Improved deal notifications and filters, automated sales process templates, and Slack AI capabilities provide better visibility into performance so sales executives can stay informed, take action, and guide their teams to close deals faster
76% of early users report Sales Elevate makes them more productive
Customers like Rochester Electronics collaborate better and sell more efficiently with Sales Elevate
Salesforce today unveiled a new user experience for sales leaders within Slack Sales Elevate, a sales workspace that centralizes Salesforce insights and automates opportunity tasks by natively integrating Sales Cloud with Slack.
Sales Elevate’s enhanced experience now gives sales leaders a personalized overview of their team’s performance, pipeline data, and account records. Leveraging automation and Slack AI, the custom interface standardizes winning strategies, elevates and accelerates key deals, and makes it easier to update forecasts, allowing sales leaders to effectively support their teams in achieving targets and closing deals.
Why it matters: Sixty-nine percent of sales professionals say selling is harder now, yet the pressure to hit targets continues to rise, according to the State of Sales report. Sales leaders are adapting by looking for new ways to improve high-quality deal data and pipeline management, while boosting team efficiency so reps can focus on selling. Sales Elevate is helping address these challenges, and among early users, 87% of managers report that it’s easier to stay updated on deals, and 76% of users report that it makes them more productive.
Innovation in action: Sales leaders can access a consolidated view of the information they need from any device, all directly in Slack. With these new enhancements, sales leaders can:
The Slack perspective: “We launched Slack Sales Elevate last year to help sellers reduce admin work, save time with process automation, and improve deal collaboration – directly in Slack,” said Kaylin Voss, Chief Revenue Officer at Slack. “Since then, we’ve innovated Sales Elevate further to support sales leaders. This new set of capabilities, coupled with the recent launch of Slack AI, will be a breakthrough in time savings and real-time insights.”
Since then, we’ve innovated Sales Elevate further to support sales leaders. This new set of capabilities, coupled with the recent launch of Slack AI, will be a breakthrough in time savings and real-time insights.
Kaylin Voss, Chief Revenue Officer at Slack
Forward-Looking Statement: The above is intended for informational purposes. Please do not rely on this information in making your purchasing decisions. The development, release, and timing of any products, features or functionality not currently available remain at the sole discretion of Slack and are subject to change.
Salesforce today announced the launch of Salesforce Spiff, incentive compensation management functionality built right into the world’s #1 AI CRM, to automate commissions and motivate sellers. With this additional capability, Sales Cloud provides sellers and sales leaders with a complete growth platform from pipeline to paycheck.
Recently acquired by Salesforce, Spiff empowers organizations to drive more revenue by helping sales leaders manage complex incentive compensation plans and understand the many factors influencing revenue performance. The product features an intuitive user interface, real-time visibility, transparency into key financial data, in-depth analytics and reporting, and seamless integration with other Salesforce applications.
Why it matters: Sixty-four percent of organizations report correct quota setting as a major challenge for their sales compensation programs. As incentive-based pay is a standard piece of total compensation, with 90% of top-performing companies using incentive programs to reward their sales associates, this hurdle runs counter to optimal sales performance and can impact organizational goals. Additionally, incentive packages vary by level and business objective. Without compensation management technology, these packages can be difficult and time-consuming to manage manually.
Innovation in action: Salesforce Spiff addresses these challenges by allowing sales and finance teams to automate every step of the commission process. Sales reps benefit from:
Commission administrators can save time and reduce administrative headaches with:
Salesforce perspective: “Sales leaders know the importance of compensation in driving rep behavior. The challenge these leaders face is in how to align these compensation plans to desired outcomes – all while navigating data across siloed-point solutions,” said Ketan Karkhanis, EVP & GM, Sales Cloud. “Spiff connects what sellers want – transparent compensation – with what sales leaders want – compensation planning built into CRM that aligns behaviors to strategic outcomes.”
Spiff connects what sellers want – transparent compensation – with what sales leaders want – compensation planning built into CRM that aligns behaviors to strategic outcomes.
Ketan Karkhanis, EVP & GM, Sales Cloud
Reaction to the News: “One of our biggest challenges was getting our sales reps invested in their comp plans, so they would work harder to meet their goals. Spiff has given us the platform to showcase our investment in our culture and employees. Spiff has truly catapulted our commission program.” – Lindsey Sanford, Senior Director of Sales and Marketing, RadNet
“Success in the sales industry is defined by how well reps meet and exceed their quotas. Accordingly, sales organizations almost universally operate on an incentive model. With Salesforce Spiff, organizations have a simple interface to give their reps the same access to and understanding of their compensation breakdown that is provided to a salaried employee. This transparency nurtures internal trust and provides seller motivation, helping companies exceed their quotas and drive revenue for their bottom line.” – Rebecca Wettemann, Valoir
Expanded offering highlights Salesforce’s commitment to building the largest and most successful community of sales professionals
The Salesblazer Community is one of the fastest-growing communities of sales professionals, helping millions advance their careers and drive sales excellence worldwide.
Today, Salesforce added new capabilities to provide real-time collaboration using Slack, empowering members to connect, engage, and share best practices. With today’s launch, sellers can now:
The additional capabilities build upon the successful April 2023 launch of Salesblazer.com, a learning and community hub featuring content from hundreds of sales thought leaders, free sales training courses, and invites to exclusive in-person and online community events. In the past year, Salesblazer.com has reached more than 8 million people across web, social, newsletter, community, learning, and event platforms.
Why it matters: Lack of advancement opportunities has long been one of the top reasons why sales professionals consider leaving their jobs. Salesforce’s Salesblazer initiative, modeled after the proven Trailblazer platform, seeks to help remedy this issue by building, connecting, and advancing the largest and most successful community of sales professionals.
Salesforce perspective: “We are in a digital-first, AI-comes-standard world, and many sales professionals are left on their own to figure things out. The Salesblazer Community is the next evolution of our commitment to support our users throughout their entire career. Salesblazer aims to foster the largest-ever community for sales professionals to learn and grow their careers, all while connecting with industry peers. It’s a game changer.” — Ketan Karhkanis, EVP and GM, Sales Cloud
The Salesblazer Community is the next evolution of our commitment to support our users throughout their entire career.
Ketan Karhkanis, EVP and GM, Sales Cloud
What they’re saying:
What else is new: For professionals in the service industry, Salesforce has also launched Serviceblazer, a premier destination for service and field service professionals. With cutting-edge content and a global community, Serviceblazers get resources to help them grow their careers, learn about technology, stay ahead of the latest trends, and connect with fellow service and field service professionals.
Learn more
Both service and field service organizations are increasing their investment in AI to meet rising customer expectations and unlock revenue-generating opportunities, according to new research from Salesforce’s sixth State of Service report.
Gathering insights from over 5,500 service professionals in 30 countries, the State of Service report highlights the priorities, challenges, and strategies shaping customer service in the AI era.
“Service and field service teams are getting more proactive and productive with the power of data and AI,” said Kishan Chetan, EVP and General Manager of Service Cloud. “They’re deflecting more issues with smarter self-service. And they’re devoting more time and energy to generating revenue — pointing to a fundamental shift in their role within the business.”
They’re deflecting more issues with smarter self-service. And they’re devoting more time and energy to generating revenue — pointing to a fundamental shift in their role within the business.
Kishan Chetan, EVP and General Manager of Service Cloud
The trend of viewing service as a revenue driver instead of a cost center is accelerating. Eighty-five percent of service decision makers now say their teams are expected to contribute a larger slice of revenue over the coming year through upselling, cross-selling, and customer retention. This parallels a jump in the number of organizations tracking service-driven revenue — from 51% in 2018 to 91% in 2024.
The strategic emphasis on revenue generation is fueling bigger budgets and larger teams. Overall, service decision makers expect budgets to grow by an average of 23% over the next year. Meanwhile, over three-quarters (76%) anticipate expanding their headcount.
With 88% of customers saying good service makes them more likely to purchase from the same company again, it’s clear that customer experience is key to driving revenue. However, delivering on expectations isn’t as simple as in years past:
This may be why service decision makers cite keeping up with changing customer expectations as their organizations’ top challenge.
One expectation is especially tricky. Over half of customers (53%) — and nearly three-quarters of business buyers (73%) — want companies to predict their needs before they arise. However, there’s a disconnect between what businesses think they’re doing and what customers actually experience. While 61% of service teams believe they are proactive in addressing issues, only a third of customers (33%) agree that companies generally anticipate and act on their needs ahead of time.
On top of increasingly sophisticated demands from customers, 76% of service organizations anticipate higher case volumes in the year ahead. The risk of burnout or failure in this scenario is a major factor for agents, who are already stretched thin.
79% of agents, and 73% of mobile workers, say they support more products/services now than they did a year ago.
77% of agents, and 74% of mobile workers say their workload has increased over the past year.
65% of agents, and 66% of mobile workers, say their cases are more complex than a year ago.
AI and automation may present solutions for over-burdened agents tasked with revenue generation. Currently, agents spend just 39% of their time servicing customers amid competing demands like internal meetings, administrative tasks, and manually logging case notes.
Ninety-three percent of service professionals at organizations with AI say the technology saves them time. By responding to simple queries and crafting self-help knowledge articles, AI clears the way for human agents to focus on more fulfilling and higher value work, such as building customer relationships and resolving complex cases.
Service organizations also report using AI to directly help with revenue generation by providing intelligent recommendations and offers to agents.
Savvy service organizations are taking note of the promise AI and another efficiency driver, automation can bring. Currently, 79% of organizations have invested in AI while 81% use workflow or process automation. Looking ahead, 83% of decision makers plan to increase their AI investments over the next year, with the same amount planning to boost automation investments.
For both AI and employees, personalizing service interactions requires customer knowledge, which often is drawn from many different data sources. Empowering AI and employees with a complete view is a clear priority for decision makers, 83% of whom plan to boost investments in data integration over the next year.
Existing investments appear to be paying off. As AI, automation, and data capabilities mature, organizations are getting better at striking the right balance between service speed and quality — a notoriously difficult challenge. In 2022, 76% of agents cited juggling these competing priorities as difficult, but that percentage dropped to 69% in this year’s report.
Salesforce conducted a double-anonymous survey of over 5,500 professionals in roles including service operations, service agents, mobile workers, service managers/directors, and service leadership/head of service. Respondents were sourced from 30 countries and five continents. The data was collected between December 8, 2023, and January 22, 2024.
The AI Implementation Bundle provides essential development tools that help customers build, test, deploy, and extend AI experiences across their organizations
The Data Governance Bundle combines key security and privacy capabilities to help IT teams protect and manage sensitive customer data
Salesforce today announced two bundled solutions to make it easier for IT teams to develop and deploy trusted AI capabilities in a secure and trusted way: the AI Implementation Bundle, which includes Einstein Copilot, Einstein 1 Studio, Platform licenses, Sandboxes, and Data Mask, and the Data Governance Bundle, which includes Shield, Security Center, and Privacy Center.
As part of the Einstein 1 Platform and integrated with Data Cloud and the Einstein Trust Layer, the new AI Implementation and Data Governance bundles allow customers to garner significant savings on products to enhance their security posture as they prepare for AI, and to help implement AI safely across their entire organization.
Why it matters: Nearly 80% of IT professionals say they face business pressures to implement AI and more than 80% of workers believe having trusted data is key to accomplishing that, according to Salesforce’s recent “Your Data, Your AI” study. But nearly half of IT professionals worry their security infrastructure won’t be able to keep up with AI development demands.
Salesforce perspective: “Every company wants to get started with AI, but a lot of companies don’t know where to begin. The possibilities with AI are vast, but so are the decisions that accompany every aspect of its development and implementation, from data governance to use case testing. These bundles make it easy for companies to get started on their AI journey by allowing teams to safely experiment and test the technology, while ensuring their data and AI are secure before and after deployment.” – Alice Steinglass, EVP and GM, Salesforce Platform
These bundles make it easy for companies to get started on their AI journey by allowing teams to safely experiment and test the technology, while ensuring their data and AI are secure before and after deployment.
Alice Steinglass, EVP and GM, Salesforce Platform
Salesforce as customer zero: Leading by example for customers, Salesforce used the tools available within these bundles to internally test and deploy several AI powered solutions for the company, including its AI powered V2MOM business planning app.
With the AI Implementation and Data Governance bundles, Salesforce is combining the tools that it uses internally to safeguard data, building AI-powered solutions like the V2MOM app, and making it easier for customers to follow the same set of best practices.
Customer testimonial: “As we integrate AI technology into our operations, thorough testing is crucial to ensure security and viability. Our approach involves testing the AI meticulously before deployment, alongside the Einstein Trust Layer for data protection and Shield for governance throughout the deployment process. Testing the technology in a Sandboxes environment will also be instrumental in building the necessary capabilities to customize prompts and implement AI safely and effectively. These steps allow us to accelerate the delivery of AI solutions across our organization, with safety and security top of mind.” – Brim Basom, Managing Director of Technology and Innovation, Goosehead Insurance
Availability: The AI Implementation Bundle and the Data Governance Bundle are available for customers to purchase today.
Any unreleased services or features referenced in this or other press releases or public statements are not currently available and may not be delivered on time or at all. Customers who purchase Salesforce applications should make their purchase decisions based upon features that are currently available. Salesforce has headquarters in San Francisco, with offices in Europe and Asia, and trades on the New York Stock Exchange under the ticker symbol “CRM.” For more information please visit https://www.salesforce.com, or call 1-800-NO-SOFTWARE.
Newly-established Sustainable AI Policy Principles aim to guide advocacy with regulators and lawmakers
Five nonprofits will develop AI-focused climate solutions with Salesforce support, prioritizing mitigation, adaptation, resilience, and finance
Salesforce today introduced a series of new initiatives designed to foster a more sustainable and equitable future through the use of AI. The company has rolled out its Sustainable AI Policy Principles, a framework aimed at guiding AI regulation to minimize environmental impact and spur climate innovation. Salesforce has also selected five new nonprofits for its Salesforce Accelerator – AI for Impact, which focuses on climate action. This accelerator cohort will empower purpose-driven organizations to leverage AI solutions in addressing the urgent challenges of climate change.
Why it matters: Prioritizing responsible AI development allows organizations to make a positive impact with the technology while ensuring equity and sustainability stay at the forefront of their efforts.
Go deeper on the principles: The new Sustainable AI Policy Principles build on Salesforce’s commitment to advocate for clear and consistent science-based policies for a just and equitable global transition to a 1.5 degree future.
Behind the accelerator: As part of the new AI for Impact cohort, Salesforce is supporting climate nonprofits with technology, investments, and philanthropy to help them create AI solutions that benefit the planet.
Accelerator participants include:
Together, we can accelerate efforts to ensure technology benefits everyone, everywhere.
Suzanne DiBianca, EVP and Chief Impact Officer, Salesforce
Soundbites:
Learn more:
Slack strengthens its AI-powered platform as it continues rolling out native generative AI capabilities to users, including enhanced search, channel and thread summaries, and a new recap feature
Customers like Wayfair, Beyond Better Foods, and more depend on Slack AI to stay ahead of the workday and better surface and prioritize information
Salesforce today announced that Slack AI — which uses a company’s conversational data to help users work faster and smarter — is now available to all paid Slack customers with expanded language support. Now, businesses of all sizes can access a trusted and intuitive generative AI experience built natively on the secure platform where their work already happens.
Why it matters: Customers are already saving an average of 97 minutes per user each week using Slack AI to find answers, distill knowledge, and spark ideas, according to an internal analysis. Yet, while 94% of executives say that incorporating AI into their organization is an urgent priority, only 1 in 4 desk workers report that they have tried AI tools at work, according to the latest research by the Workforce Lab from Slack.
Customer deep dive: From large enterprises to small businesses, customers use Slack AI to prioritize exactly what they need to know, when they need to know it.
Wayfair replaced its messaging software with Slack in 2016, and now uses Slack AI to help distill its collected data more efficiently. The global retailer uses enhanced search to ask questions in natural language and find concise answers in relevant channels, without having to sort through lengthy messages.
Slack AI gets people accurate information faster, from any channel.
– Taylor Keck, Senior Engineer, Enterprise Solutions, Wayfair
Beyond Better Foods uses Slack as its primary communication platform. As a healthy dessert brand, their operations team uses Slack AI’s enhanced search capabilities to fast-track answers for logistics planning and recaps to keep track of select channels, saving time and keeping them focused.
HR provider ProService Hawaii employees use Slack AI conversation summaries to stay informed and catch up after attending multiple back-to-back meetings.
Being able to pick the day, week, or month I’d like to catch up on with Slack AI has been so impactful.
– Jason Morita, Product Owner, ProService Hawaii
What’s next: In the future, Slack AI’s search and summarization capabilities will tap into new data sources – including files, Slack apps, canvases, and clips – to enhance the breadth and depth of context that Slack AI can access. For example, Slack AI will help users get more value out of huddles, Slack’s feature for lightweight audio or video calls. Slack AI will deliver a summary of key takeaways and action items, making it easy to turn live discussions into next steps.
Slack will also become the best place for users to engage with assistants. This includes an integration with Einstein Copilot, a conversational AI assistant for Salesforce CRM. Users will be able to bring AI-powered CRM insights directly into Slack so they can talk to Salesforce data as easily as they talk to their teams.
Trust and security: Slack AI runs on Slack’s infrastructure and upholds the same security practices and compliance standards that customers expect from Salesforce. Slack AI’s large language models (LLMs) are hosted in Slack’s own virtual private cloud (VPC), ensuring customer data remains in-house and exclusively for that organization’s use. Customer data will not be used to serve other clients, directly or indirectly, and Slack AI does not use customer data for LLM training purposes.
Pricing and availability:
Forward-looking Statement: The above is intended for informational purposes. Please do not rely on this information in making your purchasing decisions. The development, release, and timing of any products, features or functionality not currently available remain at the sole discretion of Slack and are subject to change.
Healthy dessert brand Beyond Better Foods is a fully remote small business that relies on Slack as its primary communication platform, and it’s become the central hub for knowledge sharing across the organization. With the newly launched Slack AI, a trusted and intuitive generative AI experience directly in Slack, they’re getting more value out of that collective data to save time, become more efficient, and stay ahead of the workday.
Why it matters: As a fully-distributed business with several brands, including Enlightened Frozen Treats and Bada Bean Bada Boom Snacks, coordinating logistics and freight planning is mission-critical. Before Slack AI, essential details were stuck in disparate silos, and tracking down information was slow, cumbersome, and impossible to scale.
The impact: Beyond Better Foods found that with Slack AI’s enhanced search capabilities, its operations team has better visibility into its logistics and can quickly and easily find key information like product locations and shipping times.
Customer perspective: “Slack is crucial for us. The enhanced search capabilities of Slack AI have been really helpful to fast-track answers, especially when it comes to logistics. When I need to get my CEO a fast answer at 2 p.m. on a Friday, I can use Slack AI’s search function. I’ve only been using Slack AI for about a month, but it’s already helped me quickly find answers countless times and is saving me at least 30 minutes a day.” – Andy Kung, VP of Operations, Beyond Better Foods
I’ve only been using Slack AI for about a month, but it’s already helped me quickly find answers countless times and is saving me at least 30 minutes a day.
Andy Kung, VP of Operations, Beyond Better Foods
Zoom out: Small businesses like Beyond Better Foods are eager to adopt AI tools to solve common user problems. A new Slack survey of 2,000 small business owners found that:
Forward-looking Statement: The above is intended for informational purposes. Please do not rely on this information in making your purchasing decisions. The development, release, and timing of any products, features or functionality not currently available remain at the sole discretion of Slack and are subject to change.
“Your Data, Your AI” study finds that more than half of workers believe generative AI lacks the data needed to be useful
A Salesforce survey of nearly 6,000 global knowledge workers suggests that AI has a data problem. Nearly 6 in 10 AI users say it’s difficult to get what they want out of AI right now, with over half claiming they don’t trust the data used to train today’s AI systems. In fact, more than three-quarters of workers say that accurate, complete, and secure data is critical to building trust in AI.
Why it matters: Eight in ten business leaders believe generative AI will lower business costs and increase revenue. However, the majority of workers believe that trusted customer data is required for generative AI to be used successfully in their role and at their organization. This new survey indicates that generative AI outputs must be grounded in trusted data to close this gap — and reap the benefits of the technology across the enterprise.
Salesforce perspective: “The future of enterprise AI isn’t about more data – it’s about the right data. When AI is grounded in a company’s own data, it delivers more useful results and ultimately drives greater trust and adoption.”– Wendy Batchelder, SVP, Chief Data Officer
When AI is grounded in a company’s own data, it delivers more useful results and ultimately drives greater trust and adoption.
Wendy Batchelder, SVP, Chief Data Officer
Methodology: Salesforce conducted a double-anonymous survey in partnership with YouGov from March 20 to April 3, 2024. It included nearly 6,000 full-time knowledge workers representing companies of a variety of sizes and sectors in nine countries, including the United States, the United Kingdom, Ireland, Australia, France, Germany, India, Singapore, and Switzerland. The survey took place online.
A media feeding frenzy over the exploding scale and increasing costs of artificial intelligence (AI) has created a divide between the reality of the technology and its perception among decision makers, explained Silvio Savarese, Chief Scientist of Salesforce AI Research in a recent interview.
Whether these concerns come from worries about the bottom line, the impact of AI on the environment, or even basic questions of fairness and access, Savarese believes that changing these misconceptions requires an understanding of when scale is necessary to deliver high-quality AI outputs — and when it isn’t.
In the interview, Savarese also shared why the environmental and financial price tag of AI doesn’t have to be as head-spinning as the headlines might suggest, and how understanding the different scales and performance of AI models can help any business responsibly harness this transformative technology to boost productivity, build deeper relationships with customers, and enhance daily workflows and processes.
Rather than asking if the scale of today’s LLMs is necessary, let’s ask what it’s necessary for.
The scale and size of an AI deployment isn’t inherently advantageous. Rather, when implementing AI, there’s a range of possibilities and trade-offs that should be explored. Remember, the ChatGPTs of the world are designed to do more or less everything, and that makes them very different from most enterprise applications. They can help with homework, suggest holiday recipes, and even reimagine La bohème’s libretto as Socratic dialogues. It’s a great party trick — albeit an expensive one. Training Open AI’s ChatGPT 4 cost more than $100 million. But that isn’t what enterprises are using AI for.
The scale and size of an AI deployment isn’t inherently advantageous. Rather, when implementing AI, there’s a range of possibilities and trade-offs that should be explored.”
Silvio Savarese, Chief Scientist of Salesforce AI Research
There’s also the environmental impact of these large LLMs. The hypothetical long-term benefits of AI in combating climate change in areas such as monitoring emissions and optimizing the transportation of goods are significant, with the potential to reduce global emissions 5 to 10% by 2030. However, the utilization of LLMs, while groundbreaking in their capabilities, requires enormous computing resources, exacerbating pressing concerns such as the release of greenhouse gasses, the depletion of water resources, and the extraction of raw materials along the supply chain. Given the urgency of the climate crisis and the imperative to combat planet-warming emissions, it’s paramount that the development and implementation of AI technologies doesn’t surpass the capacity of our planet’s resources.
In contrast to LLMs like ChatGPT or Anthropic’s Claude, an AI model like our own CodeGen 2.5, has a limited set of tasks — helping developers write, understand, and debug code faster. Despite its deliberately small scale, its performance is on a par with models literally twice its size, boasting remarkable efficiency without compromising on utility. So even as it helps developers work faster, it also reduces costs, latency, and, crucially, environmental impact compared to larger-scale LLMs.
Businesses should not be asking whether they need scale, but how they want to apply that scale. Depending on the task, the answer may vary wildly — and bigger is most certainly not always better.
Believe it or not, even this isn’t a clear-cut answer. Large models do generally outperform their smaller counterparts when it comes to flexibility. But therein lies the nuance that is so often left out of conversations around LLMs: as tasks become more narrow, more well-defined, and more unique to a specific organization or domain — exactly what enterprise AI is all about — it’s possible to do more with less.
In other words, most models aren’t meant to be everything to everyone, which frees up enterprises to focus on their needs while saving huge amounts of resources in the process.
Not all the time, no. But under the right circumstances, small models really can offer the best of all worlds: reduced cost, lower environmental impact, and improved performance. Small models are often neck-and-neck with large ones when it comes to tasks like knowledge retrieval, technical support, and answering customer questions.
Small models are often neck-and-neck with large ones when it comes to tasks like knowledge retrieval, technical support, and answering customer questions.”
Silvio Savarese, Chief Scientist of Salesforce AI Research
In fact, with the right strategy, they can even perform better. This includes models from the open-source world, including Salesforce’s own XGen 7B. Our model is specifically trained on a sequence of data with suitable length, helping it with tasks like the summarization of large volumes of text and even writing code — and it consistently exceeds the performance of larger models by leveraging better grounding strategies and better embeddings. Additional small-scale models from our AI research org are planned to be released soon and will be powering generative AI capabilities for critical customer use cases.
Scaling down models isn’t just about saving money. It’s one of the best ways to ensure AI outputs are reliable. Large models are exciting, but they often don’t provide much information about the data they use. This leaves companies with no choice but to monitor deployments closely to catch harmful or inaccurate outputs. Needless to say, this falls far short of the standard most businesses expect from their technology.
Scaling down models isn’t just about saving money. It’s one of the best ways to ensure AI outputs are reliable.”
Silvio Savarese, Chief Scientist of Salesforce AI Research
Instead, consider a simple, intuitive fact: smaller models are trained on smaller data sets, which are inherently easier to document and understand — an increasingly important trust and transparency measure as the role of LLMs grows to include mission-critical applications that don’t just require reliability, but accountability as well.
Additional steps for verifying that generative AI produces trusted results are of course, critical: the Einstein Trust Layer is Salesforce’s guaranteed accountability model assisting businesses in efficiently managing data privacy, security, and transparency. The Einstein Trust Layer serves as a secure middleman for user interactions with LLMs. Its functions include obscuring personally identifiable information (PII), monitoring output for harmful content, guaranteeing data privacy, prohibiting the storage or use of user data for future training, and unifying discrepancies among various model providers.
There are, of course, times when increasing scale is simply unavoidable, and the power of small models doesn’t negate the potential of bigger ones. But again, let’s ask the right questions: rather than simply asking whether we need scale, let’s ask what you need it for. The answer will inform your strategy from the very first steps, because there are, ultimately, two very different ways to scale: increasing the parameter count of a single model, or orchestration — the connection of multiple models into a single, larger deployment, analogous to multiple human workers coming together as a team.
Orchestration has the potential to offer the power of scale while still keeping its pitfalls in check. After all, even small models can do amazing things when combined with one another, especially when each is geared toward a specific strength that the others might lack: one model to focus on information retrieval, one to focus on user interactions, another to focus on the generation of content and reports, and so on. In fact, smaller models are arguably a more natural choice in such cases, as their specialized focus makes their role in the larger whole easier to define and validate.
In other words, small models can be combined to solve ever-bigger problems, all while retaining the virtues of their small size — each can still be cleanly trained, tuned, and understood with an ease large models can’t touch. And it’s yet another example of why a simple parameter count can often be misleading.
LLMs are a hugely complex topic, and there’s room for any number of voices in the conversation. But we’re overdue for a more balanced, strategic perspective on the question of how much we need to get what we want: how much time, how much compute, and, ultimately, how much cost. The answer isn’t anywhere near as simple as the impression one might get from the headlines, and I believe amazing things can be done on just about any budget. It’s just a matter of knowing what’s possible.
Today, Salesforce announced it has named Jason Yau as SVP and Architect, within the Office of the CEO. In his new role, Jason will partner deeply with Salesforce’s customers on their digital transformations and drive innovation in the Salesforce roadmap to further accelerate its position as the #1 AI CRM.
Jason is a seasoned, three-time CTO who has a proven track record leading successful B2C and commerce transformations. Most recently, he was the Global Enterprise CTO at Shopify and prior to that, he was CTO at Dollar Shave Club and SPARC Group (parent company of retail brands Eddie Bauer, Nautica, and Reebok among others).
At Salesforce, Jason will work with Fortune 500 customers spanning multiple industries. He’ll leverage the full breadth of the Salesforce Platform—as well as Data Cloud and Einstein 1 — to help customers with their large scale digital transformations.
Jason is a one-of-a-kind consumer visionary with an exceptional track record of technical innovation.
Kendall Collins, Chief Business Officer and Chief of Staff to the CEO
“Jason is a one-of-a-kind consumer visionary with an exceptional track record of technical innovation. By combining his unique skillset and perspective with the breadth of the Salesforce Platform, he will be able to help global brands transform their customer experiences with AI across Commerce, Marketing, Sales and Service,” said Kendall Collins, Chief Business Officer and Chief of Staff to the CEO. “Salesforce has an incredible opportunity ahead with AI and data, and I’m thrilled to partner with Jason to help drive this next phase of our growth.”
With its values driven culture, deep customer relationships and transformative Einstein 1 and Data Cloud technology, no company is better positioned to lead the AI and data revolution than Salesforce.
Jason Yau, SVP and Architect, Office of the CEO
“It’s a pivotal time for enterprise technology—every brand is looking to harness the power of AI and data at scale,” said Jason Yau, SVP and Architect, Office of the CEO. “With its values driven culture, deep customer relationships and transformative Einstein 1 and Data Cloud technology, no company is better positioned to lead the AI and data revolution than Salesforce. By joining Salesforce, I’ll be able to drive a much broader, deeper, and global impact for brands and I can’t wait to get started.”
The Massachusetts Department of Transportation (MassDOT) is working with Salesforce to deploy innovative technology systems that support internal collaboration, drive efficiencies in service delivery across various business divisions, and provide real-time visibility into different projects.
The impact: Massachusetts has one of the highest rates of public transit use in the United States, and MassDOT provides safe and reliable transportation for this volume of ridership with a strong focus on customer service. This starts with having scalable and future-proof technologies accessible to employees and constituents.
Go deeper: MassDOT has a number of divisions with different priorities, processes, and systems. The team needed to shift their focus away from legacy systems and migrate their business applications onto low-code/no-code configurable platforms. MassDOT launched a FedRAMP-authorized application development platform on Salesforce to optimize processes agency-wide and personalize the customer service experience across each of its business divisions, including the Registry of Motor Vehicles (RMV), Aeronautics, Planning and Enterprise Services, and Highway.
MassDOT perspective: “We’ve been using Salesforce’s government solutions to drive our own productivity. Salesforce’s built-in compliance lets us innovate quickly in a safe and secure environment so we can deliver highly-personalized services to the millions of constituents we serve.” – Anu Goutham, Deputy CIO of Information Technology
We’ve been using Salesforce’s government solutions to drive our own productivity. Salesforce’s built-in compliance lets us innovate quickly in a safe and secure environment so we can deliver highly-personalized services to the millions of constituents we serve.
Anu Goutham, Deputy CIO of Information Technology
Salesforce Perspective: “Modernization and innovation are top-of-mind for the public sector, and MassDOT was in need of consolidated, real-time systems that help create better collaboration across business divisions. By implementing these solutions, Salesforce will continue to help MassDOT drive greater efficiency and transparency for its employees and constituents.” – Nasi Jazayeri, EVP & GM, Public Sector
