SAP has been named a leader in the IDC MarketScape: Worldwide Agentic Marketing for B2C Enterprises 2026 Vendor Assessment (doc #US54113426, September 2026). Deliver personalized, AI-driven engagement across every channel and touchpoint Learn how We believe this recognition reflects SAP&#8217;s commitment to helping enterprises deliver intelligent, personalized customer experiences, enhanced by AI, built on trusted data and connected end-to-end business processes, and grounded in real-life business context. Joule Agents, Joule Assistants, SAP Business Data Cloud, and SAP Engagement Cloud together provide the agentic marketing backbone to advance this vision of the Autonomous Enterprise. AI is evolving from assisting marketers to executing and acting on their behalf, creating a fundamental opportunity for organizations to completely rethink how they engage customers. Agentic marketing enables AI agents and assistants to understand context, take action, and help brands deliver more relevant personalized experiences. Trust is the foundation to this shift. As AI takes on greater responsibility, organizations need solutions built with security, compliance, governance, and trusted business data so customers can confidently rely on AI-driven interactions and decisions. Why agentic marketing matters Consumers are becoming more comfortable using AI, reshaping their expectations of the brands they love and the organizations they interact with. SAP&#8217;s 2026 Global Customer Loyalty Index indicates that 33% of consumers now trust AI assistants to complete purchases on their behalf, while 31% say interactions with AI assistants make them more loyal to a brand. The trend is even more pronounced across younger consumers. Across generations, we see AI playing an influential role in purchasing decisions and long-term customer loyalty. For example, a consumer might ask an AI assistant to reorder a household product, book a trip, or recommend a new product based on past preferences. For those interactions to succeed, customers must trust that the AI is acting on accurate information, protecting their data, and making decisions within appropriate guardrails. Organizations from manufacturers to fashion brands are seasoned in using generative AI to create content, build audiences, and improve marketing efficiency. Attention is turning to agentic marketing: moving from AI that recommends to AI that acts. IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of technology and suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each supplier’s position within a given market. The Capabilities score measures supplier product, go-to-market and business execution in the short-term. The Strategy score measures alignment of supplier strategies with customer requirements in a 3-5-year timeframe. Supplier market share is represented by the size of the icons. Marketing context meets business reality Most marketing platforms understand customer interactions well: clicks, opens, purchases, and browsing behavior. Those signals matter, but they only tell part of the story. The most valuable signals often live elsewhere across the enterprise in inventory availability, order status, fulfillment performance, product profitability, return behavior, and subscription and replenishment cycles. Marketing often sees customer behavior, while ERP systems see business reality. The greatest opportunity for AI emerges when those worlds come together. Because SAP sits at the heart of how many of the world&#8217;s leading enterprises operate, marketers can access the operational intelligence that lives within the systems running the business, enabling AI to make decisions based not only on what customers are doing, but on what the business can actually deliver. In an age of agentic marketing, that distinction matters. Why SAP was recognized as a Leader SAP was recognized for the following strengths by the IDC MarketScape: With the agentic layer of Joule, marketers get real-time, context-aware personalization without having to reconcile SAP and non-SAP data themselves, since Joule Agents and Joule Assistants draw on a harmonized SAP Business Data Cloud foundation. Teams receive role-based guidance for navigating complex workflows across Autonomous Domains, while everyday tasks such as retrieving information, connecting enterprise data, and completing multi-step processes happen independently, freeing staff from manual execution. In SAP Engagement Cloud, this means personalization and segmentation scale on their own, and the 4Q25 CX release lets customer service issues resolve autonomously, backed by SAP&#8217;s enterprise data layer and SAP Knowledge Graph, instead of requiring escalation. Because Joule Studio is low code, organizations with reasonable levels of in-house development capabilities can build custom agents themselves, without waiting on IT or writing code, while still connecting to Joule, the SAP Customer Experience portfolio, and third-party systems grounded in SAP Knowledge Graph and SAP Business Data Cloud. For sales and marketing teams, this means faster deal cycles and higher conversion: a sales-assistant agent, for example, automatically surfaces buying patterns and product recommendations from purchase history, sparing reps the manual analysis. Marketers running campaigns across email, digital ads, wallet, conversational, and web can manage them all from a single platform instead of stitching together point solutions. Because the engagement events service connects external data sources into real-time segmentation, campaigns stay relevant as customer behavior shifts. AI-assisted tools such as natural-language product search, AI-generated consumption summaries, and an AI-assisted report builder cut down the manual work of building segments and reporting, enabling marketers to focus on strategy while AI handles recommendations, translation, prediction, and reporting at scale. AI that drives business results Organizations around the world are using SAP&#8217;s AI-powered customer engagement capabilities to create more connected experiences and drive business results. For example: Adler Mannheim uses an AI-powered data foundation to boost fan subscriptions by 50% and improve pre-game fan engagement by 80%. Molton Brown combines AI-enhanced commerce and personalization to create seamless customer experiences that strengthen loyalty while maintaining 100% uptime during peak periods. Douglas connects AI-driven shopping experiences, such as virtual beauty tools, and personalized product recommendations to deliver engaging and elevated customer experiences. Advancing toward the Autonomous Enterprise Being named a Leader in the IDC MarketScape: Worldwide Agentic Marketing for B2C Enterprises 2026 Vendor Assessment is an important milestone for SAP and the evolution of agentic marketing as a category. As organizations move from experimenting with AI to deploying agents that can reason, recommend, and act, we believe this recognition validates SAP&#8217;s approach of combining AI, trusted business data, and connected business processes to help customers navigate this next phase of transformation. The vision of the Autonomous Enterprise is one in which AI-powered agents help organizations connect customer, operational, and business data to make and execute decisions with greater speed, relevance, and scale. In this future, AI doesn&#8217;t replace human expertise; it amplifies it. Intelligent assistants and agents can help teams act faster and more effectively, with governance and guardrails that keep people responsible for the strategy, creativity, and judgment that build lasting customer relationships. Ultimately, the success of agentic AI will depend not only on what it can do, but on how much customers and businesses trust it to act on their behalf. Learn more Download the IDC MarketScape: Worldwide Agentic Marketing for B2C Enterprises 2026 Vendor Assessment excerpt. Sara Richter is chief marketing officer for SAP Engagement Cloud at SAP. Sign up for the SAP News Center newsletter to receive stories, highlights, and updates each week Subscribe here

SAP Named a Leader in the IDC MarketScape: Worldwide Agentic Marketing for B2C Enterprises 2026 Vendor Assessment

Condé Nast’s editorial teams had no fast way to do multimodal video discovery. They were spending an average of 250 minutes per content discovery task, manually scrubbing through a library of more than 140,000 videos. They relied on titles and descriptions to find relevant clips. In a media environment where speed-to-market directly determines revenue capture, this process created measurable operational drag across brands such as Vogue, GQ, Vanity Fair, and Wired. The core problem was structural: Existing search tools can’t look inside video content. Teams depended on institutional knowledge to locate assets, creating single points of failure when specific individuals were unavailable. Meanwhile, underutilized content sat in the archive undiscoverable because no keyword in a title or description connected it to the queries editors were actually running. To solve this, Condé Nast partnered with the AWS Generative AI Innovation Center (GenAIIC) to build an AI-powered multimodal video discovery solution. Built on Amazon Bedrock and Amazon OpenSearch Service, the solution runs intent-based semantic search across video transcripts, visual elements, and audio. The team selected the TwelveLabs Marengo embedding model for its native ability to jointly encode visual, audio, and transcript signals. Marengo powers all five capabilities described in the following section. This reduced discovery time from 250 minutes to under 2 minutes per task. In this post, we describe the architecture, explain why we chose specific technology choices, and share the business outcomes the solution delivered. Why semantic search (and why this architecture) When the team scoped the problem, two constraints shaped the solution design. First, keyword search was fundamentally insufficient. Editorial teams don’t search for “yoga_tutorial_march_2024.mp4.” Instead, they search for “beginner yoga content with calming backgrounds” or “behind-the-scenes fashion week moments.” The search layer needed to understand intent, not match strings. This pointed directly to vector embeddings that capture semantic meaning across modalities (visual, audio, and transcript). Human-authored metadata alone could not provide that level of understanding. Second, the video library was large enough (over 140,000 videos) that any solution needed to separate the expensive, compute-heavy work of generating embeddings from the low-latency work of serving search results. A monolithic architecture would force a tradeoff between ingestion throughput and query responsiveness. Decoupling them meant each plane could scale, fail, and evolve independently. This decision proved essential during backfill processing. These two constraints led to the solution’s core design: multimodal vector embeddings generated by the TwelveLabs Marengo model on Amazon Bedrock, indexed in Amazon OpenSearch Service, and served through a purpose-built query tier. This tier converts natural language into vector searches and returns precise timestamps. We accessed Marengo through Amazon Bedrock because Bedrock offers a range of foundation models (FMs) through a single API. It also applies AWS governance controls, including AWS Identity and Access Management (IAM) for access, Amazon Virtual Private Cloud (Amazon VPC) for network isolation, and AWS CloudTrail for auditability. That combination let the team adopt a specialized embedding model without building or operating its own model-serving infrastructure. For the vector index, Amazon OpenSearch Service provides managed k-nearest neighbor (k-NN) search with multi-AZ replication and metadata filtering for hybrid queries. This let the team run low-latency similarity search across more than 140,000 videos without managing the underlying search cluster. Solution capabilities The solution gives editorial teams a fundamentally different way to interact with their video library. The following capabilities are available through the solution: Intent-based search: Users describe what they need in natural language (for example, “beginner yoga content” or “celebrity interview about sustainability”) and receive contextually relevant clips with precise timestamps. Multimodal understanding: Search spans video transcripts, visual elements, and audio simultaneously. A clip is discoverable by what’s said, what’s shown, or what’s heard. Image-based queries: Users can upload a reference image to find visually similar content across the archive. Typo tolerance and intent resolution: The search layer handles imprecise queries and focuses on meaning rather than exact keyword matches. Timestamp precision: Results pinpoint exact moments within videos, alleviating the need to watch entire clips. The following diagram illustrates the high-level architecture, showing the ingestion and indexing plane and the query and serving plane. Figure 1: High-level architecture of the multimodal video discovery solution, showing the ingestion and indexing plane and the query and serving plane. Architecture walkthrough The solution consists of two decoupled planes: an asynchronous ingestion pipeline that makes videos searchable, and a synchronous serving tier that handles user queries. Ingestion and indexing plane When a new video is uploaded, the following sequence executes: Upload: The video lands in an Amazon Simple Storage Service (Amazon S3) bucket that holds source video (Video Outputs in the diagram). Orchestration: A new upload raises an event (Video Search Events in the diagram). That event pushes the video’s metadata into the ingestion process and coordinates the pipeline. Preprocessing: The ingestion service validates the video and extracts metadata (format, duration, resolution). Chunking: The same ingestion service, running on Amazon Elastic Container Service (Amazon ECS) with AWS Fargate, splits the video into segments. An Auto Scaling group scales it horizontally across multiple Availability Zones to process chunks in parallel. Embedding generation: Each chunk is processed through the TwelveLabs Marengo embedding model on Amazon Bedrock using asynchronous invocation. Marengo generates multimodal vector embeddings capturing the semantic content of each segment across visual, audio, and transcript dimensions. Indexing: Generated embeddings are written to an Amazon S3 bucket for durability (Video Embeddings in the diagram) and indexed into an Amazon OpenSearch Service cluster for vector similarity search. The pipeline is event-driven and orchestrated end to end. This gives the team per-step retry logic, parallel processing across chunks, and full traceability for debugging. These were capabilities the team relied on heavily during the initial 140,000-video backfill. Query and serving plane When a user performs a search, the following sequence executes: Request routing: The request enters the VPC through an internet gateway and reaches the external Application Load Balancer (ALB). Backend processing: The external ALB distributes requests to the frontend service on Amazon ECS with AWS Fargate. The frontend service calls the search service through an internal ALB. Auto Scaling groups scale both services across multiple Availability Zones. Vector search: The search service converts the user’s query into a vector embedding and performs a k-NN similarity search against the Amazon OpenSearch Service index. Metadata enrichment: The search service reads video metadata (titles, thumbnails, references) from Amazon DocumentDB (with MongoDB compatibility) to enrich results. Response: Combined results return to the user with relevant clips and precise timestamps. High availability (HA) design The solution is multi-AZ throughout. The following components are deployed for resilience: Amazon OpenSearch Service: Synchronous replication across three Availability Zones (two active, one standby). Amazon DocumentDB: Primary node and standby replica across two Availability Zones. Compute tiers: The ingestion service and the frontend and search services use Auto Scaling groups distributed across Availability Zones, with the ALBs routing around failures. Network isolation: Compute and data workloads reside in private subnets. Only the external ALB is publicly reachable, and outbound traffic flows through NAT gateways. Results Condé Nast conducted a benchmarking workshop in May 2026 to quantify impact. The following results reflect Condé Nast’s measurements from that workshop. 99.2 percent reduction in content discovery time, from 250 minutes to approximately 2 minutes per task (measured in the May 2026 benchmarking workshop described earlier). Over 90 percent reduction in manual video review effort. Teams receive targeted clips with precise timestamps instead of scrubbing through individual videos (May 2026 benchmarking workshop described earlier). Approximately $800,000 in estimated annual operational savings, based on productivity gains from alleviating repetitive manual search across the organization (estimate derived from the May 2026 benchmarking workshop described earlier). Improved asset discoverability: Previously underutilized videos surfaced through semantic connections that metadata-only search could not identify. Accelerated revenue capture: Faster discovery means faster response to advertiser requests and sales opportunities, shortening the path from inquiry to monetization. “The AWS team, along with the GenAI and TwelveLabs attendees, helped us explain the business case clearly and concisely. I am optimistic about notching more progress towards our workflow goals in the near future.” — Billy Keenly, Global Senior Director, Creative Optimization, Condé Nast What we learned Building this solution across an over 140,000 video library surfaced practical lessons that go beyond what architecture diagrams capture. Early user research shaped the entire embedding and query design. The team interviewed editorial staff to understand how they describe content in their own words. Those conversations defined the right abstraction level for semantic search. Without that grounding, the system risked optimizing for queries no one actually types. Separating ingestion from serving proved essential at this scale. Each plane could evolve, scale, and fail independently. The solution has been running in production for six months, and production search stayed available while the ingestion pipeline reprocessed backfill content or received updates. Finding the right video segment length required deliberate experimentation. Short segments lost context. Long segments diluted the semantic signal. Iterative benchmarking against real editorial queries drove the team toward a duration that balanced precision and recall. Asynchronous embedding generation was non-negotiable at this scale. Synchronous calls to the embedding model would have created bottlenecks across over 140,000 videos. Asynchronous invocation through AWS Step Functions allowed the pipeline to process the backlog without blocking, and it remains the pattern for ongoing ingestion. Building in multi-AZ availability from day one avoided costly retrofits. For a solution that editorial teams rely on throughout the workday, even brief outages translate directly to lost productivity. It’s cheaper to build this in from the start than to add it later. Conclusion This post described how Condé Nast and AWS built a multimodal video intelligence solution using Amazon Bedrock, Amazon OpenSearch Service, and TwelveLabs Marengo. The solution replaces metadata-only search with intent-based semantic search across visual, audio, and transcript modalities. The result: a 99.2 percent reduction in discovery time and an estimated $800,000 in annual operational savings across an over 140,000 video library. This pattern applies to organizations managing large video libraries, including broadcasters, streaming services, sports leagues, and enterprise media teams. The decoupled architecture adapts to different embedding models and content types as multimodal AI capabilities evolve. To get started: Learn more about Amazon Bedrock and explore the Amazon Bedrock User Guide for implementation guidance. Explore Amazon OpenSearch Service and the k-NN vector search documentation for similarity search. Read about TwelveLabs models on Amazon Bedrock for multimodal video understanding. Review the AWS Step Functions documentation for orchestrated processing pipelines. Contact the AWS Generative AI Innovation Center to discuss your use case or reach out to your AWS account team. To get started with multimodal video discovery on AWS, visit Amazon Bedrock and Amazon OpenSearch Service. To learn how the AWS Generative AI Innovation Center can help your team build AI-powered solutions, visit the AWS Generative AI Innovation Center page. Related posts Unlocking video understanding with TwelveLabs Marengo on Amazon Bedrock Multimodal embeddings at scale: AI data lake for media and entertainment workloads Acknowledgements We would like to thank the following contributors for their work on this solution: Shinan Zhang, Xiaoye Qian, Jessica Malca, Elvis Joseph, and Jason Janetzke. About the authors Mariah Miller Mariah is a Senior Generative AI Strategist at the AWS Generative AI Innovation Center, where she leads AI innovation strategy for technology, media, entertainment, sports, and gaming. She drove the overall solution architecture strategy and customer engagement for this project, aligning multimodal AI capabilities to Condé Nast’s content discovery requirements. Sirajus Salekin Sirajus is an Applied Scientist at the AWS Generative AI Innovation Center, where he builds generative AI solutions with enterprise customers, turning early prototypes into scalable production systems across multimodal AI use cases. In this project, he led the multimodal video understanding and semantic search work, designing how video content is embedded, indexed, and made searchable by intent across Condé Nast’s library. He also ran the evaluation and validation of the solution’s results. Dan Alvarez Dan is a Senior Solutions Architect based out of New York City. He enjoys working with enterprise customers to revolutionize how they think about technology and their journey on the cloud. Dan has always been a builder at heart and loves diving deep to solve difficult business and technical problems. In his spare time, you’ll find him riding his bike or enjoying all the pizza NYC has to offer. Shashank Nigam Shashank is a Customer Solutions Manager in AWS covering media and entertainment customers and helping them re-invent their businesses. He led the implementation and deployment of the solution on the customer’s AWS environment.

How Condé Nast built multimodal video discovery with Amazon Bedrock

In the leadup to Dreamforce 2026, dozens of announcements outlined what enterprises look like when software meets AI, but the event itself filled in the picture. Organizations are being transformed. Ways of working within them are transforming just as quickly. To see how headless architecture, Slack Code, purpose-trained models, and more are going to shape the future, read on for the most compelling takeaways from Dreamforce 2026. 1. Software used to make you come to it. Now it comes to you. The way people interact with enterprise applications hasn’t really changed for decades, said Khushwant Singh, SVP of Product Management for Platform, at the AIforce Keynote. “You open an application, you navigate to the right page, you complete the task &#8230; and you do that all over again.” AI inverts that: Agents don’t need a screen. They need the capabilities beneath the screen — the APIs, metadata, business semantics, permissions, and governance that Salesforce has spent 27 years building. And now with AIforce, all those capabilities are available to agents through any AI interface — no need to log into Salesforce at all. “Agents need a different entrance into the enterprise,” Singh said. “That’s why we opened up Salesforce and all of its trusted capabilities to any AI, any interface, any agent, without requiring a Salesforce UI.” “Other software companies, they think their product is the UI,” Patrick Stokes, Salesforce President of Applications and Marketing, said during the Main Keynote. “At Salesforce, our product is the trust that all of you, our customers, put into us to hold your data, to hold your workflows, your business processes, your permissions, your security rules. That is the Salesforce product.” Giving agents direct access to the information and metadata inside Salesforce unlocks tremendous value and powerful new ways of working. For one thing, you no longer have to know how to use Salesforce to use Salesforce. Building an app or updating a record used to require specialized training. Now, it’s available to anyone via natural-language prompt, making the power of the platform accessible to a new generation of builders. And those builders can now tap into the entire platform, pull out the relevant information and functionality, and create custom interfaces that give them everything they need and nothing they don’t. That’s because AIforce turns all the metadata that admins and developers have encoded inside Salesforce over the last 27 years — the definitions of objects, fields, relationships, permissions, and business logic — into composable elements. So those same underlying capabilities can now be built into dynamic, intelligent, open, and composable AI interfaces without rewriting the core business logic. As Salesforce Chair and CEO Marc Benioff put it in his keynote presentation, “You’re going to see AI interfaces that are dynamic and intelligent, that are composable and alive. … The interface itself is alive.”Read what they’re saying. 2. Purpose-trained models provide rightsized intelligence for every task. You can certainly use a slotted screwdriver to twist in a crosshead screw, but a Phillips performs better because it’s made specifically for the job. That’s the thinking behind Koa, Salesforce’s first purpose-built CRM reasoning model. Through a partnership with NVIDIA, Koa is built on the NVIDIA Nemotron 3 Super architecture, and it’s optimized specifically for enterprise tasks. One of the reasons Salesforce partnered with NVIDIA for this project is that Nemotron is pretrained on datasets that have clear provenance — training data that is open and available for review. “It’s not a dataset that’s been scraped from somewhere,” said Jayesh Govindarajan, EVP of Salesforce AI and Agentforce, at a press Q&A session. “You could check what data it was pretrained on, which is really important.” Then, Salesforce created synthetic simulation environments for over 14 industry personas across sales, service, marketing, and commerce to simulate real-world user interactions. This leveraged decades of Salesforce’s business context on CRM workflows without using any private customer data — a level of expertise no general-purpose model can match. As Govindarajan and Silvio Savarese, EVP and Chief Scientist of Salesforce AI Research, wrote: “We know what a service escalation looks like from the inside, what qualifying a lead actually involves, what ‘resolved’ means when a customer is on the other end. That … is exactly what we could give Koa.” In internal pilots, Koa proved 14% better than internal benchmarks at retaining context during complex, lengthy, multi-turn conversations. It delivered 15% more relevant answers to end users and was 11% more precise in executing the correct tool calls, meaning autonomous agents make fewer mistakes and resolve tasks faster. Read what they’re saying. 3. Slack is the front door to the enterprise. “There’s a new work operating system for the AI era, and that is Slack,” said Rob Seaman, EVP and GM of Slack, at the Slack Keynote. “It’s the only place where AI becomes multiplayer.” In Slack, teams can work together with AI in shared channels, where agents can also connect with all the conversational context inside Slack, as well as the data, agents, and workflows inside Salesforce. Without that shared interface, AI stays stuck in “single player” mode, where one person queries a model in an isolated terminal. The individual learns; the organization doesn’t. Bring agents into Slack instead, and intelligence compounds in the open.Agents can read across channels and DMs; act on Salesforce data; and even help teams write, review, and ship code together, turning what used to be a single‑player sport into a multiplayer one. Decisions become visible, reusable, and improvable by the whole organization. “Your company becomes smarter, and your company moves faster,” Seaman said. This multiplayer dynamic is already reshaping software engineering. With Slack Code, developer teams can collaborate with coding agents directly in shared channels instead of separate environments. Because the work happens in public, nontechnical staff like marketers and product managers can review the builds and guide the project alongside engineers without needing specialized developer tools. As Slack CMO Ryan Gavin explained during the session, “We are in the age of builders, and we need to turn everyone in our companies into builders.” Read what they’re saying. Related Futures Issue 5: Agentic Enterprise Futures Building the Agentic Enterprise: Secrets from Early Adopters 1 min read Older Posts 4. The harness and the model work together to run the Agentic Enterprise. “We’ve been running our little eight-figure business with three humans and 21 agents on Salesforce for over a year,” said SaaStr CEO Jason Lemkin at the keynote session, “The Enterprise AI Harness for the Agentic Enterprise.” “Agents are great, right? But you can’t trust them. You need a harness.” The issue isn’t whether AI agents can be powerful or productive — they clearly can — but whether enterprises can safely trust them with real revenue, real customers, and real data. The AI harness is the crucial layer between raw model intelligence and the operational realities of complex organizations. Rohan Kumar, Salesforce President and Chief Platform and Engineering Officer, laid out the six core capabilities that any enterprise harness needs to make AI trusted and governable at scale: Trusted models: Includes Salesforce’s new CRM-tuned model, Koa, as well as frontier models like Fable so enterprises can select the right model for each use case based on price and performance. Trusted context: Brings together customer context with data, metadata, semantics, knowledge, real-time signals, memory, and an understanding of how work gets done across the enterprise. Trusted agency: Captures how agents plan and decide — the plans, steps, and runtime logic that translate a desired outcome into an executable path. Trusted actions: Securely connects AI to applications, APIs, workflows, tools, and business processes so agents can move from understanding what needs to happen to getting it done. Trusted governance: Covers data quality, classification, and policy enforcement so only appropriate information is exposed to AI. Trusted security: Applies identity, permissions, privacy, data protection, and runtime security to what AI can access and what agents are allowed to do. Salesforce is pulling all of these capabilities together into one composable harness, all unified by an AI control plane to discover, manage, evaluate, and cost-govern agents across the enterprise stack. Models alone can’t run a business. They reason and can show what’s probable, but they can’t determine what’s allowed. The Enterprise AI Harness is a way to wrap raw model intelligence in enterprise-grade context, control, and trust so organizations can safely move from experimentation with agents to running their businesses with them. Read what they’re saying. 5. ROI can happen fast. It took one hour for Fulton Bank to go from hearing about Agentforce to setting up its first agent. Within a week, it had rolled it out to power users. Six weeks later, 3,000 employees were using it. How? By following the three-step Agentforce ROI playbook outlined in the Agentforce Keynote, “From Fast Start to Real ROI.” The playbook, said Mark Wakelin, EVP and GM for Agentforce, unfolds in three steps: Pick a use case and deploy it fast; customize and extend that use case across the enterprise; and observe and optimize agents in real time. Fulton Bank used Hunter, a prebuilt outbound sales agent, to get going quickly. Avanthika Ramesh, Senior Director of Product Management for Agentforce, described how Hunter helped set concrete revenue goals (like reengaging $340,000 of at‑risk pipeline), automatically generate and schedule tasks, and draft outreach in the seller’s own voice by connecting to tools like Gmail. For Fulton Bank, this meant they could turn Hunter on and have it “literally hunting for new opportunities” almost immediately. Southwest Airlines built a focused AI assistant to answer customer questions, freeing human reps for high‑empathy interactions. Then it expanded into more complex processes like name changes, receipt requests, and travel vouchers, orchestrated via subagents within a unified “superagent” experience. Southwest achieved millions in productivity gains and seven times the ROI in a relatively short time frame. Better still, Southwest improved its agents by observing how they were interacting with its customers. “Your customers will provide you their needs a lot faster in real time than all the planning sessions you could have had internally,” said Justin Bundick, VP of AI & Intelligence Platforms at Southwest Airlines. “You do that by analyzing the conversations that are happening between the customer and that AI product and then making tweaks and changes on a very rapid basis. It’s a brand-new operating model that those companies are going to have to adopt.” Got seven minutes? Watch the Dreamforce keynote highlights here. Go deeper: How higher-education institutions are putting agents to work. Why Salesforce post-trained its own reasoning model. Read all the announcements from Dreamforce 2026.

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