Salesforce raises list prices across clouds by about 9%

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Salesforce is raising prices across its clouds starting in August.

In a blog post, Salesforce said it will be increasing list prices about an average of 9% across Sales Cloud, Service Cloud, Marketing Cloud, its industry clouds and Tableau.

The news was good for Salesforce shares as the company has cut costs, kept customers and is now raising prices.

Salesforce noted that the company's last list price increase was 7 years ago. The primary argument for the price increase is that Salesforce has been rolling out new innovations around generative AI. Recent headlines include the launch of AI Cloud, Einstein GPT, Sales and Service GPT as well as other updates.

• Salesforce rolls out Sales GPT, Service GPT
• What Movies Get Wrong…and Salesforce Gets Right…About AI
• Salesforce launches AI Cloud, aims to be abstraction layer between corporate data, generative AI models
• Salesforce launches Marketing GPT, Commerce GPT, aims to connect generative AI to ROI
• Salesforce Q1 better than expected, margins improve

According to Salesforce, Professional Edition list prices will increase $5 to $80, Enterprise Edition will jump $15 to $165 and Unlimited Edition will go for $330, up $30. Similar increases will be implemented across Industries, Marketing Cloud Engagement and Account Engagement, CRM Analytics and Tableau.

Constellation Research CEO Ray Wang said:

"SaaS pricing was supposed to get cheaper with time and Salesforce was the standard bearer. 

Unfortunately customers now pay for licenses before implementation, they over buy licenses and can’t reduce them, and now they are subject to vendor lock-in and price increases despite record profits by the cloud providers. 

Customers should ban together and reject these price increases before it's too late.

Salesforce had amazing profits and customers entrusted Salesforce to achieve economies of scale and past cost savings to the customer not hold them in vendor lock-in."

While many enterprises have multi-year contracts and discounts across multiple clouds, Salesforce's increases will add up when it's time to renew.

Salesforce has said that 20% of its customers have more than 4 clouds.

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A look at the surprises of 2023 so far...

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly. 

We're careening into the second half of the year and it's always worth a bit of reflection. What stood out for me were the surprises.

Here's a quick look.

?? The private cloud is kind of cool now. You've seen the cloud providers all report slowing growth (except for Oracle coming off a smaller base), but the real surprise was that CIOs were thinking a lot more about the private cloud. Dion Hinchcliffe's research report on the private cloud's newfound popularity highlighted how platforms like HPE Greenlake were garnering demand. In a nutshell, public cloud providers haven't been passing on savings and encouraging enterprises to move workloads such as AI on-premise.

🤖 The intensity of the generative AI theme. Exiting 2022, it was clear that OpenAI captured lightning in a bottle. What wasn't clear: Generative AI euphoria would fuel the stock market, revive tech stocks and create a groundswell of press releases and product rollouts. When it comes to generative AI, the technology sector is taking a "build it and they will come" approach. Nvidia is the biggest winner in the generative AI buildout, but the wealth is starting to spread around. Enterprise tech buyers remain cautiously optimistic about generative AI, but acknowledge the potential risks too.

🏁 Big vendors move fast. In technology lore, the storyline is usually one that revolves around a startup upending a sleeping giant. Generative AI is an area that's highlighting how fast the giants are moving. Microsoft caught Google off guard and the latter rallied after a rough start. Salesforce outlined a generative AI roadmap in a hurry as did a bevy of other vendors. Quantum computing is being driven by giants too. Bottom line: It's way harder to sneak up on a giant today.

📉 The recession never happened. When the calendar turned to January, there was a wide consensus that the economy was going to struggle. Interest rates were surging, layoffs hit key sectors like technology and CFOs were hitting the brakes. Instead, inflation cooled a smidge, earnings weren't as horrible as expected and technology vendors saw stable demand. We're not out of the economic woods yet, but the first half didn't produce the slowdown expected.

👩🏽?💻 The future of work isn't the present of work yet. I can't believe we're still debating the all-in-office approach vs. the all-remote approach. Like everything in life, the extreme cases are the few and the middle is the many. Downtown isn't booming, commercial real estate is a mess and clearly, we all didn't run back to the office. Nevertheless, the entirely remote work life is becoming rarer. It's obvious that a hybrid approach has emerged. Nevertheless, we'll keep arguing. Also see: How a writing-based culture can rewrite work

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JPMorgan Chase: Digital transformation, AI and data strategy sets up generative AI

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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JPMorgan Chase will deliver more than $1.5 billion in business value from artificial intelligence and machine learning efforts in 2023 as it leverages its 500 petabytes of data across 300 use cases in production.

"We've always been a data driven company," said Larry Feinsmith, Managing Director and Head of Technology Strategy, Innovation, & Partnerships at JPMorgan Chase. Feinsmith, speaking with Databricks CEO Ali Ghodsi during a keynote at the company’s Data + AI Summit, said JPMorgan Chase has been continually investing in data, AI, business intelligence tools and dashboards.

Indeed, JPMorgan Chase said it will spend $15.3 billion on technology investments in 2023. JPMorgan Chase's technology budget has grown at a 7% compound annual growth rate over the last four years.

Feinsmith said the bank's AI/ML strategy is one of the big reasons JPMorgan Chase migrated to the public cloud. "If you look at our size and scale, the only way to deploy at scale is to do it through platforms," said Feinsmith. "Everyone has an opinion on data platforms, but you can efficiently move the data once and manage. Once you start moving data around it's highly inefficient and breaks the lineage."

JPMorgan Chase, a customer of Databricks, Snowflake and MongoDB, has multiple platforms, according to Feinsmith. It has an internal platform, JADE (JPMorgan Chase Advanced Data Ecosystem) for moving and managing data and one called Infinite AI for data scientists. "Equally as important as the data is the capabilities that surround that data," said Feinsmith, adding that data discovery, data lineage, governance, compliance and model lifecycle are critical.

 

According to Feinsmith, JPMorgan Chase's AI efforts start with a business focus with data scientists and AI/ML experts embedded into each business.

Feinsmith said JPMorgan Chase is leveraging streaming data and said he was a fan of Databricks' Lakehouse architecture and new AI features because it's easier to move and process data in one environment instead of two architectures, a data warehouse for business intelligence and a data lake for AI. JPMorgan deploys a central but federated data strategy and interoperability between data platforms is important. "Data has to be interoperable," Feinsmith told Ghodsi. "Not all of our data will wind up in Databricks. Interoperability is very important."

That comment rhymes with what other enterprise technology buyers have said. Despite a lot of talk about consolidating vendors--mostly from vendors looking to gain share--enterprise buyers want to keep options open. How JPMorgan Chase has approached its tech stack is instructive.

The digital transformation behind the AI

At JPMorgan Chase's Investor Day in May, Lori Beer, Global CIO at the bank, gave an overview of the bank's technology strategy. In 2022, JP Morgan launched a plan to deliver leading technology at scale with its team of 57,000 employees.

"Products and platforms need a strong foundation to be successful, and ours are underpinned by our mission to modernize our technology and practices," explained Beer. "We are already delivering product features 20% faster than last year, and we continue to modernize our applications, leverage software as a service and retire legacy applications."

JPMorgan Chase is moving to a multi-vendor public cloud approach while optimizing its owned data centers. The company is also embedding data and insights throughout the organization, said Beer. Those efforts will pave the way for large language models (LLMs) and other advances in the future.

"We have driven $300 million in efficiency through modern engineering practices and labor productivity, and we have developed a framework that enables us to identify further opportunities in the future. Our infrastructure modernization efforts have yielded an additional $200 million in productivity, driven by improved utilization and vendor rationalization," said Beer.

Here's a look at the key pillars of JP Morgan Chase's digital transformation.

Applications. Beer said the bank has decommissioned more than 2,500 legacy applications since 2017 and is focusing on modernizing software to deliver products faster. The bank has more than 560 SaaS applications, up 14% from 2022. By using industry-leading SaaS applications, Beer said it will be easier to scale new products to more than 290,000 employees.

Infrastructure modernization. Beer said:

"To date, we have moved about 60% of our in-scope applications to new data centers, which are 30% more efficient, and this translates to 16,000 fewer hardware assets. We are also migrating applications to utilize the benefit of public and private cloud. 38% of our infrastructure is now in the cloud, which is up 8 percentage points year-over-year. In total, 56% of our infrastructure spend is modern. Over the next three years, we have line of sight to have nearly 80% on modern infrastructure. Of the remainder, half are mainframes, which are highly efficient and already run in our new data centers."

JPMorgan Chase has been able to maintain infrastructure expenses flat even though compute and storage volumes have increased 50% since 2019, said Beer. One example is Chase.com is now being served through AWS and has an average of 15 releases a week.

Engineering. Beer said JPMorgan is equipping its 43,000 engineers with modern tools to boost productivity. JPMorgan Chase has adopted a framework to speed up the move from backlog to production via agile development practices.

Data and AI. Beer said:

"We have made tremendous progress building what we believe is a competitive advantage for JPMorgan Chase. We have over 900 data scientists, 600 machine learning engineers and about 1,000 people involved in data management. We also have a 200-person top notch AI research team looking at the hardest problems in the new frontiers of finance."

Specifically, Beer said AI is helping JPMorgan Chase deliver more personalized products and experiences to customers with $220 million in benefits in the last year. At JPMorganChase's Commercial Bank, AI provided growth signals and product suggestions for bankers. That move provided $100 million in benefits, said Beer.

The data mesh

To capitalize on AI, JPMorgan Chase created a data mesh architecture that is designed to ensure data is shareable across the enterprise in a secure and compliant way. The bank outlined its data mesh architecture at a 2021 Data Mesh Learning meetup.

JPMorgan said its data approach is to define data products that are curated by people who understand the data and management requirements. Data products are defined as groups of data from systems that support the business. These data groups are stored in its product specific data lake. Each data lake is separated by its own cloud-based storage layer. JPMorgan Chase catalogs the data in each lake using technologies like AWS S3 and AWS Glue.

Data is then consumed by applications that are separated from each other and the data lakes. JPMorgan Chase said it makes the data lake visible to data users to query it.

At a high level, JPMorgan Chase said its approach will empower data product owners to manage and use data for decisions, share data without copying it and provide visibility into data sharing and lineage.

In a slide, this architecture looks like this.

According to JPMorgan Chase, its architecture keeps data storage bills down and ensures accuracy. Since data doesn't physically leave the data lake, JPMorgan Chase said it's easier to enforce decisions product owners make about their data and ensure proper access controls.

How JPMorgan Chase will address generative AI

Given JPMorgan Chase's data strategy and architecture, the bank can more easily leverage new technologies like generative AI. Feinsmith at the Databricks conference said JPMorgan Chase was optimistic about generative AI but said it's very early in the game.

"There's a lot of optimism and a lot of excitement about generative AI. Businesses all know about it and generative AI will make us more productive," said Feinsmith. "But we won't roll out generative AI until we can do it in a responsible way. We won't roll it out until it's done in an entirely responsible manner. It's going to take time."

In the meantime, JPMorgan Chase's Feinsmith said the bank is working through the generative AI risks. The promise for JPMorgan Chase is obvious: Take 500 petabytes of data, train it, make it valuable and then add value to open-source models.

Beer outlined the JPMorgan Chase approach during the bank's Investor Day in May.

"We couldn't discuss AI without mentioning GPT and large language models. We recognize the power and opportunity of these tools and are committed to exploring all the ways they can deliver value for the firm. We are actively configuring our environment and capabilities to enable them. In fact, we have a number of use cases leveraging GPT4 and other open-source models currently under testing and evaluation.”

With Databricks, MongoDB and Snowflake all adding generative AI and large language model (LLMs) capabilities to the data stack, enterprises will have the tools when ready.

JPMorgan Chase has named Teresa Heitsenrether its chief data and analytics officer, a central role overseeing the adoption of AI across the bank. Heitsenrether oversees data use, governance and controls with the aim of harnessing AI technologies to effectively and responsibly develop new products, improve productivity and enhance risk management.

Heitsenrether is a 35-year veteran at JP Morgan Chase and previously was Global Head of Securities Services from 2015 to 2023.

Beer said explained JPMorgan Chase’s approach to responsible AI:

“We take the responsible use of AI very seriously, and we have an interdisciplinary team, including ethicists, data scientists, engineers, AI researchers and risk and control professionals helping us assess the risk and build appropriate controls to prevent unintended misuse, comply with regulation, and promote trust with our customers and communities. We know the industry is making remarkably fast progress, but we have a strong view that successful AI is responsible AI."

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How a writing-based culture can rewrite work

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Adam Nathan, CEO of Almanac, said modern work is broken and needs to be fixed by reframing remote work, creating writing-based cultures and processes and providing enough space for the magic of human collaboration.

Nathan's approach, which was outlined on DisrupTV Episode 328, is worth a listen starting at the 40 minute mark. Here are some of the takeaways.

Work itself is broken. "It was broken before Covid but since then it's very clear that where we work has changed but how we work hasn't. Teams are experiencing a ton of burnout, a ton of chaos at work. People are just not being able to get stuff done," said Nathan.

Creating a modern work method. Nathan said Almanac set out to conduct 5,000 interviews with organizations that have mastered collaboration. The research informed Almanac's modern work method. "We largely found that regardless of what a team does, purpose of the company or location is that the teams that are working the fastest and delivering the most value work with a lot more structure, more transparency and can't wait for meetings," said Nathan.

That tired remote work debate. "I think there has been a very loud push especially in the New York Times and from what I call old white guys on Twitter to return to the office but if you actually look at the data on this remote work a percent of the workforce has actually continued to grow even after the end of the end of Covid-era restrictions in September 2022. If you look at white collar professional jobs before the pandemic about 22% of the workforce was working in a remote or hybrid fashion. Today that number is 66%. I don't love this debate between office versus remote. It's a tiring one if you think about remote work as internet work," said Nathan. He added:

"Internet work is a disruptive and inexorable trend. Just like our consumer lives have moved from shopping in person to e-commerce and hanging out in person to social media, the same thing is happening to work. Working on the internet is not going away anytime soon. I think the questions we're asking are almost all the wrong ones. Theory and data don't support this idea that life is going to return to how it was."

7 future of work themes to know now | 12 lessons for when work, life collide | Future of work research and insights

Advantages of internet work. "For workers, there's obviously flexibility and freedom. You can work when you want and where you want and that gives people a lot more time to get into focus and flow to balance their lives better between work, family and friends and hobbies. I think that's why CEOs and owners are pushing back so much," said Nathan. "There's another chapter in this tension between capital and labor and who controls the leverage. Labor has gotten broad new freedoms and I think CEOs, people who own real estate and maybe some elected officials that there's discomfort with how this new normal is going to work out. There's not the same sense of control anymore over people's time and location. For the last 50 to 70 years managers have been managing my presence by butts and seats and did you attend meetings. I don't think that was at all correlated with effectiveness, growth or value delivery."

Well managed teams do well remote or in person (the office hides dysfunction). "The other silly thing is that remote is not a place--it's the absence of one. What we've seen in the data is that remote work and network really expose how teams are functioning. Well-managed teams tend to do better in remote settings because they already have good systems and structures and processes in place," said Nathan. "There's a high trust level. Teams that were dysfunctional don't have the theater of the office to cover it up, so all the dysfunction is exposed. These teams are often facing the choice of do we want to improve how we're working or revert and ignore it by going back into the office. There are bosses that clearly don't know how to operate in a distributed environment and would prefer the control an office creates."

Culture of writing. Amazon is well known for requiring employees to draft a memo before any meeting. Bridgewater is another example of an organization with high performance and a culture and decision-making process based on writing. "There's this misconception that the only way to get stuff done in stressful environments is to get everyone together, create a lot of chaos and move really fast," explained Nathan. "In the Marines slow means smooth and smooth means fast. A lot of organizations we've interviewed and observed are calm working environments. Everything feels really smooth, everyone's really calm and yet they're moving extremely fast in part due to a culture of writing."

How to get there? Nathan said high performing organizations start with a doc before a meeting. "Sometimes the doc obviates the need for a meeting. Even when there is a meeting everyone has read beforehand and commented. It makes the synchronous time they're spending more effective," he said.

Another move is to understand what recurring meetings aren't useful anymore. "What happens in organizations is that back-to-back meetings are just an accumulation of things that were once useful," said Nathan. Use documents to cancel meetings and store them so they can find answers easily.

Generative AI's impact on writing cultures. "I think the main thing LLMs are doing right now is producing fuzzy first drafts. It's the average of everything out there to give you an answer. Now we have a better chat interface that's going to get us to look over a larger amount of information much faster and produce a better outcome," said Nathan. "I think the productivity curve of what we can do with writing is to move up and into the right."

"What happens in the future is there are going to be some people who are going to really be able to exploit this technology to their advantage and some people who fall behind. Teamwork and collaboration are still a deeply human exercise. The human brain is constantly rewiring based on interactions it has with other people." 

• How Generative AI Has Supercharged the Future of Work

The magic of human collaboration. "What makes collaboration so magical is we don't know what will happen when we get together to work on a problem together. We might see AI almost like a collaborator in some ways but LLMs are just looking at past information, decisions, and knowledge," said Nathan. "I think the magic of human collaboration will always be there and what we do together might be more elevated because we have better technology to automate the overhead work."

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Rivian: AI, data power customer experiences

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Rivian is betting that data, AI and machine learning will continually improve its customer experience and detect and prevent future vehicle issues.

Speaking at the Databricks Data + AI Summit, Wasssym Bensaid, SVP of Software Development at Rivian, walked through how the electric vehicle manufacturer created an architecture that enables it to ingest telemetry data from vehicles, boost battery life and roll out new features with over-the-air (OTA) updates.

Bensaid said that the data ecosystem for EV makers goes well beyond autonomy and other technologies that grab headlines. "With software defined vehicles and an amazing hardware platform you can have all-in-one vehicles," he said. "Everything at Rivian is data driven from our supply chain to manufacturing to the customer relationship."

Rivian said it produced 13,992 vehicles in the second quarter and said it is on track to produce 50,000 for the year.

Speaking at an investor conference June 15, Rivian CFO Claire McDonough said the EV maker is looking to own "the full end-to-end ownership experience for commercial customers and for consumers as well." On the commercial side, Rivian counts Amazon as its flagship customer with a purpose-built delivery vehicle connected to fleet management software called FleetOS.

Data from the Rivian vehicle is shared with Amazon's back-end software system to improve efficiency and make life easier for drivers with perks like cooled seats.

For consumers, McDonough outlined:

"What we started with was how to create a seamless transaction experience. If you go online and you buy a Rivian, you can purchase, right, insurance, financing, trade-in your vehicle in about 6 minutes. Is that convenient and fast? Some of the early investments we’ve made have really ensured that we had this integrated experience for our customers with services like financing and insurance. Over time, we’re constantly updating our vehicles with over-the-air updates. And we’ve added incremental drive modes and feature sets to the vehicles. Over time, we will have the opportunity to create features that can be bundled or paid features for consumers. But right now, we’re really excited about offering continuous value accretion for Rivian owners that have seen the range of their vehicles, increase over the lifespan of their ownership and true enhancements to some of the new drive modes that we’ve offered as well."

The architecture

Bensaid said Rivian initially struggled with data silos and multiple systems, data types and tools. Rivian also had a team of experts focused on Rivian's data strategy and ultimately became the bottleneck. As a result, Bensaid said Rivian moved to democratize access to data while ensuring security, privacy and governance.

Rivian used Databricks and its Lakehouse platform to build a new architecture on top of data lakes that could scale. Bensaid said Rivian also uses Databricks Unity Catalog to create one version of the truth.

The EV maker has automated more than 95% of its Databricks provisioning workflows. Rivian's stack includes a data and analytics layer that runs through Rivian technology, cloud, product development and operations, products and services.

"We're using data and AI to achieve and unlock business outcomes," said Bensaid.

Ultimately, Rivian is planning to improve the customer experience and become more predictive about maintenance and improving performance. "Imagine a world where a vehicle will self-monitor its health and schedule its own appointments to deliver an amazing experience."

 

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How JetBlue is leveraging AI, LLMs to be 'most data-driven airline in the world'

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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JetBlue is actively using artificial intelligence and machine learning across its business and actively using generative AI for its internal operations and ultimately revenue-producing products.

Speaking at the Databricks Data + AI Summit, Sai Ravuru, Senior Manager of Data Science and Analytics at the airline, walked through how the company was using Databricks Lakehouse on multiple fronts. Databricks launched a series of new additions to its platform and said it will acquire MosaicML. 

"Over the last two years, we've made investments in data science and data refinement so raw data is continuously hydrated and reliable," said Ravuru. He said that AI and machine learning teams at JetBlue work alongside data scientists. "AI/ML scouts for the next use case before handing off to the data science team," explained Ravuru, who noted the goal for JetBlue is to be the most data-driven airline.

Ravuru said that data touches every part of JetBlue's business including operations, commercial and support functions. JetBlue is creating a unified digital twin of its business with cross-team collaboration and process-driven data science fueled by data from multiple systems.

Databricks Lakehouse is what absorbs and creates modeling across JetBlue's data footprint.

 

The airline has leveraged Databricks' platform to create an ecosystem of models called BlueSky to enable decision making. "The BlueSky product was built from scratch internally," said Ravuru. "It is a continually refreshed network with embedded LLM and real-time components for frontline staff."

BlueSky serves as JetBlue's AI-driven operating system.

Ravuru also said that JetBlue has created a unified LLM called BlueBot that uses open-source models complemented by corporate data integrated with BlueSky. BlueBot can be used by all teams at JetBlue since access to data is governed by role. For instance, the finance team may see data from SAP and regulatory filings, a new employee may just be served FAQs, and operations would see maintenance information, explained Ravuru.

"BlueBot brings crew members much closer to data and insights without change management," he said.

JetBlue is using Databricks for generative AI use cases that are experimental as well as production.

What's next? JetBlue is looking at LLMs to create new revenue channels so customers "can book from BlueBot or plan trips better." In addition, JetBlue is looking at efficiency gains by using LLMs to provide the "technical operations team with WebMD style diagnoses for each and every aircraft."

Related:

• AI is everywhere including your supermarket, homebuilder and soup
• How Home Depot blends art and science of customer experience

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Salesforce rolls out Sales GPT, Service GPT

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Salesforce launched the latest installment of generative AI across its clouds with the launch of Sales GPT and Service GPT.

The news follows Salesforce's rollout of AI Cloud this month as well as the Einstein GPT Trust Layer, which enables customers to keep data secure while leveraging large language models. 

What Movies Get Wrong…and Salesforce Gets Right…About AI | Salesforce launches AI Cloud, aims to be abstraction layer between corporate data, generative AI models | Salesforce launches Marketing GPT, Commerce GPT, aims to connect generative AI to ROI

Sales GPT includes the following:

• Auto-generated sales emails personalized for customers based on data.
• Call summaries and transcription for sales calls and follow-up actions.
• A sales assistant to summarize each step of the sales cycle including account resarch, meeting prep and contract drafts.

Service GPT and Field Service GPT will include the following generative AI tools:

• Service replies that are auto-generated with real-time data and personalization.
• Work summaries of service cases and customer engagements.
• Knowledge articles that are auto-generated and updated based on real-time data.
• Mobile work briefings for field service team appointments and summarization of issues.

Sales GPT and Service GPT are expected to be generally available this year.

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Synthetic biology: What you need to know

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Synthetic biology promises to be disruptive across multiple industries because it can be used to develop new biological parts and devices while reengineering existing systems.

DisrupTV caught up with three experts in the field on a recent episode. The roundtable captured some of the promise and peril with synthetic biology, a multidisciplinary field of science focused on reengineering organisms.

Here's a look at the key themes from the DisrupTV discussion and what you need to know.

What is synthetic biology? "Synthetic biology is the ability to design biological systems," said Dr. Megan Palmer, Senior Director for Public Impact at Ginkgo Bioworks and Adjunct Professor of Bioengineering at Stanford University. "We've been able to cut and paste DNA because we know the underlying code biology runs on for the last 50 years or so. Humans have been modifying biology for selective breeding of plants and animals for much longer than that."

"Now scientists and engineers are developing even better tools to be able to read, write, edit and evolve biological systems in ways that are easier, faster, more precise and predictable," said Palmer.

The upshot is that scientists and engineers are unlocking this ability to partner with biology in new ways.

The promise of synthetic biology. Palmer said she considers biology already the most powerful technology on the planet and the ability to program it means "we can use biology to manufacture nearly everything that is currently made with petrochemicals in ways that are more sustainable." Palmer said synthetic biology can impact multiple sectors in the economy such as health, food and manufacturing. There's even potential for data storage using DNA.

8 takeaways from Constellation Research's Healthcare Transformation Summit

Use cases for synthetic biology. Panelists noted a bevy of use cases ranging from space travel to food production to biomanufacturing and sustainability. Dr. Divya Chander, Anesthesiologist, Neuroscientist, and Data Scientist, said synthetic biology could play a role in space travel and "developing astronaut resilience." Chander said:

"We as humans aren't really very good at traveling in space because of microgravity in the radiation environment. There is a possibility of using gene editing tool, which are part of the synthetic biology toolkit, and turn genes on and off to give us more tolerance. Synthetic biology could also enable food production systems in space to improve nutrition and use fewer inputs like water or pesticides. We can even engineer our plants in space to do things like scrub toxins from the environment. CO2 is a big thing for astronauts, but also could do similar things on earth."

Chander said this gene editing could also be good for patients who are undergoing chemotherapy for cancer. With biomanufacturing, synthetic biology could print drugs that are more targeted.

Dr. David Bray, Distinguished Fellow at the Stimson Center and Business Executives for National Security, said sustainability will be a big use case for climate change. "I'm a big believer that the only way we're going to deal with climate change is with synthetic biology," said Bray, who noted that the technology could address the following:

• Clean water.
• Sustainable supply chains.
• Preventing pandemics before they start developing.

"We can organize ourselves in new ways so we can biologize industry instead of just industrializing biology," said Bray.

Chander said other use cases for synthetic biology include:

• Addressing chronic diseases such as cancer and extending longevity.
• Editing biologic machinery to address things like antibiotic resistance.
• Bio manufacturing targeted pharmaceuticals.

Growing the ecosystem and next generation. Palmer said iGEM, a non-profit focused on advancing synthetic biology, education and competition, has gone a long way to developing community in the industry. Palmer said iGEM competitions have encouraged students to design biological machines that are modular to solve problems. "Thousands of students across dozens of countries every year are developing biological innovations that are cool technologies, but also bake in social responsibility, safety and security into designs," said Palmer.

Risks with synthetic biology. Bray said it's promising that biology technologies are being democratized, but there will need to be some guardrails. "You know unleashed and craziness is going to happen, but we have multiple revolutions happening in parallel," said Bray, who added that the combination of AI and synthetic biology could be powerful for good uses and bad. "We are going to need the equivalent of smoke detectors for the biological space," he said. "Technologies like synthetic biology will be a tremendous force for good, but we also need to be ready for when some people try to use it for not so good purposes."

According to the US Government Accountability Office, synthetic biology presents safety and security concerns such as biological and chemical weapons and product tampering, environmental effects and public acceptance.

Ethics will be critical, said Palmer. Synthetic biology will require transparency and permission to use an individual's data. Society as a whole will have to think through ethics and biological data best practices, she said. Data trust will also be a key concept since individuals will be data producers. If you are going to read or write from someone's brain, you'll need full consent. The GAO noted that regulatory frameworks will be needed to address future applications of synthetic biology.

Multiple disciplines needed. The panelists said that synthetic biology will need multiple skills and sits at the intersection of data science, biology as well as ethics experts and technologists. Depending on the use case, industry expertise will also be needed.

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Databricks Data + AI Summit: LakehouseIQ, Lakehouse AI and everything announced

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Databricks infused its data platform with generative AI capabilities across its Lakehouse Platform with tools to enable customers to leverage large language models (LLMs), federate and govern data and knowledge engines that learn corporate cultures.

The company outlined the news at its Data + AI Summit. Databricks' news follows announcements from Snowflake and MongoDB designed to land more workloads and enable customers to leverage generative AI. Leading up to Databricks' conference, the company announced the acquisition of MosaicML and launch of Lakehouse Apps. The big takeaway is that data platforms and generative AI capabilities are converging.

Constellation Research analyst Doug Henschen summed up the data platform game: "All three (Snowflake, Databricks and MongoDB) want customers to do as much as possible on their platforms so they are invading each other’s turf. But their original (and still predominant) dance partners are data warehouse for Snowflake, data science for Databricks and developers for MongoDB."

Here's a roundup of Databricks' enhancements to its platform.

• The company said it will make it easier to deploy and manage LLMs with Lakehouse AI additions to offer monitoring and governance for LLM development. Databricks is adding Vector Search, a collection of opensource models, LLM-optimized Model Servicing, MLflow 2.5 with LLM tools and Lakehouse monitoring.
• Lakehouse AI will unify the AI lifecycle from data collection and preparation to model development. Databricks Vector Search will manage and automatically create vectors in Unity Catalog. Databricks AutoML will feature a low-code approach to fine tuning LLMs. And Databricks will curate a list of open-source models in its marketplace.
• Databricks outlined MLflow 2.5, a new release of the Linux Foundation open-source project MLflow. Updates include MLflow AI Gateway, which allows developers to swap out backend models and switch between LLM providers, and MLflow Prompt Tools, a no-code set of visual tools.
• Databricks Lakehouse Monitoring will monitor and manage data and AI assets within Lakehouse.
• LakehouseIQ adds a natural language interface to the Lakehouse Platform. LakehouseIQ uses generative AI to understand company specific jargon, data usage and organizational structure to answer questions within the context of a business. The goal is to democratize data analytics across a corporation. According to Databricks, LakehouseIQ will learn from signals embedded in corporate data including schemas, documents, queries, popularity, lineage, notebooks and dashboards.
• Lakehouse Federation in Unity Catalog will include query federation across data assets and platforms outside of Databricks. Databricks is also offering governance outside of its platform via Unity Catalog.
• Delta Lake 3.0, the latest contribution to Linux Foundation's Delta Lake project, will add Universal Format (UniForm), which will allow data stored in Delta to be read as if it were Apache Iceberg or Apache Hudl.

Doug Henschen's take:

• Databricks is, first and foremost, a platform for data scientists and it’s used by many of its 10,000+ customers as a platform for a significant chunk, if not a majority, of their data. Databricks is doing everything it can do to enable those customers to innovate with their data using AI, ML, and analytics, and it’s doing a great job of it.
• Databricks has spend the last three years building up the warehouse side of its Lakehouse platform, but this year the generative AI tsunami has rightly refocused Databricks on what has always been its greatest strength: data science including ML and AI. It’s very clear to me that Databricks customers are building AI models with Databricks today and there’s a deep well of capabilities that are generally available and now-emerging capabilities that are just becoming GA or are in public preview and very close to becoming GA.
• What was very clear during today’s Databricks keynote is how far along Databricks customers such as JPMorgan Chase, Jet Blue Airways and Rivian are in building innovative ML, AI and even generative AI capabilities using Databricks. A bunch of new enablers were announced today, several of which are already in public preview and are expected to go GA this year.

 

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