deal architect

IFS 2023 R2 Cloud Update

IFS provided analysts a well-paced session which covered new features available in the recently released cloud version.

I liked a few things in particular

• While everyone else is bragging about GenAI use cases it is nice to see continued investment and focus on transactional capabilities
• It was crisply done – about half an hour of features over 4 solution areas
• They plan to update every 6 months

Few things which I would have done differently

• Instead of focusing on features as released, I would prefer a highlighting of areas where IFS is differentiating in the market – particularly in asset intensive markets and in service management
• Bringing out more of unique vertical and geography specific features like they did with DNV certification for oil and gas and maritime sectors
• Reassess their ESG messaging. As I discussed with Brian Sommer in a recent ESG focused conversation here, to me it risks becoming a money pit and political hot potato. Brian pointed he is seeing environmental “doing” v merely “reporting”. Given its operational capabilities across industrial sectors, IFS would do better to highlight operational use cases rather than focusing like so many vendors on the emissions reporting part.

 

Analyst Cam: Salesforce Industries & Revenue Excellence – a Post-Dreamforce Update

As we’ve moved to virtual briefings, I have increasingly been excerpting short video segments (with permission) as part of my Analyst Cam series.

I attended Dreamforce last month fully expecting to be inundated with AI messaging–and I was, with Salesforce taking a sensible “trust-first” approach. I also wanted to get an in-person perspective on progress within Salesforce Industries and its Revenue Lifecycle Management offerings. While there, I watched several Industry Cloud presentations (Salesforce is now up to 13-plus Industry Clouds and solutions) and also spent time with many  industry-focused customers and partners, in addition to walking the show floor which offered plenty of industry-specific product demos.

Jujhar Singh, EVP and GM, Industries & Revenue Lifecycle Management for Salesforce, generously followed up with me in a post-Dreamforce video interview to share the latest on Salesforce’s Industry Strategy. See below for links to each of the eight segments, including an annotated description of each.

For more information about Salesforce Industry Clouds, click here; watch on-demand Dreamforce Industries sessions on Salesforce+ here.

Segment One: Introduction (3:46 minutes)

Jujhar sets the stage and explains the meaning of its five Product Pillars. He also emphasizes the importance of Salesforce bringing together all its core clouds based on one common metadata, stating “that’s our special sauce.” Finally, he stresses the company’s focus on AI, saying “the AI revolution is a data revolution more than anything else.”

Segment Two: AI Strategy (2:31 minutes)

Jujhar says the goal is to make sure that AI is a continuum from predictive to generative, not segmented in different boxes. The key to that is a solid data foundation. Salesforce was one of the first enterprise vendors to bring predictive AI to customers. Via Einstein, it claims to have made a trillion predictions. Now around vertical scenarios, it is packaging 31 industry AI applications out-of-the-box and looking at industry-focused LLMs and other verticalization. This segment includes examples of AI use cases.

Segment Three: Deep AI + Data Cloud Applications (5:46 minutes)

Jujhar highlights four vertical AI and Data investments announced at Dreamforce across a wide range of sectors:

• Connected Twins for Auto and other industrial markets (in a partnership with Qualcomm)
• Personalized Finance for Banking and Financial Services
• Trade Promotion Optimization & Effectiveness for Consumer Goods
• Unified Health Score for Healthcare – both at an individual and population level

He also emphasizes the “trust” factor which was repeated early and often at Dreamforce. “We don’t own the data. Customers do.”

Segment Four: Generative AI Scenarios (1:37 minutes)

Here, Jujhar highlights 10 industry-specific GenAI use cases. He also drills into Education Cloud, which is being built to support lifelong learning. Jujhar explains that as an institution designs courses, GenAI can recommend skills needed as a baseline for each course. As individuals complete different courses, they can see the aggregation of skills they’re building.

Segment Five: Revenue Management Foundations (1:44 minutes)

Jujhar discusses three solutions: Enterprise Product Catalog, Pricing, and Contracts. This segment includes Generative AI to generate contract language and collaborative redlining among other features.

Segment Six: Strengthen Scale Clouds: Financial Services and Health Cloud (5:07 minutes)

Here, we talk about two of Salesforce’s most successful verticals: Financial Services Cloud and Health Cloud, with the latter being complemented by the company’s latest industry offering: Life Sciences Cloud. He explains that Life Sciences Cloud isn’t just about the Pharma sector, but also includes MedTech. Jujhar also shares what’s going up-market with Group Insurance, expanding into compliance with more Know Your Customer (KYC) integrations for banks, and other new functionality for the Financial sector.

Segment Seven: Impact Portfolio: ESG, Nonprofit, Education, and Public Sector (6:00 minutes)

Jujhar covers four pillars in what the company calls its “Impact Portfolio.” For ESG, he talks about how the “E” is covered by the company’s Net Zero Cloud. He shares that Nonprofit Cloud provides a lifecycle approach, from fundraising, to program execution, and to a scorecard on outcomes. For Education Cloud, he says the company is starting to focus on lifelong learning, emphasizing that “You cannot be in a model of ‘one and done.’” And for Public Sector, he says “It’s one of our fastest-growing Industry Clouds. We’re getting traction across the board, really, whether it’s state and local, or the whole domain of federal, and across functions from benefits management to social, as well as licensing permits and inspections.”

Segment Eight: Maturing & Emerging Portfolio: Manufacturing, Connected Cars, Loyalty by Industry, Referral Marketing by Industry, Clean Energy (1:35 minutes)

Here, Jujhar talks about other recent investments, and they span across sectors. He says “the next set of innovation is going to come through the focus on clean energy.” For industrial sectors, he talks about a focus on assets and their maintenance, warranty management, and the concept of connected costs. He also explains how the company is investing in loyalty programs by vertical, and a referral marketing module for Education, Nonprofit, and Financial Services.

Burning Platform: Insights from Fall 2023 Industry Events

For this 104^rd  episode of Burning Platform, we host Pete Tiliakos of 3Sixty Insights and Jon Reed of Diginomica. I invited them both as we approach the end of Fall slate of Industry Events to get a distillation of perspectives from 25 or so events the three of us were privileged to be part of.

It is a rapid-fire 40 minutes and we broadly go over 5 topics. As smart analysts are prone to do, we have strong opinions, we disagree on several points, and take off on some tangents including one on the potential global scenario of “de-dollarization” starting around 31.00

The first section summarizes our “sentiment” and the attendee vibe we picked up from the events. Vendor AR and event managers will get some good, if contradictory, nuggets from this segment

Starting at around 9.15 we discuss the barrage of Generative AI coverage at most of the events. Lots of use cases and other angles here you may not have previously considered

Starting at around 18.50 we ask whether vendors are too focused on AI and not enough on transactional, vertical, localization and other customer needs including what Pete calls “blocking and tackling”

Around 27.20 we discuss economics of software projects. It is a timely discussion because there are expectations of premium AI pricing in the midst of the reality that IT budgets are under macroeconomic pressures. BTW, my episode with Geoff Scott, CEO of ASUG earlier this week also covered the economic angle extensively with a focus on the SAP customer base.

At 34.00 we have a broader conversation around automation and future of work, beyond AI and RPA technology.

Very insightful – no wonder both Pete and Jon are repeat guests on my episodes.

Burning Platform: Another Inflection Point for SAP Customers

In this 103^rd  episode of Burning Platform, we host Geoff Scott, CEO of ASUG and discuss a number of recent SAP announcements and what they mean to the ASUG and broader SAP customer base.

We discuss the SAP Q2 earnings announcement with its focus on product and pricing and the impact of RISE and GROW on ECC and early S/4 adopters. They have to factor change in one direction or another.

Next. we discuss Gen AI and other forms of AI. We talk about potential AI and automation to streamline SAP implementations, upgrades and other projects. We talk about planned Joule use cases and AI economics. More change.

We discuss stylistic differences in the way ASUG communicates with SAP executives compared to DSAG and other user groups around the world. Having dealt with vendor sensitivities for decades in my analyst role, I personally find SAP to be one of the more mature ones in dealing with all kinds of feedback. Some vendors only want positive comments – must be nice to have 100% market share😊

We discuss the sentiment in ASUG customers given all the global challenges we are facing. More changes.

However, as I tell Geoff we saw incredible innovation in the middle of so much chaos in the last few years caused by COVID, Ukraine crisis, climate change urgency and massive digital transformation – as vividly seen in Business as Unusual by Thomas Saueressig and Peter Maier we covered in a previous episode that Geoff hosted.

Importantly, we also find time to discuss baseball and college football.

As always, it is an educational and entertaining episode with Geoff – no wonder he is a regular guest in this series!

 

Burning Platform: All Things GenAI

In this 102^nd  episode of Burning Platform, we host repeat guest Tamas Hevizi, now in a new role heading the Office of the CEO at Automation Anywhere. As he says it was perfect timing with his focus on strategic initiatives with all the buzz around Generative AI

We explore many different aspects of GenAI and AI in general

We talk about a wide range of use cases across several industries - advertising, financial services, healthcare, railroads, retail and semiconductor among others 

We talk about the value of data. As he says we need to move away from ‘safe areas’ of the enterprise we have traditionally been comfortable in. Let’s focus instead on so many problems we haven’t been able to solve in the past 

We talk about technologies from Anthropic Claude to Microsoft CoPilot and approaches of enterprise vendors like Salesforce and Workday.

We talk about the economics of AI. He says ““There is this race going on in AI between data and compute. I don't know who's going to win that but so far, I'm betting on the hyperscalers and not the LLMs.”

We talk about the ethics of AI. Lots of perspectives you likely have not heard before.

Well worth spending half an hour on our back and forth. 

We end with the comment that we need to reconvene in a few months. This market segment is evolving so rapidly that we will likely have a completely different set of perspectives!

The State of Enterprise AI

“In the midst of winter, I found there was, within me, an invincible summer.

And that makes me happy. For it says that no matter how hard the world pushes against me, within me, there’s something stronger – something better, pushing right back.”

As I caught a glimpse of the Bay Bridge from my plane on yet another gorgeous afternoon last Friday, I happened to remember Albert Camus’ inspirational words.

I have spent nearly half of September in California, mostly at AI focused events. From a distance, it is a state which appears, especially after the tech meltdown last year, to be caught in a “doom loop”. But walking with my wife amongst its Redwood giants and surveying the majestic Pacific from Pigeon Point we felt nothing but awe. Even more, I was impressed by San Francisco which has cleaned up and stepped up to host several of those AI focused events. Quite a bounce back for a city which has descended to depressing lows. As Marc Benioff of Salesforce, ever a cheerleader for the city, has tweeted “San Francisco is the #1 AI City! SF will soon be sold out again! Never seen commercial real estate leasing at this rate. # of companies looking for huge amounts of space is incredible.”

You have to marvel at the power of people and places to self-heal.

Of course, as an analyst I hang around colleagues who slap me when my optimism gets carried away. I told a fellow analyst I love the term “joule” and SAP’s use of the term to brand its recently announced Generative AI digital assistant. It conveys momentum, energy and work, I said. He reminded me “it is a tiny unit of measure.”  He is right -  it is a force of a newton to move something only about a meter.

That is a good metaphor for the state of Enterprise AI between all the news coming out of Salesforce’s Dreamforce, Workday Rising, Google Next, Oracle OpenWorld, SAP’s announcements among others. Lots to be optimistic about, but realistically we are just getting started. Lots of moving and pushing ahead.

Let’s start with the positives first:

To me, the noticeable thing is how enterprise tech is threading the needle – taking advantage of the excitement generated by OpenAI and other LLMs and NVIDIA GPUs and yet distancing itself from the “wild west’ which is dependent on unstructured and often unreliable data from the consumer web.

The message from Rising was their AI harnesses “the largest, cleanest data set from 65M+ users and 629B+ transactions a year.” Dreamforce used even more eye-popping numbers for their Data Cloud, the underpinning for their AI: “900 trillion records processed per quarter”.

I heard the word “Trust” over and over.  Not just about the data, but the ability to transition customer bases across yet another architectural shift.

Aneel Bhusri of Workday expressed it pretty well in a session with analysts

“From mainframe to client server and then to the cloud (the shifts were about) re-platforming for business processes. When cloud got started, very early on, we said this is not just about business processes, it's about data. And so really, since day one, we've been building the foundation for data … I can't say we knew this world was coming but we were architected for the way that this world has materialized and you can't do the AI stuff if you don't have a really strong foundation.”

Two SAP executives made an even more compelling case of their “shock absorbing” capability in a book I helped them write last year. The preface said

“In 2022, we celebrated SAP’s 50th anniversary. Fifty years in the software industry means that generations of technology have transformed enterprise computing—from mainframes to client/server architectures, to the internet, and into the cloud. And we see the crests of new technology waves on the horizon.

Our solutions support and enable the daily operations of more than 400,000 customers. And what we consider “business as usual” today once was revolutionary and very un-usual. Real-time computing with SAP R/2 was disruptive when most of the world ran yesterday’s data and last month’s analytics on homegrown software. SAP R/3, with its scalable client/server computing, displaced the monolithic mainframes. Integrated 24/7 internet-based businesses replaced the usual 9-to-5 weekday schedules. The cloud is paving the way to scalable and adaptable computing power, high-security data management, and continuous innovation, giving companies the agility and resilience they need for an increasingly volatile environment. The pace of change has increased dramatically in the last few years as enterprises navigate one shock after another resulting from COVID-19, the war in Ukraine, climate change concerns, and massive digital transformations.”

One caveat – the trust factor would be much higher if vendors shared metrics on how many explicit customer permissions they have gathered to use their data to train their machines.

The use cases I heard in the last month were not just about Generative AI.  The vast majority of enterprise data is structured. Between SAP’s early Leonardo and Salesforce’s Einstein examples, we have early successes around demand forecasting, anomaly detection, next-best action and other scenarios. We need to turbocharge these incorporating newly available external datasets. And when it comes to GenAI a killer app will be around DevOps productivity, especially with hyperscaler platforms as George Gilbert pointed out in this Burning Platform episode. Talking of leveraging a wider group of developers, I liked Workday’s focus on AI in its Extend toolkit and its focus on startups in a new AI Marketplace.

I also liked Salesforce’s comments about “bring your own models” and commoditizing LLMs and SAP’s investment in Aleph Alpha, Anthropic and Cohere. I want application vendors to build more use cases, collect more relevant datasets and build more domain expertise, not invest as much in the plumbing.

Specific to use cases I saw many which should appeal to individual users – to help craft better email responses or job descriptions or career paths. I did not see them expressed enough in enterprise terms. “You can use the functionality for 10,000 instances a month and save xxx of $ in people time as a result”. We need that to show clear value from AI applications. In a recent conversation, I heard we are going through a phase of “AI fog” – a good way to describe that we have hundreds of potential AI use cases, but not enough value analysis around each so enterprises can easily prioritize them.

And yet, the reality is even after 25 years of cloud applications, if you chart a grid of available solutions by industry and global regions, 75% of the squares have very little coverage.  Most vendors do not have the specific domain expertise or access to relevant data as a result.

In a LinkedIn post about Oracle I recently wrote

“Brian believes Oracle’s earlier acquisitions - JDE, Siebel, MICROS, Primavera and others have brought it plenty of vertical knowledge. To me, those are all legacy applications, many from the 1980s. Made more contemporary at a cloud architectural level, but not always reflecting rapidly changing sectors. As is the core Cerner EHR. I have been particularly interested in new vertical applications which have become viable in the last 5 years”

My view is enterprises will find more AI value in the other “squares” on that grid. Vendors will need to move out of their functional comfort zones to help customers better understand these “unknown unknowns”. Their machines cannot be expected to provide truly valuable insights without curiosity about these unexplored domains. Even SAP which covers more squares in that industry/country grid tends to focus more on well-trodden horizontal areas. Over the next month we will hear at its events about use cases around HCM, procurement and CRM, when vertical and geographic operational use cases are much more elusive to find.

Another area for improvement- too many software vendors have taken Marc Andreesen’s comments on “software will eat the world” too literally. Software in concert with satellites, sensors, robotics, drones and other physical automation delivers X times more value to customers.

Mercifully, I did not see too many demands for premium AI pricing at these events. In earlier months, I had heard talk of that to mimic inflated pricing around GPUs and AI talent, but it appears to have quietened down a bit. Focus on expressing crisply the value from an AI use case and the pricing discussion becomes much easier.  

Over the last couple of decades we have seen consumer tech lead the enterprise through waves of cloud, social and mobile adoption. With this wave of AI, I see a caution around blindly adopting consumer tech. That’s healthy. Next, we need to broaden our horizons to help enterprises with data and analytics around way too many blind spots they continue to live with.  But do so quickly at the speed of consumer tech.

PS – I am not a political analyst, but if I were I would deliver a similar objective scorecard on the state of the State of California 😊

The new Enterprise Applications Gold Rush

I arrived at Gartner in 1995. My colleagues there were doing Congressional Hearings, had thrown out a number of $600 billion for enterprises to fix the Y2K bug – stuff which woke up CEOs and Boards to their technology challenges. Over the next 5 years, our applications analysts handled a fire hose of client calls about TLAs we had coined – ERP, CRM etc. In my case, it approximated 10,000 30 minute calls (yes!) related to the software vendors and related Sis – we logged every call in a database Gartner had built. The patterns across those calls meant our Magic Quadrants basically wrote themselves.

At Deal Architect, we have already been tracking a stream of new applications this decade. COVID. Ukraine war, climate change and massive digital transformations have made many vertical edge applications viable – telemedicine and personalized medicine in healthcare, EV battery management and billing in utilities, intelligent returns and reverse logistics around eCommerce, Direct to Consumer and related last-mile, small-lot logistics in consumer industries, CPQ for industrials to handle complex outcome based pricing which bundles product, spare parts, all kinds of monitoring and maintenance services, financing etc.  And many more for other sectors.

There is also a growing number of applications aimed at rapidly growing economies around the globe – they factor unique business practices and digital trends, local languages, currency, scripts. taxes, regulatory and other nuances.

Beyond these new vertical and geographic applications we are seeing a new generation of AI and data enabled applications.  This week at Dreamforce I saw Marc Benioff interview Sam Altman of OpenAI – the man who without needing any industry analysts has single handedly started a new Y2K type stampede. And in addition to the multitude of use cases I heard about during the event, Sam threw out another potential category. In his keynote, Marc had said “They call them hallucinations; I call them lies. These LLMs are very convincing liars.” Sam basically said hallucinations are not a bug, they are a feature. “A lot of value from these systems is heavily related to the fact that they do hallucinate….If you want to look something up in a database, we already have good stuff for that…The thought before was that physical and repetitive tasks would be transformed first before moving on to creative endeavors. The model takes existing data and presents it in different, novel ways (so can be used for creative and strategic work as well)

In a different session, Peter Schwartz, Futurist at Salesforce responded to my question how his Shell (the global oil company) Scenarios Group in the 70s (and comparable ones today) could benefit from today’s AI

“Most people are uncomfortable with uncertainty. They'd rather deny the uncertainty that's inherent in most of the methodologies around things like scenarios. It's not about predicting the future. It's about making better decisions today by looking at the alternatives and managing the risks both the upside and downside far better. I think AI will be a very useful tool in that. Think about it as a current conversational strategic planning partner that presents alternatives. In fact, one of my very first applications of ChatGPT was writing an article on the future of electric vehicles. And I said, give me three scenarios on the future of electric vehicles. And it gave me three really smart ones. I didn't use them literally in what I was writing, but it was a great start. And it was really amazing and actually being able to create alternatives. So when I think about it, frankly, most companies, especially small and medium sized companies, don't have somebody like me working for them. Now imagine an AI strategic planning partner that's trained on various alternatives on the methods. the mathematics of various kinds of models, and can generate real alternatives for small and medium sized businesses”

To Peter’s point about uncertainty, Gartner had taken those scenarios a step further. We assigned probabilities to them and were actively discouraged from publishing any which had a probability of 0.4 or higher. That meant it likely was not bold, edgy enough – it likely did not have enough uncertainty factored.

Couple of weeks ago, reacting to Snowflake CEO’s comments about Nvidia’s blowout quarter, I had written the premium costs driven by GPUs and scarce AI talent would lead to “premium AI applications” being prioritized. Drug discovery, mineral insights, trading patterns, design nuances are some of the use cases which would justify them. I also said “For many vendors, their predictive AI may offer more value to customers than generative AI. If you can preclude unplanned shutdowns of expensive assets with preventive maintenance AI or you can reduce production and logistics footprint, waste and scrap through better demand forecasting AI,  that may be exactly what your customers need.”

The good news about Dreamforce (and likely again at Workday Rising coming up in a couple of weeks) I heard plenty of generative AI use cases. Between the vertical, geographic and now AI enabled applications, it should allow the Gartners to come up with a new gen of TLAs. I also heard about CEOs and Boards really energized (trust me, they grudgingly go along with compliance, ESG type projects).   Except, that given the shocks of the last few years – COVID, Ukraine etc. – most enterprises have seen their industries and countries radically transformed and they expect a lot more agility than they did around Y2k.

Besides the agility, I hope companies are also less wasteful than they were around Y2K. In the 90s, I saw massive project overruns, plenty of shelfware, suites which underperformed and had to be ring-fenced with best of breed solutions.  I also expect enterprises will want their projects to be much more automated. AI should also allow SIs to rethink their labor-intensive delivery models.

I am looking forward to another fire hose of application functionality, architecture and implementation conversations. It’s the got the same feeling I got when I got to Gartner in 1995.

As Yogi Berra famously said: "It's déjà vu all over again."

Enterprise Software: Don’t stop thinking about tomorrow!

In my role as industry analyst, I have always pushed vendors to look ahead, rethink what they had in their bags over 5 years old and to cannibalize legacy revenues. Especially after the turmoil of the last few years. No industry or country has come out unscathed from the recent shocks of COVID, the Ukraine crisis, increasing climate change urgency and massive digital transformations. No surprise, as I wrote here, we are tracking 4 large software and services markets – hundreds of vertical “edge” applications, applications for fast-growing global economies, custom development using low and no-code tools and Generative and other AI.  

So imagine my surprise when I got from DSAG (the German speaking SAP user group) an email which led off with

“At SAP's annual press conference on July 20, 2023, Christian Klein, CEO and member of the Executive Board, announced that SAP's latest innovations would only be available in the cloud. Specifically, they are to be accessible only to customers using SAP S/4HANA Cloud, Public Edition or SAP S/4HANA Cloud, Private Edition via GROW-with-SAP or RISE-with-SAP contracts. This does not mean that on-premise solutions in general will not be functionally enhanced. But on-premise customers cannot, for example, benefit from major innovations such as artificial intelligence (AI) and green ledger. This also applies to larger function modules and extensions based on the Business Technology Platform (BTP). At the same time, SAP plans to increase maintenance fees. From the point of view of the German-speaking SAP User Group (DSAG), SAP is leaving numerous loyal customer companies in the lurch with this approach.”

And of course, several bloggers and journalists have piled on top of the DSAG reaction.

Huh? By our research benchmarks, SAP is one of the few vendors to have kept expanding its portfolio of vertical edge applications and global functionality. Try finding anything close with its on-prem or cloud competitors. In fact without the vertical and global functionality these vendor’s AI talk runs a huge risk – their machines and LLMs will mostly have access to what will increasingly be commoditized back office data. The next battle field will be around operational data, highly verticalized and regionalized.  BTW, AI will be lot easier in the enterprise as cloud data grows along with customer permissions – today, it is mostly squirreled away in proprietary data centers – highly fragmented and not easy to train machines with.

User groups play a very useful role in our industry and DSAG has faithfully represented the voice of the customer, especially mid-market ones in Europe, but this commentary sounded like Rip Van Winkle waking up, shocked after a long slumber. The enterprise world has been moving to the cloud for 3 decades now, and contrary to industry myth, SAP and its customers have been a major force in this transition. Some data points:

1. In my 2014 book, SAP Nation, I pointed out one of the most common strategies at many SAP customers was to “ring-fence” core ECC functionality with cloud solutions – 10, 20, 50 of them.
2. SAP, under Bill McDermott, Rob Enslin et al acquired a number of cloud properties in the HCM, procurement and CRM areas. The Ariba and Concur acquisitions have created one of the largest business networks in the industry – which SAP is increasingly verticalizing to include direct material suppliers. You think continuing to expand ECC or even S/4HANA would have easily accomplished that?
3. SAP has been delivering vertical edge applications in the cloud – intelligent returns around eCommerce, cell and gen therapy orchestration in healthcare, e-mobility and EV charge point billing for auto and utilities sectors among others
4. Countless customers are taking advantage of hyperscaler clouds as part of signing up for RISE with SAP.
5. In my 2019 book, SAP Nation 3,0, we had discussed the growing emphasis on cloud functionality and increasing talent challenges recruiting and retaining ECC and on-prem talent. Holger Mueller of Constellation had looked ahead and asked “Do you want to be in 2030…with an ERP system that cannot run machine learning, which does not have a good IOT story and so on?”

So, not sure I understand the DSAG surprise about Christian’s comment given all these signals going back so long.

About SAP’s economics – no CIO I have worked with pays full list price. There is no single enterprise software price point. Given SAP’s vertical investments and country presence it has a dominant position in many segments and can demand a premium. In many other segments it will not be able to do so. It has a very capable Value Advisory team which will have to help it justify its pricing.  I may be off base, but I don’t see a repeat of the customer pushback to maintenance price increases in 2008. That was boneheaded in the middle of a recession. This is aspirational and each customer will do their own value analysis and in case of AI, I am already seeing plenty of risk mitigation being factored in ROI assessments.

Not just picking on DSAG, I am also increasingly impatient with industry analysts. As I wrote a few weeks ago “I go to too many events where we still use dated TLAs (I admit I coined some of them in the mid-90s at Gartner). Two flagrant ones are ERP and CRM. Research firms have armies of analysts who still create Magic Quadrants, Waves and other tools around those TLAs and they parachute in droves to industry events. Worse, vendors spend months responding to questionnaires from these analysts to justify microscopic changes in their annual scorecards.” And “Let’s get away from umbrella, fossilized terms. Let’s talk the language of today’s business.”    

Every customer decides its own technology footprint and path. For many staying with the true and tested is the best path. But let’s not shackle our vendors to that past. Let’s push them to keep thinking about the future. The world has changed dramatically in the last few years and the pace of change will only accelerate.

Burning Platform: A tour of enterprise software world in 30 minutes!

In the 96th episode of Burning Platform, we host John Wookey, a long time veteran of enterprise software world

John is one of the rare execs who has worked in a wide range of software and services companies which have defined the enterprise tech sector over the last few decades. He describes his role at Andersen (now Accenture), Ross Systems, Oracle, Salesforce and SAP. He has worked closely with some of the people who have shaped this industry including Larry Ellison, Marc Benioff, Hasso Plattner, Keith Block and Ken Ross. He has also had a chance to integrate and influence the trajectory of companies like Siebel and Concur after they were acquired.

So, it is not on overstatement to call John one of the best industry market analysts. It was an honor to have him on the show. We broadly cover 4 topics

1. How the next gen of applications will be “data-influenced” – Generative AI is giving vendors a chance to reimagine applications, but it is early days. We discuss challenges and adoption speed among other topics.
2. The verticalization opportunity for application vendors – he explains why vendors need to overcome their caution “ they feel it limits them. If you say you are going after financial services, then they're by default, not going after healthcare or retail. So in a way, I think they feel like by declaring that they actually are or in some ways limiting themselves in other markets. But you have to take that step for a couple of reasons. One is, I think your relevancy to customers in those industries grows tremendously. When you declare and you make that investment. Second, I think the even more important reason is, in order to build especially new types of products based on artificial intelligence and leveraging the data, you will not do that well if you don't have a deeper understanding of customers in those industries.”
3. The localization opportunity for applications. He describes his own experience helping Oracle prepare applications for the birth of the European Union early in the century. He also explains the balancing act “You may not have justification for (a country like Nigeria) as a standalone market. The question is, where the multinational companies you work with? What's their relative importance? And how much do you want to invest to make sure that they have the ability to operate in all the countries that are critical for them? So you're always kind of balancing those two things, the what is the organic (local) market? And then what's the support market you have from a multinational perspective?”
4. We discuss the service sector and why the business model is still so people dependent. John explains why it is so difficult to give up on existing business models. He cites the example of Concur. “At some point, the Singh brothers said, this isn't the future and their stock price took a hit for a couple of years. They made an incredibly courageous decision to shift to the cloud. And I think in the case of systems integrators, I think it will be the same thing. When someone new comes along and says we're going to be tied more to something more value based, something that's more objective outcome driven, as opposed to, you know, hours based, you know and think more about lifetime value of a customer. Until someone else does that and gets to scale, it'll be difficult for them to make that shift.”

Overall, I really enjoy John’s continued optimism and excitement for our sector

“We've always been trying to better approximate the businesses that we're supporting. How do we shape information in a way that makes them more successful. The job really hasn't changed in a lot of ways. It's continued to adapt and evolve, but ultimately, what we're really trying to do is help (customers) be more successful.”

Real honor to have John. I need to bring him back more often.

Watch the entire 30 minutes. He is a delightful font of wisdom. Decades of industry experience and close working with some of the industry’s best talent distilled into this conversation.

 

Analyst Cam: SAP - Farm to Consume Walkthrough

As we have moved to virtual briefings, I have increasingly been excerpting short video segments (with permission) as part of my Analyst Cam series.

Most vendors have software demos as part of their events. SAP has taken that up a notch by blending the digital with the physical world. They showcase robotics, sensors and other physical world technologies in the many experience centers Sven Denecken had described in this episode.

Last year at Sapphire, the show floor had the Trilogy fashion/apparel demo company on the show floor which showcased next-gen tech for product innovation, sustainability, robotic manufacturing and retail experiences.

This year they went much further and actually served 25,000 helpings of (real) ice cream to attendees while showing off the Farm to Consume process flow.

I invited Andre Bechtold architect of the setting and other similar experiences to present. He has a 4 minute video of the show floor in Orlando then double clicks into the Farm-Source-Make-Deliver-Consume flow across agribusiness, CPG, logistics, retail and other industries. 

Andre also talks about a similar setting he is working on which simulates the energy-mobility sector convergence around EV charging. He also discusses the investment and logistics of repeating the experience in 3 consecutive weeks of Sapphire in Orlando, Barcelona and Sao Paolo.

Very nicely done in about 30 minutes - he makes the setting look so easy - took lots of coordination across partners, technologies and locations. Brings to life the art of the possible to re-imagine business processes in the midst of industry convergence.