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
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
Editor’s note: Visualisations included throughout this article are set to global responses to the State of Service report by default. UK responses are available via filter
Both service and field service organisations 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, including 300 in the UK, 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. Globally, 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.
In the UK, the strategic emphasis on revenue generation is fueling bigger budgets and larger teams. Seventy-three percent of UK decision makers expect budgets to grow over the next year. Meanwhile, just over two-thirds (68%) of UK decision makers anticipate expanding their headcount.
With 88% of customers wordwide 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. Ninety percent of UK service professionals say customer expectations are getting higher.
On top of increasingly sophisticated demands from customers, 72% of UK service organisations 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.
AI and automation may present solutions for over-burdened agents tasked with revenue generation. Currently, UK agents spend just 43% of their time servicing customers amid competing demands like internal meetings, administrative tasks, and manually logging case notes.
88% of UK service professionals at organisations 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 organisations also report using AI to directly help with revenue generation by providing intelligent recommendations and offers to agents.
Savvy service organisations are taking note of the promise AI and another efficiency driver, automation can bring. Currently, 62% of UK organisations have invested in AI. Seventy-eight percent of UK 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, personalising 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 UK decision makers, 74% of whom plan to boost investments in data integration over the next year.
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 — including 300 from the UK. 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.
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.
13% of European nonprofit organisations are already using AI, and 22% are ‘optimistic but cautious’ about the technology, citing concerns around data security and privacy, loss of human expertise and job displacement. This according to the latest annual Nonprofit Pulse report from the European Fundraising Association released today, in partnership with the UK’s Chartered Institute of Fundraising and Salesforce.
The survey of 671 senior representatives of nonprofit organisations across 20 European nations explores how nonprofits are responding to economic headwinds. For the first time, it includes a focus on how nonprofits are using AI, or plan to, and their view on its opportunities and the challenges around its use.
Why it matters: With increased workload, fundraising, and supporting staff and their wellbeing among the biggest challenges facing nonprofit organizations, many nonprofits are responding by seizing the opportunities available to them – from advances in technology and AI to greater collaboration between organisations.
The Salesforce perspective: “AI represents a tremendous opportunity for nonprofits of all sizes, and will be the key to reducing workloads for overburdened staff, improving fundraising outcomes, accelerating mission impact, and so much more. But, successful adoption in the sector depends on the use of trusted AI that can help nonprofits safely take advantage of their data with confidence,” said Lori Freeman, VP & GM of Nonprofits, Salesforce.
By embracing AI and educating employees on how to use it in a trusted and ethical way, nonprofits have a once-in-a-generation opportunity to modernize their operations and impact.
Lori Freeman, VP & GM of Nonprofits, Salesforce
Learn more: Find out more about the report’s findings here
New Caseworker Narrative Generation helps government employees work cases faster by automating manual tasks with AI
Salesforce now offers several FedRAMP-compliant features for products like Field Service, Privacy Center, Security Center, and GovSlack
Salesforce today announced Public Sector Einstein 1 for Service, including CRM, trusted AI, and data capabilities to help government employees automate administrative tasks and provide faster service to constituents. Built on Salesforce’s Einstein 1 platform, public sector organizations can now quickly and easily generate case reports, capture real-time call transcriptions, and document and format case interactions, all in a single offering.
Why it matters: BCG estimates that generative AI could unlock a $1.75 trillion productivity opportunity annually across many functions and levels of government. However, 62% of IT decision makers across industries, including those in the public sector, feel their organization’s data systems are not ready to leverage AI.
Innovation in action: Public Sector Einstein 1 for Service offers government contact center agents and case managers trusted conversational and generative AI, enabling them to be more productive and efficient. Features include:
High-quality AI requires high-quality data and insights: Public Sector Einstein 1 for Service also includes Data Cloud, which connects and harmonizes data and uses it to power government agency applications.
What’s new in compliance: Salesforce also now offers several Federal Risk and Authorization Management Program (FedRAMP) compliant tools to help government agencies drive efficiency and productivity while meeting regulatory requirements. These tools include:
With Public Sector Einstein 1 for Service, organizations can implement trusted AI to become more efficient, better manage and harmonize their data, and give employees the tools they need to better serve their constituents, all while driving their mission forward.
Nasi Jazayeri, EVP & GM, Public Sector
Salesforce perspective: “Public sector organizations want to simplify their technology stack, better engage with constituents, and reduce employees’ administrative burdens while improving employee productivity. With Public Sector Einstein 1 for Service, organizations can implement trusted AI to become more efficient, better manage and harmonize their data, and give employees the tools they need to better serve their constituents, all while driving their mission forward.” – Nasi Jazayeri, EVP & GM, Public Sector
Any unreleased services or features referenced here are not currently available and may not be delivered on time or at all. Customers should make their purchase decisions based upon features that are currently available.
In a recent Salesforce survey, a striking 60% of public sector IT professionals identified a shortage of artificial intelligence (AI) skills as their top challenge to implementing AI.
Why it matters: AI could save hundreds of millions of government staff hours and billions of dollars annually, according to Deloitte. The benefits of AI are only possible if the public sector workforce has the skills to harness the technology. Government agencies are already being directed to implement guidelines and build teams to support the use of AI. This includes expanding and upskilling their AI talent and designating a new Chief AI Officer, which every federal agency was recently ordered to hire.
Salesforce perspective: “Training and skills development are critical first steps for the public sector to leverage the benefits of AI. By investing in new skills like prompt development, public sector leaders can empower their workforce to use AI to increase productivity, build deeper relationships with constituents, and improve the quality of public services.” – Casey Coleman, SVP, Global Government Solutions
By investing in new skills like prompt development, public sector leaders can empower their workforce to use AI to increase productivity, build deeper relationships with constituents, and improve the quality of public services.
Casey Coleman, SVP, Global Government Solutions
IT professionals in the public sector are about a third more likely to say there’s an AI skills gap in their organization, compared to the industry* average.
By bridging the AI skills gap, organizations can create new efficiencies in the public sector. Salesforce’s survey shows that the public sector’s main goal with AI is to automate routine tasks.
*Methodology: In partnership with Vanson Bourne, Salesforce conducted a double-anonymous survey of 600 IT professionals (200 IT leaders and 400 IT individual contributors) in Australia, France, Germany, the United Kingdom, and the United States. Respondents work across industries, including technology, financial services, media and entertainment, manufacturing, retail, healthcare, the public sector, and more. The survey was fielded in December 2023 and January 2024.
Salesforce today released new bug bounty learning content on Trailhead, Salesforce’s free online learning platform. This content provides the resources for any company to build their own bug bounty program as the cyber security landscape rapidly evolves.
Why it matters: Bug bounty programs, which provide financial rewards to ethical hackers who discover software vulnerabilities, are an effective way for companies to gain insights into bad actors and stay ahead of evolving AI-powered security threats.
Go deeper: The bug bounty series on Trailhead breaks down the process for developing programs into bite-sized learning, including:
The bigger picture: From the volume of identified potential vulnerabilities to the firsthand intel on how hackers are using AI, bug bounty programs offer substantial ROI for organizations. Salesforce’s program, for example, has awarded over $18.9 million in bug bounties since 2015 to its ethical hackers, who have reported nearly 30,600 potential vulnerabilities.
Salesforce perspective: “As a trusted advisor to our customers, we share security tools and information they need to be successful. By providing the resources they need to establish their own bug bounty program and engage with ethical hackers, we are empowering companies to increase customer trust in the age of AI,” said Brad Arkin, Chief Trust Officer.
By providing the resources they need to establish their own bug bounty program and engage with ethical hackers, we are empowering companies to increase customer trust in the age of AI.
Brad Arkin, Chief Trust Officer, salesforce
The Trailblazer perspective: “As the cybersecurity landscape continues to evolve rapidly, Trailhead has been an incredible resource to continually learn new skills. Having a playbook to seamlessly set up a bug bounty program will unlock new capabilities and reshape how BACA Systems thinks about strengthening security practices,” said Andrew Russo, Salesforce Architect, BACA Systems.
