• Thought Leadership
  • How to Bridge the Digital Transformation Execution Gap

Watch to this Webinar to Learn:
  • What happens to most digital transformations today: the biggest barriers, where efforts go wrong
  • How CEO-led digital transformations should be set up
  • How most DT efforts get started
  • The one most important technology decision to get right
Transcript:
Erica:

Hello, and thank you for joining. This webinar is entitled How to Bridge the Digital Transformation "Execution Gap". This webinar is a result of research from Forrester entitled Realizing CEO-Led Digital Transformations. And the research was commissioned by C3. Joining us from Forrester Research is Mike Gualtieri, VP and Principal Analyst. Mike led the research and he'll provide a summary of the conclusions, putting them in context with today's challenges and the decisions organizations must face to successfully digitally transform. With Mike is Ed Abbo, President and CTO of C3. Ed will describe what he sees in the market today with enterprises across many industries undergoing digital transformation.

Together, Mike and Ed will cover the following topics. What happens to most digital transformations today? What are the biggest barriers? And where do digital transformation efforts go wrong? How should CEO-led transformations be set up? How do most digital transformations efforts get started? And what are the most common ways to get going? What's the one most important technology decision to get right? And how does this landscape evolve? And what's the most important thing listeners should take away?

So with that, we'll get going. And we'll start out by talking about what digital transformation is. And I'll hand it over to Mike.

Mike:

Alright, thank you. And hi everyone. My name is Mike Gualtieri. And I'm going to talk to you about some of the results of the study we did along with C3, especially focused around digital transformations. And one of the data points we found is that the C-suite, when they look at digital transformation efforts, you know, the top goal is ultimately focused on customers. Of course, that's where the revenue comes from. And you can see a lot of other activities and goals here. It accelerates delivery of products. I mean, all of these are in the higher goals of winning and retaining customers.

They also understand more than anyone else that digital technology forces all businesses to reevaluate their priorities. So you can see here we asked them, to what extent have the following trends influenced your organization's current and overall business priorities? And you can see number one: advances in IoT. And the darker blue are C-level executives, the lighter blue are VPs and directors, under the C-level. And they more than anyone, are very focused on the IoT opportunity.

Now, what's also interesting at the bottom of this slide are advances in artificial intelligence. In virtually all of these categories, the C-suite has a very good grasp on the opportunity there. We want to start you out on the digital transformation with the role of some of the different technologies as well. And you can see here that we asked, how important are the following technologies to drive those successful digital transformations?

Cloud computing, of course, transcends digital transformation. It's an important trend in everything. Streaming and real time analytics. We're living more in a real time world. Distributed storage, unified platforms, you can see the rest of them here. Again, the C-level has a grasp on the importance of this. And they're the ones leading the organization.

So if we drop down specifically into artificial intelligence, we also see that that is gaining in momentum in terms of adoption. And you can see that last set of data points there, implementing and implemented. And we didn't define what artificial intelligence is in this survey, we just asked them about artificial intelligence. So we think that most of the organizations that say they're doing it, are doing it in smaller ways. Because essentially there are lots more applications.

Now, what do we mean by artificial intelligence? Well, at Forrester we believe it will dominate enterprise innovation and that it's also critical for IoT applications. But there's still some confusion over what it is. At Forrester, we think of AI defined in two ways. One we call Pure AI and the other Pragmatic. Pure AI essentially is the sci-fi stuff. Anything you see in a sci-fi movie. And that's not what enterprises are doing. That's robotics, that's Star Wars. We're not really on the precipice of that. And that's where Pragmatic AI comes in.

Pragmatic AI is what enterprises are doing now. It's narrower in scope, meaning it's not generalized intelligence. But it very often exceeds human intelligence. And there are many examples. There's Jeopardy, there's the game of Go. But there are also the mundane examples. Not so mundane if you're a company and using it to predict customer churn, using it to predict machine failure, and numerous other applications.

So Pragmatic AI is what companies are focused on. And it's critical to digital transformation efforts as you saw in the data. And it's not one particular technology. And that's very important to understand. It's an umbrella of technologies. Front and center, machine learning, deep learning. But sensory perception from the world of IoT. Robotics. If there's physicality involved in knowledge engineering, not ignoring the human rules as well. You don't have to do all of these technologies to do an AI system. You could just do one. So it's an umbrella. So essentially for digital transformation, we're not talking about Pure AI, we're talking about the Pragmatic AI. And what you also have to realize is that data is a huge prerequisite to that.

The other critical technical piece of this is IoT. Because you cannot have intelligence of any kind if you're not connected to the real world, you're not connected to those data sources. So what IoT allows enterprises to do is to connect the physical and the digital world. And digital transformations can absolutely not ignore the physical world. They have to be brought together. They have to be linked together. And that's why IoT is so important.

Another data point we have here is that the C-level understands also that digital technology trends are forcing all businesses to re-evaluate their priorities, as you can see here. And again, you can see the contrast between what C-level executives think about this and what VPs and Directors think about it.

The industry interest and adoption for both IoT and AI is across the board. It tends to accelerate in purely digital companies like internet and e-commerce giants. It takes a little bit longer for supply chain and transportation because they are physical. They are a combination of physical and digital organizations. And that's where IoT becomes an even more important factor in that.

The other thing is that AI, it learns from historical data. It's true, but it has to decide and act in real time. So it would be very wrong to think about AI as using AI to find insights or to do analytics. AI does find insights and you can do analytics to build models. But for digital transformation, it's not about insight alone. It's about insight to action. So AI technologies for digital transformation connect with the real world to make decisions and act in real time.

Some of those use cases being used to hyper-personalize customer experiences with targeted offers. So situations where you have both digital and physical information from stores. Understanding customers, taking actions, making offers. It's used to prevent cyber and fraud. Again, right now in the real time, IoT and AI, customer service, proactive customer service and self-service to customers. It's used to predict supply chain issues.

Insurers use deep learning, a technology of AI, to automatically assess damages and costs from images. Automated instead of just an adjuster. IoT and AI are used in combination to predict machine failures in various environments. It's used in financial services. The number of use cases are basically anything that you could predict in a business process. It's a use case that connects IoT and AI. So AI-infused application, that's where digital transformation is going. That's what we call the second act of digital transformation. So you can't have a digital transformation if you're not using AI and connected to the IoT physical world.

Now, the problem is that traditional data analytics and application platforms for that matter, are largely geared towards the old world of analytics and insights and not geared towards insights to action. And when we ask organizations, the decision-makers involved in digital transformation, do they have an integrated AI or AIT platform? Only 22% say they do. So that is a very small number. And we further look through this same survey data and we found that integrated platforms, those that say they have integrated platforms of the 22%, that is correlated to higher rates of collaboration as well. Which is also an essential quality of successful digital transformations.

So we think companies at the highest level have a few decisions to make. And fortunately, the C-suite is on top of this in terms of understanding the priorities and needs. But the rest of the organization has to follow, has to understand from a technology standpoint of the types of platforms they need to make this happen.

So the first decision is to stop wasting time and money on unactionable analytics, insights, and patterns. Again, a lot of people are saying, "I'm going to use AI to find insight." If these are un-actionable, they don't have an effect on the business, so why do it? That's going to free up budget, it's going to free up resources and effort to focus on what really matters.

And so, the second decision is to feed enterprise and IoT data -this enterprise data is the digital world, the IoT data is the physical world - to feed that to an integrated platform that can do machine learning. That can find those models that will lead to that second digital transformation. And that's the third decision, which is to infuse enterprise applications, business process, and customer experiences with AI models created by that integrated platform.

And then finally the fourth decision is to use a unified AI platform to accelerate implementation. Now what we mean by this is that there's many moving parts when you're infusing AI into applications. You've got data scientists who build the models. You've got application development teams who are building the software. You've got data management people who are getting the data in. And of course, you have the business people defining what these digital transformations should look like. And there's friction when all of these moving parts, these organizations and platforms are separate. So a unified AI platform will accelerate implementation by breaking down that friction.

With that, I'm going turn it over to Ed Abbo to share some of C3's digital transformation stories. Ed?

Ed Abbo:

Thanks for that, Mike. I appreciate you sharing the insights from the research. What I thought would be interesting to do is to take you through some concrete specific examples of where we've seen digital transformation effectively implemented. And as Mike alluded to there are four elements that need to come together for this to effectively occur within a company. The first obviously is leadership. And this needs to come from the CEO of the organization given the change management that needs to transpire and accompany digital transformation.

And the second Mike touched on is really the technology platform. Not just to be able to do prototypes of machine learning but to operationalize decision-making with the assistance of AI and machine learning across a company. The third element is assembling a team with the right skill set. And again, Mike touched on data scientists, application developers, and the business. And the fourth element is a structure to be able to start with a small team and then operationalize it at-scale through a center of development or a center of excellence if you will. I'll touch on those elements through a real world example.

Just to start with some perspective on the opportunity as it essentially enables through the delivery of next generation of the technologies and we've seen this trend, over the past 40, 50 years where, on the introduction of new technology, there's an opportunity for businesses to rethink how they operate. And that is definitely the case of the technology disruptions that are significant as it pertains to the cloud and IoT.

Having said that, with each new technology, the initial reaction from, you know, chief information officers and others is this can't be that significant. But the reality is they underestimate the disruption that actually occurs. And this generation of technology that got introduced over the last decade, whether it's elastic cloud computing or machine learning and IoT, the implications are significant.

And I like to essentially position this as the big change is really predictions. The ability to analyze, you know, all available data and inform business workflows through predictions. And that's gotten a lot cheaper given the price of computing and the availability of machine learning algorithms. And essentially IoT, which is not just sensors but also computing everywhere. Ubiquitous computing.

So with that, we can now rethink everything about the business. How we engage customers, how we design products, how we manufacture products, supply chains, et cetera. And it used to be that with all the prior technology changes, the CEO essentially delegated this to the CIO and the executive team essentially got an update from the CIO on a quarterly basis, potentially during the planning meetings.

This kind of technology is different given the disruption to business and I'm not going to dwell on it, but whether it's Michael Porter with his Smart, Connected Products or Transforming Competition and Companies, or whether it's Tom Siebel's article in McKinsey Quarterly, this really is a CEO mandate. And the reason is the technology enables dramatic changes in business. And there are significant change management implications.

So, the structure of the organization changes and today most companies have either put in place or have recognized a need for a chief transformation officer, a chief digital officer. And that individual or executive reports directly to CEO, is empowered to drive change through the company, and reports progress to the CEO. Not quarterly but essentially weekly on the change management.

Now, I want to talk a little bit about a specific use case in a specific industry to talk through some of the learnings here. And we'll pick the power industry as an example. But as Mike pointed out, this really is across all industries. And they all present a similar value chain. Now, the reason the power industry is interesting is because it's effectively a highly complex system. But nevertheless, the concept of a value chain exists across industries.

Now, this value chain is undergoing an upgrade this decade and two trillion dollars are being spent to upgrade the smart grid. This is essentially IoT and AI meets the power grid. And the opportunity through the digital transformation of the power industry is really to drive significant efficiency across the entire value chain. And, working with McKinsey and others, we essentially have determined that by transforming this grid, we can drive 33% efficiency across the entire value chain from generation, transmission through to the customer.

And we'll talk a little bit about the specific areas that essentially by metering the entire infrastructure, the example that most people are familiar with is smart meter, where instead of getting a manual reading every month, we actually are getting a pulse on the network every 15 minutes. But that's not the only sensor that's on the system. There are actually sensors and actuators across the distribution transmission all the way through generation and significant investments in buildings and residences. And by essentially aggregating those data from the sensors and the underlying systems, we can drive significant value through that power generation system.

And so to really understand the site, I'll talk about Enel. And Enel is effectively the largest power utility, power company outside of China. And the the size I would say is about 85 billion dollar company in revenue. They have 62 million customers or customer endpoints as they like to refer to, as well as two million kilometers of distribution grid. And so what we did with them is essentially implement the C3 platform and delivered significant value in five areas.

The first is customer analytics, so this is really looking at customer churn. And what you can do to retain customers, acquire customers. An area that is referred to as Revenue Protection is really fraud, so who's stealing power from the power company? When did they start? How are they doing it? How much have they stolen? And then reliability of that two million kilometers of power grid and reliability of the power generation, both renewable and conventional.

Now, the way the system operates is on the left hand side, it ingests data continuously from the source systems and sensors. And they have, as I mentioned, 60 million customers with sensors that are reading data on consumption. They're integrating that with data from their SAP systems, their billing systems, as well as legacy systems that include the work management system, outages on their network, the way that the network is configured both design as well as how it's operated through SCADA systems, power quality systems.

All of those data are ingested at peak rates of a million messages per second and correlated into a unified federated data image. And upon which we're essentially applying machine learning to make predictions on customer churn, predictions on fraud, predictions on reliability. Which equipment are likely to fail next so that they can be maintained in advance of the lights going out.

So with that, we'll get going. And we'll start out by talking about what digital transformation is. And I'll hand it over to Mike.

So this is the largest commercial AI and IoT deployment out there and is delivering significant economic value. Specifically from Enel's investor presentation, they're looking at 1.9 billion Euro in cumulative EBITDA, earnings improvements, over the next two to three years from these systems and digitalization programs that they have underway. And they've also trained their developers now to ... They've trained about a hundred data scientists, application developers to actually build out applications across their system.

Now, the way that they've structured this is essentially a central team that they refer to as their Enel Digital Team, and that team works with the business units, the retail networks, generation renewable units to identify and prioritize these machine learning and IoT applications across the business. And then that team, that central team of data scientists and application developers that are trained on the C3 platform, then design, develop, deploy, and operate those applications across the company. So that's the structure that they have put in place.

And they also have a road map across the four or five business units that essentially tackles one or two new application areas initially every six months. And then basically expanding that capacity as they train more and more developers and data scientists centrally in that center of development or center of excellence team. And so they can expand the deployment of these applications gradually and then accelerate, if you will, as they train more and more teams. But the initial starting point is a team of 10 to 15 people, and then it expands very rapidly to about a hundred. And they're developing and deploying lots of applications very quickly.

Now, across all of Enel when the digitalization program is fully deployed, the economic value is in excess of six billion Euro to Enel and their customers. And you can see that it's across cross-selling and up-selling energy products and services to their customers, reducing fraud, and improving reliability in general operations of their assets and improving productivity of their people. So it's a very significant effort and one that's led by their CEO and Chief Digital Officer across the company.

Now, we talked about the power, we obviously can take exactly the same idea of improvement across the value chain and apply that to manufacturing. And in this case, we're doing projects across manufacturers where we're improving the reliability of products in customers' hands, where they're using whether it's construction equipment or aircraft. And then basically improving aftermarket sales and service, optimizing warranty, et cetera.

But also internally within a manufacturing company, looking at improvements in on-time and in-full delivery. If you can use machine learning and AI to better predict demand for products, which products to manufacture for which regions and locations, you can then drive significant improvement across the back office or value chain. So that's manufacturing and inventory optimization as well as supply network growth, if you will.

And so you've done projects and manufacturers ... I'll talk about inventory optimizations -- by looking at the demand and supply signals for all parts across the manufacturing plants, actually looking at that using optimization and machine learning algorithms on a daily basis for each part, we actually demonstrated a reduction of 30 to 50% in inventory carrying costs there. So those are examples of products that we've done.

And so this applies, whether it's across the automotive industry, oil and gas, all the way from upstream to downstream explorations, development through to retail. We can drive significant opportunity by integrating systems, integrating sensor data, correlating those and essentially looking for valves and compressors and other equipment that are likely to fail in conventional and offshore rigs. And this applies in mining to the yield optimization for end product if you will.

And as always, in the services industry such as healthcare, where we're looking at adverse drug reactions, connected patients, and optimization across hospitals and healthcare in general. Financial services, again, whether it's looking at improving the credit and loan processes or whether we're looking at identifying money laundering, the opportunity to apply the same techniques of aggregating data and applying machine learning to improve decision processes is significant.

So those are the concepts. I'll summarize or conclude by proposing the CEO Action Plan. And here I would say the first step is to assemble the executive management team and to really look at the areas of disruption and potential approaches to handling those. I would suggest visits to the disruptors, if you will. So spending time at an Apple or an Uber or a Tesla is time well spent. And we tend to host executive teams through C3 on a weekly basis looking at the opportunities and what they can do with the technology.

Benchmarking a digital plan against peers with the help of management consultants. And then really envisioning what is likely to disrupt the industry. And using that to inform your digital road map of what areas to prioritize and then to essentially ensure stakeholders are on board with that. And as I mentioned earlier, another key decision is finding the right chief digital officer that has credibility with the lines of business executives and can help drive the change management across the company and then basically making it happen.

So from one perspective, we have delivered the technology to simplify the IT piece of this. The difficult piece is now in the hands of the CEO, the chief digital officer, and the heads of business. And the difficult part is obviously the change management, which is required to actually unlock the economic value.

So with that, I'll turn it back to Erica.

Erica:

Okay. Thank you, Ed. And thank you, Mike. That was a fantastic discussion of not only what's going on across the industry and the perspectives of CEOs and their staff and how to think about digital transformation - some of the trends as well as a deep understanding. Plus, an example in one industry, the utility industry. And how digital transformation is applicable across a variety of industries.

For more information, if you're interested in the research, please go to c3iot.ai/digitaltransformation. And of course, for information about C3's products, including the C3 Platform and Applications, please go to our website at c3iot.ai. And thank you for listening!