • The founding team tells Herbie their decision. Herbie then tracks and records the results and impact of that decision; and
  • Based on this data, Herbie learns lessons and adjusts his approach to thinking so that the team will achieve better results and outcomes next time.

With Herbie’s help, Keith and his team have enough information to put forward their first product vision. Their first draft sounds something like this:

  • The United States used to be number one for high school graduation, but times have changed. In 2009, the U.S. ranked 21st out of 26 OECD countries when it came to high school graduation rate according to Andreas Schleicher, Deputy Director for Education for the Organization for Economic Co-operation and Development (OECD);
  • Portugal and Slovenia were tied for first in the rankings, Japan and Finland hold the number two spot, and the Czech Republic ranks 17th;
  • We believe that we can build a cognitive computing system that acts both as an early warning system for students at-risk of dropping out of high school and as a virtual education “doctor” capable of diagnosing in real-time the reasons why a student is likely to dropout, and then be able to tell all relevant stakeholders what specific actions they could and should take to help them qualify for graduation;
  • Millions of students, parents, educators and third party stakeholders will be able to collaborate at a level that was not possible before; and
  • By proactively communicating and executing best practice strategies for reducing dropout rates through the medium of their choice in real-time, we can leverage the entire global community to make sure that every child has a 100% chance of graduating.

This is just an example of the many different ways that Keith and his team could have drafted their product vision, but the documentation process is a crucial first step. It is important during these early stages that everyone agrees on a specific product vision so that the product development team has as clear a mandate as possible before building the MVP.

We can leverage the entire global community to make sure that every child has a 100% chance of graduating Click To Tweet

Now that the team has established a certain level of clarity on the product vision, it is now time to move forward with formally defining the product features and benefits. We will cover that in my next article.

Do you understand the power of prescriptive analytics and how it might be utilized to make sure that the right action is taken at the right time? Do you agree that focusing on the five demographic areas listed above will have the biggest impact on increasing our graduation rates?

Thanks in advance for your continuing feedback.

Lean Market Development: How to turn an educated guess into a scalable business model

In my previous article, our fictional startup founder Keith continued his quest to discover a solution that will increase our national high school graduation rate from just over 80% in 2014 to 90%+ by the year 2020. He narrowed the team’s focus to four key elements of the problem and with the assistance of his cognitive computing partner Herbie, the team was able to establish a strong Enterprise Data Architecture (EDA) for the project. All of this puts them in a great position to take the next step towards bringing a solid Minimum Viable Product (MVP) to market: Hypothesis Development & Testing.

An MVP is a concise summary of the smallest possible features that will work as a stand-alone product while still solving the core problem and demonstrating the product’s value. As this is our final destination for this phase of customer discovery we have a few questions we need to answer in the context of the graduation problem before we get there:

  1. What is the long-term product vision for the solution?; and
  2. What are the initial product features & benefits? (Why should people use or buy it?)
Your long-term product vision for your MVP must clearly solve the graduation problem Click To Tweet

According to GradNation, the high school graduation rate, as measured by the Averaged Freshman Graduation Rate, increased from 71.7 % in 2001 to 81 % in 2012. While we are making great progress, minorities, low-income and the handicapped continue to lag behind. Our ability to reach our goal of 90%+ by 2020 will depend almost entirely on our ability to increase our performance in these three demographics. Under the new criteria set by the Every Student Succeeds Act (ESSA), low-graduation-rate high schools (dropout factories) are defined as schools that enrol 100 or more students and have graduation rates of 67 % or less.

The Every Student Succeeds Act addresses the problem of high school dropout factories Click To Tweet

ESSA mandates that these schools use evidence-based reforms that correlate with measurable improvements. There is an endless combination of factors that could account for why these students are dropping out. The only way to be sure is to scientifically test each and every possible theory and to record each and every outcome. This is an extremely time-consuming and resource intensive activity that is often skipped due to either lack of resources or lack of manpower. Herbie can bring his entire cognitive computing infrastructure to bear for Keith in order to alleviate the need he would normally have for these resources. Lower costs and higher profits will be a direct result.

Herbie utilizes some of the latest machine learning algorithms available (i.e. Linear Regression, Logistic Regression, k-Means, Support Vector Machines, Random Forests, Matrix Factorization/ SVD, Gradient Boosted Decision Trees / Machine Naïve Bayes, Artificial Neural Networks, Expectation Maximization etc.) to develop and test the veracity of hundreds and thousands of hypotheses. There are 3 types of predictions typically sought from using these algorithms and they include:

  1. Binary classification which is used to predict the answer to a Yes/No question;
  2. Multiclass classification predicts the correct category from a list; and
  3. Regression predicts the value of a numeric variable.

The standard scientific method of putting forward a testable hypothesis that the team will use is as follows:

“If _____[I do this] _____, then _____[this]_____ will happen.”

For example, “If we assign an adult tutor to an at-risk student the very first time they score below average in a core ‘subject’ test area, then we expect that student to experience a ‘letter grade’ increase over the course of a school term”. This is one of many educated guesses that will need to be made in order to perform the necessary experiments to turn these guesses into verified facts so that real-life decisions can be made in real-time.

Asking the “right” questions is critical if we want cognitive computing to give us the right answers Click To Tweet

The goal of standardizing a specific scientific hypothesis testing methodology is to ensure both data and results integrity throughout the analytical model construction process. Herbie will leverage these models to simulate and test countless variations on Keith’s hypotheses until acceptable patterns of causation begin to emerge. This will allow the team to quickly identify complex business problems that he and his team can profitably address.

Achieving a 90% graduation rate by 2020 will require focus on five key areas:

  1.  Closing the opportunity gap for low-income students;
  2.  Tackling big city ‘dropout factory’ challenges;
  3.  Making handicapped students part of the solution;
  4. Focusing on ‘urban’ California; and
  5. Accelerating the lagging graduation rates for young men of color.

My next article will discuss how Herbie will help Keith the best approach to gaining unique insight into these challenges and how that insight will affect their product vision.

Cognitive Computing can help close opportunity gaps for impoverished students Click To Tweet


Do you see how extensive hypothesis testing very early in the startup development process can drive long-term success? Can you see the potential time and cost savings that a cognitive computing system could offer a founder?

I look forward to hearing your feedback. Stayed tuned for more on this story.


Building the ‘perfect’ tech startup: The ‘customer discovery’ process

Imagine that you are out one night, having your favorite beverage, when inspiration hits (as it sometimes does at these institutions) and you say out loud “It would be great if only people could do “xxxx” which would give them the benefit of “yyyy”. You have just told the universe your big plan for changing the world. Unfortunately you couldn’t find a pen in time to write it down and the moment and the thought passes.

Fortunate for you, the cognitive system (let’s call them Herbie), listening through your Apple watch, heard everything you said.

“Sounds good to me. Do you also want to know if anyone else also feels that this is a problem?” asks Herbie.

“Sure, and while you are at it, can you also let me know if there is anything that exists in the market today that solves this particular problem,” you respond.

“Cool, just give me a few minutes and I’ll let you know what I find,” and Herbie is off to research your great idea.

Herbie begins to systematically search every social media application from Facebook, Twitter, and Instagram to slideshare decks, YouTube videos, chat rooms and forums. It’s listening in on what potential customers and competitors are saying, analyzing the context of those conversations, drawing out insights about what problems they are experiencing. It’s trying to determine whether one of those challenges being discussed “sounds” like your problem, and the level of success they are having in trying to resolve it.

A few minutes later Herbie calls your watch and tells you that he has compiled a list of customer problems that your idea might solve, along with potential markets, target customers, preferred distribution channels and even preferred pricing models. You tell Herbie that you’d prefer the information was collated into your preferred cloud project management tool so you can review it, and Herbie begins to execute your request. Because Herbie has already taken the liberty of ranking each problem and solution based on criteria that you helped create, all you have to do now is review the information and tell Herbie which theoretical or hypothetical problem you would like help testing first. This is the future of collaboration enabled by collaborative systems

This is also the future of the customer discovery process. As the first phase of the Customer Development Process, it involves taking a founders’ vision and turning it into a series of business model hypothesis. These are then used to develop a plan to test customer reactions, with the ultimate goal of turning the theory into facts. When you consider that 9 out of 10 startups fail, it makes sense to take the time to do some hypothesis testing before you go too far down the track.

9 out of 10 startups fail so it makes sense to do some hypothesis testing before you go too far down the track. Click To Tweet

After all, it’s at the beginning of the startup process, where many founders make their biggest mistakes. Some fall so in love with their big idea that they rush to build something hoping that customers will come in the future. According to CB Insights, the number one reason startups fail, a whopping 42%, is because there’s no market need for their solution. There are still others who wrongly assume who their target customers will be, and develop a business model to suit the wrong market. A spear used for fishing in streams doesn’t work so well in the open sea. As a founder, you owe it to yourself and your future investors to constantly test your assumptions about your target market. Unfortunately, most startups skip this crucial step.

42% of startups fail because there’s no market need for their solution Click To Tweet

This is where having access to a cognitive system like Herbie can add tremendous value. With real-time access to virtually unlimited amounts of information, combined with the capability to contextualize, understand and learn from it. Herbie will help you experiment with new concepts that were previously either too expensive and or time consuming to pursue

Sound like the plot of next summer’s science fiction movie blockbuster? The answer is yes (I’ll be buying my advance tickets next week), but there’s more than a little bit of reality in these concepts.

Scientists have already created hypothesis generation software that finds new flavor combinations (pork and strawberries work a treat together, who knew?). And software like BrainSCANr is helping neuroscientists select and hypothesize research projects that may solve serious medical problems based on a range of seemingly disconnected information. These innovators have used the technology to sift through millions of recipes and research papers, to identify trends and clues about what flavor profiles work well together or what research gaps exist.

Cognitive systems can now filter information from much broader data sources, including videos, sentiment in social media and verbal conversations. With 80% of the world’s data now unstructured, and with technology become smarter everyday, it’s only a matter of time before they can bring all of this together to create more complex hypotheses.

It’s only a matter of time before #cognitive systems can create more complex hypotheses Click To Tweet

Once you have a hypothesis, the next step is to test it, refine it, and iterate on it until it’s ready for the next phase, customer validation. This involves getting out of the office and conducting hands on market research with your target customers. Herbie can help you execute a highly coordinated outbound marketing campaign in the most optimal time and cost efficient manner possible. Herbie will work with you to provide a list of probing questions to ask potential customers to help test the hypothesis. And if requested, Herbie can actually make the calls for you, ask the questions, record the responses, analyze the results and give you an opinion on what was and what wasn’t a good hypothesis. But I don’t know if I’m ready to advocate for the latter, as there’s no replacement for direct customer interaction (at least not yet).

When you meet with your customers, tell Herbie to listen and record the interview, and help guide you through the interview process. This might include putting together a list of questions like these; Do they feel the same way you do about the nature of the problem? What do they believe is the source of the problem? How much pain is this problem causing them both professionally and personally? And most importantly, how much would they be willing to pay to fix it?

During the course of the interview, Herbie sends a vibration alert through your phone with a new question to ask, based on the previously received responses. Herbie has been constantly evaluating your prospects responses, checking them against previously received responses, and relevant data sources in order to assist you in refining your hypothesis in real-time. As a result, a bigger picture begins to form on an alternative or more refined market application for your idea. It will suddenly be possible to have multiple “Ah-Ha” moments during this phase of the process. And what entrepreneur wouldn’t want to have multiple “big ideas” to choose from?

Consider a cognitive system like Herbie more like a business conscience than a market prophet. It can’t predict the future, but it can tell you the success potential of your ideas, based on the past and supported by the present. Herbie analyzes massive amounts of information, identifies patterns hidden within, weighs all available options, and offers plausible ideas. Herbie’s ultimate goal is to give you both the insight and the time to convert the best hypothesis into an actionable set of facts. This is the best way to begin your startup journey.

Cognitive systems can tell you the success potential of your ideas Click To Tweet

Of course, as much as we like the cognitive computing power Herbie bring us, it does have a few “kinks” in its innovative armor:

  1. It’s not easy to obtain or validate the petabytes of data required to properly feed this system. After all, not everyone will have a “data lake” in their backyard. As you may not have your own customers yet, you’ll need to access to a diverse set of publicly available data sources. Although I see a future where raw, validated market data may become generally available, I see challenges in the near-term. As a founder, it is also important to step back and look for information that may be relevant in new places. The Internet is awash with data, as over-sharing has become both a burden and a blessing. The challenge may be just finding where potential customers are voicing their problems and discussing potential solutions; and
  1. The challenge of active listening. Listening and responding to another person in a way that improves mutual understanding is difficult enough between humans. The communication between a passionate founder and Herbie may involve working through some trust issues, as natural human bias may color receptivity to “machine” feedback. It may take some time for Herbie to acquire both the data and the “experience” before a founder trusts their opinions. Until that day comes, it may be hard for the typical “control freak” founder to loosen their grip on the current reality and see things rationally.

By leveraging big data assets, machine learning algorithms and natural language processing, cognitive systems can quickly guide you through the first and most important phase of the customer development process. An effective Customer Discovery process builds a strong foundation that will allow you to build the strongest business plan and enterprise possible. After all, the most successful founders are 78% more likely to have created a formal business plan first. Don’t be the founder that builds their “dream” application first, only to then try and find no-one besides themselves actually wants it. Cutting this process down from months to a matter of days, allows an aggressive founder to quickly pivot, either forward or backwards, before betting “against the house” and begin their startup journey.

Successful founders are 78% more likely to have created a formal business plan first Click To Tweet

Now that Herbie has gotten you through the first phase of the Customer Development process, it’s time to take it to the next level. The level where you validate whether your business model is capable of being both repeatable and scalable. This will represent the first true pivotal moment for your startup.

Next stop? Customer validation.

I’d love to hear at least two valuable pieces of feedback from you. Do you think the “customer discovery” process is an effective way to begin your startup journey? Can you imagine the value of having a cognitive computing system like Herbie guiding you through this critical phase of the start-up process?

Welcome to the era of cognitive systems….


Blank & Dorf, “The Startup Owners’ Manual: The Step-by-Step Guide for Building a Great Company.”

Can a cognitive system build the ‘perfect’ tech startup?

At this very second, somewhere in the world,  there are 3 new high tech startups opening up for business . At that very next second, there are 3 that are waving goodbye.  Every day 3 new high tech startups open for business and another 3 wave goodbye Click To Tweet To a tech insider this is hardly breaking news but to the casual observer it’s scary business. Everyone knows that “starting-up” is extremely risky, so imagine what it would be like if everyone suddenly had the ability to bring their “big idea” for changing the world to market in the shortest possible time, with the least amount of staff and at low cost. Even better, what if you could speak to an “advisor” that could definitively tell you within a few short weeks/months of being in business whether you were riding a “unicorn” or you were feeding a potential “money pit”. Imagine if an advisor could tell you whether you're riding a unicorn or feeding a money pit Click To Tweet This is the promise of the cognitive era, and that future is right around the corner.

In the story of Pinocchio, Jiminy Cricket acted as both his spiritual advisor and sage. Although it can’t mimic his personality (at least not yet), a cognitive computing system will be able to come close. It has the ability to listen to your questions, quickly sift through massive amounts of information to give you relevant answers, and continuously learn from the decisions you make so that its next answer is better. Imagine the power of having a “partner” that has access to every lesson learned by all the startups that ever existed. Lessons that they used to give you insightful advice at every stage of your building process. Your partner will help you evaluate your big idea, validate the need in the market, let you know if your idea can scale, and finally help you graduate to a fully functional enterprise.

Sound like a Tony Stark invention from the last Iron Man movie? The fantasy is not so far from becoming a reality. Very soon now, a well-designed enterprise cognitive system will be able to accomplish this and more.

It is common for most startups to stumble for months or even years trying to build the perfect product, identify their target audience and find the ideal business model, while simultaneously burning through mountains of cash and time as they go. There are many published “how to” manuals advocating a structured approach to creating a viable startup. One of the best is put forward by startup strategy specialists Steve Blank and Bob Dorf in the their tremendous reference guide “The Startup Owners’ Manual: The Step-by-Step Guide for Building a Great Company,” These guides offer an excellent methodology and terrific insights, but it’s extremely difficult to both absorb and leverage 1000’s of pages of detailed and actionable content while also executing a business in real-time. Consider the sheer volume of information involved, the expertise required to determine the veracity of the information, the variety of actions you could potentially pursue, and the velocity of change you would be required to manage, This is a key area where cognitive systems will clearly add value.

There are four phases that every startup must manage and navigate through in order to achieve success:

Customer Discovery: In this phase the business is not only trying to test the founder’s vision but also identify markets, customers, channels and pricing. Is there a market?

Customer Validation: A business model is identified and a startup sees whether it can actually sell its product or service. Is it repeatable and scalable?

Customer Creation: Once the business model has been tested, it’s time to scale. This involves building end-user demand and converting sales. What is the potential?

Company Building: Once a valid business model is found it’s time to graduate into a fully-fledged company. How can the operation scale?

The goal of a cognitive system is to proactively gather massive amount of information, offer active business insights and offer a clear path for a “founder” looking to quickly build a successful company.

A cognitive system can provide a clear path for a founder looking to build a successful company quickly Click To Tweet

As defined by Sue Feldman, founder and CEO of Synthexis, cognitive computing “allows people and machines to work together in easy, intuitive ways.” Cognitive systems learn at scale, reason with purpose, and interact with humans naturally. With massive amounts of information available at our disposable in structured and unstructured formats, cognitive computing enables people to effectively interact with and leverage that data.

In order to be effective in the startup world, a cognitive system must exhibit and offer four key capabilities:

It must be Adaptive: Being able to pivot your strategy as necessary to respond to rapidly changing market conditions is key to a startup. A cognitive solution will be able to monitor and predict these external changes and offer real-time advice on how to adapt.

It must enhance Interaction: There are many stakeholders (both people and technology) that must interact seamlessly in order for the business to grow. Being able to absorb and translate feedback in real-time is a key capability.

It must offer Iterative and Stateful feedback: By helping to identify operational business problems, proactively seeking supportive data to determine impact, and applying lessons learned from past actions to recommend “tweaks” in business strategy, it will significantly increase the chances of success for your enterprise.

It must provide Contextual Information: Being able to able to intuitively judge how complex elements could impact your business. For example, being able to understand how the use of certain words by internal and external stakeholders may affect your operations or how a seemingly simple change in government regulations could derail your expansion plans. These are the real actionable insights a startup needs to survive and thrive

The end goal is to translate these “capabilities” into an active support system for a founder seeking to build a solid and scalable organization. It must be able to act as an insightful “expert advisor” capable of successfully shepherding them though all four phases of the Blank and Dorf startup life cycle.

In the future I see cognitive systems capable of either producing the next “unicorn” or, even better, letting a founder know when it’s time to “turn out the lights” and say goodbye as soon as possible.

Over the course of the next four posts, I will expand on my vision on how cognitive systems will transform the high tech startup industry beginning with how to determine if there is a market for your idea.

Welcome to the cognitive era. Are you ready?


Blank & Dorf, “The Startup Owners’ Manual: The Step-by-Step Guide for Building a Great Company.”