On the Path to a New Normal: Gain Insights and Reassurance Using Data and Artificial Intelligence

A discussion on how AI is the new pandemic response team member for helping businesses reduce risk of failure and innovate with confidence.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of AI Innovation podcast series.

Gardner

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on the latest insights into artificial intelligence (AI) strategies.

As businesses and IT strategists plan their path to a new normal throughout the COVID-19 pandemic and recovery, AI and data science are proving impactful and indispensable.

By leveraging the latest tools and gaining data-driven inferences, architects and analysts are both effectively managing the pandemic response -- and giving more people better ways to improve their path to the new normal.

Stay with us here and now as we examine how AI forms the indispensable pandemic response team member for helping businesses reduce risk of failure and innovate with confidence. To learn more about the analytics, solutions, and methods that support advantageous reactivity -- amid unprecedented change -- we are joined by two experts.

Please join me in welcoming Arti Garg, Head of Advanced AI Solutions and Technologies, at Hewlett Packard Enterprise (HPE). Welcome, Arti.

Arti Garg: Good morning.

Gardner: We’re also here with Glyn Bowden, Chief Technologist for AI and Data, HPE Pointnext Services. Welcome, Glyn.

Glyn Bowden: Thanks, Dana.

Gardner: Arti, why should we look to data science and AI to help at a time when there’s not much of a historical record to rely on? It seems we’re in a time when people are in uncharted waters in dealing with the complexities of the novel coronavirus pandemic.

AI in the fight against COVID-19

Garg: Because we don’t have a historical record, I think data science and AI are proving to be particularly useful right now in understanding this new disease and how we might potentially better treat it, manage it, and find a vaccine for it. And that’s because at this moment in time, raw data that are being collected from medical offices and through research labs are the foundation of what we know about the pandemic.

Garg

This is an interesting time because, when you know a disease, medical studies and medical research are often conducted in a very controlled way. You try to control the environment in which you gather data, but unfortunately, right now, we can’t do that. We don’t have the time to wait.

And so instead, AI -- particularly some of the more advanced AI techniques -- can be helpful in dealing with unstructured data or data of multiple different formats. It’s therefore becoming very important in the medical research community to use AI to better understand the disease. It’s enabling some unexpected and very fruitful collaborations, from what I’ve seen.

Gardner: Glyn, do you also see AI delivering more, even though we’re in uncharted waters?

Bowden: The benefits of something like machine learning (ML), for example, which is a subset of AI, is very good at handling many, many features. So with a human being approaching these projects, there are only so many things you can keep in your head at once in terms of the variables you need to consider when building a model to understand something.

But when you apply ML, you are able to cope with millions or billions of features simultaneously -- and then simulate models using that information. So it really does add the power of a million scientists to the same problem we were trying to face alone before.

Gardner: And is this AI benefit something that we can apply in many different avenues? Are we also modeling better planning around operations, or is this more research and development? Is it both?

Data scientists are collaborating directly with medical science researchers and learning how to incorporate subject matter expertise into data science models.

Garg: There are two ways to answer the question of what’s happening with the use of AI in response to the pandemic. One is actually to the practice of data science itself.

One is, right now data scientists are collaborating directly with medical science research and learning how to incorporate subject matter expertise into data science models. This has been one of the challenges preventing businesses from adopting AI in more complex applications. But now we’re developing some of the best-practices that will help us use AI in a lot of domains.

In addition, businesses are considering the use of AI to help them manage their businesses and operations going forward. That includes things such as using computer vision (CV) to ensure that social distancing happens with their workforce, or other types of compliance we might be asked to do in the future.

Gardner: Are the pressures of the current environment allowing AI and data science benefits to impact more people? We’ve been talking about the democratization of AI for some time. Is this happening more now?

More data, opinions, options

Bowden

Bowden: Absolutely, and that’s both a positive and a negative. The data around the pandemic has been made available to the general public. Anyone looking at news sites or newspapers and consuming information from public channels -- accessing the disease incidence reports from Johns Hopkins University, for example -- we have a steady stream of it. But those data sources are all over the place and are being thrown to a public that is only just now becoming data-savvy and data-literate.

As they consume this information, add their context, and get a personal point of view, that is then pushed back into the community again -- because as you get data-centric you want to share it.

So we have a wide public feed -- not only from universities and scholars, but from the general public, who are now acting as public data scientists. I think that’s creating a huge movement.

Garg: I agree. Making such data available exposes pretty much anyone to these amazing data portals, like Johns Hopkins University has made available. This is great because it allows a lot of people to participate.

It can also be a challenge because, as I mentioned, when you’re dealing with complex problems you need to be able to incorporate subject matter expertise into the models you’re building and in how you interpret the data you are analyzing.

And so, unfortunately, we’ve already seen some cases -- blog posts or other types of analysis -- that get a lot of attention in social media but are later found to be not taking into account things that people who had spent their careers studying epidemiology, for example, might know and understand.

Gardner: Recently, I’ve seen articles where people now are calling this a misinformation pandemic. Yet businesses and governments need good, hard inference information and data to operate responsibly, to make the best decisions, and to reduce risk.

What obstacles should people overcome to make data science and AI useful and integral in a crisis situation?

Garg: One of the things that’s underappreciated is that a foundation, a data platform, makes data managed and accessible so you can contextualize and make stronger decisions based on it. That’s going to be critical. It’s always critical in leveraging data to make better decisions. And it can mean a larger investment than people might expect, but it really pays off if you want to be a data-driven organization.

Know where data comes from 

Bowden: There are a plethora of obstacles. The kind that Arti is referring to, and that is being made more obvious in the pandemic, is the way we don’t focus on the provenance of the data. So, where does the data come from? That doesn’t always get examined, and as we were talking about a second ago, the context might not be there.

All of that can be gleaned from knowing the source of the data. The source of the data tends to come from the metadata that surrounds it. So the metadata is the data that describes the data. It could be about when the data was generated, who generated it, what it was generated for, and who the intended consumer is. All of that could be part of the metadata.

Organizations need to look at these data sources because that’s ultimately how you determine the trustworthiness and value of that data.

We don't focus on the provenance of the data. Where does the data come from? That doesn't always get examined and he context might not be there.

Now it could be that you are taking data from external sources to aggregate with internal sources. And so the data platform piece that Arti was referring to applies to properly bringing those data pieces together. It shouldn’t just be you running data silos and treating them as you always treated them. It’s about aggregation of those data pieces. But you need to be able to trust those sources in order to be able to bring them together in a meaningful way.

So understanding the provenance of the data, understanding where it came from or where it was produced -- that’s key to knowing how to bring it together in that data platform.

Gardner: Along the lines of necessity being the mother of invention, it seems to me that a crisis is also an opportunity to change culture in ways that are difficult otherwise. Are we seeing accelerants given the current environment to the use of AI and data?

AI adoption on the rise 

Garg: I will answer that question from two different perspectives. One is certainly the research community. Many medical researchers, for example, are doing a lot of work that is becoming more prominent in people’s eyes right now.

I can tell you from working with researchers in this community and knowing many of them, that the medical research community has been interested and excited to adopt advanced AI techniques, big data techniques, into their research.

It’s not that they are doing it for the first time, but definitely I see an acceleration of the desire and necessity to make use of non-traditional techniques for analyzing their data. I think it’s unlikely that they are going to go back to not using those for other types of studies as well.

In addition, you are definitely going to see AI utilized and become part of our new normal in the future, if you will. We are already hearing from customers and vendors about wanting to use things such as CV to monitor social distancing in places like airports where thermal scanning might already be used. We’re also seeing more interest in using that in retail.

So some AI solutions will become a common part of our day-to-day lives.

Gardner: Glyn, a more receptive environment to AI now?

Bowden: I think so, yes. The general public are particularly becoming used to AI playing a huge role. The mystery around it is beginning to fade and it is becoming far more accepted that AI is something that can be trusted.

It does have its limitations. It’s not going to turn into Terminator and take over the world.

The fact that we are seeing AI more in our day-to-day lives means people are beginning to depend on the results of AI, at least from the understanding of the pandemic, but that drives that exception.

The general public are particularly becoming used to AI playing a huge role. The mystery around it is beginning to fade and it is becoming far more accepted that AI is something that can be trusted.

When you start looking at how it will enable people to get back to somewhat of a normal existence -- to go to the store more often, to be able to start traveling again, and to be able to return to the office -- there is that dependency that Arti mentioned around video analytics to ensure social distancing or temperatures of people using thermal detection. All of that will allow people to move on with their lives and so AI will become more accepted.

I think AI softens the blow of what some people might see as a civil liberty being eroded. It softens the blow of that in ways and says, “This is the benefit already and this is as far as it goes.” So it at least forms discussions whenever it was formed before.

Garg: One of the really valuable things happening right now are how major news publications have been publishing amazing infographics, very informative, both in terms of the analysis that they provide of data and very specific things like how restaurants are recovering in areas that have stay-in-place orders.

In addition to providing nice visualizations of the data, some of the major news publications have been very responsible by providing captions and context. It’s very heartening in some cases to look at the comments sections associated with some of these infographics as the general public really starts to grapple with the benefits and limitations of AI, how to contextualize it and use it to make informed decisions while also recognizing that you can go too far and over-interpret the information.

Gardner: Speaking of informed decisions, to what degree you are seeing the C-suite -- the top executives in many businesses -- look to their dashboards and query datasets in new ways? Are we seeing data-driven innovation at the top of decision-making as well?

Data inspires C-suite innovation 

Bowden: The C-suite is definitely taking a lot of notice of what’s happening in the sense that they are seeing how valuable the aggregation of data is and how it’s forwarding responses to things like this.

So they are beginning to look internally at what data sources are available within their own organizations. I am thinking now about how do we bring this together so we can get a better view of not only the tactical decisions that we have to make, but using the macro environmental data, and how do we now start making strategic decisions, and I think the value is being demonstrated for them in plain sight.

So rather than having to experiment, to see if there is going to be value, there is a full expectation that value will be delivered, and now the experiment is how much they can draw from this data now.

Garg: It’s a little early to see how much this is going change their decision-making, especially because frankly we are in a moment when a lot of the C-suite was already exploring AI and opening up to its possibilities in a way they hadn’t even a year ago.

And so there is an issue of timing here. It’s hard to know which is the cause and which is just a coincidence. But, for sure, to Glyn’s point, they are dealing with more change.

Gardner: For IT organizations, many of them are going to be facing some decisions about where to put their resources. They are going to be facing budget pressures. For IT to rise and provide the foundation needed to enable what we have been talking about in terms of AI in different sectors and in different ways, what should they be thinking about?

How can IT make sure they are accelerating the benefits of data science at a time when they need to be even more choosy about how they spend their dollars?

IT wields the sword to deliver DX 

Bowden: With IT particularly, they have never had so much focus as right now, and probably budgets are responding in a similar way. This is because everyone has to now look at their digital strategy and their digital presence -- and move as much as they can online to be able to be resistant to pandemics and at-risk situations that are like this.

So IT has to have the sword, if you like, in that battle. They have to fix the digital strategy. They have to deliver on that digital promise. And there is an immediate expectation of customers that things just will be available online.

With the pandemic, there is now an AI movement that will get driven purely from the fact that so much more commerce and business are going to be digitized. We need to enable that digital strategy.

If you look at students in universities, for example, they assume that it will be a very quick fix to start joining Zoom calls and to be able to meet that issue right away. Well, actually there is a much bigger infrastructure that has to sit behind those things in order to be able to enable that digital strategy.

So, there is now an AI movement that will get driven purely from the fact that so much more commerce and business is going to be digitized.

Gardner: Let’s look to some more examples and associated metrics. Where do you see AI and data science really shining? Are there some poster children, if you will, of how organizations -- either named or unnamed -- are putting AI and data science to use in the pandemic to mitigate the crisis or foster a new normal?

Garg: It’s hard to say how the different types of video analytics and CV techniques are going to facilitate reopening in a safe manner. But that’s what I have heard about the most at this time in terms of customers adopting AI.

In general, we are at very early stages of how an organization is going to decide to adopt AI. And so, for sure, the research community is scrambling to take advantage of this, but for organizations it’s going to take time to further adopt AI into any organization. If you do it right, it can be transformational. Yet transformational usually means that a lot of things need to change -- not just the solution that you have deployed.

Bowden: There’s a plethora of examples from the medical side, such as how we have been able to do gene analysis, and those sorts of things, to understand the virus very quickly. That’s well-known and well-covered.

The bit that’s less well covered is AI supporting decision-making by governments, councils, and civil bodies. They are taking not only the data from how many people are getting sick and how many people are in hospital, which is very important to understand where the disease is but augmenting that with data from a socioeconomic situation. That means you can understand, for example, where an aging population might live or where a poor population might live because there’s less employment in that area.

The impact of what will happen to their jobs, what will happen if they lose transport links, and the impact if they lose access to healthcare -- all of that is being better understood by the AI models.

As we focus on not just the health data but also the economic data and social data, we have a much better understanding of how society will react, which has been guiding the principles that the governments have been using to respond.

So when people look at the government and say, “Well, they have come out with one thing and now they are changing their minds,” that’s normally a data-driven decision and people aren’t necessarily seeing it that way.

So AI is playing a massive role in getting society to understand the impact of the virus -- not just from a medical perspective, but from everything else and to help the people.

Gardner: Glyn, this might be more apparent to the Pointnext organization, but how is AI benefiting the operational services side? Service and support providers have been put under tremendous additional strain and demand, and enterprises are looking for efficiency and adaptability.

Are they pointing the AI focus at their IT systems? How does the data they use for running their own operations come to their aid? Is there an AIOps part to this story?

AI needs people, processes 

Bowden: Absolutely, and there has definitely become a drive toward AIOps.

When you look at an operational organization within an IT group today, it’s surprising how much of it is still human-based. It’s a personal eyeball looking at a graph and then determining a trend from that graph. Or it’s the gut feeling that a storage administrator has when they know their system is getting full and they have an idea in the back of their head that last year something happened seasonally from within the organization making decisions that way.

We are therefore seeing systems such as HPE’s InfoSight start to be more prominent in the way people make those decisions. So that allows plugging into an ecosystem whereby you can see the trend of your systems over a long time, where you can use AI modeling as well as advanced analytics to understand the behavior of a system over time, and how the impact of things -- like everybody is suddenly starting to work remotely – does to the systems from a data perspective.

So the models-to-be need to catch up in that sense as well. But absolutely, AIOps is desirable. If it’s not there today, it’s certainly something that people are pursuing a lot more aggressively than they were before the pandemic.

Gardner: As we look to the future, for those organizations that want to be more data-driven and do it quickly, any words of wisdom with 20/20 hindsight? How do you encourage enterprises -- and small businesses as well -- to better prepare themselves to use AI and data science?

Garg: Whenever I think about an organization adopting AI, it’s not just the AI solution itself but all of the organizational processes -- and most importantly the people in an organization and preparing them for the adoption of AI.

I advise organizations that want to use AI and corporate data-driven decision-making to, first of all, make sure you are solving a really important problem for your organization. Sometimes the goal of adopting AI becomes more important than the goal of solving some kind of problem. So I always encourage any AI initiative to be focused on really high-value efforts.

Use your AI initiative to do something really valuable to your organization and spend a lot of time thinking about how to make it fit into the way your organization currently works. Make it enhance the day-to-day experience of your employees because, at the end of the day, your people are your most valuable assets.

Those are important non-technical things that are non-specific to the AI solution itself that organizations should think about if they want the shift to being AI-driven and data-driven to be successful.

For the AI itself, I suggest using the simplest-possible model, solution, and method of analyzing your data that you can. I cannot tell you the number of times where I have heard an organization come in saying that they want to use a very complex AI technique to solve a problem that if you look at it sideways you realize could be solved with a checklist or a simple spreadsheet. So the other rule of thumb with AI is to keep it as simple as possible. That will prevent you from incurring a lot of overhead.

Gardner: Glyn, how should organizations prepare to integrate data science and AI into more parts of their overall planning, management, and operations?

Bowden: You have to have a use case with an outcome in mind. It’s very important that you have a metric to determine whether it’s successful or not, and for the amount of value you add by bringing in AI. Because, as Arti said, a lot of these problems can be solved in multiple ways; AI isn’t the only way and often isn’t the best way. Just because it exists in that domain doesn’t necessarily mean it should be used.

AI isn't an on/off switch; it's an iteration. You can start with something small and then build into bigger and bigger components that bring more data to bear on the problem, and then add new features that lead to new functions and outcomes.

The second part is AI isn’t an on/off switch; it’s an iteration. You can start with something small and then build into bigger and bigger components that bring more and more data to bear on the problem, as well as then adding new features that lead to new functions and outcomes.

The other part of it is: AI is part of an ecosystem; it never exists in isolation. You don’t just drop in an AI system on its own and it solves a problem. You have to plug it into other existing systems around the business. It has data sources that feed it so that it can come to some decision.

Unless you think about what happens beyond that -- whether it’s visualizing something to a human being who will make a decision or automating a decision – it could really just be hiring the smartest person you can find and locking them in a room.

Pandemic’s positive impact

Gardner: I would like to close out our discussion with a riff on the adage of, “You can bring a horse to water but you can’t make them drink.” And that means trust in the data outcomes and people who are thirsty for more analytics and who want to use it.

How can we look with reassurance at the pandemic as having a positive impact on AI in that people want more data-driven analytics and will trust it? How do we encourage the perception to use AI? How is this current environment impacting that?

Garg: The fact that so many people are checking the trackers of how the pandemic is spreading and learning through a lot of major news publications as they are doing a great job of explaining this. They are learning through the tracking to see how stay-in-place orders affect the spread of the disease in their community. You are seeing that already.

We are seeing growth and trust in how analyzing data can help make better decisions. As I mentioned earlier, this leads to a better understanding of the limitations of data and a willingness to engage with that data output as not just black or white types of things.

As Glyn mentioned, it’s an iterative process, understanding how to make sense of data and how to build models to interpret the information that’s locked in the data. And I think we are seeing that.

We are seeing a growing desire to not only view this as some kind of black box that sits in some data center -- and I don’t even know where it is -- that someone is going to program, and it’s going to give me a result that will affect me. For some people that might be a positive thing, but for other people it might be a scary thing.

People are now much more willing to engage with the complexities of data science. I think that’s generally a positive thing for people wanting to incorporate it in their lives more because it becomes familiar and less other, if you will.

Gardner: Glyn, perceptions of trust as an accelerant to the use of yet more analytics and more AI?

Bowden: The trust comes from the fact that so many different data sources are out there. So many different organizations have made the data available that there is a consistent view of where the data works and where it doesn’t. And that’s built up the capability of people to accept that not all models work the first time, that experimentation does happen, and it is an iterative approach that gets to the end goal.

I have worked with customers who, when they saw a first experiment fall flat because it didn’t quite hit the accuracy or targets they were looking for, they ended the experiment. Whereas now I think we are seeing in real time on a massive scale that it’s all about iteration. It doesn’t necessarily work the first time. You need to recalibrate, move on, and do refinement. You bring in new data sources to get the extra value.

What we are seeing throughout this pandemic is the more expertise and data science you throw in an instance, the much better the outcome at the end. It’s not about that first result. It’s about the direction of the results, and the upward trend of success.

Gardner: I’m afraid we’ll have to leave it there. We have been exploring how AI and data science are proving impactful and indispensable as business architects chart their path to a new normal.

And we have learned how AI is the new pandemic team member for helping businesses reduce risk of failure and innovate with confidence. So please join me in thanking our guests, Arti Garg, Head of Advanced AI Technologies and Solutions at HPE. Thank you.

Garg: Thank you.

Gardner: And we have been with Glyn Bowden, Chief Technologist for AI and Data at HPE Pointnext Services. Thank you.

Bowden: Thanks, Dana.

Gardner: And a big thank you as well to our audience for joining us for this sponsored BriefingsDirect Voice of AI Innovation discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of Hewlett Packard Enterprise-supported discussions.

Thanks again for listening. Please pass this along to your IT community, and do come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard Enterprise.

A discussion on how AI is the new pandemic response team member for helping businesses reduce risk of failure and innovate with confidence. Copyright Interarbor Solutions, LLC, 2005-2020. All rights reserved.

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How Modern Operational Services Leads to More Self-Managing, Self-Healing, and Self-Optimizing for IT

A discussion on how Hewlett Packard Enterprise Pointnext Services is reinventing the experience of IT support to increasingly rely on automation, analytics, and agility.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of Innovation podcast series.

Gardner

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on how enterprise IT has entered a new era for services and support.

General digital business transformation and managing the new normal around the COVID-19 pandemic have hugely impacted how businesses and IT operate. Faced with mounting complexity, rapid change, and striking budgets, IT operational services must be smarter and more efficient than ever.

Stay with us here as we examine how Hewlett Packard Enterprise (HPE) Pointnext Services is reinventing the experience of IT support to increasingly rely on automation and analytics to help enable continued customer success.

Here to share the HPE Pointnext Services vision for the future of IT operational services are Gerry Nolan, Director of Portfolio Product Management, Operational Service Portfolio, at HPE Pointnext Services. Welcome, Gerry.

Gerry Nolan: Hi, Dana. Thank you for having us.

Gardner: We are also here with Ronaldo Pinto, Director of Portfolio Product Management, Operational Service Portfolio, at HPE Pointnext Services. Welcome, Ronaldo.

Ronaldo Pinto: Thank you, Dana. It’s a pleasure to be with you.

Gardner: Gerry, is it fair to say that IT has never had a more integral part of nearly all businesses and that therefore the intelligent support of IT has never been more critical?

IT delivers digitally in the new normal 

Nolan: We’ve never seen a time like this, Dana. Pretty much every aspect of our life has now moved to digital. It was already moving that way. Everyone is spending more hours per day in various collaboration platforms, going through various digital interactions, and we’re seeing that in our business as well.

Nolan

That applies to whether you are ordering a pizza, booking time at your gym, getting your morning coffee -- pretty much your life has changed forever. We see that dramatically impacting the IT space and the customers we deal with.

So, yes, it’s a unique time, we have never seen it before, and we believe things will never be the same again.

Gardner: So, we are reliant on technology for commerce, healthcare, finance, all across many of these scientific activities to combat the pandemic, not to mention more remote education and more remote work -- basically every facet of our modern life.

Consequently, how enterprise IT uses services and support has entered a new phase, a new era. Please explain why a digital environment requires more tools and opportunity to the people delivering the new operational services.

Nolan: The IT landscape is very dynamic. There is an expanding array of technology choices, which brings more complexity. Of course, the move to cloud and edge computing introduces new requirements from an IT operations point of view.

Then we got hit with COVID-19 and a whole new set of challenges -- huge increases in remote workforce, and all creating problems with networks, performance, and security.

For example, a retail customer that I just met with -- they don’t even have a four-walls data center anymore, most of their IT is distributed throughout their retail stores -- and another customer, a large telco, is installing edge-related servers on their electricity pylons on the sides of mountains in very remote areas. These types of use-cases need very different operational processes, approaches, and skills.

Then we got hit with COVID-19, and that brings a whole new set of challenges, with locking down of IT environments, huge increases in remote workforces, all creating problems with network capacity, performance, and security challenges.

As a result, we are seeing customers needing more help than ever while they try and maintain their businesses. At the same time, they need to plan and evolve for the medium- to long-term. They need solutions both for today -- to help in this unique lockdown mode -- but also to accelerate transformation efforts to move to a digitally enabled customer experience.

Gardner: Ronaldo, this obviously requires more than a traditional helpdesk and telephone support. Where does the operational experience, of even changing the culture around support, kick in? How do we get to a new experience?

Pinto

Pinto: Dana, many people associate traditional support to telephone support, but today it needs to be much more. As Gerry described, we are moving toward a very distributed, remote, low-touch to no-touch world, and COVID-19, the pandemic, just accelerated that.

To operate in such an environment, companies depend on an increasing number of tools and technologies. You have more variables today, just to control and maintain your performance. So it’s extremely important to arm the people that provide technical support with the latest artificial intelligence (AI) tools and digital infrastructure so they continue to be effective in the work they do.

Gardner: Gerry, how has the pandemic and emphasis on remote services accelerated people’s willingness to delve into the newer technologies around automation, AI, predictive analytics and AIOps? Are people more open now to all of that?

Nolan: No question, Dana. Consider any great customer experience that you have today -- from dealing with your mobile phone provider to, in my case recently, my utility company. The great experiences offer a variety of ways to access the information and the help you may need on a 24-7 basis. Typically, this has involved a whole range of different elements -- from a portal or an app, to some central hub -- for how you engage. That can include getting a more personalized dashboard of information and help. Those experiences also often have different engagement options, including access to live people who can answer questions and provide guidance to solve issues. That central hub also provides a wealth of helpful, useful information and can be AI-enabled to provide predictive alerts via dashboards.

There are companies that still provide only a single channel, such as, for example, the utility company I had to call yesterday, which kept me on hold for 45 minutes until I hung up. I tried the website, and they had multiple websites. I sent an e-mail; I am still waiting for a response!

The great customer experiences have multiple elements and dimensions to them. They have great people you can talk to. You have multiple ways of getting to those people. They have a great app or website with all sorts of information and help available, personalized to your needs.

That’s the way of the future. Those companies that are successful and have already started on that path are seeing great success. Those that have not are struggling -- especially in this climate. Now, not only is there more need to go digital, the pressure on revenue limits the investment dollars available to move in that direction if you haven’t already done so.

So, yes, there’s a multitude of different challenges here we are dealing with.

Gardner: It’s amazing nowadays when you deal, as a customer, with companies, how you can recognize almost instantly the ones that have invested in digital business transformation and are able to do a lot of different things under duress -- and those who didn’t. It’s rather stark.

Ronaldo, dealing with these complexities isn’t just a technology issue. Oftentimes it includes a multi-vendor aspect, a large ecosystem of suppliers. Pointing fingers isn’t going to help if you’re in a time-constrained situation, a crisis situation.

How do the new operational experiences include the capability to bring in many vendors and even so provide a seamless experience back to that customer?

Seamless collaborations succeed 

Pinto: HPE has historically collaborated. If you look at our customers today, they have best-of-breed environments and there are many emerging tools that make those environments more efficient. We also have several startups.

So, it’s extremely important for us to serve our customers by being able to collaborate seamlessly with all of those companies. We have done that in the past and we are expanding the operational capabilities, including tools we have today, to better understand performance, integration between our products, and with third-party products. We can streamline all of that collaboration.

Gardner: And, of course, the complexity extends across hybrid environments, from edge to cloud -- multi-cloud, private cloud, hybrid cloud. Is that multi-vendor and multi-architecture mix something that you’re encountering a lot?

Nolan: Today, every customer has a multi-vendor IT landscape. There are various phases of maturity in terms of dealing with legacy environments. But they are dealing with new IT on-premises technologies, they are trying to deploy cloud, or they may be moving to public cloud. There’s a plethora of use cases we see globally with our customers.

The classic issue is when there's a problem, the finger-pointing or blame-game starts. Even triaging and isolating problems in these environments can be a challenge, let alone the expertise to fix the issue. The more vendors you work with the more dimensions you have to manage.

And the classic issue, as you point out, is when there’s a problem, the finger-pointing or the blame-game starts. Even triaging and isolating problems in these types of environments can be a challenge, let alone having the expertise to fix the issue. Whether it’s in the hardware, software layer, or on somebody else’s platform, it’s difficult. Most vendors, of course, have different service level agreements (SLAs), different role names, different processes, and different contractual and pricing structures.

So, the whole engagement model, even the vocabulary they use, can be quite different; ourselves included, by the way. So, the more vendors you have to work with, the more dimensions you have to manage.

And then, of course, COVID-19 hits and our customers working with multiple vendors have to rely on how all those vendors are reacting to the current climate. And they’re not all reacting in a consistent fashion. The more vendors you have, the more work and time it’s going to take -- and the more cost involved.

We call it the power of one. Our customers see huge value in working with a partner who provides a single point of contact, that single throat to choke or hand to shake, and a single focal point for dealing with issues. You can have a single contract, a single invoice, and a single team to work with. It saves a lot of time and it saves a lot of money.

Organizations already in that position are seeing significant benefits. Our multi-vendor business is growing very, very well. And we see that moving into the future as companies try to focus on their core business, whatever that might be, and let IT take care of itself.

Edge to cloud to data center 

Pinto: To your question, Dana, on hybrid environments, it’s not only hybrid, it’s edge to cloud and to the data center. I can give you two examples.

We have a large department store customer with the technology in each of the many stores. We support not only the edge environments in those stores but all the way through to their data center. There are also hybrid environments for data management where you typically have primary storage, secondary storage, and your archiving strategy. All of that is managed by a multitude of backup and data-movement software.

The customer should not be worried with component by component, but with a single, end-to-end solution. We help customers abstract that by supporting the end-to-end data environment and collaborating with the third-party software vendors or platform vendors that will inevitably be a part of the solution.

Gardner: Gerry, earlier you mentioned your own experience with a utility company. You were expecting a multi-channel opportunity to engage with them. How does the IT operational services as an experience become inclusive of such things? Why does that need to be all-inclusive across the solutions and support approaches?

Have it your way

Nolan: An alternative example that I can give is my bank. I have a couple of different banks that I work with, but one in particular invested early in a digital platform. They didn’t replace their brick and mortar models. They still have lots of branches, lots of high-tech ATMs that allow for all types of self-serve.

But they also have a really cool app and website, which they’ve had for a number of years. They didn’t introduce digital as a way of closing down their branches, they keep all of those options available because different people like to integrate and work with their service providers in different ways, and we see that in IT, too.

The key elements to delivering a successful experience in the IT space, an AI-enabled experience, includes having lots of expertise and knowledge available across the IT environment, not just on a single set of products.

Of course, a digital platform provides that personalized view. It includes things like dashboards of what’s in my environment, ongoing alerts and predictions -- maybe capacity is running out or maybe costs are beyond what was forecast. Or maybe I have ways of optimizing my costs, some insights around updates to my software, licenses or some systems might be reaching the end of their support life. There is all sorts of information that should be available to me in a personalized way.

And then in terms of accessing experts, the old model is to get on the phone, like I was yesterday for 45 minutes talking to somebody, and in my case, I wasn’t successful. But customers, in some cases, they like to deal with the experts through a chat window or maybe live on the phone. Others like to watch expert technical tips and technique videos. So, we have developed an extensive video library of experts wherein you can pick and choose and listen to some tips and techniques about how to deal with certain key topics we see that customers are interested in.

Moderated forums: Customers actually like sharing their experiences with each other. And then our experts get involved and you mix and match with partners and end-customers and you get this very rich dialogue that goes on around particular topics, best practices, ideas, or there could be problems that somebody else has solved.

AI is at the heart of all of this because it's constantly learning. It's like a self-propelling mechanism that just gets better over time. The more knowledge it gains, the more answers are provided.

AI is at the heart of all of this because it’s constantly learning. It’s like a self-propelling mechanism that just gets better over time. The more people come on board, the more knowledge it gains, the more questions they ask, the more answers are provided.

The whole thing just gets better and better over time. It’s key, of course, to have that wide portfolio of help for customers. If they have a strategy, make it work better; if they don’t have a strategy and need help building one, we can help them do that all the way through to designing and implementing those solutions.

And then they can get the ongoing support, which is where Ronaldo and I spend most of our life. But it’s important as a vendor or as a partner to be able to offer customers help across the value chain or across the lifecycle, depending on where they need that help.

Gardner: Ronaldo, let’s dig more deeply into the specifics of the new HPE Pointnext Services’ operational services’ approach, modernizing operations for the future of IT. What does it include?

Meet customers’ modernization terms 

Pinto: We are doing all of this modernization with the customer in mind. What is really important for us is not only accomplishing something, but how you accomplish it. At the end of any interaction the customer needs to feel that their time was used effectively. HPE shows a legitimate concern with the customer success and in feeling positive at the end of the interaction.

Gerry mentioned the AI tools and alerts. We are integrating all of the sensors, telemetry we get from products in the field, all the way up to our operational processes in the back end so that customers can accomplish whatever they need with us on their own terms.

For example, if there’s an alert or a performance degradation in a product, we provide tools to dig deeper and understand why. “Hey, maybe it’s a component in the infrastructure that needs to be updated or replaced?” We are integrating all of that. We see into our back end operational processes so that we can even detect issues before the customer does. Then we just notify the customer that an action needs to be performed and, if needed, we dispatch the part replacement.

If the customer needs someone at the site to do the replacement, no problem. The customer can schedule that visit easily in a web interface and we will show up in the window that the customer chooses.

It’s offering the customer, as Gerry mentioned, multiple channels and multiple ways to interact. For customers, it means they may prefer a remote automated web interface or the personal touch of a support engineer, but it should be on the customers’ own terms.

Gardner: I have seen in the release information you provide to analysts like myself the concept of a digital customer platform. What do you mean by a digital customer platform when it comes to operational services?

A focused digital platform 

Nolan: It’s all of the things that Ronaldo just mentioned coming together in a single place. Going back to my bank example, they give you a credit card where you typically have a single place that you go from a digital point of view. It’s either an app and/or a website and that provides you all of this personalized information that’s honed to your specific needs and your specific use case.

For us, from a digital point of view and from a customization platform, we want to provide a single place regardless of your use case. So, whether you are a warranty level customer or a consumption customer, buying your IT on a pay-as-you-go basis, all of the help you need, all of the information, dashboards, all of the ways of engaging with us as a partner, it’s all through a single portal. That’s what we mean when we say the digital platform, that central place that brings it all to life for you as a customer.

Gardner: Why is the consumption-based approach important? How has that changed the game?

Pinto: It’s the same idea, to provide customers the option to consume IT and to use IT on their own terms. HPE pioneered the hybrid IT consumption model. Behind that is Pointnext through all the services we provide -- whether the customer chooses to consume or not, on an as-a-service basis, consuming an outcome, or if the customer wants to consume the traditional way, where the customer takes ownership of their underlying infrastructure. We automate those more transactional, repeatable tasks and help the customer focus on innovation and meeting their business objectives through IT. So that is going to be consistent across all the consumption models.

Nolan: What’s important to recognize here is, as a customer, you want choice and choice is good. If the only option you have is, for example, a public cloud solution, then guess what? Every problem you as a customer have, then that public cloud provider has one toolbox. It’s a public cloud solution.

I have just been speaking with a large insurance company and they are moving toward a cloud-first strategy, which their board decided they needed. So, everything in their mind needs to move to the cloud. And it’s interesting because they decided the way they are going to partner to get that done is directly with a public cloud vendor. And guess what? Every problem, every workload in that organization is now directed toward moving to public cloud, even where that may not be the best outcome for the customer. To Ronaldo’s point, you want to be assessing all of your workloads and deciding where is the best placement of that workload.

You might want to do that work inside your firewall and on your network because certain work will get done better, more cost effectively, and for all sorts of security, network latency, and data regulatory reasons. Having multiple different choices -- on-premises, you can do CAPEX, you can do as-a-service -- is important. Your partner should be able to offer all those choices. We at HPE, as Ronaldo said, pioneered the IT as-a-service mode. We already have that in place.

 Our HPE GreenLake offering allows you to buy and consume all of your IT on a pay-as-you-go basis. We just send you a monthly bill for whatever IT you have used. Everything is included in that bill -- your hardware, software, and all of the services, including support. You don’t really need to worry about it.

You care instead about the outcomes. You just want the IT to take care of itself, and you get your bill. Then you can easily align that cost with the revenue coming in. As the revenue goes up, you are using more IT, you pay more; revenue goes down, you are using less IT, you pay less. Fairly simple, but very powerful and very popular.

Gardner: Yes, in the past we have heard so many complaints about unexpected bills, maintenance add-ons, and complex licensing. So, this is something that’s been an ongoing issue for decades.

Now with COVID-19 and so many people working remotely, can you provide an example of bringing the best minds on the solutions side to wherever a problem is?

Room with a data center view 

Nolan: One that comes to mind sounds like a simplistic use case, but it’s valuable in today’s climate, with the IT lockdown. Inside of HPE, we use multiple collaboration environments. But we own our own collaboration platform, HPE MyRoom.

We launched a feature in that collaboration platform called Visual Remote Guidance, which allows us to collaborate like we are in the customer’s data center virtually. We can turn on the smart device on the customer side, and they can be enabled, through the camera, to actually see the IT situation they are dealing with.

It might be an installation of some hardware. It could be resolving some technical problem. There are a variety of different use cases we are seeing. Of course, when a system causes a problem and the company has locked-down their entire IT department, they don’t want to see engineers coming in from either HPE or one of our partners.

Visual Remote Guidance allows us to collaborate like we are in the customer's data center virtually. We can turn on the smart device on the customer side and they can be enabled to see the IT situation that they are all dealing with.

This solution immediately became very useful in helping customers because we now have thousands of remote experts available in various locations around the world. Now, they can instantly connect with the customer. They can be the hands and eyes working with the customer. Then the customer can perform the action, guided all the way through the process by their remote HPE expert. And that’s using a well-proven digital collaboration platform that we have had for years. By just introducing that one new additional feature, it has helped tremendously.

Many customers were struggling with installing complex solutions. Because they needed to get it done and yet didn’t want to bring anybody onto their site, we can take advantage of our remote experts and get the work done. Our experts guide them through, step by step, and test the whole thing. It’s proving to be very effective. It’s used extensively now around the world. All of our agents have this on their desktop and they can initiate with any customer, in any conversation. So, it’s pretty powerful.

Gardner: Yes, so you have socialized isolation, but you have intense technology collaboration at the same time.

Ronaldo, HPE InfoSight and automation have gone a long way to helping organizations get in front of maintenance issues, to be proactive and prescriptive. Can you flesh out any examples of where the combination of automation, AI, AIOps, and HPE InfoSight have come together in a way that helps people get past a problem before it gets out of hand?

Stop problems before they start

Pinto: Yes, absolutely. We are integrating all our telemetry from the sensors in our technology with our back-end operational processes. That is InfoSight, a very powerful, AI and machine learning (ML) tool. By collecting from sensors -- more than 100 data points from our products every few seconds -- and processing all of that data on the back end, we can be informed by trends we see in our installed base and gather knowledge from our product experts and data scientists.

That allows us to get in front of situations that could result in outages in the environment. For example, a virtual storage volume could be running out of capacity. That could lead to storage devices going offline, bringing down the whole environment. So, we can get ahead of that and fix the problem for the customer before it gets to a business-degradation situation.

We are expanding the InfoSight capabilities on a daily basis throughout the HPE portfolio. We also should be able to identify, based on the telemetry of the products, what workloads the customer is running and help the customer better utilize all those underlying resources in the context of a specific workload. We also could even identify an improvement opportunity in the underlying infrastructure to improve the performance of that workload.

Gardner: So, it is problem solving as well as a drive for continual IT improvement, refinement, and optimization, which is a lot different than a break-fix mentality. How will the whole concept of operational services shift in your opinion from break-fix to more of optimization and continuous improvement?

Pinto: I think you just touched on probably the most important point, Dana. Data centers today and technology are increasingly redundant and resilient. So really break-fix is becoming table stakes very quickly.

The metaphor that I use many times is airlines. In the past, security or safety of the airline was something very important. Today it’s basically table stakes. You assume that the airline operates at the highest standards of safety. So, with break-fix it’s the same. HPE is automating all of the break-fix operations to allow customers to focus on what adds the most value to their businesses, which is delivering the business outcomes based on the technology -- and further innovating.

The pace of innovation in this business is unprecedented, both in terms of tools and technologies available to operate your environment as well as time-to-market of the digital applications that are the revenue generators for our customers.

Gardner: Gerry, anything additional to offer in terms of the vision of an optimization drive rather than a maintenance drive?

Innovate to your ideal state 

Nolan: Totally. It’s all about trying to stay ahead of the business requirements.

For example, last night Ronaldo and I were speaking with a customer with a global footprint. They happen to be in a pretty good state, but it was interesting talking to them about what would a new desired state look like. We work closely with customers as we innovate and build better service capabilities. We are trying to find out from our customers what is their ideal state, because it’s all about delivering the customer experience that maps to each customer’s use case -- and every customer is different.

I also just met with a casino operator, which at the moment is in a bit of a tough space, but they have been acquiring other casinos and opening new casinos in different parts of the world. Their challenge is completely different than my friend in the insurance industry who was going to cloud-first.

The casino business is all about security and regulation. They are really not in the business of IT -- but IT is still critical to their success. They are trying to understand all the IT that they have.

The casino business is all about security, and a lot of regulation. In his case, they were buying companies, so they are also buying all of this IT. They need help controlling it. They are in the casino business, they are not really in the business of IT, but IT is still critical to their success. And now they are in a pandemic-driven shutdown, so they are trying to figure out how to manage and understand all of the IT they have.

For others in this social isolation climate, they need to keep the business running. Now as they are starting to open up, they need help with all sorts of issues around how to monitor customers coming into their facilities. How do they keep staff safe in terms of making sure they stay six feet apart? And HPE has a wealth of new offerings in that space. We can help customers deal with opening up and getting back to work.

Whether you are operating an old environment, a new environment, or are in a post COVID-19 journey -- trying to get through this pandemic situation, which is going to take years -- there are all sorts of different aspects you need to consider as an organization. Trying to paint your new vision for what an ideal IT experience feels like -- and then finding partners like HPE who can help deliver that -- is really the way forward.

Gardner: I’m afraid we will have to leave it there. We have been exploring how digital business transformation and managing the new normal around COVID-19 pandemic issues have hugely impacted how businesses and IT operate. And we have learned now how HPE Pointnext Services has reinvented the experience of IT support to increasingly rely on automation, analytics, and agility.

So please join me in thanking our guests, Gerry Nolan, Director of Portfolio Product Management, Operational Service Portfolio, at HPE Pointnext Services. Thank you so much, Gerry.

Nolan: Thank you very much, Dana. It was a pleasure talking to you today.

Gardner: We have also been here with Ronaldo Pinto, Director of Portfolio Product Management, Operational Service Portfolio, at HPE Pointnext Services. Thank you as well, Ronaldo.

Pinto: Thanks for the opportunity Dana and stay safe.

Gardner: And thanks, as well, to our audience for joining this sponsored BriefingsDirect Voice of Innovation discussion. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HPE-supported discussions.

Thanks again for listening. Please pass this along to your IT community, and do come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett Packard Enterprise.

 A discussion on how Hewlett Packard Enterprise Pointnext Services has reinvented the experience of IT support to increasingly rely on automation, analytics, and agility. Copyright Interarbor Solutions, LLC, 2005-2020. All rights reserved.

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