Human-Centered AI

As with many families, education, the future of jobs, technology and continuous learning are major topics of conversation around our home these days.  With two high-school seniors about to start their college application process, a wife in the middle of graduate school and my required continuous education across the latest and greatest in technology, our dining room table is frequently covered in books, laptops and research papers.  With my professional responsibilities including the use of emerging technologies, AI is usually at the center of our conversations.  We debate the widespread fear that robots and AI will eliminate jobs in the near future and alter career opportunities for college graduates and senior executives alike.

According to a 2014 Pew Research Center study¹ asking almost 1,900 experts about the impact of emerging technologies, half of the experts interviewed (48 percent) envisioned a future in which robots and AI will have displaced large numbers of both blue- and white-collar workers.  Their feedback went on to express concern that these changes will also lead to massive income inequality, masses of “unemployable” people and breakdown in our social order. Although this report is now a few years old, numerous other more recent studies highlight the very uncertain future jobs market and paint a dystopian future.  So, should my teenage sons be concerned about their future opportunity? After 25 years in the technology industry, should I?

I choose to take an optimistic view of the future.  I believe AI and other similar technologies will create new opportunities (albeit very different opportunities) that will only enhance the human experience.  “Human-centered AI” is all about unlocking the full potential for human and machine interaction and driving a future in which economic opportunities are expanded.

In the terrific book Human+Machine, authors Paul Daugherty and H. James Wilson argue that there are certain activities, such as leadership and judgement, that will remain human-only.  On the other hand, activities such as prediction or repetitive transaction management are ideal for machine-only.  The real power, and our collective future opportunity, comes from those activities that bridge the two silos, which the authors call “human-machine alliances.” Activities in this space are driven by humans enabling machines and machines augmenting humans.  In both cases, new economic opportunities are created.

A perfect example of this human-machine alliance is GE’s digital twin implementation.  In this example, power plant workers converse with AI agents to determine the required maintenance activities for a plant’s turbines.  Because the workers wear AI headsets, the computer can show the human exactly where damage repair or maintenance is required. The AI can also suggest next-best actions for resolving the issues in real time.

So, should my sons and I be worried about the future?  I believe the cup is half full.  Those of us currently in organizational leadership roles should be fostering organizational cultures of creativity, collaboration and competency to capture the exponential power that is possible through AI.  In most cases, this will require re-skilling and continuous learning, but this is the opportunity of the experienced workforce.  Organizational leadership should be strengthening the alliances between humans and machines now, so that when the next generation of employees finish college, including my sons, they face a job market and a future filled with growth.

1 Aaron Smith and Janna Anderson, “AI, Robotics and the Future of Jobs,” Pew Research Center, August 6, 2014.

AI - The Transformational Technology of the Digital Age

Remember SMAC?  It wasn’t long ago that those of us working on “digital” solutions were almost entirely engulfed in a future focused on four key technologies – Social, Mobile, Analytic and Cloud. Organizations were trying to incorporate social into their customer service operations, deliver responsive experiences over every device and overcome the security concerns that paralyzed their decision to move to the cloud.  While all four remain incredibly important, it seems few business conversations these days focus exclusively on one of these domains.  Instead, today, Artificial Intelligence (AI) if the focus from boardrooms to basements.  AI now stands out as the transformational technology of the digital age.

There are many reasons why this shift has happened so quickly.  Obviously, storage costs continue to fall, the proliferation of data and data sources continues to sky-rocket and compute continues to become more powerful.  Just as important, public cloud providers continue to improve, and add to, the impressive machine learning and deep learning capabilities that they make available to the masses.  When you combine all of the technological improvements with the growing corporate investment in this space, it becomes clear why AI is expected tol be the defining technology of our future.   The number of AI use cases, from enhancing the client experience in call centers (improved language processing and speech recognition) to predictive maintenance (fixing equipment before failures) is resulting in another powerful wave of business improvement driven by technology.  In a recent study by McKinsey, the firm estimates that AI has the potential to create between $3.5 trillion and $5.8 trillion in value, annually, across various business functions and industries.¹

So, with all of this promise, why aren’t more firms adopting AI at scale and growing the number of AI solutions across their business processes?  Yes, there are a lack of skills in the data science discipline.  Yes, there are regulatory issues.  Yes, there remains a trust issue (transparency in how AI decisions were reached).  From my experience, however, the primary reason for the lack of AI scale comes back to the quality of Artificial Intelligence “nutrients” that the algorithms require for ingestion.  That is, many organizations just do not have their data in a state of readiness to take advantage of this AI-powered world.

The first step in creating value from any applied intelligence solution is accessing all of the information relevant to a given problem.  The concept underpinning all of machine learning is giving an algorithm a massive number of “experiences” (training data) and a generalized strategy for learning, and then letting the AI identify patterns, associations and insight from that data.  But, if the data is siloed in an organization and inaccessible, or if it is difficult to obtain data sets sufficiently large and comprehensible to be used for training, then the AI value cannot be realized.

To overcome these challenges, many organizations need to get back to the basics before attempting the AI “leap.”  There are three areas that must be addressed:

1) Data Strategy. To build out the required data collection and data architecture, an organization must understand what the data (and associated analytics) will be used for.  In many cases, executives worry about their ability to choose the most effective systems for their needs and they get lost in a state of paralysis. Data is no longer about just measuring and managing. Data is core to a firm’s innovation.  Defining the data strategy is core organizational function.

2) Data Generation and Aggregation.  I have met with numerous firms lately that are sourcing and collecting large amounts of data but that still do not have a plan or a platform to consolidate the information in a useful way.  Organizations struggle with creating the right structure for any meaningful synthesis to take place.  This is why cloud platforms, such as Microsoft Azure, are fundamental.  The ability to generate and aggregate becomes only more important with AI since the quantity of available data is core to the machine learning.

3) Driving Insight.  Driving insight is all about revealing the invisible and gleaning new information from data that can be acted upon.  While insight is obviously the output, understanding that business problem upfront is important.  In understanding what insight is required, an organization can balance the requirements for traditional analytics and developing AI-powered solutions.

Artificial Intelligence is here and now and advancing quickly.  The technology can drive significant value and the opportunity is tremendous.  For organizations wishing to deploy AI to realize that value, however, there are some basics that must be in place.  Developing the data strategy, collecting and aggregating the information in a thoughtful manner and focusing on the insight required to address specific business problems are table stakes.  From there, the value of AI can be mined.  All companies have the opportunity in front of them.  As Mark Twain wrote, “there’s gold in them thar hills!”

¹ McKinsey Global institute, “Notes from the AI Frontier.”  April 2018.

AI in Steps

I could not understand why Alexa wasn’t responding with the current temperature.  After shuffling across the room to see if the device was plugged in, I realized the reason for the lack of response.  I had made the request of my son’s water bottle vs. our Alexa device (to be fair, they are very similar looking).  While my family got a good laugh at my expense, I became a bit introspective on the importance of AI.  How easy it was to assume there was a device in the room that would provide information immediately at my request, and how quickly frustrated I became when the data wasn’t readily available!  Although I see the growing and pervasive use of AI in the clients I meet with daily, this personal event highlighted, for me, the new environment that we are all living in.

Algorithms are, without a doubt, becoming more pervasive in our lives, both personally and professionally, and we are all starting to rely on AI driven capabilities whether we know it or not.  The technology is providing tailored customer experiences, increased client engagement and significant differentiation for those organizations that implement it.  But, according to a recent survey conducted by my employer, 88% of global c-level executives and IT decision makers believe companies incorporate AI only because it is trendy, and most admit they don’t actually know how to use it!  So, how should organizations prepare for this new AI dynamic?  There are 6 recommended steps for getting ready for this AI-drive world:

Step 1:  Understand where you are:
Most clients I meet with continue to share that the top three challenges they face, with regards to preparing for AI, are accessing the appropriate data, determining the best way to analyze the data that they do have, and finding the right skill sets to implement AI solutions.  It is so important for organizations to evaluate how their data is being captured and address the governance in place around that collection.  Understanding the data, improving the data management practices and then preparing to “start small” are foundational steps.

Step 2:  Determine potential:
Once an organization has an inventory of the data they do have, it is time to understand the data’s potential value and how it relates to the businesses goals and challenges.  If there are gaps between the data held and the strategic objective, organizations should consider acquiring data from external sources to help close the gap.  This is also the step in which organizations should consider privacy laws and their “digital ethics.” (Digital Ethics)

Step 3: Get focused:
Now that you know the business problem and what data is required to help address the challenges, it is time to get focused.  Prioritize the data that matters to meeting your objectives.  The amount of data we already have is overwhelming…but continues to increase.  By 2020, it is expected that individuals will generate 1.5GB of data per day.  That doesn’t include the amount of data generated by connected devices, smart homes, smart buildings or autonomous vehicles!  The ability to focus the data is critical.

Step 4: Prototype:
The path is now clear and it is time to start training algorithms.  Look for patterns and behaviors.  Think outside the box.  Start small.  Be Agile.  Evangelize early successes so that leadership sees the potential of AI (vs. considering it “trendy”).

Step 5:  Operationalize
Once your prototypes add value, it is time to integrate the work into existing business processes and start measuring the impact the changes are making.  At this point, you know exactly where to implement AI (bots, predictive analytics, etc.) because your data is organized and the business use case is clear.

Step 6: Do it all again.
It is important to continually repeat the steps above to sharpen the data and expand the number of AI use cases.  This slow expansion allows an organization’s leadership to get comfortable with the value-add business impact of the technology, and it allows the employees to feel more comfortable with the change management aspects of the new solutions.

In summary, we should all embrace the endless potential of AI.  As already noted, the technology (and required human-centered AI design) is providing tailored customer experiences, increased client engagement and significant differentiation for those that implement it.  There is no hotter topic at the intersection of business and technology than AI, and the high-level steps above will help an organization get started on addressing this brave new world.  My only other suggestion might be to start your AI journey by designing/developing AI-powered water bottles.

Digital Ethics

I am a news junkie, and these days, that is not necessarily a good thing.  Everywhere you turn it, it feels people are arguing over something...especially politics and religion.  In my own community, where it is local election season, people are arguing over mass development vs. controlled development and whether roads should be expanded from four lanes to six.  There is passion on both sides and the arguments can get nasty.

It is impossible to escape debate. It appears on social media, on TV, in our schools and in our local town halls.  In many ways, the debate is healthy, and it is obviously critical to our system of government.  But it can also be incredible sharp, personal and vindictive.  "There are two sides to every story" as we all know, and those "sides" are usually based on an individual's world view.  World views are formed early and they entrench all of us into our perspectives on life.  They are the basis from which we all behave and provide the framework from which we make decisions.

As the world becomes more connected, and as Artificial Intelligence becomes more pervasive, it is critical that we take a step back and consider the importance of the world views, ethics and morals and their influence on the technology we use on a daily basis.  We call this area "digital ethics," and most organizational leadership overwhelmingly agree that their organizations are behind in their readiness to address the challenges arising from digital ethics.  The speed at which ethics issues arise will only increase with the expanded use of AI and automation.  Our world is more digital and connected than ever before, and the effects of ignoring digital ethics will be significant.  

Think of Microsoft's Tay bot.  As many of us know, Microsoft overlooked the simple challenge of letting the bot know exactly what kind of input to trust and what kind of input to reject.  As a result, in less that 24 hours of Twitter conversations, Tay, a "conversational understanding" bot, became both misogynistic and racist.  Tay quickly assimilated our worst human tendencies into its personality and had to be disconnected from the on-line world.

We are reaching what some call a tipping point, a moment where the powers of technology surpass our ethical capabilities, as well as our capacity to foresee the consequences they will have on our personal life and our business.  So how do we address these concerns in a common way, given we all have a different worldview?

First, we have to agree on what to watch out for.  In my opinion, management of data will be the Achilles' heel.  Gartner predicts that by 2018, as many as half of business ethics violations will be caused by the improper use of big data and analytics.  Businesses must closely examine the data they keep and the reasons they do so.  Individuals must examine the data they share and consistently ask themselves, "am I willing to share something personal, for a better on-line experience, even if the use of the data, in the future, is unknown?"

Second, even with all the debate and various perspectives, we must start somewhere.  As with any risk in life, we need a framework in order to react effectively.  With increased transparency, compliance and privacy, companies that step up and take responsibility and accountability will lead the markets of tomorrow.  To do so, they need to develop and implement a digital ethics framework.  This framework should include guidelines on how to handle sensitive data, how to obtain client information and how the data or information collected will be used.  Companies should develop a framework that holds accountable the algorithms that drive the automation, robots and artificial intelligence solutions being rolled out.  They should also address, in my opinion, offering a fair value exchange for the data they collect.

A digital ethics framework, focused on the use of data, is only a starting point for this complex subject.  Moving forward, as AI becomes more pervasive in our lives, I expect automation debates to fill television news, social media and even our local town halls.  We will all bring a different perspective to the argument, but ultimately we must agree on the ethics that will be incorporated into the digital economies of the future.  Otherwise, we will have misogynistic, racist bots deciding the road should actually have 6 lanes.

CMO vs CIO

"Coming together is a beginning.  Keeping together is progress.  Working together is success." - Henry Ford

Customer Experience is still the battleground on which all companies compete (including service providers).  In fact, the battleground is becoming more fierce as, according to Forrester, 64% of organizations cite competition as the most important factor driving their work on customer experience.  Increasingly, the digital "experience" is becoming the product.

Because of this shift, digital conversations continue to migrate away from the strict domain of the CIO.  Of course, the CIO remains a critical part of the dialogue because any digital transformation requires a strong technology backbone.  That is, powerful operational capabilities are a prerequisite for delivering empathetic digital-powered client experiences....and any company with better operations has a competitive advantage.  In my experience, the majority of the CIO digital conversations focus on integration capabilities and how service providers can help them enhance their existing technology investments and be more responsive to their business clients.

That said, the CMO role continues to be an equally critical driving force in the delivery of digital solutions.  CMOs are obviously much more focused on initiatives that engage with end customers and drive revenue.  They are focused on personalized solutions that enhance the company brand, and for more and more organizations, the digital budget resides with the CMO.

So who wins in the digital landscape moving forward?  Some analysts expect the CMO technology spend to outstrip the technology spend of CIOs in the near future.  Other analysts believe that view is nonsense because the capital required to enhance or maintain existing systems will always keep the CIO in charge of any technology discussion.

I believe that it can not be one or the other, and that any successful digital transformation must include both the business and IT.  CMOs and CIOs should have joint alignment on the vision they want to deliver to the end customer and they should be partners in delivering exceptional end-user experiences.  Digital transformation is fundamentally not about just the technology or just the brand, it is about the strategy...and all executives must be aligned on the strategy to win the battle.

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