Cognitive Automation and AI in Business


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Cognitive technologies such as Artificial Intelligence (AI) offers businesses an incredible opportunity to rethink traditional processes. By automating rote tasks and accelerating standard workflows, companies can free employees to pursue innovation in other capacities.

But are AI and intelligent automation different than other enterprise technologies? Does AI’s potential pose far more dramatic threats than previous technological innovations? Why are technologists and business leaders so excited and simultaneously apprehensive about a technology that, despite its creation in the 1950s, is still in its relative infancy?In this video, we speak with Fred Laluyaux, CEO and President of Aera Technology and David Bray, Executive Director of the People-Centered Internet, about these critical topics.

During our conversation, we define some of the industry buzzwords and scientific terms that may still mystify the business world. We discuss the ways in which humans and machines should work in collaboration, both now and in a future that may give rise to machines that become responsible for many of the tasks humans handle today. The conversation touches on industries such as retail, manufacturing, automotive and more. We also examine how AI will revitalize current business technology including supply chain solutions, e-commerce platforms, and the still-nascent Internet of Things.

To learn more about how, when, and why your business should jump into the world of AI and automation, be sure to watch this video. In it, you’ll also find valuable advice from David and Fred on how to handle the ethical implications of AI adoption, and how you should treat your employees as AI becomes ubiquitous.


The transcript below has been lightly edited for clarity and length.

Michael Krigsman: We’re exploring the concept of cognitive automation. I’m delighted to speak with two gentlemen who will explain these concepts and what they mean for business. Fred, tell us briefly about Aera Technology.

Fred Laluyaux: We build the technology that enables self-driving enterprise. I’ll speak more about what it is, but it’s fundamentally a cognitive operating system. I’m being a bit jargon-y here, but a system that automates how decisions are made and executed primarily in large organizations.

Michael Krigsman: David, tell us about your background and what you’re doing.

Dr. David Bray: My background is Executive Director for what’s called the People-Centered Internet coalition. We strive to do projects that demonstrate how the Internet can be used to make a measurable improvement in people’s lives. I’m also faculty at Singularity University focusing on impact and disruption as well as Senior Fellow at what’s called the Institute for Human Machine Cognition.

What are cognitive automation and artificial intelligence?

Dr. David Bray: The term “artificial intelligence,” which right now has been used a lot in the last few years, we need to recognize the longer history, that this is really the third wave of AI. The first wave of AI occurred about 40 to 50 years ago when Herb Simon was using it to demonstrate how machines could help solve with games and they could actually solve those problems. Then about 20 to 30 years later, it was actually then being used to actually solve what was called “expert systems” in 1970s and 1980s, decision support systems.

Then finally, now, in this third wave, we’re looking at sort of the idea of neural networks, deep learning, the idea that what the machine can really do is begin to sort of pattern match, what a human would typically do, at a much higher volume and a much higher scale than would be possible if a human was to do it by themselves. This then now gets to the idea of sort of augmenting what a human does as a way of pairing the human with the machine so that the human is learning from the machine and, at the same time, the machine is learning from the human and, together, you’re getting better outcomes from them both.

Fred Laluyaux: We’re moving from an era of people doing the work supported by computer systems to an era where actually computers are doing a lot of the work, the thinking that is required to make a decision and controlled by the humans. IT is becoming a reality, so people doing the work supported by computers-to-computers doing the work controlled by people. That’s really the result of the acceleration at which decisions have to be made, the increased level of accuracy, the increased level of complexity that’s surrounding all the companies.

They’re facing complex challenges and people organized in a network are just not efficient enough to decide in real time with the right level of accuracy. This is where cognitive augmentation comes in again. Again, as David said, it’s a very interesting process where the algorithms are getting enriched by the decisions that humans are making and vice versa. We have a closed loop solution here.

How is cognitive computing different from traditional computing?

Fred Laluyaux: Think about it as moving from an era where users would log into software and get the computers to actually process some computation–push data up and down, forward, and so on and so forth–to having a system that’s completely autonomous. We’re digitizing the process of driving. Well, here, that’s the same difference that you see when you’re digitizing the decision-making process in an organization. You have a hands-free system that is actually able to process the data, analyze it, come up with the recommendation, potentially go to a user for control or, in some cases, automatically, in a touch-less manner, execute that recommendation or that decision. Moving from, as I said before, people getting involved in every decision to actually a touch-less planning, a touch-less forecasting event or re-optimization, all these kind of use cases that required people that now can be run completely autonomously.

How important are the autonomous aspects of cognitive computing?

Dr. David Bray: I think it’s a dimension. You can think about systems that are fully autonomous. You can also think about some that are partially autonomous. It’s also the dimension, though, of just the sheer amount of data that you’re able to process now as a result of advances in terms of cloud computing, edge computing that just were not possible in the past.

Fred Laluyaux: The ability to process a massive amount of data in real time at a reasonable cost is what really enabled the digitization of the decision-making process.

Michael Krigsman: Computing infrastructure kind of set the stage but it’s the data that brings it to life. Would that be an accurate way of describing it?

Dr. David Bray: I’m going to make your analogy even a little bit more complex. We’ve talked about two dimensions so far, Michael. There is a third if you can imagine a 3D graph. The third dimension is the increasing, what would be called, instrumentation of the planet with the Internet of Things, especially the Industrial Internet of Things, although the commercial IoT is slowly picking up, especially with voice-activated devices, with small satellites.

We are now at a point in which the ability to actually receive the data from the infrastructure is now conceivable where you can have this augmented intelligence occur. It’s both the automation; it’s both the Internet-scale computing power. Then it’s just simply the ability that we are increasingly instrumenting the planet.

Now, there are some cautions that come with that. In some cases, there is a risk of becoming either a surveillance state or surveillance capitalism, as some might say, if we’re not careful. But this also allows organizations to begin to actually be smarter about how they operate and have this augmented intelligence applied to their processes.

What is the impact of cognitive computing on business?

Fred Laluyaux: Yeah, it’s a very good question. It’s very interesting. When we started that journey, we were really exclusively looking at the impact on work. Oh, we’re going to be able to automate a lot of decisions. We’re going to be able to augment the decision-making capabilities on some very complex problems. You have to think about it in the context of very large organizations that are being profoundly disrupted by the e-commerce giants, as an example.

We thought about the impact on the business, on performance. We thought about the business on the work and how people actually value their time at work. They sit inside this very large pyramid in the case of a corporation, and part of their value is knowing how to work the system, how to actually operationalize a decision and so on and so forth. All of that gets digitized.

Where is the value left and how do you create additional value for yourself? How do you monetize your subject matter expertise and your time at work? That was the initial angle when we started on that journey.

But more and more, talking with our clients, we realized that the impact is on the waste. We’re actually cutting waste in the entire supply chain. We’re optimizing how decisions are made and our trucks basically hit the road and, very pragmatically, how we’re consuming energy and raw resources. The impact of that cognitive automation, enabling companies to make better, faster decisions closer to the point of impact.

To David’s point, there still need to be humans in the mix. There are still some corner cases that are not being properly addressed by data or by cognitive automation. But, in some cases, we can actually run better, faster decisions with a massive impact on the environment, resource consumption and, of course, on the way work is being done.

The organization design, we can foresee in the next few years that it’s going to fundamentally change from those very large organization pyramidal structure to a network of smaller groups that will be tightly connected with the ability to measure the impact of a decision on the different metrics in real time. I think you’ll see a deconstruction of the large organizations, the way they’re actually structured. You’ll see an impact on the way people work and you’ll get, of course, an impact on the environment in general.

How does cognitive computing help the enterprise?

Fred Laluyaux: We have a platform on top of which we build different skills. One of the skills we build is called a perfect forecast. What it basically did is it’s now proven that it’s delivering the right forecast for our client, which means what? If you know exactly what you need to manufacture because that’s the right number that you’re going to sell, think about the savings across the value chain from sourcing to manufacturing to the entire supply chain. Delivering the perfect forecast is actually the key to a massive amount of savings.

I’ll give you another example around promotion planning. If you think about the way this process was done in the past, people would build their promotion plan and roll them out once a year, twice a year, and it would take a while for that to impact and hit the stores. But you have now consumers walking into stores with their cell phone being able to check online coupons and their behavior, the way they are actually consuming has completely changed. You have a complete disconnect between what the organizations could do in terms of planning their promotions, which drives, in some cases, 50% of their sale, and the way the consumers are actually buying stuff today.

Cognitive automation enabled these companies to actually plan their promotions quasi-real time with end-to-end visibility in their supply chain, understand the demand and matching the two. It’s their answer, so to speak, to the e-commerce giants who have really built their success on incredibly sophisticated consumer analytics and a very agile supply chain.

Michael Krigsman: David, it sounds like we’re not talking science fiction here. There are actual use cases today of these systems having a dramatic impact in many different areas.

Dr. David Bray: Yes. In fact, Fred’s example that he gave of the shopping situation where maybe you’re either going to a grocery store or you’re going to a clothing store, that’s something that’s only possible now and it’s happening now because you do have sensors and devices in the store that are monitoring where the different customers are going. Maybe you’re dwelling for an extra long time at the vegetable aisle, and so then you could actually push to the customer specifically and say, “Would you be interested if there is a special deal on broccoli or on lettuce?” whatever you’re actually looking at right then and there. It’s targeted just to you and that’s only possible because of the speed, that looks at your pattern of buying behaviors, looks at what you’re interested in, and delivers it to you if you’ve given consent to receive that targeted advertisement. The same thing for shopping.

Another example that also Fred mentioned too is supply chains. Up until now, supply chains were kind of something that you had different sort of checkpoints along the way but you didn’t have real-time visibility into the location and the timing of both things that you had and things that you might need to have based on forecasts. You can actually begin to see how weather might impact buying behavior, how weather might impact delivery behaviors.

Again, this is the idea that what really is happening is it’s augmenting the intelligence of the organization relative to how it engages both its human assets as workers as well as how it interacts with humans as customers such that it’s bringing together both Internet scale, assessments of data, it brings together the sensors themselves that are bringing in this information, and then producing a result that is not just about more efficiency but, also, about either more effectiveness or more delivery of information or offering of services that are tailored to that individual.

Again, this then raises questions in terms of ethics, thinking about, “Well, when do we want our data used for this purpose? When do we want to actually have the sensors being aware of what we’re doing and our buying behaviors?” These are huge questions to make sure that we’re doing it with choice and consent, going forward, as opposed to people that may not be aware of it and may not necessarily buy into having their data used for that purpose.

Michael Krigsman: Fred, as you talk with your customers, to what extent do they appreciate or recognize the extent of the implications for the extent of change that it may bring to their business as well as their industry and competitors?

Fred Laluyaux: We talk to a lot of customers. The first thing I would say is that debate that we’re having right now is a true C-level discussion. We’re engaging with the CIOs, the CEOs of some of the largest companies in the world around that topic. I think they intuitively know that the way they organize and the way decisions are being made in their organization is not sufficient anymore.

There is a drive for change. There is an impending event. When we launched Aera a couple of years ago, it was like, “Are we out there?” The answer is no. The answer from these execs is more like, “Where have you been? We’ve been waiting for a new set of tools.”

The way decisions are made has not really fundamentally evolved. We’ve got better collaboration tools. We’ve got better spreadsheets. We’ve got better planning tools that allow us to compute faster, but the organization has not evolved. It’s the first time, with really the concept of augmentation and automation, that we are seeing a leapfrog, a step change in the way organizations are deciding on very simple and very pragmatic stuff on supply chain, manufacturing, and the way they sell, as we discussed earlier. It’s going to change the business model and the organizations very profoundly in the next few years.

What are the technology aspects of cognitive automation?

Dr. David Bray: Right. The reality is, the actual techniques that are being used, whether it’s deep learning versus neural networks or something such as that, that is actually less important than really three things. The first is the data and the data that’s being used to sort of drive the automated decision-making. The old adage in computer science is, “Garbage in, garbage out.” If the data doesn’t have the robustness and diversity necessary to answer the questions as to whatever direction or process that you’re trying to drive, then it’s not going to be sufficient.

The second, though, is, of course, then the sensors or how you’re acquiring the data. If the sensors either are missing something or are not accurately pointing in the right direction or are not adequate enough to provide the data that you need, then you will fall behind as well.

Then, finally, it really is thinking about how your organization changes how it operates. I think oftentimes we miss how old legacy technologies can become a source of ossification for an organization not just because they’re old and falling behind in terms of technology capability, but because organizations often instantiate their processes in their legacy technologies. If that process itself needs to change, just moving to a newer technology and not changing that process will pull the organization further behind.

What makes this really interesting, at the end of the day, is the feedback loops that occur between the data, how you’re collecting the data through your sensors, and how the organization itself responds as a result of what the data is informing you to do next. If you have that cycle of feedback loops, the actual implementations, the nice thing is you can rely on someone else to help make that happen, but you’ve got to have those three things in key and then actually have that quicker feedback loop so you can be responsive enough to adapt.

Now, again, going back to the idea of this exploration versus exploitation happening in organizations.

I think what Fred said a little bit earlier is absolutely right that the nature of how organizations themselves are structured is fundamentally going to change; that we organized in the past with hierarchies. Hierarchies are absolutely the wrong thing to have for this type of environment because they’re very good at efficiency and repetition across the different organizational units, but that’s the last thing you want because you actually want things to be fluid and adapt as necessary, which a hierarchy is not conducive of. It’s really going to be interesting to see where we go next with how organizations reshape themselves.

Fred Laluyaux: The three pillars that he just described are spot on. What I would say is, if you want to get to a cognitive automation or augmentation, you need an end-to-end system. The algorithm that you just positioned in your question are just a part of it. The closed loop that David described is exactly right.

Think about it as, again, you go back to my self-driving car analogy. Having a sight of sensors and lighters and GPS, all of that sitting there on the dashboard is not enough. You have to make it work together. You have to make it work in real time. The ability to actually process all this data coming from inside the organization through the ERPs and the other transactional systems and outside, the ability to process the data in real time so that you go to the users with a proper recommendation to take an action, get the feedback from the users, and automate the execution, that’s the problem that we’ve set ourselves to resolve, which is really creating this end-to-end system. That’s why we call that a cognitive operating system and not just a single piece of software.

How do ethics affect how companies use data and cognitive technologies?

Dr. David Bray: Sure. I think, with the term “accountability,” that can be a loaded term but it’s really saying, “If you’re going to start relying on this interplay between data, sensors, and what the organization actually does and that feedback loop, you have to think about it in terms of accountability, in terms of, one, who in the organization is responsible for making sure what the machine is doing in an automated fashion is appropriate and is right both for the company as well as for whatever customers or members of the public that it’s interacting with.”

There’s another pillar, though, too. The second pillar is thinking about if you’re interacting with customers or members of the public, are they aware that they are either giving their data to the sensors or are they aware that they are being serviced in this fashion? Some may actually object to that or may have qualms about that, and so it’s sort of the choice mechanisms that go with that.

I think what you’re going to see, right now, that the first forays with augmented intelligence are going to be in areas in which it’s a little less controversial. It doesn’t actually begin to impede too much into your lives, but it is things like supply chain behind the scenes to make sure what gets delivered is what needs to get delivered.

Finally, the third leg, as you look forward to this, is really thinking about what is the future of human autonomy in the midst of all this, whether you’re a human worker, whether you’re a human customer. Do you have any autonomy? Hopefully, you do. But what does that mean relative to all those things that are happening in terms of automation and augmented intelligence?

In terms of ethics, well, ethics is simply the socially accepted, normative practices that we see that are appropriate here. We’ve seen changes before. The idea of privacy really is actually a 20th century idea that came about. It did not exist, say, in the 1600s or the 1700s. There may be other ethics that start to arise that involve this and, ideally, thinking about what we can do to uplift as many people as possible through what augmented intelligence provides. That, I think, is an obligation of CEOs, of organizations, and of the public as a whole.

What advice do you have for policymakers?

Dr. David Bray: My answer is, they should have started paying attention to these trends about a year or two ago because those companies that start to invest right now in thinking about how they’re going to use data to drive decisions, thinking about how they’re going to actually begin to instrument it, whether it’s their supply chain, whether it’s what they’re doing in their stores. If you don’t put in place the investments in the sensors, then you’re not going to be able to have that first mover advantage as this rolls forward, and it is rolling forward now. The first idea is, start to be hungry, start to explore this space, and see what’s out there.

This is dramatic change in a very small period of time. It’s not going to be like the 100 years that happened with the Industrial Revolution. It’s going to be happening so fast that we’re going to have to figure out ways to do both governance as well as organization in thinking about what companies do and thinking about how communities do too. That’s going to have to happen in parallel as we move forward together and it’s going to require looks across multiple sectors to try and figure out the best way to move forward because not only will it be those companies that do this first that will succeed; it will also be those countries that do this first will have that first mover advantage as well.

Finally, at the end of the day, it really is about doing this to free the human to do more of the creative work, not the rote work, not the repetitive work. We need to make sure, in the midst of this whole conversation, we are talking about augmenting intelligence and making organizations operate smartly. Think about what we can do to also embrace that human spirit and uplift humans so they can either have more time to do more creative work, more of the in-depth problem solving, more of the in-depth, “Why is this occurring?” or also thinking about how they can give back to society in other ways as well, as we go forward into this.

It’s not going to be necessarily something that means humans don’t still have value and purpose, but I think that’s going to be a seismic shift because so much of our jobs right now will probably eventually be displaced by this and we have to start having the conversation now about what does it mean to be human in this era, have a sense of purpose, and actually be a member of society as well.