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The Cookiepocalypse Has Been Cancelled: What’s Next?

The Cookiepocalypse Has Been Cancelled: What’s Next?

Google has canceled its cancellation of the third-party cookie. Advertisers: You may resume your baking. Marketers: Hang back a minute…we’ve got something to chat about.

Third-party cookies are stuck to us like we forgot to use the Silpat before popping the last batch into the oven. After all these years of fretting over a cookie-less tomorrow, we are now stuck with them. Or is this an opportunity to right size the cookie into a more appropriate position in the overarching world of marketing and experience? Can we finally stop following which way the cookie could crumble?

For years now we have rethought and rearchitected our data strategies to more intentionally and directly amass a better understanding and record of our customer. And let’s be clear…this record is so much more than the transactional history we once coveted in CRM and extends beyond the golden profile in our CDPs.

We didn’t just think about a cookie-less world, we architected how to deliver more robust, profitable and creative worlds where third-party cookies got put in their place. That’s not to say we banished every cookie. In fact, those first-party cookies started to taste a bit sweeter. Instead of stalking our prey across the Serengeti we call the internet, we got smaller, more focused and more intentional to understand what our customer did with us. We focused our gaze to settle on “our” crowd instead of watching the whole crowd. We enriched and expanded strategy on creating moments for customers to share their preferences. We rewarded them with going above and beyond those volunteered requests with consistency of purpose and respect of relationship.

We stopped talking about if we were too close to the creepy line. In fact, we banished creepy to the relief of our customer. We got back to the business of marketing and the opportunity of experience to purposefully craft durable, profitable relationships with our customers. And now, thanks to advancements in AI and more pointedly with GenerativeAI, we can now apply customer data in new and even more creative, contextual and personalized ways to advance those hard-won relationships.

So why go backwards? Why lean back into the thrill of the third-party cookie?

I would argue we have plenty of reason to be glad that advertising teams have access to tried and true tools to bring precision and accountability to the table. The third-party cookie, when used responsibly and in line with overarching experience and relationship building strategies are a great tool. But can we afford to revert to bad behavior, tracking for tracking sake?

Instead of moving backwards, let’s spark these conversations instead:

Rightsizing the Third-Party Cookie: This is the first line in the sand. For today’s CMO, this is where we say that the third-party cookie is a tool in the advertising tool kit. It is NOT the backbone of marketing or experience strategy. We built its replacement and should continue along that path. The reintroduction of the third-party cookie should be rightsized into the tool and tactic for optimization and visibility that it is. It is one signal source of many. And don’t assume this is just a conversation for B2C marketers…you B2B leaders can’t afford to allow ABM to return to the land of retargeted advertising as its one-trick pony. Account based advertising can rejoice, but account-based marketing needs to continue to advance the analytics and intelligence around account-based buying and influence centers to create those impact and growth opportunities that true ABM can deliver. Stay the course.

Draw Clear Lines in the Privacy Sand: In this cookie-rich future, it will be critical to fully understand the difference between privacy and safety. Privacy puts the control of engagement and the value exchange of customer data in the hands of the customer. It expects that a brand will do more with less data about the customer. Safety is the promise that the data that is exchanged is secure and used responsibly and to the benefit of both parties involved in the exchange. With a focus on the first-party data we now rely upon to have first party relationships with our customers (aka the second party), the third-party cookie becomes a responsibility we must maintain and respect to stay within Digital Safety boundaries. For those striving for true digital privacy as a strategy, we may need to make the hard call to do away with the third-party cookie all together. No matter your brand’s decision, make it, state it and then stick with it, repeatedly proving your dedication to that pact consistently and over time.

Never Be Stuck with a Single Point of Panic Again: If there is any lesson I hope we all come out of this with is that regardless if you ever used a third-party cookie in your advertising or marketing initiatives, the proposed crumbling of the cookie sent our industry into shock. Products and whole companies have been lost to this chaos. The panic-driven campaigns to hype the horror of a cookieless tomorrow remain seared in many minds. For some, that capacity to track across the web was their only view into the behaviors and journey of their customer…without it they felt blind. In this age of data, automation and AI, we can’t afford to have a cookie be our definition of the customer. We can’t afford a single point of panic while we are supposed to be architecting growth.

So…go ahead. Go bake a batch of cookies. Mix up the flavors. But don’t forget that the marketing and experience toolkit is far more diverse than the cookie-monsters of the past several years would want us to remember. We have built that depth of first-party understanding and knowledge. We don’t have to revert or settle back into bad behavior. Cookies are delicious…but bake them in moderation…and choose wisely when you decide to dine on them before your actual meal.

 

 

 

Image Credit: Image has been AI generated using Adobe Firefly Image 3 model and further edited in Adobe Express. 

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AMD: Strong Q2, raised Q3 guidance, data center revenue doubles

AMD: Strong Q2, raised Q3 guidance, data center revenue doubles

AMD's second quarter was paced by $2.8 billion in data center revenue, up 115% from a year ago.

The chipmaker, which is chasing Nvidia in GPUs and accelerated computing, reported second quarter net income of $265 million, or 16 cents a share, on revenue of $5.8 billion, up 9% from a year ago. Non-GAAP earning in the second quarter was 69 cents a share.

Wall Street was expecting AMD to report second quarter earnings of 68 cents a share on revenue of $5.72 billion.

In a statement, AMD CEO Lisa Su said company saw record data center revenue in the second quarter. Su added:

"Our AI business continued accelerating and we are well positioned to deliver strong revenue growth in the second half of the year led by demand for Instinct, EPYC and Ryzen processors. The rapid advances in generative AI are driving demand for more compute in every market."

As for the outlook, AMD said it expects revenue of $6.7 billion, give or take $300 million. For the third quarter Wall Street analysts were modeling earnings of 94 cents a share on revenue of $6.61 billion.

Data center revenue in the second quarter accounted for the bulk of AMD's operating income followed by embedded. 

By the numbers for the second quarter:

  • AMD reported client revenue of $1.5 billion, up 49% from a year ago. Demand was driven by AMD Ryzen processors. 
  • Gaming revenue fell 59% in the second quarter from a year ago to $648 million. 
  • Embedded revenue in the quarter was $861 million, down 41% from a year ago.
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Microsoft Q4 strong, Azure growth 29% and a bit light

Microsoft Q4 strong, Azure growth 29% and a bit light

Microsoft delivered a better-than-expected fourth quarter and said Azure revenue growth was up 29%.

The company reported fourth quarter net income of $22 billion, or $2.95 a share, on revenue of $64.7 billion, up 15% from a year ago.

Wall Street was expecting Microsoft to report fourth quarter earnings of $2.93 a share on revenue of $64.39 billion. Microsoft’s cloud business was expected to show fourth quarter growth of about 30%.

Microsoft CEO said the company is focused on meeting at-scale workloads for AI. CFO Amy Hood noted that Microsoft Cloud revenue in the quarter was $36.8 billion, up 21% from a year ago.

For fiscal 2024, Microsoft reported net income of $88.1 billion, or $11.80 a share, on revenue of $245.1 billion, up 16% from a year ago.

By the fourth quarter numbers:

  • Intelligent Cloud operating income was $12.86 billion followed by Productivity and Business Processes operating income of $10.14 billion. 
  • More Personal Computing operating income was $4.92 billion. 
  • Office Commercial revenue ws up 12% and Office Consumer revenue was up 3%.
  • LinkedIn revenue was up 10%. 
  • Dynamics 365 revenue growth was 19%.
  • Windows revenue was up 7%. 

Microsoft said first quarter revenue will be between $63.8 billion and $64.8 billion. 

Takeaways from the conference call include:

  • Microsoft is adding Azure AI customers, but remains capacity constrained. Nadella said: "We now have over 60,000 Azure AI customers, up nearly 60% year-over-year, and average spend per customer continues to grow."
  • Models as a service customers doubled sequentially in the fourth quarter. 
  • Copilot accounted for 40% of GitHub's revenue growth in the fourth quarter. Nadella said Copilot was driving growth across the portfolio. He said: "Copilot customers increased more than 60% quarter-over-quarter. Feedback has been positive, with majority of enterprise customers coming back to purchase more seats. All-up, the number of customers with more than 10,000 seats more than doubled quarter-over-quarter, including Capital Group, Disney, Dow, Kyndryl, Novartis. And EY alone will deploy Copilot to 150,000 of its employees."
  • Capital expenditures will continue to grow for the AI buildout. Hood said: "Capital expenditures including finance leases were $19 billion, in line with expectations, and cash paid for PP&E was $13.9 billion. Cloud and AI related spend represents nearly all of total capital expenditures. Within that, roughly half is for infrastructure needs where we continue to build and lease datacenters that will support monetization over the next 15 years and beyond. The remaining cloud and AI related spend is primarily for servers, both CPUs and GPUs, to serve customers based on demand signals. For the full fiscal year, the mix of our cloud and AI related spend was similar to Q4."
  • The genAI payoff will take time for Microsoft and will drive growth across the product line, said Nadella. He added: "At the end of the day, GenAI is just software. So it is really translating into fundamentally growth on what has been our M365 SaaS offering with a newer offering that is the Copilot SaaS offering, which today is on a growth rate that's faster than any other previous generation of software we launched as a suite in M365. That's, I think, the best way to describe it."

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Why enterprise, process workflows are the new battleground

Why enterprise, process workflows are the new battleground

Enterprise workflows are quickly becoming the new battleground for tech vendors as providers that have the customer data race to ensure they don't merely become systems of record.

In just a few hours last week, the importance of enterprise workflows and streamlining processes was laid out.

First, Salesforce and Workday unified their data foundations in a move that will make it seamless to connect financial, HR and customer data within Data Cloud. The companies didn't mention workflows directly, but it's clear they wanted to secure their data advantage and be that workflow engine.

A quote from Sal Companieh, Chief Digital and Information Officer, Cushman & Wakefield, indicated that the Salesforce-Workday pact was as defensive as it was about customer value. “The ability to streamline workflows across two of our most used platforms – Workday and Salesforce – and deliver more personalized AI-powered employee experiences will be a game changer for us," said Companieh.

I read that quote to mean that ServiceNow was becoming a pain in HR and CRM with its platform that's aimed at genAI, process and enterprise workflows. When ServiceNow reported its second quarter earnings that threat to category incumbents became clear.

ServiceNow CEO Bill McDermott talked about manufacturing workflows, efficiency and AI. He talked CPG. And the ServiceNow vision applies to any industry. Talking about CPG, McDermott said:

"Think about consumer goods. They want AI-powered chatbots to deliver personalized shopping experiences. And just think about your own shopping experience. You could buy a great product, but if you can't return it in a streamlined way. You drop the brand. You're not doing business with them anymore. So, it's a virtuous cycle to think about the quality of the product, the service experience of the customer and ultimately advocating for the customer and giving them what they want, that's all workflow, and we're going to automate that entire industry. And so rethink health care, rethink manufacturing, rethink utilities, you rethink consumer goods. We're going for it all."

Indeed, ServiceNow is selling its genAI NowAssist at a rapid clip. ServiceNow is becoming included as an automation platform across systems. ServiceNow is landing more deals valued at more than $1 million ACV and its workflow businesses across security, IT service management, operations and customer and employee experiences are all faring well.

What remains to be seen is whether the enterprise software vendors that have the customer data can defend against a ServiceNow coming over the top.

The final data point in the workflow machinations was ServiceNow's partnership with Boomi. Boomi has done a nice job of expanding its iPaaS to becoming an AI-driven API platform and enterprise connector. Boomi will be to ServiceNow what MuleSoft will be in the Salesforce and Workday partnership.

As noted previously, generative AI can become the interface that rewires enterprise software. There's an argument that automation and workflows will be in that mix too.

 

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The generative AI buildout, overcapacity and what history tells us

The generative AI buildout, overcapacity and what history tells us

The spending on generative AI infrastructure is accelerating at a breakneck pace, but it's quite possible that the "build it and they will come" approach may lead to overcapacity or some serious indigestion.

That is the argument from MIT's Daron Acemoglu and Goldman Sachs Research's Jim Covello. In a recent Goldman Sachs podcast, the two were skeptical about whether the $1 trillion expected to be spent on AI capex is going to pay off. And there are some incremental items that point to more skepticism about the AI buildout.

Overcapacity ahead?

Covello noted that the internet buildout was huge and led to multiple companies and services like Uber that arrived later. The issue is that this buildout didn't pay off big for about 30 years. "It ends badly when you build things the world is not ready for," said Covello. "When you wind up with a whole bunch of capacity because you build something that isn't going to get utilize it takes a while to grow into that supply."

He said: "One of the biggest lessons I've learned over 25 years here is bubbles take a long time to burst. So, the build of this could go on a long time before we see any kind of manifestation of the problem. I'm very respectful of how long they can go on."

Clearly, the AI buildout is underway. Alphabet, Amazon Web Services, Oracle and Microsoft are all spending billions to build genAI capacity. Companies like OpenAI and Anthropic are also spending. Most of the genAI infrastructure profits are going to Nvidia for now.

This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly and is brought to you by Hitachi Vantara.

This buildout could lead to indigestion should genAI fail to deliver the returns. "Very few companies are actually saving any money at all doing this," said Covello. "How long do we have to go before people start to really question?"

To hear Covello tell it, we're clearly in the FOMO stage with genAI. It's likely that genAI won't fizzle like the metaverse, but you never know.

For now, genAI infrastructure spending isn't going to ease. Alphabet CEO Sundar Pichai crystallized the FOMO-fueled AI infrastructure boom when he answered a question about the company's genAI capital spending. Speaking on Alphabet’s second quarter earnings call, Pichai said:

"We are at an early stage of what I view as a very transformative era. When we go through a curve like this, the risk of under-investing is dramatically greater than the risk of over-investing. Even in scenarios where it turns out that we are over-investing the infrastructure is widely useful for us. I think not investing to be at the frontier definitely has much more significant downside. Having said that, we obsess around every dollar we put in. Our teams work super hard and I'm proud of the efficiency work, be it optimization of hardware, software, model deployment across our fleet."

Covello said: "AI is pie in the sky big picture. If you build it, they will come, just you got to trust this because technology always evolves and we're a couple of years into this. And there's not a single thing that this has been used for that's cost effective at this point. I think there's a unbelievable misunderstanding of what the technology can do today."

The fundamental issue with genAI is that the buildout starts from a different point. E-commerce started out cheaper. Mobile turned out to be cheaper. The internet made everything cheaper. "With AI, you're starting from a very high-cost base. I think there's a lot of revisionist history about how things start expensive and get cheaper. Nobody started with a trillion dollars," said Covello.

GenAI can get cheaper, but first Nvidia needs real competition. "Why is AI so expensive? It's really the GPU costs," said Covello. "I think the big determination in whether AI costs ever become affordable is whether there are other players that can come in and provide chips alongside Nvidia."

The economics

Acemoglu recently penned a research paper that questioned the economic benefit from genAI. Acemoglu said genAI economic prognostications depend on how quickly the technology can be integrated into an organization. Some genAI projects will boost productivity. Other companies will find that genAI was a waste of time and money.

The big issue is time horizon. Acemoglu said most genAI economic prognostications, which range from a 1.5% to 3.4% boost to average annual GDP over the next decade, have too many uncertainties. His paper concludes that AI and productivity improvements at the task level will increase total factor productivity by 0.71% over 10 years. That amount is nontrivial but too modest to justify the genAI building boom today. You can find plenty of folks who disagree here, here and here.

Acemoglu said: "I think economic theory actually puts a lot of discipline on how some of these effects can work once we leave out those things like amazing new products coming online, something much better than silicon coming, for example, in five years. That's good. That happens. All right, that's big. But once you leave those out, the way that you're going to get productivity effects is you look at what fraction of the things that we do in the production process are impacted and how that impact is going to change our productivity or reduce our costs."

Acemoglu is that genAI isn't going to be big enough to replace humans in the short run. Transport, manufacturing, utilities and other industries have people interacting with the real world. GenAI can offload some of that work, but not much in the next few years. Pure mental tasks can be affected and that work replaced isn't trivial but it's not huge, he said.

In 10 years, genAI may be cost effective and move GDP, but a lot has to happen first. Acemoglu said: "I am less convinced that we're going to get there very quickly by just throwing more GPU capacity. Any estimate of what can be achieved with an anytime horizon is going to be uncertain. There's a view among some people in the industry that you double the amount of data, you double the amount of compute capacity, same number of GPU units or their processing power, and you're going to double the capacities of the AI model."

There are a few issues with scaling genAI and driving this alleged productivity boom. For starters, it's unclear what doubling capabilities of genAI will do in economic terms. Meanwhile, more data doesn't matter if it doesn't improve predictions and help you solve problems. It's also not clear whether data or compute will be cheap enough to really move productivity.

Acemoglu said: "I think there is the possibility that there could be very severe limits to where we can go with the current architecture. Human cognition doesn't just rely on a single mode. It involves many different types of cognitive processes, different types of sensory inputs, different types of reasoning. The current architecture of large language models has proven to be more impressive than many people would have predicted, but I think it still takes a big leap of faith. There are all the sorts of uncertainties."

In the end, genAI's march to super intelligence and a productivity boom depends on time horizon. Acemoglu said.

A Sequoia report makes you go hmm

In June, venture capital firm Sequoia chimed in on the AI buildout. Sequoia partner David Cahn noted the big gap between revenue expectations implied by the AI infrastructure buildout and actual revenue growth, which Cahn used as proxy for end user value.

Cahn first raised the issue in Sept. 2023, but updated his argument since conditions have changed. He noted that the supply shortage for GPUs has been "almost entirely eliminated."

In addition, GPU stockpiles at large cloud providers is growing. If these stockpiles grow enough demand will decrease--and so will Nvidia's valuation. Cahn's other concern is that most of the direct AI revenue is going to OpenAI. Microsoft, Google, Apple and Meta need a big leap in direct AI revenue. Here’s a look at Sequoia’s payback figures.

Cahn said: "Speculative frenzies are part of technology, and so they are not something to be afraid of. Those who remain level-headed through this moment have the chance to build extremely important companies. But we need to make sure not to believe in the delusion that has now spread from Silicon Valley to the rest of the country, and indeed the world. That delusion says that we’re all going to get rich quick, because AGI is coming tomorrow, and we all need to stockpile the only valuable resource, which is GPUs."

My take

First, this topic has been on my mind for a few months. The data center buildout, machinations of bitcoin miners suddenly pivoting to AI with a new narrative and the lack of trickle-down economics beyond Nvidia have given me pause. After all, I've seen this movie before with the dotcom boom and bust, 2008 financial crisis, the COVID-19 pandemic component panic and current housing bubble.

All of these bubble cycles include a gold rush that leads to too much capacity. This capacity usually pans out in the long run, but squeezes investors that aren't first movers.

Nvidia is priced for perfection and it's only logical that there will be some indigestion ahead as the genAI supply chain starts optimizing for costs. There's a rush to buy GPUs now, but you can only build data centers so fast. Nvidia GPUs have become their own asset class, but competition looms.

The big genAI spenders are touting massive capex budgets that may not be tolerated by Wall Street forever. This spending is fine until sales growth and/or productivity gains slow. Spending on genAI will always work--until it doesn't.

And perhaps the biggest reason to play contrarian about the genAI boom. I've heard cashiers, grandparents and randoms on the street all talking about Nvidia and genAI--and I don't live in Silicon Valley.

In the end, it's worth paying attention to these genAI contrarians but keep in mind that bubbles last longer than you think. Also keep in mind no one rings a bell at the top, but there are signs worth watching.

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7 takeaways on emerging trends from ARX 2024

7 takeaways on emerging trends from ARX 2024

Constellation Research analysts outlined emerging trends for the second half of 2024 at ARX 2024. Here's a look at the themes that were surfaced.

Constellation Research CEO Ray Wang outlined the following macro economic backdrop influencing enterprise technology buying decisions.

  • Business decisions are on hold due to the election cycle, interest rates and the need to push for exponential efficiency.
  • Companies are wrestling with AI arbitrage and when it makes sense to insert humans into automated processes.
  • Vendors will need to enable either 10x growth improvement for enterprises or be able to deliver at 1/10th of the cost. "You'll have to be either better or cheaper," said Wang. As a result, there will be some interesting duels between vendors trying to preserve margins and those enabling margin compression and value.

Themes to watch across the Constellation Research coverage areas.

Growth operations are becoming critical to AI strategies. Constellation Research analyst Martin Schneider said sales automation, revenue operations and customer success teams are converging into a growth ops org. AI is breaking down silos between those teams and there's a need for enterprises to forge a holistic strategy for growth operations. "Chief growth officers will emerge to drive holistic strategy," said Schneider. Chief growth officers will be more than chief revenue officers and sales leaders. There will be a more holistic view.

Cybersecurity will be about resilience and response instead of prevention, said Constellation Research analyst Chirag Mehta. The cybersecurity stories and best practices that emerge will revolve around how enterprises bounce back from data breaches. You're not going to avoid them.

Infinite computing and AI will enable new opportunities. Constellation Research analyst Holger Mueller said that infinite computing has become a reality now and genAI is democratizing data and making sense of it. Data contained in documents and transactional systems will enable new processes and expand what enterprises can do. "We'll move from infinite computing to infinite deep learning and automation," said Mueller.

The intersection between CX and AI is going to surface a host of issues including data droughts, personalization and a bunch of trust concerns, said Constellation Research analyst Liz Miller. Privacy regulation will also be a big issue as AI and CX converge. "CX will be a proving ground for AI and where the business meets the customer," said Miller.

There's a buy vs. build debate emerging around generative AI. As genAI is operationalized, enterprises are focusing on industry use cases and verticalization. It's currently unclear whether vendors will be able to go vertical or whether enterprises will build. The other wrinkle to ponder is vendor pricing models as generative AI goes vertical, according to Constellation Research analyst Andy Thurai, who noted value-based and usage pricing will be in the mix.

Data platform vendors have invested heavily in AI and they can't pull back even though they're not necessarily seeing a payoff, said Constellation Research Doug Henschen. "Vendors are way ahead of what customers are doing," said Henschen.

Open vs. closed models. As enterprises develop their data and AI strategies one key debate will revolve around open-source large language models and proprietary offerings. If AI is to be democratized, the industry has to move toward an open system.

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Salesforce’s State of Sales Survey Reveals Roadblocks Still Remain with AI: Can RevOps be the Heroes?

Salesforce’s State of Sales Survey Reveals Roadblocks Still Remain with AI: Can RevOps be the Heroes?

Salesforce released its annual “State of Sales” survey this week. The company surveys more than 5,500 sales professionals each year from around the world to gain insights on the pain points and opportunities facing sellers. This year, as in recent years, AI dominates the headspace, but other positive surprises were revealed as well. 

The most positive news was that 79% of respondents said sales have increased over last year. As we fully cycle out of supply chain and other post-pandemic issues, this is not a huge surprise, but positive nonetheless. Challenges still remain for sellers, as they cited changing customer needs and expectations, competition with other businesses, lingering supply chain issues, macroeconomic conditions, and inadequate or ineffective tools/technology as their biggest barriers to success.

Perhaps less surprising was the fact that while 81% of respondents claim to be using AI in some form today - sellers still cited that up to 70% of their time is spent on non-selling activities. There seems to be a lag between AI becoming the productivity booster vendors are claiming it to be. 

Lack of budget, headcount, and training to effectively implement AI were the main reasons cited by those not seeing strong returns from AI investment to date. Nearly a third of RevOps professionals also have concerns about data security, completeness, and accuracy. The same amount expressed concerns about having sufficient human oversight of AI — for example, monitoring AI outputs to ensure they’re correct. RevOps respondents also pointed to customer distrust as a common obstacle they've faced while implementing AI. Only 55% of business buyers trust AI to be as accurate as a human, according to survey results.

While AI may not be a silver bullet, the survey did note that 83% of sales teams with AI saw revenue growth in the past year — versus 66% of teams without AI. 

For those looking to improve upon existing AI investment, or just getting started - RevOps teams have the ability to take a more strategic and phased approach to where AI should be implemented. They can work with IT to provide both the training and guardrails that improve usage, effectiveness and security. RevOps leaders need to be a critical stakeholder when building out strategy, evaluating technology, and providing effective rollouts of AI to sales people as part of a larger enablement initiative. 

 

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IBM's Q2 led by software revenue

IBM's Q2 led by software revenue

IBM reported a better-than-expected second quarter fueled by software revenue growth.

Big Blue reported second quarter net income of $1.8 billion, or $1.96 a share, on revenue of $15.8 billion, up 2% from a year ago. Non-GAAP earnings were $2.43 a share.

Wall Street was looking for IBM to report second quarter earnings of $2.17 a share on revenue of $15.62 billion.

As for the outlook, IBM projected annual revenue growth in the mid-single digit range with free cash flow topping $12 billion.

By the numbers for the second quarter:

  • Software revenue was $6.7 billion, up 7%. Red Hat revenue was up 7% with automation sales growth of 15%. Data and AI revenue in the quarter fell 3%.
  • Consulting revenue was $5.2 billion, down 1% from a year ago.
  • Infrastructure revenue was $3.6 billion, up 0.7% from a year ago. IBM Z revenue was up 6%.

IBM CEO Arvind Krishna said the company saw strength in hybrid cloud demand and software.

"Technology spending remains robust as it continues to serve as a key competitive advantage allowing businesses to scale, drive efficiencies and fuel growth. As we stated last quarter, factors such as interest rates and inflation impacted timing of decision making and discretionary spend in consulting. Overall, we remain confident in the positive macro-outlook for technology spending but acknowledge this impact."

Krishna added that watsonx and IBM's generative AI has been infused across its business. He said genAI has been used in consulting, Red Hat and even IBMz. IBM is also focusing on offering models suited to enterprises. 

He said:

"Choosing the right AI model is crucial for success in scaling AI. While large general-purpose models are great for starting on AI use cases, clients are finding that smaller models are essential for cost effective AI strategies. Smaller models are also much easier to customize and tune. IBM's Granite models, ranging from 3 billion to 34 billion parameters and trained on 116 programming languages, consistently achieve top performance for a variety of coding tasks. To put cost in perspective, these fit-for-purpose models can be approximately 90% less expensive than large models."

IBM's book of business related to generative AI is now $2 billion inception to date. 

Tech Optimization IBM Chief Information Officer

ServiceNow Q2 strong, Desai out

ServiceNow Q2 strong, Desai out

ServiceNow reported better-than-expected second quarter earnings and announced that president and chief product officer CJ Desai will leave the company after an internal investigation.

Long-time ServiceNow executive Chris Bedl will serve as interim chief product officer. Bedl had previously served as Chief Digital Information Officer and Chief Customer Officer.

Here's what ServiceNow had to say about Desai's departure and its internal investigation that stemmed from an employee complaint:

"As a result of the investigation, the Company’s Board of Directors determined Company policy was violated regarding the hiring of the former Chief Information Officer of the U.S. Army. As such, the hired individual, who led the company’s public sector thought leadership and business development efforts since March 2023, departed the company. In addition, the Company and CJ Desai, President and Chief Operating Officer, came to a mutual agreement that Desai would resign from all positions with the Company effective immediately. The company believes this was an isolated incident."

ServiceNow reported second-quarter earnings per share of $3.13 a share on revenue of $2.627 billion, up 22% from a year ago. Wall Street was expecting second quarter earnings of $2.84 a share on revenue of $2.61 billion.

As for the outlook, ServiceNow said third quarter subscription revenue will be between $2.66 billion to $2.665 billion, up about 20%. For 2024, ServiceNow said subscription revenue will be between $10.57 billion to $10.58 billion, up 22%.

ServiceNow earlier in the day announced the acquisition of Raytion, a genAI search tool that will be integrated into the Now Platform. Boomi and ServiceNow also formed a strategic partnership that will blend Boomi's application programming interface management and automation platform with ServiceNow's Now Platform.  In addition, Salesforce and Workday said it will combine data to enable employee workflows in a move aimed at ServiceNow. 

Speaking on an earnings conference call, ServiceNow CEO Bill McDermott said the company has signed 11 NowAssist deals more than $1 million in ACV. "Enterprises are investing in business transformation. They are investing in AI. They are building a new reference architecture for the decades to come. This is the largest, most compelling business opportunity in the world. We are bullish on what's ahead," said McDermott.

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Salesforce, Workday form unified data foundation aimed at employee workflows

Salesforce, Workday form unified data foundation aimed at employee workflows

Salesforce and Workday formed a strategic partnership that revolves around a unified data foundation that connects Workday financial and HR data with Salesforce CRM data to streamline workflows.

With the move, Workday and Salesforce are combining forces to deliver an AI-powered assistant for employee use cases that include onboarding, health benefits and career development.

The upshot here is that the Salesforce and Workday partnership adds seamless integrations across the platforms to extend into workflows. ServiceNow has become an increasing threat to HR and CRM use cases championed by Workday and Salesforce.

Constellation Research CEO Ray Wang said the integration of business processes and data is critical to CxOs. "The shared data foundation between ;Workday and Salesforce will enable these partners to deliver AI capabilities that could completely transform the employee experience," said Wang. Wang added:

"Organizations are having a tough time bringing all their datasets from multiple systems into one place.  Workday and Salesforce are often in the same organization and represent a large bucket of data that will be needed for AI. Executives want to ask how many FTE's do they have in an area and whether or not they should add more people in sales lead generation or marketing. To do that, the data and the process have to come togeether in one place."

Here are the moving parts of the Salesforce and Workday partnership:

  • The partnership combines Salesforce's Agentforce Platform with Einstein AI with Workday AI and the platform.
  • The AI agent will be powered by the unified data foundation and natural language. The AI employee service agent will run on Einstein 1 Platform and Workday AI.
  • According to the companies, the AI employee service agent will use LLMs built on the common data platform, which is built on Salesforce Data Cloud.
  • Salesforce and Workday said the combination will enable employees to take action and automate tasks.
  • Workday will be natively integrated inside Slack.
  • The partnership is aimed at boosting productivity for joint customers.

Salesforce CEO Marc Benioff said the partnership with Workday "to jointly build an employee service agent" will enable employees to "get answers, learn new skills, solve problems and take action quickly and efficiently."

Carl Eschenbach, CEO of Workday, added that the partnership will boost employee experiences with genAI. For employers, the common Salesforce and Workday data layer will improve workforce planning, financial planning and sales enablement.

 

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