deal architect

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!

If Data is the New Oil, how should Enterprise Data and AI be priced?

Over my career, I have been in the middle of or on the sidelines of countless technology negotiations. Deal Architect loves that back and forth and yelling and conniving, so no wonder my ears have perked up quite often with some of the wild talk I have been hearing about enterprise AI pricing

I have heard Wall Street analysts goad technology CEOs “You have to pay for NVIDIA GPUs which are premium priced, so better charge your customers a premium for your AI”

I have heard tech execs, driven by FOMO or OpenAI-envy say “We deserve a premium. We are bringing highly sanitized and reliable book of record data unlike that in the “wild west” consumer web”

I have heard from customer executives “Wait, its OUR data and the vendor is just going to massage it and sell it back to us at a significant premium?”

Every byte of data and related AI is not worth the same. There is a reason why the Coca Cola formula, why pharma molecular data, pixels of high resolution satellite imagery and other strands of data are highly protected and likely will never be available for training of vendor machines. There is a clear hierarchy of data value – IP/product related and specific customer data is worth a lot more than back office benchmark data. If you can help identify signals from all the noise in IoT data, that’s worth a lot especially to prevent unplanned downtime of expensive capital equipment. Ditto for gigabytes of parametric data that comes out of semiconductor fabs. The value of each byte of data varies by industry and country. And over time depending on demand and supply. Everything does not neatly fit into the “it deserves a 25% premium” bucket.  

Many enterprises will think like energy companies when it comes to selling access to their data to train machines. Many energy companies drill for oil and gas and sell that in the open market. Depending on whether you are in Saudi Arabia where you can poke a finger in the sand and its oozes black gold or you are in Norway and have to spend plenty of capital for infrastructure to drill in the rough, dangerous North Sea, market prices determine how much they sell in the open market. The amount of sulfur in the oil – whether it is “sweet” or “sour” - affects the price even further.

Other oil companies refine their crude into literally thousands of products.  According to the IPRB “There are more than 6,000 everyday products refined from an/or manufactured with natural gas liquids and crude oil, including electronics, paint, cosmetics, synthetic fabrics and medicines. A 42-gallon barrel of crude oil is typically used to make 19.4 gallons of gasoline, roughly 11 gallons of diesel fuel and four gallons of jet fuel. The rest is used to make petrochemical feedstocks, waxes, lubricating oils, and asphalt, just to name a few.”

Some highly integrated oil companies own their gas stations and sell diesel and gasoline to consumers. Others sell refined products through retailers like Wawa and Costco.

This complex marketplace has taken decades to evolve. And it may be in gradual decline as we move to alternate fuels.

The challenge for data and AI pricing is there are no trading exchanges or supply chains (yet) like there are for crude or chemicals. And those are somewhat regulated, though by no means stable. Data trading, especially consumer data, on the other hand is associated with the Dark Web. We are a long way from regulated markets.

But first principles are emerging. Not all data is worth the same. Data will be worth more or less over time. Customers will expect to be compensated for data to train vendor machines. Some customer data will never be available for vendors to mine.  Many customers will mine their own data and offer it for sale to others.

I expect a lot of head butting between technology customers and vendors over the first few years of enterprise AI applications. I also expect vendors to be held accountable for outcomes if they are being paid premium prices.

The cloud revolution came with a new business model – subscription contracts. Cloud-native vendors enjoyed several years of advantage over on-prem vendors who could not adapt to the new business model.

I think we may see similar with the emerging AI market. A new business model based on outcomes or performance based contracts. It’s something pioneers like Rolls-Royce have been fine tuning for decades with their “Power by the Hour” and TotalCare offerings. And it will give results-based vendors a few years of advantage compared to those that just glibly expect premiums for their AI.

More about that coming up.

Burning Platform: Enterprise Architecture & Communities in SAP world

In this 101st episode of Burning Platform, we host Paul Kurchina, SAP Community Evangelist, Connector and Analyst. Paul has been a fixture in SAP World for 3 decades – as a customer, as an SAP Mentor, active in various user groups around the world, omnipresent at various Sapphires, and more recently conducting 19 road show gatherings across N. America and others around the world.

We spend the longest time talking about enterprise architects and their complex job as application portfolios keep growing and as application lifecycle management becomes more critical. We also discuss SAP’s recent acquisition of the enterprise architecture tools company, LeanIX, the impact of new AI applications on the EA role, the changing world economy and leveraging SAP’s global functionality and reach.

We kick off talking about communities and their changing role. He walks through his 8 Cs of community building  – conversations, coaching, content etc. He says “the half-life of education has gone down tremendously given all the rapid changes in technology”  In addition to curriculum, coaching is as important given the multiple generations of talent in the SAP community with its 50 year history and widespread customer base.

As he says “Community was important in the early days of R/3. We'd get together as customers to talk about different aspects of our journeys, whether we were implementing, or post go live, deriving value … we used to think R/3 was pretty complicated back in the day. Roll forward, 30 years later, oh, my God, it's off the charts. The importance of us learning together and working together, especially in the age of AI, has never been more important.”

We next discuss the changing SAP ecosystem which has exploded beyond the big SIs. We discuss some of the strategy firms, robotics firms, SAP.iO startups and newer players as what he calls the SAP “solar system” keeps expanding.

Finally, we talk about the resurgence of focus on Centers of Excellence.

Every step of this 30 minute conversation. I felt in awe of SAP’s vital role in our amazingly complex, global economy and admiration for enterprise architects who keep up with all the rapid changes and especially folks like Paul who have mentored so many for so long in the communities that make SAP run.

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 😊