Salesforce Unveils AgentForce: AI-Powered Productivity Boost for Businesses

Dreamforce 24 - are Salesforce customers ready for Agentforce?

As we head into Dreamforce, we'll be hearing a lot more from Salesforce about its Artificial Intelligence (AI) strategy and investments, and Salesforce will be pushing Agentforce with autonomous agents across all cloud. But are Salesforce customers ready? At Valoir, we've had the opportunity to assess Salesforce customers' adoption of AI - you can read the full report here. While there is a growing excitement around the integration of generative AI into Salesforce, it's clear that organizations are embracing it at different speeds, depending on their industry, cloud usage, and how prepared their data and systems are. Since Salesforce launched Einstein in 2016, the company has been steadily adding AI capabilities to its platform. More than one in four Salesforce customers we surveyed earlier this year are currently experimenting with generative AI features, and close to 20% are already using these tools in production, albeit often in a limited capacity. But AI adoption is no flip-of-the-switch scenario. For every company diving into the deep end of generative AI, we found that almost as many aren't quite ready. Nearly one in four respondents admitted that either their organization or their data wasn't prepared for AI just yet. The takeaway here is clear: AI has massive potential, but organizations need to walk before they run -- starting with data hygiene, processes, and training. As Salesforce CEO Marc Benioff mentioned in the recent earnings call, and will mention again - I'm sure - this week, a lot of the early enthusiasm companies had about building their own models and custom AI platforms has waned as they've realized how complex and costly (not to mention risky) a DIY approach can be. It's not surprising that few, if any, Salesforce customers are using the more complex Model Builder in production today. Salesforce's initial approach, providing a soup-to-nuts approach from pre-built capabilities to Prompt Builder to Einstein Copilot to Model Builder, is likely to continue to evolve to reflect the demand of the market for more turnkey solutions with less cost and risk. One of the more interesting insights we gathered was how AI adoption varies across industries. Not surprisingly, professional services and tech companies are leading the way in experimenting with AI. However, when it comes to fully operationalizing AI, construction and engineering firms stand out as being early adopters. On the flip side, non-profits are far behind in AI adoption, with most saying they simply aren't ready for it. These industry differences show us that AI readiness is often tied to sector-specific challenges and needs. For Salesforce, this creates both a challenge and an opportunity: there's clearly room to tailor AI solutions even more effectively for certain industries. When we analyzed adoption trends across Salesforce's various cloud offerings, some clear patterns emerged. Users of the Experience Cloud are leading in experimentation, likely due to their early access to new features. Meanwhile, Analytics Cloud and Industry Cloud users are further along in embedding AI into their processes, thanks in part to their rich datasets, which make them more AI-ready. Service Cloud users, in particular, are showing a lot of enthusiasm for Prompt Builder, which allows them to streamline routine service tasks. This is where the magic of AI comes alive -- freeing up service agents to focus on high-value interactions rather than repetitive tasks. We're seeing companies realize that automation can offer real, measurable improvements in both efficiency and customer satisfaction. Although Valoir's analysis of early adopters has found customers are achieving significant value, a lot of organizations are proceeding with caution. Nearly one-third of Salesforce customers we surveyed said they don't plan to adopt AI features within the next year. This hesitation speaks to the need for companies to get their data in order and address internal readiness, both technically, politically, and culturally. Companies need to invest not just in the tools themselves but in training, leadership buy-in, and process changes to get the most out of Salesforce's generative AI capabilities. Companies that have already invested in strong data practices and are more prepared to integrate generative AI will see not just incremental but exponential benefits. Others will be looking to Salesforce's leadership to help them bridge the gaps so they don't get left behind.

Salesforce launches Agentforce AI Partner Network and updates to Data Cloud - SiliconANGLE

Salesforce launches Agentforce AI Partner Network and updates to Data Cloud Salesforce Inc. recently announced the launch of its new artificial intelligence autonomous agent platform Agentforce that can handle tasks for employees, and for enterprises to do this in a useful way, the AI agents need to connect to external tools and partners. Today at its annual Dreamforce conference in San Francisco, the company announced the launch of the Agentforce Partner Network, which will allow users to quickly get access to connections to external data sources, specialized third-party actions and unique AI skills and deploy partner-built agents. These agents can plan and take action on their behalf across multiple systems and channels, even outside Salesforce. "Customers want AI that they can deploy with trust that drives value," said Clara Shih, chief executive officer of AI at Salesforce. "So far, they haven't seen ROI from their investment in competitor products, that's because those products are disconnected from the data and metadata and cannot take action. Our own customer testing shows that Agentforce drives higher accuracy and higher time to value than building on an LLM like OpenAI and Azure." Among the new partners, IBM Corp. will launch prebuilt agents for regulated industries. The IBM banking agent will speed up loan approvals by being able to use credit checks, comply with local tax laws and generate onboarding documents. Google LLC has joined the Agentforce Partner Network to allow agents access to its ecosystem to generate Google Docs, search Gmail or trigger from Google calendar events. "In summary, we are leaving the era of disconnected copilots behind and moving into a future of a broad open connected network of interoperable third-party systems and agent forces," said Shih. The same partner network will also enable Agentforce agents to connect through Salesforce's Zero Copy Partner Network, which permits businesses high data connectivity from partners into Data Cloud. The "zero copy" means that none of the data that moves across the locations of the network is moved or copied between platforms, saving on storage costs. Partners in that network include Amazon Web Services Inc., Databricks Inc., IBM, Microsoft Corp.'s Azure, Google BigQuery and Snowflake Inc. In order to keep up with the soaring data needs to feed the burgeoning demand for AI agents that Salesforce is already seeing, the company also announced multiple feature updates to its Data Cloud hyperscale data platform today. "Data Cloud is the heartbeat of the Salesforce platform. With it our customers can build a unified view of every customer of theirs so that they can put their data to work with full fluidity," Rahul Auradkar, executive vice president and general manager for Data Cloud at Salesforce. "Data Cloud brings humans together with agents, which are powered by AI, data and agents to deliver customer success. All of this is done while enforcing enterprise data governance and security." Auradkar added that Data Cloud processed "multiple quadrillion records" during the last quarter and it is only continuing to grow. Data Cloud is adding more unstructured data types, including audio and video to its vector database capabilities. This will allow customers to easily scan through customer calls, meetings and more. It will also make it possible to create multimodal AI-powered apps that can use audio and video as part of their workflow. This is important because about 90% of the data produced by a business is unstructured and therefore "dark data" when it comes to search and AI. In any given amount of that is also audio and video, meaning that if it cannot be stored and accessible to AI agents then it's out of reach. By adding this to Data Cloud, suddenly it becomes a valuable resource. Salesforce also announced that Data Cloud is getting 50 new data connectors allowing real-time data insights from different types of data streams. This adds to the already existing 150 prebuilt connector pipelines that Data Cloud already has available. Connectors could be fairly generic as something that connects to a hyperscaler such as AWS, Google or Azure, such as a Kinesis data stream, Slack communications or Google documents, or something as specific as a particular financial services implementation. Data Cloud will also receive subsecond real-time data ingestion capability, which will allow data models to take immediate action when something changes. This means customers can create apps that can handle rapidly changing data streams for instantly critical information within fractions of a second with extremely low latency. Use cases include internet of things devices, telemetry and other situations where having immediately up-to-date information is important.

Dreamforce 24 - Wiley turns the page on productivity gains with Agentforce

A big question this week at Dreamforce is how ready and receptive the Salesforce customer base is to buy into the firm's Agentforce platform and its promised potential. Recent research from Valoir - see here - suggests that, in common with users of other vendors tech, the Salesforce community is enthusiastic curious about the potential of AI, but also cautious about adoption. There are some however who are, as it were, blazing a trail with Agentforce already. A prime example is publishing firm Wiley, which is boasting a 40% increase in self-service and efficiency and a 213% Return on Investment through its Service Cloud implementation. This time of the year is what the firm calls its 'Super Bowl time' as schools and colleges re-open their doors for fresh semesters, creating huge demand for Wiley text books and publications across the 20 countries in which the company operates. That also means an uptick in service calls into the company, with a seasonal increase in the firm's use of agents to tackle these. Already a user of Service Cloud with Einstein AI, Wiley has been piloting Agentforce to provide customers with conversational self-service functionality which taps into the firm's knowledge base to automatically resolve issues such as account access and password problems. It can also tackle registration and payment queries in order to route callers to appropriate handlers, freeing up human agents to work on more complex issues. Agentforce also delivers summaries of conversations so that agents can get up to speed with customer account history more quickly. To date, the company is finding positive results, with Agentforce outperforming Wiley's previous chatbot tech by around 40%. That's crucial at the busier times of the years, according to Kevin Quigley, Senior Manager, Continuous Improvement, Wiley, who says: Piloting Agentforce has made a noticeable difference during one of our busiest periods -- back-to-school season. It's been exciting to go live with our first agent thanks to the no-code builder, and we've seen a more than 40% increase in case resolution, outperforming our old bot. Agentforce helps to manage routine responsibilities and free up our service teams for more complex cases. Seasonality can pose some logistical challenges to any operation, and for us, it's really important that we're always providing the best customer experience possible. So the cool thing about the service agent is that it's actually able to provide personalized, not canned, support for our customers on common and simple inquiries, so that our service reps can focus on the complex issues. And it's just one really great example of how Wiley's embraced AI to accelerate that innovation. When it came to moving from its existing chatbot to Agentforce, Quigley says: We were not only able to move over the areas that the old chatbot already covered, from the old kind of canned version, to the new personalized platform, but we were also able to expand the topics we're able to cover to virtually everything in our knowledge base. It can do that search and apply the case by case responses. So that's been really wonderful. We've seen an over 40% increase in our case resolution when you compare the agent to our old chatbot. Whereas you used to require someone who could program, who was familiar with that type of logic and syntax, you've taken that skill set, you transferred it to the person who really knows your customers best, who knows your support operation best, and just knows how to articulate, what does good super support look like for that product. That's really exciting. It's really cool. It expedites the speed of implementation, but it also gives you some more control around maybe more subjective parts of how you provide support that are difficult to define as black-and-white and programming logic. And there was no need to create a team of new AI experts, he adds: We took our existing CRM experts, we took our product experts, and importantly, our technical and product support experts, got them all together. We said, 'How do we make sure that that we have a really clear vision for what the experience should be?' and then we were able to kind of just instruct our way through that vision. Quigley's advice to others when it comes to adopting AI is simple - master your fundamentals! He explains: I think the truth is not every company has mastered their fundamentals at this point, and this creates a brand new reason to put some urgency around that from an architectural perspective and an enablement perspective. So I think it's really exciting to see all these things culminate and come to fruition in this new technology. So I'd say that's my message to people, is if you weren't already focused on it - hopefully you were - but focus now, and here's a brand new reason to take it seriously.

Salesforce unveils 'Agentforce' AI agents to enhance employee productivity

Global customer relationship management (CRM) giant Salesforce on Monday introduced "Agentforce," a new suite of autonomous artificial intelligence (AI) agents designed to enhance employee productivity across service, sales, marketing, and commerce. The AI-powered platform features the Agentforce Atlas Reasoning Engine, which autonomously analyses data, makes decisions, and completes tasks. Click here to connect with us on WhatsApp The low-code tools in Agentforce enable organisations to easily build and deploy custom agents, said the company. Marc Benioff, chair and chief executive officer (CEO), Salesforce, said that Agentforce represents the third wave of AI, advancing beyond copilots to a new era of highly accurate, low-hallucination intelligent agents. "Unlike other platforms, Agentforce is a revolutionary and trusted solution that seamlessly integrates AI across every workflow, embedding itself deeply into the heart of the customer journey. This means anticipating needs, strengthening relationships, driving growth, and taking proactive action at every touchpoint. While others require you to DIY your AI, Agentforce offers a fully tailored, enterprise-ready platform designed for immediate impact and scalability," he added. Benioff said that the vision of the company was to empower one billion agents with Agentforce by the end of 2025. More From This Section HCLTech tops TIME's World's Best Companies 2024 list among Indian firms Adani, Wilmar likely to start stake sale in joint venture from October Tata Electronics to build two semiconductor manufacturing fabs in Gujarat Emperium targets Rs 775 cr revenue from Yamunanagar, Gurugram projects Axis Bank expands wealth management services across 15 new cities According to the company, Agentforce, in contrast to copilots and chatbots that rely on human requests, offers a new level of sophistication by operating autonomously, retrieving the right data on demand, building action plans for any task, and executing these plans without requiring human intervention. "Like a self-driving car, Agentforce uses real-time data to adapt to changing conditions and operates independently within an organisation's customised guardrails, ensuring every customer interaction is informed, relevant, and valuable. And when desired, Agentforce seamlessly hands off to human employees with a summary of the interaction, an overview of the customer's details, and recommendations for what to do next," said a press release from the company describing the solution. Salesforce said that the solutions are easy to customise and deploy with clicks, without the need for code. "They can be set up in minutes, are easily scalable, and work around the clock across any channel," said the company. Enterprises like Amazon Web Services, Box, Certinia, Copado, Coupa, Google, Honeywell, IBM, Workday, and Zoom are part of the Agentforce Partner Network, which has built more than 20 agents and agent actions that will be available through the Salesforce AppExchange. "Customers can leverage these specialised actions in Agent Builder to customise the out-of-the-box agents, build new agents with unique skills, and deploy partner-built agents to plan and take action on behalf of any organisation across multiple systems and channels, even outside of Salesforce," said the company. Also Read Zomato bans AI-generated images from food menus to preserve authenticity Premium Fact-checking, research: A case for taking AI's assistance in the law Premium AI fabrication raises existential questions about photography's reality Premium Indians make up 20% of TIME 100 AI list, but their impact in India unclear Seven GenAI startups from India selected for AWS Generative AI Accelerator

Salesforce Introduces Agentforce, the Next-Gen AI Agents for Enhanced Business Productivity

Salesforce unveiled Agentforce, a groundbreaking suite of autonomous AI agents that augment employees and handle tasks in service, sales, marketing, and commerce, driving unprecedented efficiency and customer satisfaction. Agentforce enables companies to scale their workforces on demand with a few clicks. Agentforce's limitless digital workforce of AI agents can analyze data, make decisions, and take action on tasks like answering customer service inquiries, qualifying sales leads, and optimizing marketing campaigns. With Agentforce, any organization can easily build, customize, and deploy their own agents for any use case across any industry. The future of AI is agents, and it's here.

Salesforce delivers next-generation AI models to power Agentforce

UAE - Salesforce (NYSE: CRM), announced new AI models, including xGen-Sales, a proprietary model trained and designed to power autonomous sales tasks with Agentforce, and xLAM, a new family of Large Action Models designed to handle complex tasks and generate actionable outputs. Together, these models developed by Salesforce AI Research will allow Salesforce customers to quickly set up and deploy autonomous AI agents that take action, driving unprecedented scale. By fine-tuning xGen-Sales to increase accuracy for relevant industry tasks, it is able to deliver more precise and rapid responses, automating sales tasks such as generating customer insights, enriching contact lists, summarizing calls, and tracking the sales pipeline. This model enhances the capabilities of Agentforce sales agents, allowing them to autonomously nurture pipeline and coach reps with greater accuracy and speed. xGen-Sales' abilities have already eclipsed other much larger models, according to Salesforce's own evaluations. xGen-Sales is a step toward the next generation of language models called Large Action Models (LAMs). In contrast to LLMs (Large Language Models) that require frequent human involvement and are mostly used for generating content, LAMs specialize in function-calling, which is the ability to execute capabilities within other systems and applications. In other words, they're able to trigger the actions needed for AI agents to independently perform tasks for people. In addition to xGen-Sales, Salesforce AI Research has delivered a new LAM family called xLAM. xLAM models offer lower costs, faster performance, and greater accuracy than many of the larger and more complex models that are available today. For example, the xLAM-1B model has outperformed larger and more expensive models despite consisting of just 1 billion parameters, which are the variables that models learn to generate results and insights. xLAM-1B, specifically, is a non-commercial, open-source model to help advance the science with the research community, while Salesforce uses a much more performant model for Agentforce. Why it matters: Organizations need AI agents that can take action for employees, augmenting their work so they can focus on more strategic priorities. These models not only understand the jobs they're intended to handle but also know their own limitations, so agents using them will recognize when it's time to hand a task over to a human being for quality assurance and completion. Salesforce recently launched its LLM Benchmark for CRM, providing organizations the opportunity to navigate the many models on the market and compare LLMs for CRM use cases. "Building and training your own AI models can be time-consuming, costly, and incredibly frustrating," said Salesforce Chief Scientist Silvio Savarese. "With Agentforce, we're able to deliver appropriately sized models, built specifically for your business with your data to drive outcomes." Behind the news: To train the xLAM models, Salesforce AI Research created APIGen, a robust, proprietary pipeline for generating high-quality synthetic data. Positive results were almost immediate, with xLAM 8x22b capturing a No. 1 ranking on the Berkeley Leaderboards for function calling, surpassing GPT-4, according to Salesforce's own evaluation. The xLAM-8x7b model is ranked sixth. Both beat models that are many times their size. The four language models in the xLAM family include: Tiny (xLAM-1B): The "Tiny Giant" features 1B parameters. Given the model's compact size, it is most suitable for on-device applications where larger models are more impractical. The xLAM-1B can be used to create powerful and responsive AI assistants that can run locally on smartphones or other devices with limited computing resources. Small (xLAM-7B): The 7B model is designed for swift academic exploration with limited GPU resources. It can be used to perform planning and reasoning tasks for agentic applications in a light-weight environment. Medium (xLAM-8x7B): An 8x7B mixture-of-experts model, the 8x7B is ideal for industrial applications striving for a balanced combination of latency, resource consumption, and performance. Large (xLAM-8x22B): The 8x22B is a large mixture-of-experts model that allows organizations with a certain level of computational resources to achieve optimal performance. The analyst perspective: "Open-sourcing of LAM models is a game-changer," said Rena Bhattacharyya, Chief Analyst and Practice Lead, Enterprise Technology & Services at GlobalData. "Salesforce's 'Tiny Giant' xLAM-1B exemplifies how advanced, small, action-oriented AI can revolutionize business efficiency and innovation, making high-performance AI accessible to a broader range of companies. Salesforce continues to be a leader in accelerating AI adoption across sectors." The Salesforce perspective: "We envision a future in which sellers are augmented by AI to help them drive selling efficiency, freeing up precious time to focus on their customers," said MaryAnn Patel, SVP, Product Management, SVP, Product Management at Salesforce. "The xGen-Sales model is purpose built to help companies build generative AI solutions that will augment the work of their sales teams with Agentforce." Salesforce AI Research is Salesforce's artificial intelligence research lab, which develops new technological breakthroughs in the field. The team comprises researchers, engineers, and product managers working to shape the future of AI for businesses via foundational research that directly informs product development. Availability: The non-commercial, open-source version of the xLAM suite of LAMs is available on Hugging Face for community review and benchmark testing. A significantly more advanced, proprietary version is powering Agentforce. xGen-Sales recently completed a pilot and will be generally available soon. About Salesforce Salesforce empowers companies of every size and industry to connect with their customers in a whole new way through the power of AI + data + CRM. For more information about Salesforce (NYSE: CRM), visit: www.salesforce.com. Media Contacts: Michelle Oribello Wallis [email protected]