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AI

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How new tools help any business build ethical and sustainable supply chains

The next BriefingsDirect digital business innovations discussion explores new ways that companies gain improved visibility, analytics, and predictive responses to better manage supply-chain risk-and-reward sustainability factors.

We’ll examine new tools and methods that can be combined to ease the assessment and remediation of hundreds of supply-chain risks -- from use of illegal and unethical labor practices to hidden environmental malpractices

Listen to the podcastFind it on iTunes. Get the mobile app. Read a full transcript or download a copy. 

Here to explore more about the exploding sophistication in the ability to gain insights into supply-chain risks and provide rapid remediation, are our panelists, Tony Harris, Global Vice President and General Manager of Supplier Management Solutions at SAP Ariba; Erin McVeigh, Head of Products and Data Services at Verisk Maplecroft, and Emily Rakowski, Chief Marketing Officer at EcoVadis. The discussion was moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tony, I heard somebody say recently there’s never been a better time to gather information and to assert governance across supply chains. Why is that the case? Why is this an opportune time to be attacking risk in supply chains?

Harris: Several factors have culminated in a very short time around the need for organizations to have better governance and insight into their supply chains.

Harris

Harris

First, there is legislation such as the UK’s Modern Slavery Act in 2015 and variations of this across the world. This is forcing companies to make declarations that they are working to eradicate forced labor from their supply chains. Of course, they can state that they are not taking any action, but if you can imagine the impacts that such a statement would have on the reputation of the company, it’s not going to be very good. 

Next, there has been a real step change in the way the public now considers and evaluates the companies whose goods and services they are buying. People inherently want to do good in the world, and they want to buy products and services from companies who can demonstrate, in full transparency, that they are also making a positive contribution to society -- and not just generating dividends and capital growth for shareholders. 

Finally, there’s also been a step change by many innovative companies that have realized the real value of fully embracing an environmental, social, and governance (ESG) agenda. There’s clear evidence that now shows that companies with a solid ESG policy are more valuable. They sell more. The company’s valuation is higher. They attract and retain more top talent -- particularly Millennials and Generation Z -- and they are more likely to get better investment rates as well. 

Gardner: The impetus is clearly there for ethical examination of how you do business, and to let your costumers know that. But what about the technologies and methods that better accomplish this? Is there not, hand in hand, an opportunity to dig deeper and see deeper than you ever could before?

Better business decisions with AI

Harris: Yes, we have seen a big increase in the number of data and content companies that now provide insights into the different risk types that organizations face.

We have companies like EcoVadis that have built score cards on various corporate social responsibility (CSR) metrics, and Verisk Maplecroft’s indices across the whole range of ESG criteria. We have financial risk ratings, we have cyber risk ratings, and we have compliance risk ratings. 

These insights and these data providers are great. They really are the building blocks of risk management. However, what I think has been missing until recently was the capability to pull all of this together so that you can really get a single view of your entire supplier risk exposure across your business in one place.

What has been missing was the capability to pull all of this together so that you can really get a single view of your entire supplier risk exposure across your business.

Technologies such as artificial intelligence (AI), for example, and machine learning (ML) are supporting businesses at various stages of the procurement process in helping to make the right decisions. And that’s what we developed here at SAP Ariba. 

Gardner: It seems to me that 10 years ago when people talked about procurement and supply-chain integrity that they were really thinking about cost savings and process efficiency. Erin, what’s changed since then? And tell us also about Verisk Maplecroft and how you’re allowing a deeper set of variables to be examined when it comes to integrity across supply chains.

McVeigh: There’s been a lot of shift in the market in the last five to 10 years. I think that predominantly it really shifted with environmental regulatory compliance. Companies were being forced to look at issues that they never really had to dig underneath and understand -- not just their own footprint, but to understand their supply chain’s footprint. And then 10 years ago, of course, we had the California Transparency Act, and then from that we had the UK Modern Slavery Act, and we keep seeing more governance compliance requirements. 

McVeigh

McVeigh

But what’s really interesting is that companies are going beyond what’s mandated by regulations. The reason that they have to do that is because they don’t really know what’s coming next. With a global footprint, it changes that dynamic. So, they really need to think ahead of the game and make sure that they’re not reacting to new compliance initiatives. And they have to react to a different marketplace, as Tony explained; it’s a rapidly changing dynamic.

We were talking earlier today about the fact that companies are embracing sustainability, and they’re doing that because that’s what consumers are driving toward.

At Verisk Maplecroft, we came to business about 12 years ago, which was really interesting because it came out of a number of individuals who were getting their master’s degrees in supply-chain risk. They began to look at how to quantify risk issues that are so difficult and complex to understand and to make it simple, easy, and intuitive. 

They began with a subset of risk indices. I think probably initially we looked at 20 risks across the board. Now we’re up to more than 200 risk issues across four thematic issue categories. We begin at the highest pillar of thinking about risks -- like politics, economics, environmental, and social risks. But under each of those risk’s themes are specific issues that we look at. So, if we’re talking about social risk, we’re looking at diversity and labor, and then under each of those risk issues we go a step further, and it’s the indicators -- it’s all that data matrix that comes together that tell the actionable story. 

Some companies still just want to check a [compliance] box. Other companies want to dig deeper -- but the power is there for both kinds of companies. They have a very quick way to segment their supply chain, and for those that want to go to the next level to support their consumer demands, to support regulatory needs, they can have that data at their fingertips. 

Global compliance

Gardner: Emily, in this global environment you can’t just comply in one market or area. You need to be global in nature and thinking about all of the various markets and sustainability across them. Tell us what EcoVadis does and how an organization can be compliant on a global scale.

Rakowski: EcoVadis conducts business sustainability ratings, and the way that we’re using the procurement context is primarily that very large multinational companies like Johnson and Johnson or Nestlé will come to us and say, “We would like to evaluate the sustainability factors of our key suppliers.”

Rakowski

Rakowski

They might decide to evaluate only the suppliers that represent a significant risk to the business, or they might decide that they actually want to review all suppliers of a certain scale that represent a certain amount of spend in their business. 

What EcoVadis provides is a 10-year-old methodology for assessing businesses based on evidence-backed criteria. We put out a questionnaire to the supplier, what we call a right-sized questionnaire, the supplier responds to material questions based on what kind of goods or services they provide, what geography they are in, and what size of business they are in. 

Of course, very small suppliers are not expected to have very mature and sophisticated capabilities around sustainability systems, but larger suppliers are. So, we evaluate them based on those criteria, and then we collect all kinds of evidence from the suppliers in terms of their policies, their actions, and their results against those policies, and we give them ultimately a 0 to 100 score. 

And that 0 to 100 score is a pretty good indicator to the buying companies of how well that company is doing in their sustainability systems, and that includes such criteria as environmental, labor and human rights, their business practices, and sustainable procurement practices. 

Gardner: More data and information are being gathered on these risks on a global scale. But in order to make that information actionable, there’s an aggregation process under way. You’re aggregating on your own -- and SAP Ariba is now aggregating the aggregators.

How then do we make this actionable? What are the challenges, Tony, for making the great work being done by your partners into something that companies can really use and benefit from? 

Timely insights, best business decisions

Harris: Other than some of the technological challenges of aggregating this data across different providers is the need for linking it to the aspects of the procurement process in support of what our customers are trying to achieve. We must make sure that we can surface those insights at the right point in their process to help them make better decisions. 

The other aspect to this is how we’re looking at not just trying to support risk through that source-to-settlement process -- trying to surface those risk insights -- but also understanding that where there’s risk, there is opportunity.

So what we are looking at here is how can we help organizations to determine what value they can derive from turning a risk into an opportunity, and how they can then measure the value they’ve delivered in pursuit of that particular goal. These are a couple of the top challenges we’re working on right now.

We're looking at not just trying to support risk through that source-to-settlement process -- trying to surface those risk insights -- but also understanding that where there is risk there is opportunity.

Gardner: And what about the opportunity for compression of time? Not all challenges are something that are foreseeable. Is there something about this that allows companies to react very quickly? And how do you bring that into a procurement process?

Harris: If we look at some risk aspects such as natural disasters, you can’t react timelier than to a natural disaster. So, the way we can alert from our data sources on earthquakes, for example, we’re able to very quickly ascertain whom the suppliers are, where their distribution centers are, and where that supplier’s distribution centers and factories are.

When you can understand what the impacts are going to be very quickly, and how to respond to that, your mitigation plan is going to prevent the supply chain from coming to a complete halt. 

Gardner: We have to ask the obligatory question these days about AI and ML. What are the business implications for tapping into what’s now possible technically for better analyzing risks and even forecasting them? 

AI risk assessment reaps rewards

Harris: If you look at AI, this is a great technology, and what we trying to do is really simplify that process for our customers to figure out how they can take action on the information we’re providing. So rather them having to be experts in risk analysis and doing all this analysis themselves, AI allows us to surface those risks through the technology -- through our procurement suite, for example -- to impact the decisions they’re making. 

For example, if I’m in the process of awarding a piece of sourcing business off of a request for proposal (RFP), the technology can surface the risk insights against the supplier I’m about to award business to right at that point in time. 

A determination can be made based upon the goods or the services I’m looking to award to the supplier or based on the part of the world they operate in, or where I’m looking to distribute these goods or services. If a particular supplier has a risk issue that we feel is too high, we can act upon that. Now that might mean we postpone the award decision before we do some further investigation, or it may mean we choose not to award that business. So, AI can really help in those kinds of areas. 

Gardner: Emily, when we think about the pressing need for insight, we think about both data and analysis capabilities. This isn’t something necessarily that the buyer or an individual company can do alone if they don’t have access to the data. Why is your approach better and how does AI assist that?

Rakowski: In our case, it’s all about allowing for scale. The way that we’re applying AI and ML at EcoVadis is we’re using it to do an evidence-based evaluation.

We collect a great amount of documentation from the suppliers we’re evaluating, and actually that AI is helping us scan through the documentation more quickly. That way we can find the relevant information that our analysts are looking for, compress the evaluation time from what used to be about a six or seven-hour evaluation time for each supplier down to three or four hours. So that’s essentially allowing us to double our workforce of analysts in a heartbeat.

AI is helping us scan through the documentation more quickly. That way we can find the relevant information that our analysts are looking for, allowing us to double our workforce of analysts.

The other thing it’s doing is helping scan through material news feeds, so we’re collecting more than 2,500 news sources from around all kinds of reports, from China Labor Watch or OSHA. These technologies help us scan through those reports from material information, and then puts that in front of our analysts. It helps them then to surface that real-time news that we’re for sure at that point is material. 

And that way we we’re combining AI with real human analysis and validation to make sure that what we we’re serving is accurate and relevant. 

Harris: And that’s a great point, Emily. On the SAP Ariba side, we also use ML in analyzing similarly vast amounts of content from across the Internet. We’re scanning more than 600,000 data sources on a daily basis for information on any number of risk types. We’re scanning that content for more than 200 different risk types.

We use ML in that context to find an issue, or an article, for example, or a piece of bad news, bad media. The software effectively reads that article electronically. It understands that this is actually the supplier we think it is, the supplier that we’ve tracked, and it understands the context of that article. 

By effectively reading that text electronically, a machine has concluded, “Hey, this is about a contracts reduction, it may be the company just lost a piece of business and they had to downsize, and so that presents a potential risk to our business because maybe this supplier is on their way out of business.”

And the software using ML figures all that stuff out by itself. It defines a risk rating, a score, and brings that information to the attention of the appropriate category manager and various users. So, it is very powerful technology that can number crunch and read all this content very quickly. 

Gardner: Erin, at Maplecroft, how are such technologies as AI and ML being brought to bear, and what are the business benefits to your clients and your ecosystem? 

The AI-aggregation advantage

McVeigh: As an aggregator of data, it’s basically the bread and butter of what we do. We bring all of this information together and ML and AI allow us to do it faster, and more reliably

We look at many indices. We actually just revamped our social indices a couple of years ago.

Before that you had a human who was sitting there, maybe they were having a bad day and they just sort of checked the box. But now we have the capabilities to validate that data against true sources. 

Just as Emily mentioned, we were able to reduce our human-rights analyst team significantly and the number of individuals that it took to create an index and allow them to go out and begin to work on additional types of projects for our customers. This helped our customers to be able to utilize the data that’s being automated and generated for them. 

We also talked about what customers are expecting when they think about data these days. They’re thinking about the price of data coming down. They’re expecting it to be more dynamic, they’re expecting it to be more granular. And to be able to provide data at that level, it’s really the combination of technology with the intelligent data scientists, experts, and data engineers that bring that power together and allow companies to harness it. 

Gardner: Let’s get more concrete about how this goes to market. Tony, at the recent SAP Ariba Live conference, you announced the Ariba Supplier Risk improvements. Tell us about the productization of this, how people intercept with it. It sounds great in theory, but how does this actually work in practice?

Partnership prowess

Harris: What we announced at Ariba Live in March is the partnership between SAP Ariba, EcoVadis and Verisk Maplecroft to bring this combined set of ESG and CSR insights into SAP Ariba’s solution.

We do not yet have the solution generally available, so we are currently working on building out integration with our partners. We have a number of common customers that are working with us on what we call our design partners. There’s no better customer ultimately then a customer already using these solutions from our companies. We anticipate making this available in the Q3 2018 time frame. 

And with that, customers that have an active subscription to our combined solutions are then able to benefit from the integration, whereby we pull this data from Verisk Maplecroft, and we pull the CSR score cards, for example, from EcoVadis, and then we are able to present that within SAP Ariba’s supplier risk solution directly. 

What it means is that users can get that aggregated view, that high-level view across all of these different risk types and these metrics in one place. However, if, ultimately they are going to get to the nth degree of detail, they will have the ability to click through and naturally go into the solutions from our partners here as well, to drill right down to that level of detail. The aim here is to get them that high-level view to help them with their overall assessments of these suppliers. 

Gardner: Over time, is this something that organizations will be able to customize? They will have dials to tune in or out certain risks in order to make it more applicable to their particular situation?

Customers that have an active subscription to our combined solutions are then able to benefit from the integration and see all that data within SAP Ariba's supplier risk solutions directly.

Harris: Yes, and that’s a great question. We already addressed that in our solutions today. We cover risk across more than 200 types, and we categorized those into four primary risk categories. The way the risk exposure score works is that any of the feeding attributes that go into that calculation the customer gets to decide on how they want to weigh those. 

If I have more bias toward that kind of financial risk aspects, or if I have more of the bias toward ESG metrics, for example, then I can weigh that part of the score, the algorithm, appropriately.

Gardner: Before we close out, let’s examine the paybacks or penalties when you either do this well -- or not so well.

Erin, when an organization can fully avail themselves of the data, the insight, the analysis, make it actionable, make it low-latency -- how can that materially impact the company? Is this a nice-to-have, or how does it affect the bottom line? How do we make business value from this?

Nice-to-have ROI

Rakowski: One of the things that we’re still working on is quantifying the return on investment (ROI) for companies that are able to mitigate risk, because the event didn’t happen.

How do you put a tangible dollar value to something that didn’t occur? What we can look at is taking data that was acquired over the past few years and understand that as we begin to see our risk reduction over time, we begin to source for more suppliers, add diversity to our supply chain, or even minimize our supply chain depending on the way you want to move forward in your risk landscape and your supply diversification program. It’s giving them that power to really make those decisions faster and more actionable. 

And so, while many companies still think about data and tools around ethical sourcing or sustainable procurement as a nice-to-have, those leaders in the industry today are saying, “It’s no longer a nice-to-have, we’re actually changing the way we have done business for generations.”

And, it’s how other companies are beginning to see that it’s not being pushed down on them anymore from these large retailers, these large organizations. It’s a choice they have to make to do better business. They are also realizing that there’s a big ROI from putting in that upfront infrastructure and having dedicated resources that understand and utilize the data. They still need to internally create a strategy and make decisions about business process. 

We can automate through technology, we can provide data, and we can help to create technology that embeds their business process into it -- but ultimately it requires a company to embrace a culture, and a cultural shift to where they really believe that data is the foundation, and that technology will help them move in this direction.

Gardner: Emily, for companies that don’t have that culture, that don’t think seriously about what’s going on with their suppliers, what are some of the pitfalls? When you don’t take this seriously, are bad things going to happen? 

Pay attention, be prepared

Rakowski: There are dozens and dozens of stories out there about companies that have not paid attention to critical ESG aspects and suffered the consequences of a horrible brand hit or a fine from a regulatory situation. And any of those things easily cost that company on the order of a hundred times what it would cost to actually put in place a program and some supporting services and technologies to try to avoid that. 

From an ROI standpoint, there’s a lot of evidence out there in terms of these stories. For companies that are not really as sophisticated or ready to embrace sustainable procurement, it is a challenge. Hopefully there are some positive mavericks out there in the businesses that are willing to stake their reputation on trying to move in this direction, understanding that the power they have in the procurement function is great. 

They can use their company’s resources to bet on supply-chain actors that are doing the right thing, that are paying living wages, that are not overworking their employees, that are not dumping toxic chemicals in our rivers and these are all things that, I think, everybody is coming to realize are really a must, regardless of regulations.

Hopefully there are some positive mavericks out there who are willing to stake their reputations on moving in this direction. The power they have in the procurement function is great.

And so, it’s really those individuals that are willing to stand up, take a stand and think about how they are going to put in place a program that will really drive this culture into the business, and educate the business. Even if you’re starting from a very little group that’s dedicated to it, you can find a way to make it grow within a culture. I think it’s critical.

Gardner: Tony, for organizations interested in taking advantage of these technologies and capabilities, what should they be doing to prepare to best use them? What should companies be thinking about as they get ready for such great tools that are coming their way?

Synergistic risk management

Harris: Organizationally, there tend to be a couple of different teams inside of business that manage risks. So, on the one hand there can be the kind of governance risk and compliance team. On the other hand, they can be the corporate social responsibility team. 

I think first of all, bringing those two teams together in some capacity makes complete sense because there are synergies across those teams. They are both ultimately trying to achieve the same outcome for the business, right? Safeguard the business against unforeseen risks, but also ensure that the business is doing the right thing in the first place, which can help safeguard the business from unforeseen risks.

I think getting the organizational model right, and also thinking about how they can best begin to map out their supply chains are key. One of the big challenges here, which we haven’t quite solved yet, is figuring out who are the players or supply-chain actors in that supply chain? It’s pretty easy to determine now who are the tier-one suppliers, but who are the suppliers to the suppliers -- and who are the suppliers to the suppliers to the suppliers?

We’ve yet to actually build a better technology that can figure that out easily. We’re working on it; stay posted. But I think trying to compile that information upfront is great because once you can get that mapping done, our software and our partner software with EcoVadis and Verisk Maplecroft is here to surfaces those kinds of risks inside and across that entire supply chain.

Listen to the podcastFind it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: SAP Ariba.

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Inside story on HPC’s AI role in Bridges 'strategic reasoning' research at CMU

The next BriefingsDirect high performance computing (HPC) success interview examines how strategic reasoning is becoming more common and capable -- even using imperfect information.

We’ll now learn how Carnegie Mellon University and a team of researchers there are producing amazing results with strategic reasoning thanks in part to powerful new memory-intense systems architectures.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript ordownload a copy. 

To learn more about strategic reasoning advances, please join me in welcoming Tuomas Sandholm, Professor and Director of the Electronic Marketplaces Lab at Carnegie Mellon University in Pittsburgh. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tell us about strategic reasoning and why imperfect information is often the reality that these systems face?

Sandholm: In strategic reasoning we take the word “strategic” very seriously. It means game theoretic, so in multi-agent settings where you have more than one player, you can't just optimize as if you were the only actor -- because the other players are going to act strategically. What you do affects how they should play, and what they do affects how you should play.

Sandholm

Sandholm

That's what game theory is about. In artificial intelligence (AI), there has been a long history of strategic reasoning. Most AI reasoning -- not all of it, but most of it until about 12 years ago -- was really about perfect information games like Othello, Checkers, Chess and Go.

And there has been tremendous progress. But these complete information, or perfect information, games don't really model real business situations very well. Most business situations are of imperfect information.

Know what you don’t know

So you don't know the other guy's resources, their goals and so on. You then need totally different algorithms for solving these games, or game-theoretic solutions that define what rational play is, or opponent exploitation techniques where you try to find out the opponent's mistakes and learn to exploit them.

So totally different techniques are needed, and this has way more applications in reality than perfect information games have.

Gardner: In business, you don't always know the rules. All the variables are dynamic, and we don't know the rationale or the reasoning behind competitors’ actions. People sometimes are playing offense, defense, or a little of both.

Before we dig in to how is this being applied in business circumstances, explain your proof of concept involving poker. Is it Five-Card Draw?

Heads-Up No-Limit Texas Hold'em has become the leading benchmark in the AI community.

Sandholm: No, we’re working on a much harder poker game called Heads-Up No-Limit Texas Hold'em as the benchmark. This has become the leading benchmark in the AI community for testing these application-independent algorithms for reasoning under imperfect information.

The algorithms have really nothing to do with poker, but we needed a common benchmark, much like the IC chip makers have their benchmarks. We compare progress year-to-year and compare progress across the different research groups around the world. Heads-Up No-limit Texas Hold'em turned out to be great benchmark because it is a huge game of imperfect information.

It has 10 to the 161 different situations that a player can face. That is one followed by 161 zeros. And if you think about that, it’s not only more than the number of atoms in the universe, but even if, for every atom in the universe, you have a whole other universe and count all those atoms in those universes -- it will still be more than that.

Gardner: This is as close to infinity as you can probably get, right?

Sandholm: Ha-ha, basically yes.

Gardner: Okay, so you have this massively complex potential data set. How do you winnow that down, and how rapidly does the algorithmic process and platform learn? I imagine that being reactive, creating a pattern that creates better learning is an important part of it. So tell me about the learning part.

Three part harmony

Sandholm: The learning part always interests people, but it's not really the only part here -- or not even the main part. We basically have three main modules in our architecture. One computes approximations of Nash equilibrium strategies using only the rules of the game as input. In other words, game-theoretic strategies.

That doesn’t take any data as input, just the rules of the game. The second part is during play, refining that strategy. We call that subgame solving.

Then the third part is the learning part, or the self-improvement part. And there, traditionally people have done what’s called opponent modeling and opponent exploitation, where you try to model the opponent or opponents and adjust your strategies so as to take advantage of their weaknesses.

However, when we go against these absolute best human strategies, the best human players in the world, I felt that they don't have that many holes to exploit and they are experts at counter-exploiting. When you start to exploit opponents, you typically open yourself up for exploitation, and we didn't want to take that risk. In the learning part, the third part, we took a totally different approach than traditionally is taken in AI.

We are letting the opponents tell us where the holes are in our strategy. Then, in the background, using supercomputing, we are fixing those holes.

We said, “Okay, we are going to play according to our approximate game-theoretic strategies. However, if we see that the opponents have been able to find some mistakes in our strategy, then we will actually fill those mistakes and compute an even closer approximation to game-theoretic play in those spots.”

One way to think about that is that we are letting the opponents tell us where the holes are in our strategy. Then, in the background, using supercomputing, we are fixing those holes.

All three of these modules run on the Bridges supercomputer at the Pittsburgh Supercomputing Center (PSC), for which the hardware was built by Hewlett Packard Enterprise (HPE).

HPC from HPE

Overcomes Barriers

To Supercomputing and Deep Learning

Gardner: Is this being used in any business settings? It certainly seems like there's potential there for a lot of use cases. Business competition and circumstances seem to have an affinity for what you're describing in the poker use case. Where are you taking this next?

Sandholm: So far this, to my knowledge, has not been used in business. One of the reasons is that we have just reached the superhuman level in January 2017. And, of course, if you think about your strategic reasoning problems, many of them are very important, and you don't want to delegate them to AI just to save time or something like that.

Now that the AI is better at strategic reasoning than humans, that completely shifts things. I believe that in the next few years it will be a necessity to have what I call strategic augmentation. So you can't have just people doing business strategy, negotiation, strategic pricing, and product portfolio optimization.

You are going to have to have better strategic reasoning to support you, and so it becomes a kind of competition. So if your competitors have it, or even if they don't, you better have it because it’s a competitive advantage.

Gardner: So a lot of what we're seeing in AI and machine learning is to find the things that the machines do better and allow the humans to do what they can do even better than machines. Now that you have this new capability with strategic reasoning, where does that demarcation come in a business setting? Where do you think that humans will be still paramount, and where will the machines be a very powerful tool for them?

Human modeling, AI solving

Sandholm: At least in the foreseeable future, I see the demarcation as being modeling versus solving. I think that humans will continue to play a very important role in modeling their strategic situations, just to know everything that is pertinent and deciding what’s not pertinent in the model, and so forth. Then the AI is best at solving the model.

That's the demarcation, at least for the foreseeable future. In the very long run, maybe the AI itself actually can start to do the modeling part as well as it builds a better understanding of the world -- but that is far in the future.

Gardner: Looking back as to what is enabling this, clearly the software and the algorithms and finding the right benchmark, in this case the poker game are essential. But with that large of a data set potential -- probabilities set like you mentioned -- the underlying computersystems must need to keep up. Where are you in terms of the threshold that holds you back? Is this a price issue that holds you back? Is it a performance limit, the amount of time required? What are the limits, the governors to continuing?

Sandholm: It's all of the above, and we are very fortunate that we had access to Bridges; otherwise this wouldn’t have been possible at all.  We spent more than a year and needed about 25 million core hours of computing and 2.6 petabytes of data storage.

This amount is necessary to conduct serious absolute superhuman research in this field -- but it is something very hard for a professor to obtain. We were very fortunate to have that computing at our disposal.

Gardner: Let's examine the commercialization potential of this. You're not only a professor at Carnegie Mellon, you’re a founder and CEO of a few companies. Tell us about your companies and how the research is leading to business benefits.

Superhuman business strategies

Sandholm: Let’s start with Strategic Machine, a brand-new start-up company, all of two months old. It’s already profitable, and we are applying the strategic reasoning technology, which again is application independent, along with the Libratus technology, the Lengpudashi technology, and a host of other technologies that we have exclusively licensed to Strategic Machine. We are doing research and development at Strategic Machine as well, and we are taking these to any application that wants us.

HPC from HPE

Overcomes Barriers 

To Supercomputing and Deep Learning

Such applications include business strategy optimization, automated negotiation, and strategic pricing. Typically when people do pricing optimization algorithmically, they assume that either their company is a monopolist or the competitors’ prices are fixed, but obviously neither is typically true.

We are looking at how do you price strategically where you are taking into account the opponent’s strategic response in advance. So you price into the future, instead of just pricing reactively. The same can be done for product portfolio optimization along with pricing.

Let's say you're a car manufacturer and you decide what product portfolio you will offer and at what prices. Well, what you should do depends on what your competitors do and vice versa, but you don’t know that in advance. So again, it’s an imperfect-information game.

Gardner: And these are some of the most difficult problems that businesses face. They have huge billion-dollar investments that they need to line up behind for these types of decisions. Because of that pipeline, by the time they get to a dynamic environment where they can assess -- it's often too late. So having the best strategic reasoning as far in advance as possible is a huge benefit.

If you think about machine learning traditionally, it's about learning from the past. But strategic reasoning is all about figuring out what's going to happen in the future.

Sandholm: Exactly! If you think about machine learning traditionally, it's about learning from the past. But strategic reasoning is all about figuring out what's going to happen in the future. And you can marry these up, of course, where the machine learning gives the strategic reasoning technology prior beliefs, and other information to put into the model.

There are also other applications. For example, cyber security has several applications, such as zero-day vulnerabilities. You can run your custom algorithms and standard algorithms to find them, and what algorithms you should run depends on what the other opposing governments run -- so it is a game.

Similarly, once you find them, how do you play them? Do you report your vulnerabilities to Microsoft? Do you attack with them, or do you stockpile them? Again, your best strategy depends on what all the opponents do, and that's also a very strategic application.

And in upstairs blocks trading, in finance, it’s the same thing: A few players, very big, very strategic.

Gaming your own immune system

The most radical application is something that we are working on currently in the lab where we are doing medical treatment planning using these types of sequential planning techniques. We're actually testing how well one can steer a patient's T-cell population to fight cancers, autoimmune diseases, and infections better by not just using one short treatment plan -- but through sophisticated conditional treatment plans where the adversary is actually your own immune system.

Gardner: Or cancer is your opponent, and you need to beat it?

Sandholm: Yes, that’s right. There are actually two different ways to think about that, and they lead to different algorithms. We have looked at it where the actual disease is the opponent -- but here we are actually looking at how do you steer your own T-cell population.

Gardner: Going back to the technology, we've heard quite a bit from HPE about more memory-driven and edge-driven computing, where the analysis can happen closer to where the data is gathered. Are these advances of any use to you in better strategic reasoning algorithmic processing?

Algorithms at the edge

Sandholm: Yes, absolutely! We actually started running at the PSC on an earlier supercomputer, maybe 10 years ago, which was a shared-memory architecture. And then with Bridges, which is mostly a distributed system, we used distributed algorithms. As we go into the future with shared memory, we could get a lot of speedups.

We have both types of algorithms, so we know that we can run on both architectures. But obviously, the shared-memory, if it can fit our models and the dynamic state of the algorithms, is much faster.

Gardner: So the HPE Machine must be of interest to you: HPE’s advanced concept demonstration model, with a memory-driven architecture, photonics for internal communications, and so forth. Is that a technology you're keeping a keen eye on?

HPC from HPE

Overcomes Barriers 

To Supercomputing and Deep Learning

Sandholm: Yes. That would definitely be a desirable thing for us, but what we really focus on is the algorithms and the AI research. We have been very fortunate in that the PSC and HPE have been able to take care of the hardware side.

We really don’t get involved in the hardware side that much, and I'm looking at it from the outside. I'm trusting that they will continue to build the best hardware and maintain it in the best way -- so that we can focus on the AI research.

Gardner: Of course, you could help supplement the cost of the hardware by playing superhuman poker in places like Las Vegas, and perhaps doing quite well.

Sandholm: Actually here in the live game in Las Vegas they don't allow that type of computational support. On the Internet, AI has become a big problem on gaming sites, and it will become an increasing problem. We don't put our AI in there; it’s against their site rules. Also, I think it's unethical to pretend to be a human when you are not. The business opportunities, the monetary opportunities in the business applications, are much bigger than what you could hope to make in poker anyway.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript ordownload a copy. Sponsor: Hewlett Packard Enterprise.

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Women in business leadership -- networking their way to success

The next BriefingsDirect digital business insights panel discussion focuses on the evolving role of women in business leadership. We’ll explore how pervasive business networks are impacting relationships and changes in business leadership requirements and opportunities for women.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: SAP Ariba.

To learn more about the transformation of talent management strategies as a result of digital business and innovation, please join me in welcoming our guests, Alicia Tillman, Chief Marketing Officer at SAP Ariba, and Lisa Skeete Tatum, Co-founder and CEO of Landit in New York. The panel was recorded in association with the recent 2017 SAP Ariba LIVE conference in Las Vegas, and is moderated by Dana Gardner, principal analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Alicia, looking at a confluence of trends, we have the rise of business networks and we have an advancing number of women in business leadership roles. Do they have anything to do with one another? What's the relationship?

Tillman: It is certainly safe to say that there is a relationship between the two. Networks historically connected businesses mostly from a transactional standpoint. But networks today are so much more about connecting people. And not only connecting them in a business context, but also from a relationship-standpoint as well.

Tillman

Tillman

There is as much networking and influence that happens in a digital network as  does from meeting somebody at an event, conference or forum. It has really taken off in the recent years as being a way to connect quickly and broadly -- across geographies and industries. There is nothing that brings you speed like a network, and that’s why I think there is such a strong correlation to how digital networking has taken off -- and what a true technical network platform can allow.

Gardner: When people first hear “business networks,” they might think about transactions and applications talking to applications. But, as you say, this has become much broader in the last few years; business networks are really about social interactions, collaboration, and even joining companies culturally.

How has that been going? Has this been something that’s been powerful and beneficial to companies?

Tillman: It’s incredibly powerful and beneficial. If you think about how buying habits are these days, buyers are very particular about the goods that they are interested in, and, frankly, the people that they source from.

If I look at my buying population in particular at SAP Ariba, there is a tremendous movement toward sustainable goal or fair-trade types of responsibilities, of wanting to source goods from minority-owned businesses, wanting to source only organic or fair-trade products, wanting to only partner with organizations where they know within their supply chain the distribution of their product is coming from locations in the world where the working conditions are safe and their employees are being paid fairly.

A network allows for that; the SAP Ariba Network certainly allows for that, as we can match suppliers directly with what those incredibly diverse buyer needs are in today’s environment.

Gardner: Lisa, we just heard from Alicia about how it's more important that companies have a relationship with one another and that they actually look for culture and character in new ways. Tell us about Landit, and how you're viewing this idea of business networks changing the way people relate to their companies and even each other?

Skeete Tatum: Our goal at Landit is to democratize career success for women around the globe. We have created a technology platform that not only increases the success and engagement of women in the workplace, but it also enables companies in this new environment to attract, develop, and retain high-potential diverse talent.

We do that by providing each woman with the personalized playbook in the spirit of one-size-fits-one. That empowers them with the access to the tools, the resources, the know-how, and, yes, the human connections that they need to more successfully navigate their paths.

Skeete Tatum

Skeete Tatum

It’s really in response to the millions of women who will find themselves at an inflection point; whether they are in a company that they love but are just trying to figure out how to more successfully navigate there, or they may be feeling a little stuck and are not sure how to get out. The challenge is: “I am motivated, I have the skills, I just don’t know where to start.”

We have really focused on knitting what we believe are those key elements together -- leveraged by technology that actually guides them. But we find that companies in this new environment are often overwhelmed and trying to figure out a way to manage this new diverse workforce in this era of connectedness. So we give them a turnkey, one-size-fits-one solution, too.

As Alicia mentioned, in this next stage of collaborative businesses, there are really two things. One, we are more networked and more visible than ever before, which is great, because it’s created more opportunities and flexibility than we have seen -- not to mention more access. However, those opportunities are highly dependent on how someone showcases their value, their contribution, and their credibility, which makes it even more important to cultivate not only your brand and your network. It goes beyond just individual capabilities of getting at what is the sponsorship in the support of a strong network.

The second thing I would say, that Alicia also mentioned, is that today’s business environment -- which is more global, more diverse in its tapestry -- requires businesses to create an environment where everyone feels valued. People need to feel like they can bring the full measure of their talent and passion to the workplace. Companies want amazing talent to find a place at their company.

Gardner: If I’m at a company looking to be more diverse, how would I use Landit to accomplish that? Also, if I were an individual looking to get into the type of company that I want to be involved with, how would I use Landit?

Connecting supply and demand for talent

Skeete Tatum: As an individual, when you come on to Landit, we actually give you one of the key ingredients for success. Because we often don’t know what we don’t know, we knit together the first step, of “Where do I fit?” If you are not in a place that fits with your values, it’s not sustainable.

So we help you figure out what is it that fits with “all of me,” and we then connect you to those opportunities. Many times with diversity programs, they are focused just on the intake, which is just one component. But you want people to thrive when they get there.

Many times with diversity programs, they are focused just on the intake, which is just one component. But you want people to thrive when they get there.

And so, whether it is building your personal brand or building your board of advisors or continuing with your skill development in a personalized, relevant way -- or access to coaching because often many of us don’t have that unless we are in the C-suite on the way -- we are able to knit that together in a way that is relevant, that’s right-sized for the individual.

For the company, we give them a turnkey solution to invest in a scalable way, to touch more lives across their company, particularly in a more global environment. Rather than having to place multiple bets, they place one bet with Landit. We leverage that one-size-fits-one capability with things that we all know are keys to success. We are then able to have them deliver that again, whether it is to the newly minted managers or people they have just acquired or maybe they are leaders that they want to continue to invest in. We enable them to do that in a measurable way, so that they can see the engagement and the success and the productivity.

Gardner: Alicia, I know that SAP Ariba is already working to provide services to those organizations that are trying to create diversity and inclusion within their supply chains. How do you see Landit fitting into the business network that SAP Ariba is building around diversity?

Tillman: First, the SAP Ariba Network is the largest business to business (B2B) network on the planet. We connect more than 2.5 million companies that transact over $1 trillion in commerce annually. As you can imagine, there is incredible diversity in the buying requirements that exist amongst those companies that are located in all parts of the world and work in virtually every industry.

One of things that we offer as an organization is a Discovery tool. When you have a network that is so large, it can be difficult and a bit daunting for a buyer to find the supplier that meets their business requirements, and for a supplier to find their ideal buyer. So our SAP Ariba Discovery application is a matching service, if you will, that enables a buyer to list their requirements. You then let the tool work for you to allow matching you to suppliers that most meet your requirements, whatever they may be.

I’m very proud to have Lisa present at our Women in Leadership Forum at SAP AribaLIVE 2017. I am showcasing Lisa not only because of her entrepreneurial spirit and the success that she’s had in her career -- that I think will be very inspirational and motivational to women who are looking to continue to develop their careers -- but she has also created a powerful platform with Landit. For women, it helps provide a digital environment that allows them to harness precisely what it is that’s important to them when it comes to career development, and then offers the coaching in the Landit environment to enable that.

Landit also offers companies an ability to support their goals around gender diversity. They can look at the Landit platform and source talent that is not only very focused on careers -- but also supports a company in their diversity goals. It’s a tremendous capability that’s necessary and needed in today’s environment.

Gardner: Lisa, what has changed in the past several years that has prompted this changed workforce? We have talked about the business network as an enabler, and we have talked about social networks connecting people. But what's going to be different about the workforce going forward?

Collaborative visibility via networking

Skeete Tatum: There are three main things. First, there is a recognition that diversity is not a “nice to have,” it’s a “must-have” from a competitive standpoint; to acquire the best ideas and gain a better return on capital. So it’s a business imperative to invest in and value diversity within one's workforce. Second, businesses are continuing to shift toward matching opportunities with the people who are best able to do that job, but in a less-biased way. Thirdly, business-as-usual isn’t going to work in this new reality of career management.

It’s no longer one- or bi-directional, where it’s just the manager or the employee. It’s much more collaborative and driven by the individual. And so all of these things … where there is much more opportunity, much more freedom. But how do you anchor that with a problem and a framework and a connectivity that enables someone to more successfully navigate the new environment and new opportunities? How do you leverage and build your network?  Everyone knows they need to do it, but many people don’t know how to do it. Or when your brand is even more important, visibility is more important, how do you develop and communicate your accomplishments and your value? It is the confluence of those things coming together that creates this new world order.

Gardner: Alicia, one of the biggest challenges for most businesses is getting the skills that they need in a timely fashion. How do we get past the difficulty of best matching hiring?  How do we use business networks to help solve that?

Tillman: This is the beauty of technology. Technology is an enabler in business to form relationships more quickly, and to transact more quickly. Similarly, technology also provides a network to help you grow from a development standpoint. Lisa’s organization, Landit, is one example of that.

Within SAP Ariba we are very focused on closing the gap in gaining the skills that are necessary to be successful in today’s business environment. I look at the offering of SAP SuccessFactors - which is  focused on empowering the humancapital management (HCM) organization to lead performance management and career development. And SAP Fieldglass helps companies find and source the right temporary labor that they need to service their most pressing projects. Combine all that with a business network, and there is no better place in today’s environment to find something you need -- and find it quickly.

But it all comes down to the individual’s desire to want to grow their skills, or find new skills, to get out of their comfort zone and try something new. I don’t believe there is a shortage of tools or applications to help enable that growth and talent. It comes down to the individual’s desire to want to grab it and go after it.

Maximize your potential with technology

Skeete Tatum: I couldn’t agree more. The technology and the network are what create the opportunity. In the past, there may have been a skills gap, but you have to be able to label it, you have to be able to identify it in a way that is relevant to the individual. As Alicia said, there are many opportunities out there for development, but how do you parse that down and deliver it to the individual in a way that is relevant -- and that’s actionable? That’s where a network comes in and where the power of one can be leveraged in a scalable way.

Now is probably one of the best times to invest in and have an individual grow to reach their full potential. The desire to meet their goals can be leveraged by technology in a very personal way.

Gardner: As we have been hearing here at SAP Ariba LIVE 2017, more-and-more technologies along the lines of artificial intelligence (AI) and machine learning (ML) – are taking advantage of all the data and analyzing it and making it actionable -- can now be brought to bear on this set of issues of matching workforce requirements with skill sets.

Where should we expect to see these technologies reduce the complexity and help companies identify the right workforce, and the workforce identify the right companies?

Skeete Tatum: Having the data and being able to quantify and qualify it gives you the power to set a path forward. The beauty is that it actually enables everyone to have the opportunity to contribute, the opportunity to grow, and to create a path and a sense of belonging by having a way to get there. From our perspective, it is that empowerment and that ownership -- but with the support of the network from the overall organization -- that enables someone to move forward. And it enables the organization to be more successful and more embracing of this new workforce, this diverse talent.

Tillman: Individuals should feel more empowered today than ever before to really take their career development to unprecedented levels. There are so many technologies, so many applications out there to help coach you on every level. It’s up to the individual to truly harness what is standing in front of them and

Gardner: Lisa, what should you be thinking about from a personal branding perspective when it comes to making the best use of tools like Landit and business networks?

Skeete Tatum: The first thing is that people actually have to think of themselves as a brand, as opposed to thinking that they are bragging or being boastful. The most important brand you have is the brand of you.

Second, people have to realize that this notion of building your brand is something that you nurture and it develops over time. What we believe is important is that we have to make it tangible, we have to make it actionable, and we have to make it bite-size, otherwise it seems overwhelming.

So we have defined what we believe are the 12 key elements for anyone to have a successful brand, such as have you been visible, do you have a strategic plan of you, are you seeking feedback, do you have a regular cadence of interaction with your network, et cetera. Knowing what to do and how to do it and at what cadence and at what level is what enables someone to move forward. And in today’s environment, again, it’s even more important.

Pique their curiosity by promoting your own

Tillman: Employers want to be sure that they are attracting candidates and employing candidates that are really invested in their own development. An employer operates in the best interest of the employee in terms of helping to enable tools and allow for that development to occur. At the same time, where candidates can really differentiate themselves in today’s work environment is when they are sitting across the table and they are in that interview. It's really important for a candidate to talk about his or her own development and what are they doing to constantly learn and support their curiosity.

Employers want curious people. They want those that are taking advantage of development and tools and learning, and these are the things that I think set people apart from one another when they know that individually they are going to go after learning opportunities and push themselves out of their comfort zone to take themselves – and ultimately the companies that employ them - to the next level.

Gardner: Before we close out, let’s take a peek into the crystal ball. What, Alicia, would be your top two predictions given that we are just on sort of an inflection point with this new network, with this new workforce and the networking effect for it?

Tillman: First, technology is only going to continue to improve. Networks have historically enabled buyers and sellers to come together and transact to build their organizations and support growth, but networks are taking on a different form.

Technology is going to continue to enable priorities professionally and priorities personally. Technology is going to become a leading enabler of a person’s professional development.

Second, individuals are going to set themselves apart from others by their desire and their hunger to really grab hold of that technology. When you think about decision-making among companies in terms of candidates they hire and candidates they don’t, employers are going to report back and say, “One of the leading reasons why I selected one candidate over another is because of their desire to learn and their desire to grab hold of technologies and networks that were standing in front of them to bring their careers to an unprecedented level.”

Gardner: Lisa, what are your top two predictions for the new workforce and particularly for diversity playing a bigger role?

Skeete Tatum: Technology is the ultimate leveler of the playing field. It enables companies as well as the individual to make decisions based on things that matter. That is what enables people to bring their full selves, the full measure of their talent, to the workplace.

In terms of networks in particular, they have always been a key element to success but now they are even more important. It actually poses a special challenge for diverse talent. They are often not part of the network, and they may have competing personal responsibilities that make the investment of the time and the frequency in those relationships a challenge.

Sometimes there is a discomfort with how to do it. We believe that through technology people will have to get comfortable with being uncomfortable. They need to learn not only how to codify their network, but also have the right access to the right person with the right cadence, and access to that know how, that guidance, can be delivered through technology.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: SAP Ariba.

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How AI, IoT and blockchain will shake up procurement and supply chains

The next BriefingsDirect digital business thought leadership panel discussion focuses on how artificial intelligence (AI), the Internet of things (IoT), machine learning (ML), and blockchain will shake up procurement and supply chain optimization.

Stay with us now as we develop a new vision for how today's cutting-edge technologies will usher in tomorrow's most powerful business tools and processes. The panel was assembled and recorded at the recent 2017 SAP Ariba LIVE conference in Las Vegas. The discussion is moderated by Dana Gardner, principal analyst at Interarbor Solutions.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.

To learn more about the data-driven, predictive analytics, and augmented intelligence approach to supply chain management and procurement, please welcome the executives from SAP Ariba:

Here are some excerpts:

Gardner: It seems like only yesterday we were confident to have a single view of a customer, or clean data, or maybe a single business process end–to-end value. But now, we are poised to leapfrog the status quo by using words like predictive and proactive for many business functions.

Why are AI and ML such disrupters to how we've been doing business processes?

Shahane: If you look back, some of the technological impact  in our private lives, is impacting our public life. Think about the amount of data and signals that we are gathering; we call it big data.

We not only do transactions in our personal life, we also have a lot of content that gets pushed at us. Our phone records, our location as we move, so we are wired and we are hyper-connected.

Shahane

Shahane

Similar things are happening to businesses. Since we are so connected, a lot of data is created. Having all that big data – and it could be a problem from the privacy perspective -- gives you an opportunity to harness that data, to optimize it and make your processes much more efficient, much more engaged.

If you think about dealing with big data, you try and find patterns in that data, instead of looking at just the raw data. Finding those patterns collectively as a discipline is called machine learning. There are various techniques, and you can find a regression pattern, or you can find a recommendation pattern -- you can find all kinds of patterns that will optimize things, and make your experience a lot more engaging.

If you combine all these machine learning techniques with tools such as natural language processing (NLP), higher-level tools such as inference engines, and text-to-speech processing -- you get things like Siri and Alexa. It was created for the consumer space, but the same thing could be available for your businesses, and you can train that for your business processes. Overall, these improve efficiency, give delight, and provide a very engaging user experience.

Gardner: Sanjay, from the network perspective it seems like we are able to take advantage of really advanced cloud services, put that into a user experience that could be conversational, like we do with our personal consumer devices.

What is it about the cloud services in the network, however, that are game-changers when it comes to applying AI and ML to just good old business processes?

Multiple intelligence recommended

Almeida: Building on Dinesh’s comment, we have a lot of intelligent devices in our homes. When we watch Netflix, there are a lot of recommendations that happen. We control devices through voice. When we get home the lights are on. There is a lot of intelligence built into our personal lives. And when we go to work, especially in an enterprise, the experience is far different. How do we make sure that your experience at home carries forward to when you are at work?

From the enterprise and business networks perspective, we have a lot of data; a lot of business data about the purchases, the behaviors, the commodities. We can use that data to make the business processes a lot more efficient, using some of the models that Dinesh talked about.

Almeida

Almeida

How do we actually do a recommendation so that we move away from traditional search, and take action on rows and columns, and drive that through a voice interface? How do we bring that intelligence together, and recommend the next actions or the next business process? How do we use the data that we have and make it a more recommended-based interaction versus the traditional forms-based interaction?

Gardner: Sudhir, when we go out to the marketplace with these technologies, and people begin to use them for making better decisions, what will that bring to procurement and supply chain activities? Are we really talking about letting the machines make the decisions? Where does the best of what machines do and the best of what people do meet?

Bhojwani: Quite often I get this question, What will be the role of procurement in 2025? Are the machines going to be able to make all the decisions and we will have no role to play? You can say the same thing about all aspects of life, so why only procurement?

I think human intelligence is still here to stay. I believe, personally, it can be augmented. Let's take a concrete example to see what it means. At SAP Ariba, we are working on a product called product sourcing. Essentially this product takes a bill of material (BOM), and it tells you the impact. So what is so cool about it?

One of our customers has a BOM, which is an eight-level deep tree with 10 million nodes in it. In this 10 million-node commodity tree, or BOM, a person is responsible for managing all the items. But how does he or she know what is the impact of a delay on the entire tree? How do you visualize that?

Bhojwani

Bhojwani

I think humans are very poor at visualizing a 10-million node tree; machines are really good at it. Well, where the human is still going to be required is that eventually you have to make a decision. Are we comfortable that the machine alone makes a decision? Only time will tell. I continue to think that this kind of augmented intelligence is what we are looking for, not some machine making complete decisions on our behalf.

Gardner: Dinesh, in order to make this more than what we get in our personal consumer space, which in some cases is nice to have, it doesn't really change the game. But we are looking for a higher productivity in business. The C-Suite is looking for increased margins; they are looking for big efficiencies. What is it from a business point of view that these technologies can bring? Is this going to be just a lipstick on a pig, so to speak, or do we really get to change how business productivity comes about?

Humans and machines working together

Shahane: I truly believe it will change the productivity. The whole intelligence advantage -- if you look at it from a highest perspective like enhanced user experience -- provides an ability to help you make your decisions.

When you make decisions having this augmented assistant helping you along the way -- and at the same time dealing with large amount of data combined in a business benefit -- I think it will make a huge impact.

Let me give you an example. Think about supplier risk. Today, at first you look at risk as the people on the network, and how you are directly doing business with them. You want to know everything about them, their profile, and you care about them being a good business partner to you.

But think about the second, third and fourth years, and some things become not so interesting for your business. All that information for those next years is not directly available on the network; that is distant. But if those signals can be captured and somehow surface in your decision-making, it can really reduce risk.

Reducing risk means more productivity, more benefits to your businesses. So that is one advantage I could see, but there will be a number of advantages. I think we'll run out of time if we start talking about all of those.

Gardner: Sanjay, help us better understand. When we take these technologies and apply them to procurement, what does that mean for the procurement people themselves?

Almeida: There are two inputs that you need to make strategic decisions, and one is the data. You look at that data and you try to make sense out of it. As Sudhir mentioned, there is a limit to human beings in terms of how much data processing that they can do -- and that's where some of these technologies will help quite a bit to make better decisions.

The other part is personal biases, and eliminating personal biases by using the data. It will improve the accuracy of your strategic decisions. A combination of those two will help make better decisions, faster decisions, and procurement groups can focus on the right stuff, versus being busy with the day-to-day tasks.

Using these technologies, the data, and the power of the data from computational excellence -- that's taking the personal biases out of making decisions. That combination will really help them make better strategic decisions.

Bhojwani: Let me add something to what Sanjay said. One of the biggest things we're seeing now in procurement, especially in enterprise software in general, is people's expectations have clearly gone up based on their personal experience outside. I mean, 10 years back I could not have imagined that I would never go to a store to buy shoes. I thought, who buys shoes online? Now, I never go to stores. I don't know when was the last time I bought shoes anywhere but online? It's been few years, in fact. Now, think about that expectation on procurement software.

Currently procurement has been looked upon as a gatekeeper; they ensure that nobody does anything wrong. The problem with that approach is it is a “stick” model, there is no “carrot” behind it. What users want is, “Hey, show me the benefit and I will follow the rules.” We can't punish the entire company because of a couple of bad apples.

By and large, most people want to follow the rules. They just don't know what the rules are; they don't have a platform that makes that decision-making easy, that enables them to get the job done sooner, faster, better. And that happens when the user experience is acceptable and where procurement is no longer looked down upon as a gatekeeper. That is the fundamental shift that has to happen, procurement has to start thinking about themselves as an enabler, not a gatekeeper. That's the fundamental shift.

Gardner: Here at SAP Ariba LIVE 2017, we're hearing about new products and services. Are there any of the new products and services that we could point to that say, aha, this is a harbinger of things to come

In blockchain we trust

Shahane: The conversational interfaces and bots, they are a fairly easy technology for anyone to adopt nowadays, especially because some of these algorithms are available so easily. But -- from my perspective -- I think one of the technologies that will have a huge impact on our life will be advent of IoT devices, 3D printing, and blockchain.

To me, blockchain is themost exciting one. That will have huge impact on the way people look at the business network. Some people think about blockchain as a complementary idea to the network. Other people think that it is contradictory to the network. We believe it is complementary to the network.

Blockchain reaches out to the boundary of your network, to faraway places that we are not even connected to, and brings that into a governance model where all of your processes and all your transactions are captured in the central network.

I believe that a trusted transactional model combined with other innovations like IoT, where a machine could order by itself … My favorite example is when a washing machine starts working when the energy is cheaper … it’s a pretty exciting use-case.

This is a combination of open platforms and IoT combining with blockchain-based energy-rate brokering. These are the kind of use cases that will become possible in the future. I see a platform sitting in the center of all these innovations.

Gardner: Sanjay, let’s look at blockchain from your perspective. How do you see that ability of a distributed network authority fitting into business processes? Maybe people hadn't quite put those two together.

Almeida: The core concept of blockchain is distributed trust and transparency. When we look at business networks, we obviously have the largest network in the world. We have more than 2.5 million buyers and suppliers transacting on the SAP Ariba Network -- but there are hundreds of millions of others who are not on the network. Obviously we would like to get them.

If you use the blockchain technology to bring that trust together, it’s a federated trust model. Then our supply chain would be lot more efficient, a lot more trustworthy. It will improve the efficiency, and all the risk that’s associated with managing suppliers will be managed better by using that technology.

Gardner: So this isn’t a “maybe,” or an “if.” It’s “definitely,” blockchain will be a significant technology for advancing productivity in business processes and business platforms?

Almeida: Absolutely. And you have to have the scale of an SAP Ariba, have the scale from the number of suppliers, the amount of business that happens on the network. So you have to have a scale and technology together to make that happen. We want to be a center of a blockchain, we want to be a blockchain provider, and so that other third-party ecosystem partners can be part of this trusted network and make this process a lot more efficient.

Gardner: Sudhir, for those who are listening and reading this information and are interested in taking advantage of ML and better data, of what the IoT will bring, and AI where it makes sense -- what in your estimation should they be doing now in order to prepare themselves as an organization to best take advantage of these? What would you advise them to be doing now in order to better take advantage of these technologies and the services that folks like SAP Ariba can provide so that they can stand out in their industry?

Bhojwani: That’s a very good question, and that's one of our central themes. At the core of it, I fundamentally believe the tool cannot solve the problem completely on its own, you have to change as well. If the companies continue to want to stick to the old processes -- but try to apply the new technology -- it doesn’t solve the problem. We have seen that movie played before. People get our tool, they say, hey, we were sold very good visions, so we bought the SAP Ariba tool. We tried to implement it and it didn’t work for us.

When you question that, generally the answer is, we just tried to use the tool -- tried to change the tool to fit our model, to fit our process. We didn’t try to change the processes. As for blockchain, enterprises are not used to being for track and trace, they are not really exposing that kind of information in any shape or form – or they are very secretive about it.

So for them to suddenly participate in this requires a change on their side. It requires seeing what is the benefit for me, what is the value that it offers me? Slowly but surely that value is starting to become very, very clear. You hear more companies -- especially on the payment side -- starting to participate in blockchain. A general ledger will be available on blockchain some day. This is one of the big ideas for SAP.

If you think about SAP, they run more general ledgers in the world than any other company. They are probably the biggest general ledger company that connects all of that. Those things are possible, but it’s still a technology only until the companies want to say, “Hey, this is the value … but I have to change myself as well.”

This changing yourself part, even though it sounds so simple, is what we are seeing in the consumer world. There, change happens a little bit faster than in the enterprise world. But, even that is actually changing, because of the demands that the end-user, the Millennials, when they come into the workforce; the force that they have and the expectations that they have. Enterprises, if they continue to resist, won’t be sustainable.

They will be forced to change. So I personally believe in next three to five years when there are more-and-more Millennials in the workforce, you will see people adopting blockchain and new ledgers at a much faster pace.

A change on both sides

Shahane: I think Sudhir put it very nicely. I think enterprises need to be open to change. You can achieve transformation if the value is clearly articulated. One of the big changes for procurement is you need to transition yourself from being a spend controller into a value creator. There is a lot of technology that will benefit you, and some of the technology vendors like us, we cannot just throw a major change at our users. We have to do it gradually. For example, with AI it will start as augmented first, before it starts making algorithmic decisions.

So it is a change on both sides, and once that happens -- and once we trust each other on the system -- nice things will happen.

Almeida: One thing I would add to that is organizations need to think about what they want to achieve in the future and adopt the tool and technology and business processes for their future business goals. It’s not about living in the past because the past is going to be gone. So how do you differentiate yourself, your business with the rest of the competition that you have?

The past business processes and people and technology many not necessarily get you over there. So how do you leverage the technology that companies like SAP and Ariba provide? Think about what should be your future business processes. The people that you will have, as Sudhir mentioned, the Millennials, they have different expectations and they won’t accept the status quo.

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