John Sumser, aka HR Examiner, has been focusing on "intelligent technology" including AI and machine learning as it morphs the HCM function. Trends in that function are a forerunner of what we will see across the enterprise. I recently caught with him on his latest report and what he learned at the recent HR Tech event in Las Vegas
John, what are you excited about when you look at "intelligent technology"?
In the HCM world, it's pretty clear to me that data is becoming infrastructure rather than the stuff that passes through the software. Everybody I have talked to said, in one way or another, that first you have the data and then you have the software, so the software sits on top of the data . Getting the data right is the key to allowing the software to do its thing.
The second thing that was pretty uniform, although I had to prompt it sometimes, is that what now comes out of a machine is an opinion. It used to be what came out of a machine was the facts that you put into it. But, it is decreasingly the case that what the machine gives you is the facts that you gave it. It is almost always, at some level, providing an improvement on those facts, some benchmarking, forecast, context or some insight.
Rather than software being a re-packager of the data that you give it, it's now an interpreter of the data that you give it. That's a big change. That's a really big change. And so, people are starting to talk about that. People are starting to try to figure out how you deal with that.
I ran a session a called "How to Evaluate an AI Vendor," The gist of it was how do you understand data quality and what does it mean? Does the vendor understand their data models at scale? If you've got 5,000 people in your company, you're going to have a million data models.
How do you monitor their health? And, how do you make sense out of the variable quality of the overall data farm when you're making decisions?
When you say it's more opinion and less fact, is that surprising to the audiences? How did they react to that?
It was a lightbulb. It was actually a surprise. Nobody -- I've worked a long time to get that idea that simple, and the notion that machines are now not as reliable from a pure fact perspective causes lots of heads to bounce up and down. "Oh, that's what intelligence means."
One of the things that I've been probing a lot of the bigger enterprise vendors is, you don't have enough enterprise class data. Most of your data is locked up in customer, on-premise systems. You don't have permissions to access that. A lot of what they're talking about is stuff that consumer tech is doing, the image recognition, the voice recognition, and so on. There's very little enterprise data that is really being used for AI use cases.
You're right. The notion that enterprise technology is going to resemble consumer technology is one of the most damaging ideas that we've entertained in enterprise technology. I was looking the other day. I made a reservation on OpenTable for some restaurant in Las Vegas. OpenTable has handled 820 million reservations. With 820 million reservations in your database, you could do things like predict what the menu ought to be and where you ought to locate the restaurant if you want to have a successful restaurant in Western Sonoma County, California. It's a huge database of consumer interest and preference.
If you've got 10,000 people in your organization, you're never going to have that. You can't get to a trillion data points like OpenTable can in a 10,000-person organization.
What happens in those smaller settings is latency becomes a primary problem. Fidelity becomes a primary problem. You don't have those when you have a ton of data points.
We're about to see some expectations being reset in the enterprise space. That doesn't mean that there isn't cool stuff being done. It just means that you will not see what Cornerstone is busy suggesting that they're going to have a recommendation engine for learning videos. You can't do that with little tiny clients.
If you took all the clients in the world, you wouldn't have enough data to make sense out of it. If you had all of the employee data, employees don't handle their interactions with the company in the same way that they do with a consumer product. They don't have daily interactions in their learning system.
I don't have a clear picture of how it shakes out. But, the difference between consumer technology and enterprise technology is going to become increasingly apparent.
Well, tell me where it is promising in HCM AI
First of all, if you wanted to find where a technology rooted in statistics was going to take hold, you'd look at insurance and banking, maybe pharma
So, Allstate Insurance is a really good example. At Allstate, they have built by integrating - it is a patchwork quilt of vendors integrated into a single system that can go from identifying names of people who might be good employees all the way out through the entire acquisition and development process to identifying the kinds of skills that you'll need in the future and the sorts of places where you're going to need to open offices. And, if you get to the place where you're opening offices up, it can help you navigate all the way through the staff problem. That's pretty interesting.
What's even more interesting there is that team is run by librarians. The team that built that structure are the librarians they hired. There are five librarians, five Masters of Library Science on the team.
The reason that's interesting is because, in order to use the next technology, you have to be better at asking questions than having answers. This is what it means when you say that the machine gives you an opinion. But, if you have a boss or a very upset spouse, these are the times when you only need one opinion to make a decision. Every other time, you need multiple opinions when you make a decision.
The worker of the future is going to be better at gathering opinions in order to make decisions. Places where it's easier to have ambiguity be the center of the equation are the places where this stuff is going to tick.
There is a vendor called Bridge that has an integrated LMS and performance management system. The idea of performance management stops being, "Here's how you screwed up and missed your objectives last year," and starts being, "Here's how we're developing you." The manager gets coached through the available material in order to bring the employee along as a developing expert in something.
There's a great one that's for interview scheduling - Ascendify. Their interviewing system identifies the interviewing team by expertise, gives that interviewing team a set of questions to ask individually, and evaluates the results of the interview to see if the information that was asked for was obtained and then revises the question set for the people down the road. It does this while doing all of the coordination necessary to keep hiring interviews scheduled.
Ascendify only works in the Fortune 100. If you're a big company, that process is cumbersome and expensive and often has ten interviews as a part of the overall process. It takes all the administrative load out of that. That's significant. The estimate for Microsoft's interview coordination budget, so just scheduling and rescheduling the interviews, is $20 million. They can zero that out and give you a faster process.
The first wave of things look like those sorts of incremental improvements to existing processes. Over time, what's going to happen is the silos, which look to me like things that are held in place by existing enterprise technology, will start to collapse. It doesn't take very long, if you stand far enough away, to see that employment branding, recruitment marketing, onboarding and orientation training- are all the same task. Because they are understood to take a different point in a workflow or done by a different expert, you have the same task performed multiple times by five different people for different reasons. You can consolidate that under a goal that looks like, how do we shorten the time to productivity for a new employee?
If that's the goal of the entire talent acquisition process, including the training and admin functions that wrap around it, then what you do in the initial moments of contact with somebody who might come to work for you are different than what people are doing today. That becomes easier and more intelligible when you've got data as infrastructure underneath it all
I heard you say some customers are doing interesting stuff. Some startups are doing some cool stuff. Where do the mainstream vendors fit in?
The emerging battle is for the replacement generation for enterprise software. The people who are in that battle are IBM, Google, Amazon, Microsoft and -- maybe Facebook.
In that group, the name of the game is selling commodity processing and storage. And so, they all -- IBM and Google being the first into the market, they all have an idea of an overlay of technology that expresses as functionality. They all already have applicant tracking systems. They all, for the most part, believe that the world is better if you have a taxonomy as the fundamental structure for HCM.
IBM and Google are very quietly duking it out over which is the better taxonomy, but it's pretty clear that, in this particular game, taxonomy is going to be the key because you can organize everything around the taxonomy. If you go taxonomy plus competencies in the places where people want to use competencies, you start to have a magic formula. That's the initial data structure.
If you start with the initial data structure, then you can lay workflow on top of that pretty easily. The next generation of large-scale enterprise providers looks like it's coming out of that area, and they all have recognizable teams.
If you look at what they're doing, it's really interesting. The Microsoft people analytics team, which is both in HR and product development, has 140 people in it. People analytics, the HR analytics team, 140 people at Microsoft. At Facebook, it's 60, and that's 60 people doing HR analytics for 30,000 employees. These are environments where budget doesn't mean the same thing as it does in the customers you and I understand, but partly because both companies are using their internal processes as a way to accelerate product development. There's real interesting stuff up there in the sort of serious cloud providers area.
The next tier down, if you look at Workday, even Kronos -- Workday, Kronos, or Ultimate Software, the ones that pop up on my radar as the best of the bunch, it looks to me like one of those situations where you lift the house up and install a new foundation underneath it. At Workday, it's a planning and data management system. And, at Kronos, it's a layer of intelligence that uses 40 years' worth of time clock data to predict some interesting stuff.
I don't expect to see anything resembling innovation out of Oracle or SAP. They won't go out of business because of it. They have a long, long, long tail on their cash cow, but people are going to have to innovate around them in order to get real intelligence in their enterprises.
Then the rest of it is small point solutions and data manicuring plays.
That's another fairly prophetic statement that it is the infrastructure-oriented Microsoft, Amazon, Google, IBM that are leading the way on big machine learning. Are HR executives willing to accept them as their HCM providers?
Well, I think it's interesting. If you look at Google and IBM's strategy, and they're really out in the market right now, they are largely going for a sort of a Trojan horse play. IBM is inside of Workday and inside of Cornerstone. Google gets their foot in the door by being the frontend of employment websites for 3,000 companies.
I don't think that the way that it happens is these companies come out of the gate on day one selling a comprehensive enterprise solution. They come out of the gate on day one solving actual, significant problems with a meaty difference so that they have the relationships in place that they can build on. They will ankle bite their way into taking accounts. It's not going to be an RFP level competition for a long time but, by the time it becomes an RFP level competition, the game will be over.
Bruce Cleveland - Part 2 - thoughts on current enterprise market
In Part 1 yesterday, Bruce Cleveland talked about the framework in his new book, Traversing the Traction Gap. Here we talk about partnering and white spaces in the market.
Most enterprise vendors now offer their own platforms. Could they use your framework to develop a vibrant network of startups on their platforms?
At Siebel, we used partners to dominate – I know the word is overused— the industry. We grew from initial product release to $2 billion in revenue in 4.5 years. In 1999, Deloitte named Siebel the “fastest growing company in the US” with a staggering CAGR of more than 750,000%. We only accomplished this because we (by “we” I mean Tom Siebel) put significant resources (personnel and capital) into building a technical platform and a robust ecosystem to support it.
Tom knew he had to “remove the constraints on the growth of the business.” We needed systems integrators who were trained and certified on Siebel implementations. We needed to our applications tuned to run optimally on various hardware systems. And, we needed numerous third-party applications that could interface and interoperate with our application suite.
Consequently, Tom charged me with building the Siebel Alliance Program. Tom had created our first systems integration partnership with Andersen (now Accenture). He also had created our first hardware relationship with Compaq and our first software partnership with Microsoft.
I simply took Tom’s initial ideas and those partnerships and developed a scalable partner program from them. We began the Siebel Alliance Program with Accenture, Compaq and Microsoft and over the course of three years we accumulated a total of 750 partners. These partners generated about a $B of additional annual license revenue for Siebel and helped us ensure that those licenses were implemented successfully. By partners, I don't mean they were reselling Siebel applications. I mean we partnered with them to market, sell, and implement our joint solutions. We were compensated on our products/services and they were compensated on their products/services.
So, as you might imagine, I am a huge fan of partnerships. We won the Forbes award for the most innovative partnering program in the technology industry at the time and every technology company knew how powerful it was. It wasn’t a secret. I find it interesting – with all the success we had with the Siebel Alliance Program - that none of the incumbent technology companies have replicated its form or function.
I believe the current technology platform leaders have a tremendous opportunity to develop a similar program. Sure, they all have partner programs, but those programs don’t really put a lot of skin in the game. By that, I mean, they don’t obligate the leaders to do much or to invest in their partners beyond providing basic access to some technology and lightweight market awareness.
For our program, we actively recruited and hired hundreds of MBAs from the top universities as well as industry professionals and chartered them to create joint business plans with our partners, advocate for our partners with our product and sales organizations, and held the Siebel Alliance organization accountable for helping our partners achieve their revenue and market share objectives. We built specific marketing programs – brand and demand gen – that partners could sponsor to generate leads for themselves. We put skin in the game.
The program was so successful that Harvard Business School wrote a business case study about it. So, I'm pretty proud of it.
Today, though, for the most part, the large technology platform companies offer what I call “paper partnerships”. Those partnership don’t obligate either party to much of anything. They're not “skin in the game” partnerships so they are anemic, at best.
Most startup teams have little experience partnering with these large technology providers. And, even if they do, they have found that those companies offer little that their startup can benefit from. There's little provided by the large company that is likely to generate significant market awareness or revenue for the startup. For example, startups need to know who, from the partner’s organization, wakes up every morning worried about helping the startup to succeed; where are the demand gen programs they can co-invest in to generate awareness and interest in the startup’s products? Which teams inside the large company are held accountable for achieving the market share and revenue objectives of the startups in their partner community? The answer? None. Those partner programs are essentially worthless – at least as far as the startup community is concerned.
Salesforce built an App Store and prospective customers can certainly search for and find add-on applications and products for the Salesforce ecosystem there. However, that's a pretty lightweight endorsement for startups. Who is held accountable within the four walls of Salesforce to wake up every morning, ensuring that a particular partner has the leads it needs, generating a robust pipeline, and helping the startup to convert that pipeline into revenue?
I don’t mean to call out Salesforce. At least they developed an innovative approach and put a lot of energy behind it. And, they have done some advanced partnering with Vlocity. Vlocity was founded by some of my former Siebel colleagues. What's Vlocity doing? They're building vertical applications on top of the force.com platform.
Salesforce saw a need to have much deeper vertical versions of their platform. That's what we did at Siebel (create vertical solutions of Siebel) and David Schmaier led that initiative as the executive VP. David is the founder and CEO of Vlocity. David and his team are replicating what we did at Siebel for Salesforce and it's going very well.
I'm an investor in Vlocity, and, to your point about partnering, it's an example of what can – and I think should - be done between technology platform providers and the startup ecosystem. As this example shows, it's not just about partnering with systems integrators.
If you want these partnerships to amount to anything there needs to be alignment and shared objectives. For example, if you want to garner the attention of the partner’s sales organization, there is nothing like making partner revenue a part of the sales rep’s quota.
The truth is that partner programs as conceived and implemented by the current technology platform leaders just don’t do much for startups – in terms of market share and revenue - and therefore it shouldn’t be surprising that those programs aren’t valued by startups.
Apple has done well in its ecosystem – with the App Store - but the consumer world doesn’t require face-to-face interactions. In the B2B world, people's work reputations are on the line when they make a buy decision. And, in almost all cases, there are multiple decision makers who must be convinced. For B2B, startups need brand credibility from the platform provider. This happens when they implicitly endorse the startup by working closely with them.
In the consumer world, a startup can “borrow” the brand of Apple because if their application is admitted into the Apple App Store, it tells consumers, "Look, we vetted this product. It adheres to our rules."
Conversely, I don’t think that Salesforce’s App Store does a lot for startups. It’s essentially a digital catalog with some ratings/rankings. That’s not enough for most businesses to make a buy decision. If you speak with startups about the Salesforce App Store and ask them if it is a significant source of revenue for them, I think you will find that for most, it isn’t.
Startups lack credibility. They lack access to prospective customers. And, they lack sufficient marketing dollars. This is where the enterprise vendors could really help startups.
I had hoped that the technology leaders would have adopted at least some of the best practices we had created with the Siebel Alliance Program. Tom Siebel and his new company, C3, are developing a similar program to what we had at Siebel Systems. So, I believe the principles are as valid today as they were back then.
When you look at the cloud apps market there are so many white spaces - major geographies, industry verticals etc - and they do not seem to be targeted by established vendors or even by startups. How are investors like you guiding them through these white spaces?
Cloud computing with its subscription business model - and delivery and usage model -has democratized business application software. Prior to cloud computing, it was prohibitively expensive for smaller companies to acquire and implement enterprise-class applications.
But, by dramatically dropping the cost of these solutions, I suspect it has constrained the application providers’ ability to support smaller geographies and industry verticals. Going after smaller markets that require significant translation and support infrastructure at much lower price points isn’t practical.
Even with traditional (re: higher priced) enterprise software, it was always challenging to create localized versions of products for a specific market. Most US companies started out with the English speaking markets and then typically moved to countries such as Germany, Spain, and France with relatively large populations. But, when they were asked to support a Dutch or Finnish version, it was seldom worth the upfront and ongoing cost.
For a large company to continue to grow at a significant rate, it needs to acquire or build new solutions that add significant revenue streams – quickly. In that context, let's talk about SAP and Salesforce. One is a classic enterprise technology company with primarily a perpetual license business model. The other is the "modern" cloud computing company with a subscription business model.
The challenge with both of those organizations is that to grow meaningfully they have to build or acquire something of meaningful size. Moving into a small geography requires the same resources or at least the same FTE costs that might be invested in something else and could generate a much larger impact. So, it should be little surprise why we are seeing these large companies look to make large acquisitions and avoid niche industries and geographical markets.
To your point, one might think, “Hey, if these market leaders are not seeing a lot of companies growing fast enough to make a measurable impact on their top line why aren't they helping smaller companies to grow so that they can eventually acquire them?”
Technology leaders may want to look at making investments in smaller technology companies where they see strategic value. These leaders can protect their interests by exchanging an equity investment and access to their channels with a Right of First Refusal (ROFR) that allows the leader to acquire the smaller company when it reaches a certain revenue threshold, at some predetermined price based upon a number of factors such as the acquirer’s stock multiple at the time.
With the respect to investing in and expanding the number of verticals supported, there may be a dearth of deep domain expertise inside some of the larger technology companies to do that well. I don't know whether these companies have initiatives to try to hire subject matter experts from industry to help them drive their product management. They may and I'm just not privy to it.
I do know that Salesforce took a run at creating vertical solutions and it wasn't as effective as they had hoped, at least not at the time. This is one of the reasons that led to their investment in Vlocity. I think this makes the case that the technology leaders, at least those in the B2B markets and not normally inclined to partnering, could really benefit by embracing partnerships.
Instead, it looks like the majority of them sit back and wait to see which startups “make it”, without their help, and then acquire the few that do. This seems rather silly because by doing that they have to then compete in the open market – and pay a premium - to keep them out of the hands of competitors.
What is going on right now in the enterprise space that you think is interesting?
Roughly every ten years, new technology is introduced that dramatically changes our work and personal lives. In the 60s it was mainframe computing. In the 70s it was minicomputers. In the 80s it was PCs. In the 90s it was client/server computing and in the 2000s, it was largely cloud and mobile computing which spawned “digital transformation”, beginning in the consumer markets with companies such as Amazon, Apple, Facebook, NetFlix, Google, etc. Digital transformation has now moved full speed into the enterprise markets.
I believe digital transformation - digital disruption - is such a big transition it will require multiple decades. I think it has a chance to undo a lot of the current market leaders in every industry. To your point, maybe vertical providers will emerge and dominate the markets due to digital transformation issues.
The emerging opportunity in the enterprise is in systems of intelligence. This is where I think vertical solutions can dominate. Systems of intelligence use the data generated and captured by systems of record and systems of engagement to enable companies to derive better business insights, faster and far more accurately than ever before. I mean real-time insights and real-time responses, without humans involved, that can be triggered via key business events and moments of value.
Here is an example in the retail space. Let’s say I'm walking through PetSmart and I'm a repeat PetSmart customer. I’m a member of the PetSmart loyalty program. If PetSmart has implemented a system of intelligence and placed beacons throughout the store, I have PetSmart’s loyalty application on my phone, and I provide my permission to send me offers, PetSmart can enable brands to generate contextually-relevant real-time offers to me as I walk through the store. An offer could be from a brand I don’t normally buy for my dog. The system of intelligence has access to POS data from my prior store purchases. PetSmart makes $ by serving up the competitive offer and if/when I decide to redeem the offer in a coupon that could be set to expire if I walk out of the store without making the purchase. Powering this type of application requires a different type of computer and systems architecture.
The biggest opportunity may come from vertical systems of intelligence built on top of the older systems of record and systems of engagement. In my PetSmart example, it means that you've got to be able to interface with an existing point of sale system, not replace it. This is where I would advise B2B application startups to focus. Trying to replace the existing systems of record and engagement is tough to do.
Trying to rip and replace technology is hard to do. Building next-generation systems of intelligence that utilize the existing systems of record and engagement is far easier. That's what a lot of the startups we work with are doing.
March 03, 2019 in Analytics, Big Data, Cloud Computing, SaaS, Industry Commentary, Venture Capital | Permalink | Comments (0)