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BT150 CXO zeitgeist February edition: Low marks for SAP RISE, process automation, change management, AI risk

BT150 CXO zeitgeist February edition: Low marks for SAP RISE, process automation, change management, AI risk

Constellation Research held its February call with Business Transformation 150 and SAP's RISE program, process automation and AI were top-of-mind topics.

These gatherings, held under Chatham House rules, are a venue to share information and emerging trends. Here's a look at the topics from our February meetup.

Previously: BT150 CXO zeitgeist: AI trust, AI pilots to projects, VMware angst, projects ahead

SAP's RISE program

  • One CIO asked the group for opinions on SAP's RISE program and being forced from on-premises to the cloud. The goal was to have a strategy for SAP in place by the end of the year.
  • CXOs weren't thrilled about SAP RISE and items like licensing credits for legacy environments. A CIO wondered what would prevent a customer from moving away from SAP--especially since the enterprise operates in a space that doesn't garner investment from the enterprise software giant.
  • One option could be to build a homegrown ERP system to replace SAP. 
  • SAP's RISE program is viewed as an exercise in financial engineering more than something that benefits customers. 
  • Being on-premises is actually a good thing because the customer isn't beholden to SAP and already paid for the asset. Rimini Street was frequently mentioned as an option for maintenance and support to preserve the initial SAP investment and keep options open down the road.
  • For enterprises that don't want to migrate to SAP's cloud, there's an option to build an abstraction layer on top of the transactional system that's eating up budget without value.

Our BT150 CXOs aren't alone. SAP's German speaking user group takes aim at cloud contracts, BTP and more | SAP user group DSAG rips S/4HANA innovation plans, maintenance increases | SAP retools for generative AI, cuts 8,000 jobs, sets 2024, 2025 ambition

Process mining and automation

  • Some CXOs were exploring proof of concept projects for process mining and modeling tools. The concerns revolved around process mining turning into more work than it needed to be unless the underlying systems were emitting real signals.
  • Process mining was seen as an option to automate repetitive jobs. Celonis and SAP Signavio were mentioned as options.
  • Process design is critical to making automation projects work.
  • Enterprise automation needs processes that are designed by the bottoms up with employees on the front line. Enterprises should look to redesign processes completely and creatively destroy them to find new ways to work.
  • Change management is a required art form to make automation work. In many cases, the objective for employees and departments is to maintain a process that has been running for decades. Resistance to change torpedoes automation projects and CXOs need to evangelize and bring in multiple stakeholders.

Celonis launches Process Intelligence Graph, makes case process enables automation, AI applications | SAP buys LeanIX, aims to couple it with Signavio, system transformation

Management tips

  • Use regulatory and security requirements to move projects along and get stakeholders on the same page.
  • Sell teams on projects that will last beyond today and influence the future. 
  • Enterprise change is more successful if it starts from the inside than outside.
  • Plan projects assuming that the CEO has a tenure of 4 years and so does the team attached to that leader. Year one is likely to be about cost cutting. Year two is about innovation funded by the savings of year one. Years three and four are about delivering results that renew the CEO's contract. The goal is to implement technology that can span leadership teams.

Return on Transformation Investments (RTI)

AI risks

  • CXOs on our panel all referenced Air Canada's rogue bot that gave a discount that was upheld in court. There are other examples of bots giving deals that no human would approve. CXOs noted that safeguards, process maturity and a kill switch are required to prevent bots from becoming headaches.
  • Enterprises need to think through where a human goes into a process to establish trust.
  • Enterprises also need to ponder legal liability with generative AI.
  • AI applications are becoming a key theme as enterprises look to renew long-term deals with software vendors. Multi-year deals may smooth out costs and contract negotiations.

 

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Reddit's data licensing play: Do you want your LLM trained on Reddit data?

Reddit's data licensing play: Do you want your LLM trained on Reddit data?

Reddit has filed to go public in a closely watched initial public offering and one of its potential revenue streams is data licensing. The big question is whether enterprises want that data in a large language model. Perhaps the bigger question is whether enterprises will have a choice.

The social community has made a deal with Google to provide it model training data. Reddit also indicated that it'll provide training data more broadly. Reddit has sentiment, conversational data, and a lot of useful stuff to go along with the ridiculous. In some ways, Reddit data is likely to make LLMs a bit more serendipitous.

Reddit's data licensing plan highlights how training data from media-ish companies is valuable. The plan also highlights Hobson’s Choice facing these companies. Reddit gets revenue by providing training data, but also could get squashed by an LLM in the future.

According to Reddit's S-1 filing with the SEC:

"Redditors may choose to find information using LLMs, which in some cases may have been trained using Reddit data, instead of visiting Reddit directly. We believe that our ability to compete effectively for users depends upon many factors both within and beyond our control."

Not that Reddit has a choice. Data licensing is a high-margin business and Reddit lost $90.8 million in 2023 on revenue of $804.03 million. The company did pare losses from 2022 and may be headed in the right direction if data licensing works out. Advertising is a rough business.

Reddit specifically names ChatGPT (OpenAI CEO Sam Altman is a shareholder of Reddit by the way), Gemini (Google paid Reddit for data) and Anthropic as competition. Reddit's list of competitors for your attention is also extensive: Google, Meta, Wikipedia, X, Snap, Roblox, Discord and any other company that can give you an answer. Marketplaces are also cited as rivals.

In other words, every Reddit competitor will also likely have a data licensing business. It's clear that users have become the product and the data licensing model.

As these data licensing models proliferate, data lineage is going to become critical. Ultimately, I'd want to know what data the LLM was trained on and be able to back out some sources. This ability is especially critical for media sources. Media isn't objective and you need a few outlets (and preferably source documents) to even figure out what the actual truth is. Media has become like navigating a divorce. There's the first spouse's truth. There's the other spouse's truth. And then THE truth.

For companies like Shutterstock, which is going to provide training data for models on data marketplaces, the business looks more straightforward. Other providers can supply data, but in the end the LLM buyer may not actually want that data set. How do you reverse learning? How do you back out a data set? Do I want my customer service LLM dishing out some snarky comment it learned on Reddit, X or Meta properties? Is Reddit's training data really just a small subset of the company's more than 500 million visitors and 73 million daily active users that actually comment?

We should be asking these questions now because the data licensing parade is just about to leave the station. In its IPO filing, Reddit said it has "one of the internet’s largest corpuses of authentic and constantly updated human-generated experience" and is in the early stages of data licensing. Reddit said its platform is good for real-time perspective on products, market sentiment and signals. It'll provide data API access and model training from Reddit's real-time content.

Ideally, I'd want to see a nutrition label on LLMs, and a list of data sources used for training. I'd also want to check off sources too. It's unlikely we'll get those options, so enterprises are likely to find small language models aimed at specific use cases and industries more valuable. 

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Transforming Experiences in the Age of AI | Impact TV Episode 5

Transforming Experiences in the Age of AI | Impact TV Episode 5

? Watch the final episode of Impact TV: Transforming Experiences in the Age of #AI👇

Co-hosts R "Ray" Wang, founder of Constellation Research, and Teresa Barreira, CMO of Publicis Sapient, discuss the intersection of #AI and #customerexperience, and how to lead the way in creating transformational and human-centric experiences for customers...

Ray and Teresa sit down with the following #CX and #AI experts to learn more:

00:00 - Introduction
03:46 - Abby Godee, CXO, Publicis Sapient
16:19 - Melissa Falconett, Principal Director - Trust UX, Google
33:14 - Soon Yu, best selling author of Iconic Advantage and Friction

#technology #business #trends #cxos #CX #EX

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Shutterstock will bring its training data to Databricks, Snowflake, Amazon, Google Cloud

Shutterstock will bring its training data to Databricks, Snowflake, Amazon, Google Cloud

Shutterstock is best known as a stock image provider and owner of Giphy, but the money and margins may come from training data for model training.

During the company’s fourth quarter earnings call, Shutterstock CEO Paul Hennessy said the company is looking beyond wholesale data deals with the likes of Meta and OpenAI to licensing data across marketplaces. Shutterstock sees the data business as something that can grow at a compound annual growth rate of 22% through 2030.

Hennessy said:

"As we look ahead, AI and machine learning model training will continue to be a growth opportunity, especially as we look to diversify our revenue base by targeting new buyers beyond the hyperscalers. In fact, we just won our first seven figure contract involving a venture backed startup in the generative AI ecosystem, and we feel there are much more such opportunities ahead. We'll also be expanding our delivery model by leveraging our cloud marketplace partners. This will allow us to go from being a wholesale provider of data to the likes of Meta and OpenAI to a retail provider of data to the hundreds of companies we believe are going to custom train their own models.

To that end, we are in the process of rolling out Shutterstock's training data onto data marketplaces of DataBricks, Snowflake, Amazon and Google Cloud."

Shutterstock's data business had revenue of $104 million in 2023. Today, Shutterstock has 10 anchor customers for training data, but thinks it can get to 150 over time.

"The data marketplaces of Snowflake, Databricks, AWS and Google are comparatively small but fast-growing businesses for these companies as their customers learn how to enrich and monetize their own data and models," said Doug Henschen, VP and Principal Analyst at Constellation Research. "Interest in GenAI will further accelerate the growth of these data marketplaces as companies start to build their own small and midsize models, including ones harnessing image data."

Giphy has a role in this generative AI play for Shutterstock because the content platform is connected with APIs. That know-how has given Shutterstock "API relationships" with major technology players. Shutterstock will invest in 2024 to build out its training data business.

Hennessey said the plan is to bring Shutterstock data to where the customers are. "Our customers don't naturally think of Shutterstock as a place to go for computer vision training data and training their generative AI models, but they do typically go to a DataBricks or a Snowflake or an AWS or GCS in order to acquire training data. This is also the natural compute environment for these customers," said Hennessey, who added that those partners will boost distribution without hurting margins.

"The way these distribution channels make money is not by taking a cut of the data sales, it's through the compute. And so they're looking forward to having our data on their ecosystems, so they can drive additional compute in the cloud," he said.

It remains to be seen how Shutterstock's training data business develops. Contracts can last years or be shorter depending on volume-based pricing.

Shutterstock reported 2023 revenue of $874.6 million, up 6% from a year ago, with net income of $110.3 million.

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Nvidia's GPU boom continues, projects Q1 revenue of $24 billion, up more than $20 billion a year ago

Nvidia's GPU boom continues, projects Q1 revenue of $24 billion, up more than $20 billion a year ago

Nvidia's data center business continued to surge and the company continues to raise its revenue guidance at a rapid clip.

The company reported fourth quarter revenue of $22.1 billion, up 265% from a year ago, with earnings of $4.93 a share. Non-GAAP earnings for the fourth quarter were $5.16 a share.

Wall Street was looking for Nvidia to report fourth-quarter non-GAAP earnings of $4.64 a share on revenue of $20.62 billion.

For fiscal 2024, Nvidia reported earnings of $11.93 a share on revenue of $60.9 billion.

The fact Nvidia is raking in dough isn't surprising given cloud providers and Meta said they were spending heavily on GPUs to train models. Constellation Research CEO Ray Wang said "we haven't hit peak Nvidia yet." 

By business unit, Nvidia's quarterly revenue trend is almost comical. The company's fourth quarter data center revenue was $18.4 billion, up from $3.83 billion a year ago.

As for Nvidia's outlook, the company projected first quarter revenue of $24 billion with GAAP margins of 76.3%.

To date, most of the spoils from the generative AI boom have gone to Nvidia with SuperMicro being an exception.

CEO Jensen Huang said generative AI and accelerated computing has hit an inflection point with surging demand. "Our Data Center platform is powered by increasingly diverse drivers — demand for data processing, training and inference from large cloud-service providers and GPU-specialized ones, as well as from enterprise software and consumer internet companies," he said.

Nvidia CFO Colette Kress said:

"In the fourth quarter, large cloud providers represented more than half of our Data Center revenue, supporting both internal workloads and external customers. Strong demand was driven by enterprise software and consumer internet applications, and multiple industry verticals including automotive, financial services, and healthcare. Customers across industry verticals access Nvidia AI infrastructure both through the cloud and on-premises."

Kress added that sales to China declined significantly. Gross margins improved due to lower component costs. Nvidia's cash, cash equivalents and marketable securities was $26 billion, nearly double from a year ago. Regarding inventory, Kress added:

"Inventory was $5.3 billion with days sales of inventory (DSI) of 90. Purchase commitments and obligations for inventory and manufacturing capacity were $16.1 billion, down sequentially due to shortening lead times for certain components. Prepaid supply agreements were $5.0 billion. Other non-inventory purchase obligations were $4.6 billion, which includes $3.5 billion of multi-year cloud service agreements, largely to support our research and development efforts."

Kress said data center revenue was driven by the Nvidia Hopper GPU computing platform along with InfiniBand and and networking. Compute revenue grew more than 5x and networking growth revenue tripled from last year, she said. "We are delighted that the supply of Hopper architecture products is improving and demand remains very strong. We expect our next generation products to be supply constrained as demand far exceeds supply," said Kress.

Conference call highlights

Key items from the conference call with analysts:

Nvidia's networking business is at a $13 billion annualized revenue run rate. Spectrum X Ethernet has adaptive routing congestion control, noise isolation and traffic isolation. Huang said Spectrum X Ethernet will be an AI optimized system and InfiniBand will be an AI dedicated networking option.

Huang said there's strong demand across industries but cited healthcare and financial services as well as sovereign AI as key areas.

He said:

"Sovereign AI has to do with the fact that the language, the knowledge, the history, the culture of each region, are different. And they own their own data. They would like to use their data, train it to create their own digital intelligence and provision it to harness that raw material themselves. It belongs to them."

He added that the generative AI boom experienced in the US will be replicated in multiple countries.

Nvidia's supply constraints are improving. "Our supply chain is just doing an incredible job for us," said Huang. "The Nvidia Hopper GPU has 35,000 parts. It weighs 70 pounds. These things are really complicated. We call it an AI supercomputer for good reason."

Demand will outstrip supply. "We expect the demand will continue to be stronger than supply provides, and we'll do our best," said Huang. "The cycle times are improving. Whenever we have new products, it ramps from zero to a very large number. And you can't do that overnight."

Allocation depends on how soon customers can launch services and infrastructure. Kress said allocation is a moving process and Nvidia looks to get inventory to customers right away. Huang said cloud service providers have a clear view of Nvidia's roadmap and the visibility of what products can buy and when. "We allocate fairly and to avoid allocating too early. Why allocate before a data center is ready?" said Huang. "We bring our partners customers. We are looking for opportunities to connect partners and users all the time."

Nvidia will work within government constraints to reset its product offering to China. Nvidia is doing its best to compete. "Hopefully we can go there and compete for business," said Huang, who said the company is sampling with China customers.

Software is as important as hardware. "Software is fundamentally necessary for accelerated computing. If you don't have software, you can't open new markets or enable new applications," said Huang, who said Nvidia has teams working with a bevy of enterprise software companies.

Huang said Nvidia will do the optimization, patching and optimization for enterprise software companies. "Think of Nvidia AI enterprise as a runtime like an operating system. It's an operating system for artificial intelligence," said Huang.

Constellation Research's take

Wang said there's no reason why Nvidia can't keep rolling. He said:

"This is the most important stock in the world right now and a barometer of AI health. Four years ago we talked about Nvidia hitting a $1 trillion market cap and $2 trillion by 2025. We are in the age of AI and Nvidia is king. This is real demand and it's growing. We haven't hit peak Nvidia nor peak AI and we'll see software as the second wave of AI in about 6 months to a year."

Constellation Research analyst Dion Hinchcliffe said Nvidia's moat is also about software. Hinchcliffe said:

"Amid all the hoopla about NVIDIA's chip prowess, what most observers still miss is that Nvidia has carefully cultivated and strategically wielded its CUDA (Computing Unified Device Architecture). CUDA is the unique software stack that has won the hearts and minds of next-gen compute devs in gaming, HPC, and now AI.

CUDA has become the key competitive weapon that connects 3rd party apps to Nvidia GPUs. It’s the magic handshake that enables AI algorithms to work effortlessly with the massive compute power of Nvidia's modern chip architectures. 

But CUDA isn’t just an ordinary piece of software. It's a closed-source, low-level API that optimally wraps a powerful software stack around Nvidia’s GPUs. The result has created an ecosystem for parallel computing that's potent while also carefully keeping devs captive due to their code's dependency on it. Even the most formidable competitors such as AMD and Intel have struggled with only minor success as CUDA has become widely adopted in various industries like HPC, AI, and deep learning. 

I project that we'll continue to see Nvidia's lead grow as it continues to corral devs with the success of CUDA and and keeps competitors at bay as long as the API remains relatively non-portable."

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Conversational AI, Tech News, Analyst Intro | ConstellationTV Episode 74

Conversational AI, Tech News, Analyst Intro | ConstellationTV Episode 74

ConstellationTV episode 74 just in! 🎬 This week, co-hosts Liz Miller and Holger Mueller talk #enterprise tech news, then grill new Constellation analyst Chirag Mehta during a lively Salon 50 session. Round out the episode with CR analyst Andy ThurAI explaining his Conversational #AI ShortList with Liz. And this week...you won't want to miss the bloopers!

00:00 - Welcome from our hosts!
01:40 - Enterprise #technology news (#Digital strategy adoption, AI investment, upcoming events)
12:15 - Salon 50 interview with Chirag Mehta
22:25 - Conversational AI ShortList explainer with Andy Thurai
33:35 - Bloopers!

ConstellationTV is a bi-weekly Web series hosted by Constellation analysts, tune in live at 9:00 a.m. PT/ 12:00 p.m. ET every other Wednesday!

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3 Steps to Making Success More Successful: Is It Time To Up-Level With Value-Based Strategies?

3 Steps to Making Success More Successful: Is It Time To Up-Level With Value-Based Strategies?

Is Customer Success Broken?

In mid-2023 a trio of partners at the famed venture capital firm Andreessen Horowitz published a blog titled “Customer Success Is Broken. Here’s How to Fix It.” They noted that customer success failures occurred most often when problems originating outside of the team directly associated with the project took center stage and priority. Success teams were often focused on managing relationship problems, ironing out when and where needs, intentions, or goals had been miscommunicated or where promises had been broken.

According to the blog’s authors, solving this issue lies in where the success teams focus and the context of the organization’s transformation project. Success, they argue, should be “laser-focused” on the customer’s “hierarchy of needs” to ensure that customers stay “on track to realize positive business outcomes” that all involved have agreed upon. Rather than adding to processes or attempting to “fix culture,” the authors stress, success teams should realign and fix disconnected processes, paying special attention to the blind spots that stymie a shared customer focus.

For some, customer success feels in need of a refresh. Although it might not be broken, when customer success doesn’t deliver the business results a customer desires, it can be disappointing. For others, customer success is the critical resource needed to realize their digital transformation goals. Across the board, regardless of vendor or customer, there is a universal truth to customer success: No two customer success programs are the same, and one size certainly does not fit all.

Everyone focuses on the excitement of aligning on a solution, establishing that plan, and setting out into the bright new world of possibilities, but customer success programs can often become an afterthought to the main event, added on as an incidental piece of the larger transaction.

This is especially true in enterprise software, where customer success can be defined by any number of parameters—from renewing, cross-selling, or upselling to achieving lifetime revenue. Some organizations have entire departments dedicated to customer success; others have eliminated expensive, bloated, and underperforming success teams that have failed to move a needle that was never well established to begin with.

Many vendor organizations treat customer success programs as an offshoot of sales or service, a floating support service that may weave in and out of a particular customer to resolve issues, mitigate churn, or set the stage for a renewal. This doesn’t even begin to account for customer success programs that never truly start, because customers are not handed off between sales and success accurately or appropriately, are never onboarded in the first place, or have failed to fully adopt a solution.

In the end, the authors are left with their biggest question unanswered: “How do we fix customer success?”

Instead of fixing a broken machine, what if we decide to ask an entirely different question? If we could architect customer success from Moment 1, knowing the challenges and the pitfalls of where transformation success can lose focus and force, how would we purpose-build success to prioritize the needs of both the business and the technology buyer? Is there a way to turn success from a frustrating failure into a value driver?

Whereas some technology vendors have established success services as a standard offering intended to lengthen their customers’ lifetime value and increase retention and upselling, others have turned to a more premium offering that shifts the gaze of success from an internal driver of revenue for the vendor and works to center focus on the demands of the customer. In these programs, customer success delivers a deeper level of partnership for ongoing support and operational optimization. For organizations investing in complex technology environments, this opportunity to opt into premium support has created a call to reassess where and how to partner with their vendor ecosystem to recenter success on tangible business goals and operational demands.

The 3 Steps to Recentering Success

Step 1: Determine What Success Program Is Right for You and Your Business

Not all customer success programs are alike. Some are free programs based on enhanced service and support, purpose-built to accelerate implementation and adoption. For some solutions, this extra layer of success support is more security blanket than added hands—but that may be all you and your team need. Not every nail requires a heavy-duty extra-large hammer!

However, for complex business- and mission-critical systems such as enterprise resource planning (ERP), service management, enterprise content management (ECM), and enterprise asset management (EAM) or across customer experience (CX) platforms, customer success (and perhaps even more importantly, customer success strategy and planning) can be critical to outcome optimization. These customer success programs are likely longer in scope and have dedicated resources assigned to accounts.

Because customer success programs can vary in scope and vision, don’t assume that a program will be proactive in addressing needs. If you need a dedicated resource or an executive leader focused on data about implementation, utilization, support, and experience, speak up and ensure that the customer success program you are working with is proactive in nature and entrenched in data. Some are more reactive, laser-focused on customer happiness. Others are more data-driven, looking for cues and signals that could indicate that a challenge or issue is on the horizon.

There are also premium customer success programs that operate as on-demand resources for program rollout or flexible, on-call resources. These premium programs also often include significant program, goal, and resource planning and strategy sessions that a more reactive success program may be able to provide. A good example of this type of premium program is the customer success program offered by IFS, a global enterprise applications company best known for its ERP software. IFS’s premium offering actually kicks off before a contract is ever signed, establishing goals, needs, and business-value parameters with which both IFS and the customer align. The vendor’s vision is of a value-driven customer success program that is proactive in strategy but flexible and agile to be reactive to real-time business needs for resources and support. It focuses on the reality of business transformation demands to align success activities with real business outcomes and value.

Step 2: Look Beyond the SLA

For CX and operations leaders—especially those who have stood up complex, holistic, and fully integrated platforms that power critical operations of their business—the service-level agreement (SLA) is both a requirement and a potential pitfall. Time and again, SLAs overfocus on operational implementation benchmarks, which are, in turn, assigned achievement incentives or missed-mark penalties. The SLA focuses on getting to value quickly—delivering on the promise the given software or solution has provided. To be clear, the SLA and the ongoing customer success strategy should be two different, albeit connected, things.

Although SLAs are focused on benchmarks, they can often be templated and fairly standardized goals for operational implementation and project management. There is often an underfocus on business outcomes or key indicators of value. Teams focused on an SLA can lack a shared understanding of how the customer defines value and the outcomes that can make a lasting impact for the business. Alignment with key business stakeholders and business value prioritization are rarely SLA line items. In a purely SLA-driven success environment, it is easy to imagine that everyone involved is running a race with very different prizes in mind, meaning that this is often a race to a nonexistent finish line.

Success in a vacuum is simply not success: It is just a service agreement—elegant as it may be—and still in a vacuum. Outcomes evolve, especially those tied to ongoing business operations and growth. Technology in modern infrastructures aims to stay flexible, to have the ability to bend easily without breaking, in order to scale. When it comes to successful digital transformation initiatives, organizations need elasticity—the ability to stretch and shift under pressure yet return to their expected shape once the forces and pressures causing the shift are removed. Digital transformation has muscle memory that customer success and ongoing improvements and optimization should rely on to advance the key moments that matter.

Step 3: Be Realistic About Goals, Resources, and Priorities

It may sound obvious to say that a key to customer success for both the vendor and the customer is to set short-term goals with long-term visions. In reality, teams often do one or the other. This is where reality comes in: You need both and shouldn’t settle for choosing one over the other or be told that one will organically follow the other. It won’t.

Create a prioritized plan with near-term timelines and success markers and long-term outcomes and shared definitions that quantify success. By tracking priorities and goals, teams can quickly realize value in near-term priorities; report and even brag about those achievements; and instead of stalling or getting stuck in that afterglow of near-term wins, will be motivated to build on that success, even if priorities have shifted in response to business needs. With an outcomes mindset, stakeholders can focus on quick wins and track each win toward a more durable long-term transformation.

But reality also needs to be a constant partner in assessing everything from business goals to business resources. With any digital transformation effort, no matter how large or small, the reality may be that access to talent and resources is the primary challenge to moving forward effectively. Customer success programs where partnering organizations have ready access to on-call and on-demand talent help address this reality head-on without risking future success. An interesting example of this came to my attention with Associa, which happens to be the largest property management company in the world, with more than 14,000 team members in 275 branch offices across North America delivering management and lifestyle services to more than 6.5 million residents worldwide. Associa has a massive service remit that ranges from simple maintenance and repairs to larger-scale projects such as concrete and asphalt repairs.

As Associa made the transition to the cloud with the IFS Field Service Management platform, it needed rapid access to expertise to scale operations and address crisis situations. With a regular cadence of support and access to expertise, thanks to an ongoing premium success engagement, Associa could lean on IFS to be more proactive in establishing and optimizing business rules and hit the ground running to address issues. In one example, Associa experienced a significant performance degradation where billions in revenue processes had ground to a halt, creating a massive backlog and disrupting operations. IFS, being embedded in Associa’s operations and understanding the challenges, customizations, and complexities, quickly jumped in with experts and on-call business specialists who were able to rapidly identify legacy business rules that were at the root of the problem. Within a week, IFS specialists had resolved the issue and the backlog.

Conclusion: Success Isn’t a Destination

There is no last page of a customer success journey that reads “The End” and sends us on our merry way. Customer success is an ongoing process of proactive analysis and engagement that is more of a partnership than a vendor-to-customer sales contract. Customer success teams should—and can—be a vital part of digital transformation impact and outcomes. Many customer success programs focus on three core stages of a transformation initiative: strategy, planning, and execution. Others look beyond to create stages centered on the capacity to engineer business value where long-term strategy and short-term corrections can be integrated across all stages of any project, buoyed by the collective buy-in and support of senior business leaders and stakeholders.

Moreover, regardless of what type or style of customer success your technology partners may offer, customer success should be focused on long-term business success and not just near-term application achievements. Milestones are nice, but meaningful value-based metrics are better.

In an age of frenetic change and more than a few transformation and technology implementation failures, customers are looking for consistency, scalability, and focus. In the end, by adopting a posture of intentional outcome-driven success, customer success teams can help their customers fix their eyes firmly on the future: future innovations, future transformation, and future-proofed technology investments that drive tangible business value. Customers, with a shared vision of value and a clearly articulated transformation plan, can rest assured that their business value is the first priority.

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Intel Foundry sets roadmap, aims to be No. 2 foundry by 2030

Intel Foundry sets roadmap, aims to be No. 2 foundry by 2030

Intel CEO Pat Gelsinger launched Intel Foundry and a roadmap for process technology that'll manufacture artificial intelligence processors for internal and external customers. The bet is that Intel Foundry can be the manufacturer for a wide range of AI systems.

The goal is to be the No. 2 foundry by 2030. The lifetime value for Intel Foundry deals is $15 billion so far. For context, TSMC did $19.62 billion in net revenue in the fourth quarter. Intel executives argued that Intel Foundry’s systems approach will trump TSMC over time.

At IFS Direct Connect 2024, Gelsinger said "I want to manufacture every AI chip." To get there, Intel Foundry outlined a new roadmap with Intel 14A process technologies, advanced system assembly and test capabilities and a design win with Microsoft, which will manufacture a chip on Intel's 18A process.

In addition, Intel Foundry announced a series of partners including Synopsys, Cadence, Siemens and Ansys. The event features a series of speakers including ARM CEO Rene Haas and Open AI CEO Sam Altman.

The upshot to the Intel Foundry event was that Intel can enable a resilient semiconductor supply chain in the US. Gelsinger affirmed Intel's plan to launch five process nodes in four years.

Intel, which is lagging behind Nvidia in GPUs and accelerating computing and battling AMD on GPUs and CPUs, is betting that it can use its manufacturing heft to become a rival to TSMC. "There are only a few companies that can do this. That have the capital, capacity and resilience," said Gelsinger, who positioned Intel as a stabilizer amid economic and geopolitics turmoil.

Gelsinger said Intel plans to regain process leadership with Intel 18A in 2025.

Here's a look at Intel Foundry's roadmap.

Can Intel pull off its Intel Foundry plans? Certainly, Intel has a big customer in Microsoft. Microsoft CEO Satya Nadella said that the company has chosen a chip design that'll be produced on Intel's 18A process.

Intel also has electronic design automation (EDA) partners lined up with Synopsys, Cadence, Ansys and others.

The company will also work with Arm under a partnership where Intel Foundry will provide services for Arm-based system on chips.

For Gelsinger, the game plan is to expand Intel's reach into the total addressable market (TAM) for AI processors. He said:

"We're engaging in 100% of the AI TAM clearly through our products on the edge and PC and clients and then the data centers, but through our foundry. I want to manufacture every AI chip in the industry, Those being done by the cloud service providers, merchant providers and technology providers. We'll be the systems foundry for the AI era where semis are essential."

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Palo Alto Networks launches platform deals as it aims for cybersecurity share

Palo Alto Networks launches platform deals as it aims for cybersecurity share

Palo Alto Networks is changing its strategy to entice enterprises to spend on its security platform as customers consolidate security vendors. Palo Alto Networks' bet is to accelerate security vendor consolidation by offering platform deals with introductory offers and deferred payments to grab share.

When Palo Alto Networks reported its second quarter earnings it handily topped expectations. The outlook, however, was light. Palo Alto Networks projected fiscal 2024 billings between $10.1 billion to $10.2 billion, down from the earlier range of $10.7 billion to $10.8 billion. Revenue for fiscal 2024 will be between $7.95 billion and $8 billion, down from $8.15 billion to $8.2 billion.

The efforts to gain share will impact revenue growth for about 12 months to 18 months, but Palo Alto Networks CFO Dipak Golechha said the security vendor has internal expense levers to preserve operating margins. Rest assured that other cybersecurity platforms—namely CrowdStrike and Zscaler will offer enterprise deals and incentives too.

CEO Nikesh Arora said customers are seeing spending fatigue and optimizing security budgets to see returns on investment.

Here's Arora's assessment of enterprise security spending on Palo Alto Networks' earnings call.

"Most of our customers have ended up spending more on cybersecurity than they wanted to. As a consequence, they're feeling like the budget for cybersecurity keeps going up in double digits every year and you see the number of breaches continue to rise. So, customers are sitting down and saying if I spend more money, then show me how I get a lower total cost of ownership across my enterprise. How do I spend less on the services that I have to deploy? How do I get better ROI? It's more about optimizing their current cybersecurity budgets. Demand continues to be very strong, but customers are demanding more for the money allocated to cybersecurity. That's where platform consolidation kicks in."

Palo Alto Networks said it will offer programs to trade in legacy vendors, no cost deals, introductory offers and product add-ons and incentives to convince enterprises to standardize on Palo Alto Networks.

Arora said the programs and offers are designed to address customers' economic concerns and Palo Alto Networks "must bear the cost of the transition to lower upfront financial outcomes."

As a result, customers entering a platform transaction won't pay Palo Alto Networks for a time period. Over time, customers will migrate to full billing and revenue contribution.

"We believe we can sustain high growth rates for longer aspire to by sticky and broad platform relationships and incremental customers, allowing us to aspire the goal of $15 billion in next generation security in 2030," said Arora.

He added that as Palo Alto Networks puts more customers on its platform it'll gain as AI gains as a use case for cybersecurity in the years ahead. said Golechha. "Once a customer deploys the platform, it's easier to continue to consolidate vendors and adopt new innovations. We expect this to drive a significant increase in our overall revenue mix that is occurring," said Golechha.

For the second quarter ending Jan. 31, Palo Alto Networks had non-GAAP earnings of $1.46 a share on revenue of $2 billion. Wall Street was looking for earnings of $1.30 a share on revenue of $1.97 billion.

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Hitachi Vantara names HPE alum CTO

Hitachi Vantara names HPE alum CTO

Hitachi Vantara named Ayman Abouelwafa as its new chief technology officer as it continues to build out its executive team.

Abouelwafa, who was previously CTO of Hewlett Packard Enterprise's storage group, lands at Hitachi Vantara just weeks after the hybrid cloud infrastructure company named Octavian Tanase as chief product officer. Tanase was previously at NetApp.

Like Tanase, Abouelwafa will report to Hitachi Vantara CEO Shiela Rohra. Abouelwafa will also serve on Hitachi Vantara's executive committee. In a statement, Rohra said Abouelwafa will bring a deep understanding of emerging technologies and market trends as Hitachi Vantara aims to become a leading hybrid cloud company.

At HPE, Abouelwafa was responsible for commercializing next-generation data storage platforms and software as well as strategic research.

Here's more on Hitachi Vantara, a Constellation Insights underwriter.  

 

 

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