Salesforce Agentforce: The Hype vs Reality of AI-Powered Customer Service

Salesforce’s much-hyped Agentforce promised to bring large language model (LLM) intelligence directly into the CRM. But as early adopters quickly learned, the execution has fallen short. Customers report limited flexibility, integration challenges, and performance gaps that don’t align with the complexity of enterprise use cases. The result? A wave of Salesforce customers are re-evaluating their AI strategy, looking beyond Salesforce-native tools to harness the true potential of LLMs. 🔎 Why Agentforce Fell Short While Agentforce had an ambitious vision, several factors hindered adoption: • Closed ecosystem – Rigid guardrails prevented enterprises from bringing their own fine-tuned models. • Scalability concerns – Struggled to handle large-scale enterprise data. • Vendor lock-in – AI tied tightly to Salesforce’s stack, leaving little room for multi-cloud or hybrid approaches. • Limited connectors – Many organizations rely on MCP servers and diverse AI platforms (OpenAI, Anthropic, Llama, Mistral, etc.) which Agentforce did not support natively. 🌐 The New Path: External AI Platforms + Salesforce Data Enterprises don’t want to be boxed in. They need AI that can: • Plug into Salesforce data easily (Cases, Accounts, Opportunities, Knowledge). • Leverage enterprise-grade AI platforms that already fit into their ecosystem. • Support MCP (Model Context Protocol) for interoperability across AI tools, clouds, and servers. • Enable fine-tuning on proprietary datasets without vendor restrictions. By decoupling Salesforce data from Agentforce and instead exposing it via APIs, Data Cloud, or Data Federation, organizations can run LLMs where it makes the most sense: on external AI platforms that support flexibility, compliance, and scaling. ⚡ Practical Approaches 1. API + Data Cloud Integration Export Salesforce objects (Case, MessagingSession, Knowledge) securely to an external AI pipeline for training and inference. 2. n8n / MuleSoft / Middleware Orchestration Use low-code automation platforms to route data between Salesforce and LLM servers. 3. MCP Servers for Standardization Adopt MCP to allow AI models to interoperate with Salesforce data and other enterprise systems without heavy customization. 4. Fine-Tuned Enterprise Models Train LLMs on your domain-specific Salesforce data (case logs, support chats, sales playbooks) while keeping the model infrastructure in your control. 🏆 The Benefits of Going External • Freedom of Choice: Use the right LLM for the right use case. • Enterprise Security: Keep sensitive data in compliance with company policies. • Future-Proofing: Avoid being tied to a single vendor’s AI limitations. • Faster Innovation: Experiment with open-source and commercial LLMs side by side. 📌 Conclusion The Agentforce experiment showed us what’s possible—but also what enterprises truly need: flexibility, interoperability, and ownership over their AI stack. Wait is over Salesforce customers jump …

This is worth a read. The challenge with AgentForce for someone who has been directly and indirectly involved in promoting it is that more flexibility exists in emerging open standards like MCP, A2A and a plethora of smaller AI tools. These provide a viable alternative to being locked in to a vendor with a rigid framework. AgentForce might be right for some, but for those already being squeezed - looking at a suite of existing, proven and cost-efficient tools offers a much better fit.

Salesforce’s much-hyped Agentforce promised to bring large language model (LLM) intelligence directly into the CRM. But as early adopters quickly learned, the execution has fallen short. Customers report limited flexibility, integration challenges, and performance gaps that don’t align with the complexity of enterprise use cases. The result? A wave of Salesforce customers are re-evaluating their AI strategy, looking beyond Salesforce-native tools to harness the true potential of LLMs. 🔎 Why Agentforce Fell Short While Agentforce had an ambitious vision, several factors hindered adoption: • Closed ecosystem – Rigid guardrails prevented enterprises from bringing their own fine-tuned models. • Scalability concerns – Struggled to handle large-scale enterprise data. • Vendor lock-in – AI tied tightly to Salesforce’s stack, leaving little room for multi-cloud or hybrid approaches. • Limited connectors – Many organizations rely on MCP servers and diverse AI platforms (OpenAI, Anthropic, Llama, Mistral, etc.) which Agentforce did not support natively. 🌐 The New Path: External AI Platforms + Salesforce Data Enterprises don’t want to be boxed in. They need AI that can: • Plug into Salesforce data easily (Cases, Accounts, Opportunities, Knowledge). • Leverage enterprise-grade AI platforms that already fit into their ecosystem. • Support MCP (Model Context Protocol) for interoperability across AI tools, clouds, and servers. • Enable fine-tuning on proprietary datasets without vendor restrictions. By decoupling Salesforce data from Agentforce and instead exposing it via APIs, Data Cloud, or Data Federation, organizations can run LLMs where it makes the most sense: on external AI platforms that support flexibility, compliance, and scaling. ⚡ Practical Approaches 1. API + Data Cloud Integration Export Salesforce objects (Case, MessagingSession, Knowledge) securely to an external AI pipeline for training and inference. 2. n8n / MuleSoft / Middleware Orchestration Use low-code automation platforms to route data between Salesforce and LLM servers. 3. MCP Servers for Standardization Adopt MCP to allow AI models to interoperate with Salesforce data and other enterprise systems without heavy customization. 4. Fine-Tuned Enterprise Models Train LLMs on your domain-specific Salesforce data (case logs, support chats, sales playbooks) while keeping the model infrastructure in your control. 🏆 The Benefits of Going External • Freedom of Choice: Use the right LLM for the right use case. • Enterprise Security: Keep sensitive data in compliance with company policies. • Future-Proofing: Avoid being tied to a single vendor’s AI limitations. • Faster Innovation: Experiment with open-source and commercial LLMs side by side. 📌 Conclusion The Agentforce experiment showed us what’s possible—but also what enterprises truly need: flexibility, interoperability, and ownership over their AI stack. Wait is over

Intriged by reading below post from SRINI Pusuluri . Well written ! 👏 As the landscape of AI-driven CRM evolves, enterprises are seeking platforms that offer more than just promises. At DevRev, we understand the critical need for flexibility, scalability, and seamless integration that modern businesses demand. Unlike some solutions that impose rigid vendor constraints and scalability issues, DevRev provides an open ecosystem where you can fine-tune your AI models, integrate effortlessly with enterprise-grade platforms, and maintain control over your data. Happy to discuss with anyone on the how behind DevRev

I had an interesting conversation with an enterprise solution architect yesterday. He asked me: "Why wouldn't we just let the AI agents orchestrate our APIs? After all, agents know how to talk to backend systems through MCP." It's a fair question. But it reveals a fundamental misunderstanding about what AI agents can—or should—do. The Dangerous Assumption: Yes, AI agents like ChatGPT can talk to your raw APIs through MCP. But thinking they should orchestrate your enterprise systems isn't just giving away control of your brand experience—it's downright dangerous. Here's what actually happens when you expect AI to orchestrate your stack: Customer: "Add to cart with my corporate discount" ❌ What the Agent will do: - Retrieve your customer's profile information (dangerous) - Decipher discount rules from Salesforce (exposes competitive advantage) - Query inventory levels in SAP (reveals stock positions) - Makes dozens of API calls in unpredictable patterns (performance nightmare) Why "Let AI Figure It Out" Fails: When agents orchestrate your enterprise commerce stack directly: 🛑 Every AI agent gets your complete architecture blueprint 🛑 Your business rules—your competitive advantage—become public knowledge 🛑 Backend APIs get hammered with uncontrolled, inefficient call patterns 🛑 Customer privacy is compromised as agents access raw customer data 🛑 You hand your customer relationships to third-party platforms MCP is very powerful and it makes your business discoverable by AI agents. But agents should discover commerce capabilities—checkout, returns, product discovery—not your underlying technology choices. They shouldn't know you're running SAP for ERP, Salesforce for commerce, Stripe for payments, or any other system details. Why an Orchestration layer is Essential for MCP Servers: The orchestration layer abstracts backend complexity while giving you, the brand, complete control over business logic and data security. With orchestration, you expose clean capability endpoints, not a blueprint of your backend architecture: discoverProducts - not raw database queries processReturn - not internal workflow details submitOrder - not payment provider specifics Your architecture stays private. Your business logic stays protected. Your customer data stays secure. The choice is actually quite simple. Either build an orchestration layer that protects your business while enabling AI commerce, or hand over your entire operation to agents you don't control. Holly Hall Brian Browning Yann Boisclair-Roy Jasmin Guthmann Rafaela Ellensburg Paola Roccuzzo Timi Stoop-Alcala Mihaela Mazzenga Morgan Johanson Kevin Rahe Jon Irwin Skaidra Puodžiūnas Natasha Akter Justin I. Chandan K. Scott Abel Conscia Dirk Jan van der Pol Gaurav Kumar Andy Clarson Suresh Ramalingam Paige Tyrrell Suparna Sharma

Data isn’t the new oil—it’s the fuel inside your CRM. Our practical guide shows how Salesforce Data Cloud unifies profiles, builds real-time segments, and puts insight where reps and agents work—plus a 30-day plan to ship value without boiling the ocean. 👉 https://bit.ly/4ndG0Ii #Salesforce #DataCloud #RevOps #Customer360 #DataStrategy #AI

Salesforce has signed a definitive agreement to acquire Waii, a platform that converts natural language into SQL for enterprise use. Waii empowers non-technical users and AI agents to seamlessly query data using natural language, enabling more accurate and reliable business decision-making. Upon acquisition, Waii will be integrated into Salesforce Data Cloud to streamline and accelerate the way customers configure, query, and analyze their data.

✨ How Salesforce Data Cloud Works – Simple, Smart & Seamless ✨ Unlock the power of connected, intelligent customer data with Salesforce Data Cloud. This innovative platform empowers organizations to break down data silos, enrich business processes, and unlock rich insights with minimal friction. Here's how it works: 1. Access Diverse Data Sources: - Ingest structured and unstructured data through various methods like connectors, streams, scheduled loads, or zero-copy techniques that access data without moving it. 2. Harmonize and Model Data: - Transform diverse data into a unified structure: - Data Source Objects (DSOs) hold raw ingested data. - Data Lake Objects (DLOs) manage structured sources, while Unstructured DLOs (UDLOs) make transcripts, PDFs, and media files searchable. - Map everything into consistent schemas with Data Model Objects (DMOs) and Unstructured DMOs (UDMOs) for AI-ready insights. 3. Build Unified Customer Profiles: - Leverage powerful identity resolution with fuzzy, exact, and normalized matching, along with reconciliation logic to merge overlapping records into high-quality customer profiles. 4. Enrich with AI, Insights & Segmentation: - Power vector search from unstructured data for AI and retrieval-augmented generation use cases. - Utilize Data Graphs for near real-time querying. - Define Segments, generate Insights (historical, streaming, real-time), and deploy models with the Model Builder, including Bring-Your-Own-Model options. - Maintain trust with full Einstein AI audit trails and dashboards. 5. Activate and Operationalize Insights: - Share insights with partners using zero-copy access. - Access data via APIs, including advanced Models API for LLM interactions. - Trigger workflows through Data Actions such as events, marketing, MuleSoft, webhooks, or serverless functions. - Publish Segments to ads, marketing, file stores, or commerce platforms. - Embed Enrichments into Salesforce #salesforceohana #salesforce

Workflows Are the New Databases! Every platform shift creates an invisible but indispensable layer of infrastructure. In the SaaS era, it was relational databases - the backbone that quietly powered Salesforce, Workday, and every “system of record.” In the AI era, that layer looks like durable workflow engines. Why? Because AI models are probabilistic and stateless. They generate outputs, but execution - retrying, chaining steps, handling failures, and orchestrating across systems - is the real challenge. Whether it’s an AI sales agent juggling CRM data, emails, and escalations, or a support bot pulling from multiple systems, these aren’t stateless API calls. They’re living processes that need persistence, durability, and observability. Today, teams hack this together with cron jobs, queues, and glue code brittle at scale. But just as no one builds their own database anymore, no one will want to build their own execution layer. The parallels are clear: - Databases stored state → Workflow engines store progress - Databases ensured durability → Workflow engines ensure execution durability - Databases standardized access → Workflow engines will standardize orchestration In 5 years, workflow engines will be to AI apps what databases are to SaaS: not flashy, but essential. The hidden backbone that takes apps from prototype to production. Full piece in Jamin Ball 's Clouded Judgement @ https://lnkd.in/gkiVmSjq #ai #artificialintelligence

Salesforce and IBM have announced Zero Copy Integration between IBM Z and Salesforce Data Cloud. This isn’t just infrastructure news. It’s a glimpse of how the intelligence layer in pharma and medtech will be rewired. In plain terms: mission-critical data, previously locked away on mainframes, can now be activated in Salesforce without duplication, delay, or migration. That means a 360° patient or HCP view, sharper AI models, and real-time decisioning inside Health Cloud. Why this matters for pharma and medtech? Healthcare runs on data gravity. Clinical trial records, supply chain transactions, pharmacovigilance logs, real-world evidence, and market access contracts. Much of it still sits in complex, siloed legacy systems. Historically, getting that data into a CRM or engagement layer meant slow, brittle ETL pipelines. Now, with Zero Copy, Salesforce can tap IBM Z data streams directly, while IBM watsonx provides the AI horsepower to make sense of it. For pharma, this unlocks new use cases: 1. Hyper-personalised HCP engagement with data flowing seamlessly into Salesforce Health Cloud. 2. Real-time issue resolution — surfacing shipment delays, stockouts, or adverse event signals directly in service consoles. 3. Lifecycle management and compliance — using mainframe transaction data to identify renewals, flag fraud, or manage regulatory risk without moving sensitive data. Here's the bigger picture. This is not about incremental efficiency. It’s about designing the foundations for agentic AI in healthcare: intelligent agents that can orchestrate engagement, service, and compliance in real-time, powered by secure, federated data. For pharma and medtech leaders, the challenge is no longer if Health Cloud can become the engagement OS — it’s how quickly your enterprise can re-platform around this new intelligence layer. Chugai Pharmaceutical Co., Ltd., Pfizer, The Janssen Pharmaceutical Companies of Johnson & Johnson, Moderna, Fresenius Kabil, MENARINI Group, Sanofi, Amgen, Bayer, Kite Pharma, and Boehringer Ingelheim already leverage Salesforce and they are set to further scale ops. Our take at The Palindromic is that this partnership signals a reshaping of healthcare’s data operating system. Veeva Systems, IQVIA, Salesforce, and now IBM are all positioning to be the indispensable layer between data and decision. The winners in pharma will be those who act early, piloting zero-copy architectures, embedding AI agents, and re-engineering workflows around real-time intelligence. If you need help with mapping your next steps, we're here to help - and we speak pharma and tech! Thanks Mary Fratto Rowe for flagging this up. Frank Defesche this is an interesting move, keen to see how this ties into the partnership with Bernd Haas. Kuber Sharma + Narinder Singh from Salesforce (both former Microsoft), and Anson Kokkat from IBM, will have a webinar on this topic next week. #watsonx #SalesforceHealthCloud #AIagents #AgenticAI #AIinHealthcare