Salesforce Launches Agentforce 3 With Real-Time Visibility and MCP Integration
Salesforce Launches Agentforce 3 With Real-Time Visibility and MCP Integration
Salesforce has launched Agentforce 3, a transformative update to its AI agent platform. With enterprise AI scaling faster than ever, this release delivers what organizations have been waiting for: real-time visibility, seamless interoperability, robust compliance, and action-ready intelligence.
Backed by massive enterprise adoption and a growing partner ecosystem, Agentforce 3 doesn’t just iterate—it elevates. It’s no longer just about using AI agents to automate tasks; it’s about operating a secure, observable, and governable AI workforce across industries.
Why Agentforce 3 Matters Now
AI agent adoption is surging. According to Salesforce, usage of Agentforce has grown by 233% in just six months, with over 8,000 organizations adopting the platform since late 2024. From reducing customer service handling time by 15% to autonomously resolving 70% of administrative queries during tax season, Agentforce is proving its enterprise value at scale.
But as agent usage grows, so do questions around control, visibility, and integration. Agentforce 3 is Salesforce’s answer to these critical enterprise needs.
The Command Center: Observability Becomes Operational
One of the most powerful additions in Agentforce 3 is the Agentforce Command Center—a unified, real-time observability layer for enterprise AI agents.
Key Capabilities:
Live dashboards tracking latency, error rates, escalation trends, adoption, and cost.
AI-powered feedback loops for tagged conversation types.
Real-time alerts when agent behavior deviates from expected patterns.
Built on the OpenTelemetry standard, it integrates seamlessly with tools like Datadog, Splunk, and Wayfound, making it possible for IT and business teams alike to govern AI performance without friction.
Why this matters: With enterprises deploying hundreds of agents across business units, this kind of full-spectrum telemetry is no longer optional—it’s foundational.
Plug-and-Play AI: Welcome to the Model Context Protocol (MCP)
Salesforce’s integration of the Model Context Protocol (MCP)is arguably the most strategic move in Agentforce 3. Often described as the “USB-C for AI,” MCP enables secure, zero-code connections between Agentforce and external services.
Out-of-the-Box Integrations Include:
AWS Bedrock, Notion, Stripe, PayPal, IBM Watson, Google Cloud, and more.
Use cases: document summarization, video analysis, payment disputes, image processing, code generation, database querying, and beyond.
This changes the game for developers and IT teams. Instead of building custom pipelines, teams can now compose intelligent agentic workflows by linking enterprise tools directly to Agentforce.
Agentforce 3 debuts an enhanced Atlas architecture, now delivering:
50% lower latency
Streaming responses for real-time visibility
Auto-failover to backup models during latency spikes
Support for Anthropic’s Claude (with Gemini support coming soon)
Combined with web search grounding and inline citations, agents can now access and reference both internal and external data sources, making them more reliable and audit-friendly.
Industry-Ready Out of the Box
Salesforce isn’t just building generic AI tools—it’s delivering industry-specific automation kits. Agentforce 3 includes:
200+ pre-built actions, with 100 added in this release.
Industry templates for healthcare, finance, media, automotive, and retail.
Agentforce SKUs for Sales, Service, and Industry Clouds, with per-user pricing and unlimited action usage.
Example use cases:
Healthcare: AI agent for patient scheduling.
Automotive: Agent for vehicle servicing and diagnostics.
Media: Automated content summarization and distribution workflows.
This pre-configured approach helps companies avoid costly development cycles and hit the ground running.
Built for Scale, Security, and Compliance
Agentforce 3 is now available on Government Cloud Plus with FedRAMP High authorization, meeting the most stringent data security and governance standards.
Other enterprise-readiness upgrades include:
Expanded global reach (now in Canada, UK, India, Japan, Brazil)
Support for 6+ languages, with 30+ more coming
Trusted partner ecosystem with over 272,000 certified specialists across Accenture, PwC, Deloitte, and NeuraFlash
This positions Agentforce as a viable choice not only for tech-forward enterprises but also regulated industries and government bodies.
What’s Coming Next?
Salesforce has laid out a clear roadmap for continued innovation:
July 2025: Anthropic Claude general availability, MuleSoft A2A integrations
August 2025: Command Center rollout, Agent health monitoring, Agentforce Studio
Agentforce 3 marks a pivotal point in AI adoption—moving from pilot projects to enterprise-grade, governed agent deployment. For organizations serious about scaling AI, the platform offers a ready-made, trustworthy foundation with the depth and flexibility needed for complex business environments.
Whether you’re in healthcare, finance, media, or manufacturing, Agentforce 3 makes deploying intelligent AI agents as easy—and as secure—as deploying traditional software.
Salesforce Summer ’25 Release: Agentforce, Multimodal AI, and More
Salesforce Summer ’25 Release: Agentforce, Multimodal AI, and More
The Salesforce Summer ’25 release marks a transformative leap into an AI-native CRM era. With Agentforce at its core, Salesforce continues its mission to embed intelligent, multimodal agents across the entire enterprise stack—from Sales Cloud to Data Cloud, and across industries such as Education, Financial Services, and Public Sector. This release goes beyond feature updates—it’s a blueprint for rethinking how work gets done.
In this blog, we break down the most impactful Summer ’25 updates you need to know to future-proof your Salesforce strategy.
Agentforce: Expanding the AI Frontier in the Enterprise
Agentforce is no longer just for IT or development use cases. With the Summer ’25 release, Agentforce becomes a full-scale AI platform for internal employees, sales teams, support staff, and even industry verticals.
Key Highlights:
Internal Agents for Employees: Every employee can now summon personalized agents for tasks, knowledge retrieval, or guided workflows directly inside Salesforce Lightning, Slack, and mobile apps.
Agent Surfaces: Responses are no longer just text-based. Agents can return rich media, charts, tables, and images tailored to the channel and use case.
Multimodal Input Support: Upload images, documents, or unstructured files directly into the chat—Agentforce can now analyze multimodal context.
Instruction Adherence Scoring: Admins and architects can now audit and score agent behavior to ensure compliance with security and intent constraints.
Web-Search Powered Agents: Extend agent knowledge with public web search built into the Data Cloud Library.
Sales Cloud: AI Agents That Actually Sell
The Summer ’25 release redefines how sellers interact with Salesforce by introducing Agentforce for Sales, a set of autonomous assistants that directly impact pipeline velocity and revenue productivity.
New Sales Features:
Pipeline Management Automation: Agents now auto-fill opportunity stages and “Next Steps” by analyzing conversations, tasks, and activity logs.
Multilingual Sales Coaching: AI-driven coaching is now available in six additional languages—French, German, Japanese, Spanish, Portuguese, and Italian.
Sales Development Automation:
Outreach agents now support Contact, Lead, and Person Account objects.
Agent Builder preview and multilingual messaging templates.
Quoting via Natural Language: Integrated with Revenue Cloud, sales reps can now generate or modify quotes using plain English prompts.
Other notable updates include:
Auto-logging of email and meeting activity.
Advanced Conversation Insights.
Ecosystem-friendly Partner Cloud features.
Visual dashboard enhancements for Revenue Cloud users.
As AI becomes more embedded into workflows, data governance and accuracy are top priorities. The Summer ’25 Data Cloud enhancements bring next-gen governance, document intelligence, and partner connectivity.
Major Enhancements:
AI-Powered Tagging & Classification: Unstructured and structured data is now automatically tagged and categorized for discovery and use in AI prompts.
Policy-Based Governance: Fine-grained data access and masking rules that adapt in real time to user roles and contexts.
RAG 2.0 (Retrieval-Augmented Generation):
Inline citations for AI-generated answers.
Improved vague prompt handling.
Structured knowledge indexing from enterprise sources.
Unstructured Data Integration: Includes new connectors for Web Crawler, Site Maps, and Zendesk—allowing AI agents to read across websites and support channels.
Zero Copy Framework: Access external or partner data via APIs/SDKs—without duplicating or replicating data—enhancing trust and compliance.
AI Agents by Industry: From Banking to Education
Salesforce is introducing domain-specific Agentforce templates and skills that empower verticals with tailored AI capabilities:
Education Cloud:
24/7 student recruitment agents that handle FAQs, register tours, initiate applications, and route inquiries—all in natural language.
Financial Services Cloud:
AI-powered banking agents that streamline customer service case resolution with smart summarization and historical trend analysis.
Public Sector Solutions:
Complaint-handling agents with contextual summarization, intelligent case linking, and citizen engagement tools.
Life Sciences:
Site selection agents that analyze clinical trial feasibility across regions using historical success data and AI insights.
Admins, Architects, and Devs: Prepare for the Rollout
Summer ’25 is more than just front-end upgrades—it includes critical backend changes and UI transformations that demand attention from Salesforce professionals.
Key Prep Activities:
Sandbox Previews: Available since May 13—test new UI elements, APIs, and security models before deployment.
Component Migrations: Aura site components must be updated to Lightning Web Components for compatibility.
Security Enforcements: Role-based access changes and ICU locale formats now enforced by default.
API Deprecations: Retiring older API versions—ensure integrations are using supported endpoints.
Salesforce Summer ’25 signals the shift from custom configuration to intent-driven orchestration. Every new update is grounded in one key principle—AI should not just assist; it should act.
Whether you’re in sales, education, banking, or public sector delivery, this release empowers teams to:
Activate AI agents tailored to their role.
Eliminate redundant clicks and tasks.
Leverage natural language as the new UI.
Stay compliant, secure, and scalable.
Want to supercharge your Salesforce experience?
At GetGenerative.ai, we help organizations deploy AI-native Salesforce projects at 10x speed using purpose-built agents. From proposal to deployment, our platform automates every phase, while our top 1% global experts ensure human oversight and quality.
AI-Washing in IT Consulting: Buzzwords vs Real AI-First Firms
From Outsourcing to “Digital Transformation” – History Repeats
In the mid-2000s, the IT industry witnessed a significant shift. Traditional outsourcing firms began rebranding themselves as digital transformation specialists. However, beneath this new label, many continued with the same delivery models and talent pools. The term “digital transformation” has become a catch-all buzzword, often signifying superficial changes, such as migration to the cloud or updating legacy applications, without any fundamental shift in methodology or approach.
Fast forward to today, and a similar pattern emerges. The buzzword has changed to “AI-first,” but the underlying strategy remains the same. Consulting firms worldwide are now proclaiming themselves as AI-driven organizations. Yet, in many cases, this is more about branding than substance, a phenomenon aptly termed “AI-washing.”
Understanding AI-Washing in Consulting
AI-washing refers to the practice of overstating or fabricating a company’s AI capabilities to capitalize on the current hype surrounding artificial intelligence. It’s like greenwashing in sustainability, where companies exaggerate their environmental efforts. In IT consulting, AI-washing occurs when firms claim to offer AI-powered solutions without the necessary infrastructure, expertise, or genuine integration of AI technologies.
A study highlighted that 40% of “AI startups” in Europe had little to no actual AI in their products. This trend is mirrored in IT consulting, where firms boast about their AI capabilities, but upon closer inspection, the offerings are often superficial.
The Hallmarks of AI-Washing
Several indicators can help identify AI-washing in consulting:
Buzzword Overload: Proposals and pitches are filled with AI-related jargon, terms like “intelligent,” “cognitive,” “AI-driven,” and “machine learning,” without concrete explanations or demonstrations of how these technologies are applied.
Generic Solutions: Firms offer one-size-fits-all AI solutions that are not tailored to the client’s specific needs or data.
Lack of Case Studies: When pressed for examples of successful AI implementations, these firms struggle to provide tangible results or measurable outcomes.
Minimal AI Talent: The teams deployed for projects consist mainly of traditional roles, with perhaps one or two individuals labeled as AI experts, indicating a lack of depth in AI expertise.
Flashy Demos: Sales processes often include impressive AI demonstrations that aren’t directly tied to the client’s use case, suggesting a focus on theatrics over substance.
Evasive on Data Privacy: When questioned about data handling, privacy, and AI ethics, these firms provide vague or unsatisfactory answers, highlighting a lack of preparedness to address critical concerns.
What Truly Defines an AI-First Company?
To truly be AI-first, a company must undergo profound changes in three core areas:
Process: The delivery process should be reinvented to integrate AI at every stage. This includes automating manual tasks, leveraging AI for data analysis, and redesigning workflows to be intelligence-driven.
People: The workforce must be upskilled to include data scientists, machine learning engineers, and AI specialists. Training programs should be implemented to ensure AI literacy across all levels of the organization.
Technology: The adoption of AI-native tools and platforms is crucial. This means moving beyond legacy systems and investing in technologies designed with AI and automation at their core.
Engaging with firms that practice AI-washing can lead to:
Overpayment: Clients may pay premium prices for AI solutions that don’t deliver the promised value.
Wasted Time: Projects may take longer to complete due to the reliance on traditional methods, often disguised as AI-driven processes.
Eroded Trust: Repeated disappointments can lead to skepticism about AI’s potential, hindering genuine innovation and adoption.
How to Spot and Avoid AI-Washing
To protect your organization:
Ask Specific Questions: Inquire about the exact AI technologies used, how they’re integrated into the process and request demonstrations using your data.
Request Case Studies: Seek evidence of past successes, including metrics that showcase the impact of AI on similar projects.
Evaluate the Team: Ensure the team assigned to your project includes qualified AI professionals with relevant experience.
Discuss Data Handling: Engage in detailed conversations about data privacy, security measures, and compliance with relevant regulations.
Conclusion
The allure of AI is undeniable, promising efficiency, innovation, and competitive advantage. However, it’s essential to approach AI claims with a critical eye.
By understanding the signs of AI-washing and demanding transparency and proof, organizations can make informed decisions, ensuring that their investments in AI lead to genuine transformation, not disillusionment.
Is AI Replacing Developers? Not Exactly—Here’s What’s Really Happening
Is AI Replacing Developers? Not Exactly—Here’s What’s Really Happening
The belief that AI will soon replace software developers has spread rapidly, stoked by bold predictions from tech leaders and sensational headlines.
Meta CEO Mark Zuckerberg claimed AI could handle “half of the company’s software development within the next year”, while Google’s Sundar Pichai and Microsoft’s Satya Nadella each stated that 30% or more of their code is now written by AI. These declarations have triggered a wave of anxiety across the developer community.
But a closer look at recent data, industry reports, and developer surveys suggests a more nuanced truth: AI is not replacing developers. It’s reshaping the development process — improving productivity, attracting new talent, and altering team dynamics.
The future belongs not to machines alone but to developers who adapt and collaborate with AI.
The Hype vs. the Reality
Tech Executives Fuel the Narrative
Headlines citing AI’s dominance often stem from optimistic projections by tech leaders. In 2025 alone:
Meta’s Mark Zuckerberg suggested AI would write up to 50% of code.
Microsoft CTO Kevin Scott predicted AI would write 95% of all code within five years.
Anthropic CEO Dario Amodei stated AI could soon be “writing essentially all code”.
These statements, while headline-grabbing, often focus on theoretical potential under ideal conditions. They emphasize what AI could do, rather than what it’s doing today in real-world developer workflows.
Developer Sentiment Tells a Different Story
In contrast to C-suite projections, developer surveys reflect optimism, not fear:
62% reported using AI tools in 2024, up from 44% in 2023.
76% said they are currently using or planning to use AI assistants.
Developers aren’t just tolerating AI — they’re welcoming it as a tool to automate tedious tasks, accelerate prototyping, and free up time for creative and complex work.
Enterprise Usage Patterns Show Rapid Adoption
The GitHub 2024 AI Survey, conducted with over 2,000 enterprise developers from the U.S., India, Brazil, and Germany, reported:
97%+ have used AI coding tools in the workplace.
In the U.S., 88% of developers said their company encourages or permits AI tool usage.
GitHub’s conclusion? “AI doesn’t replace human jobs — it frees up time for human creativity.”
Together, these insights indicate a strong bottom-up adoption trend, where developers use AI tools regardless of whether corporate policy has formally caught up.
How Developers Are Using AI Today
AI as a Coding Assistant
The most common use case for AI in development today is code generation. In the Stack Overflow 2024 survey:
Eighty-two percent of developers using AI assistants report using them for writing code.
27% currently use AI for code testing, with many planning to expand their usage.
AI excels at writing boilerplate, filling in syntax, and providing scaffolding. As one developer noted,
“AI is like an eager junior developer — fast, but still needs supervision.”
Projected Deeper Integration Across Workflows
Expectations for 2025 and beyond suggest even deeper AI integration:
81% of developers believe AI will become a core tool for code documentation.
80% expect more AI assistance in testing.
76% foresee broader AI usage in code generation.
This indicates developers see AI as a supportive co-pilot, not a replacement, enhancing work across the software development lifecycle.
Reported Productivity Benefits
GitHub’s 2024 data underscores clear wins from AI adoption:
Developers using AI reported increased productivity and more time for collaboration and architecture.
Commonly cited benefits included:
Better code quality
Faster test case generation
Easier language learning and onboarding
In short, AI reduces friction and unlocks strategic capacity, especially for teams under time pressure.
Caveats: Security, Accuracy, and Overreliance
Despite the benefits, AI tools aren’t flawless:
A Stanford study (2022) found that developers using AI assistants were more likely to write insecure code.
The issue isn’t necessarily incorrect syntax — it’s that AI suggestions are often subtly wrong or insecure in ways beginners may miss.
Google engineer Addy Osmani coined the “70% problem”: AI helps with repetitive tasks but fails at nuanced, complex problem-solving, areas where human expertise is critical.
This underscores the need for a “review-before-use” mindset. Developers must treat AI outputs as drafts, not final code, and organizations should reinforce human-in-the-loop coding policies.
AI and Developer Productivity: By the Numbers
Hard Data from the Field
One of the largest controlled studies on AI coding assistance comes from GitHub:
Involving ~4,800 developers across Microsoft, Accenture, and a Fortune 100 firm.
Developers using GitHub Copilot:
Completed 26% more pull requests
Made 13.5% more code commits
Had 38.4% more code compilations
Crucially, no drop in code quality was observed.
Another independent review reported that Copilot users delivered code 12.6% faster than non-users. These aren’t isolated anecdotes — they’re evidence-based gains at enterprise scale.
Skill Amplification, Not Equalization
AI doesn’t flatten the playing field — it amplifies existing skills:
Senior engineers saw up to 22% faster coding speeds with Copilot.
Junior engineers saw only 4% improvement.
This reinforces the importance of developer experience and prompt engineering skills. Those who know how to frame queries and review AI output effectively will gain the most.
How AI Is Changing the Role of Developers — Not Replacing Them
From Code Monkey to Creative Engineer
AI is increasingly seen as a force multiplier, allowing developers to offload mechanical tasks and shift focus to higher-level work:
Code design
System architecture
Cross-functional planning
Stakeholder alignment
In this context, developers evolve from “doers” to “architects and problem-solvers.” As generative AI handles more of the “how,” developers focus on the “why” and “what.”
💬 “AI makes everyone operate at a higher level — but you still need humans to lead,” said Dror Weiss, CEO of Tabnine.
17.9% job growth for software developers from 2023 to 2033
Compared to the 4.0% average for all occupations
These figures indicate that while some tasks will be automated, new roles and opportunities are emerging just as fast, particularly in AI operations, integration, data infrastructure, and security.
GitHub’s Octoverse 2024: Global Developer Boom
518 million repositories created (25% YoY growth)
~1 billion open-source contributions
98% growth in generative AI-related projects
GitHub’s report shows an exploding developer ecosystem, not a shrinking one, especially in regions like India, Brazil, Nigeria, and Indonesia, where developers are increasingly learning via AI tools.
📈 “AI isn’t just helping people write code. It’s helping more people become developers.
Cursor, Codeium, or GetGenerative.ai for advanced use cases
Roll them out via internal workshops, code labs, or hackathons. Monitor usage with defined KPIs, and ensure best practices (security, licensing, accuracy) are followed.
2. Invest in Modern Skills
Encourage learning paths in:
Python, TypeScript, and Jupyter Notebooks
ML frameworks (PyTorch, TensorFlow)
Prompt engineering and model evaluation
Cloud-based AI tools (Azure ML, AWS SageMaker, Vertex AI)
Developers proficient in these areas will become increasingly valuable.
3. Balance Automation with Fundamentals
While AI can scaffold functions and write tests, engineers must still know:
How to debug, refactor, and optimize manually
How to conduct code reviews
How to design scalable and secure architecture
Ensure junior devs aren’t overly reliant on AI. Pair them with mentors and use AI-assisted outputs as teaching moments, not final deliverables.
4. Elevate the Developer Role
Prioritize team capacity in:
Solution design
Strategic decision-making
Cross-functional collaboration
AI implementation strategy
As the “grunt work” fades, developers should own more innovation and leadership responsibilities.
5. Govern Responsibly
Put in place:
Static analysis and security testing pipelines for AI-generated code
Code linting and IP checks
Clear guidelines on code review and manual overrides
Make it clear: developers remain accountable, even when using AI outputs.
6. Evolve Roles and Org Structure
Emerging positions to consider:
Role
Purpose
Prompt Engineer
Optimize LLM inputs for better code generation
AI QA Specialist
Review AI-generated outputs for bugs or logic flaws
AI DevOps Lead
Manage automated pipelines integrating AI services
AI Ethics Advisor
Ensure compliance, fairness, and transparency
Upskilling current staff into these roles builds internal capability and reduces reliance on outside consultants.
Rather than asking “Is AI replacing developers?”, the better question is:
“How can AI help developers become more effective, creative, and indispensable?”
The data is conclusive: AI augments developers. It enhances speed, encourages new entrants into tech, and shifts developer time toward solving higher-order challenges.
The future won’t be code written by AI alone — but rather, code written with AI, by developers who know how to guide it.
The Innovator’s Dilemma for GSIs: How AI Pods Are Disrupting IT Service Models
The Innovator’s Dilemma for GSIs: How AI Pods Are Disrupting IT Service Models
In a bold move, Globant has launched “AI Pods”—autonomous AI agents overseen by human experts—to automate significant portions of its delivery operations. Offered through a subscription-based, token-metered model, this innovation marks what CEO Martín Migoya describes as a “radical departure” from traditional IT services.
Instead of billing by the hour, Globant is now selling AI-driven outcomes.
But the market’s reaction has been anything but enthusiastic.
Globant’s stock has dropped over 50% in the past six months, including a single-day 23% plunge following its Q1 2025 earnings miss and reduced full-year guidance. Once trading near $238, shares now hover around $100, reflecting investor skepticism around the execution risks of this AI pivot.
Analyst sentiment is mixed. Goldman Sachs, for example, cut its price target from $225 to $120 and downgraded the stock to Neutral. Other firms retained Buy ratings, but with tempered expectations. Some observers argue that Globant is undervalued, citing solid long-term profit potential, but few dispute that the company must now prove that its AI Pods can deliver both growth and margins to justify this radical shift.
The Real Wake-Up Call for Legacy GSIs
Globant’s transformation is not just about one company—it highlights a brewing crisis for legacy Global System Integrators (GSIs). The success playbook of the past, built around scaled human labor, pyramid structures, and hourly billing, is now colliding with a new delivery paradigm: AI-native service models.
These AI-powered disruptors—be they upstart SaaS vendors or hybrid tech firms—offer faster, cheaper, and more flexible solutions. Their models decouple growth from headcount and prioritize automation over human scale. This exposes a central tension for incumbents: the very structures that once enabled their dominance now threaten their adaptability.
Consider the dilemma: if AI agents can perform tasks traditionally handled by large delivery teams, what happens to the tens of thousands of employees that legacy GSIs depend on for revenue? With billable hours as the primary metric of success, adopting AI at scale could cannibalize their existing business model.
One analyst framed it succinctly: “Most big IT services firms were built on human leverage. That leverage is now under threat.”
Why Transformation Is Harder Than It Looks
Despite widespread interest in AI, legacy GSIs face serious internal barriers to becoming truly AI-native.
Structural Inertia: Their operating models are optimized for managing large human workforces, with layers of project managers, compliance checks, and utilization tracking. This structure is poorly suited for fast, iterative AI workflows.
Cultural Resistance: Business incentives still reward increasing headcount and maximizing billable hours. Automation, paradoxically, can look like a threat to growth, especially in the short term.
Process Complexity: Long approval chains, risk-averse policies, and slow experimentation cycles make rapid AI adoption difficult.
Client Expectations: Clients still associate these firms with dependable, people-intensive service delivery. Breaking that mold requires careful repositioning and trust-building.
Globant’s leadership, however, is betting that this disruption is necessary and profitable. “We are leading a fundamental shift,” Migoya stated, highlighting their belief that AI can unlock far more value than traditional methods.
What It Really Takes to Be AI-Native
Becoming an AI-native services company is more than just adopting new tools. It’s a ground-up reinvention. Here’s what that transformation entails:
1. Reimagining the Delivery Model
Shift from time-and-materials billing to continuous, outcome-driven delivery. Instead of charging for hours worked, AI-native firms monetize results. Globant’s AI Pods, for instance, offer token-based access to AI capabilities, aligning revenue with value delivered, not effort expended.
2. Embedding AI in Tooling & Platforms
AI-native operations are powered by platforms that automate coding, testing, data processing, and more. Globant’s internal GEAI platform deploys intelligent agents like “Globant CODA” that generate code, test software, and deploy updates, shortening software cycles dramatically. Other firms must invest in similar AI infrastructure or risk falling behind.
3. Redesigning Team Structures
Hierarchical, role-specialized teams give way to smaller, agile units where AI augments every contributor. The workforce transitions from manual executors to AI trainers and curators. New roles—like prompt engineers and AI model supervisors—emerge, while traditional roles evolve to require AI fluency.
4. Overhauling Commercial Models
Billing transforms into usage-based or outcome-based contracts. Providers might charge based on delivered business KPIs, token usage, or subscription tiers. Internal incentives also shift, rewarding efficiency, reusability, and IP creation instead of hours billed.
The stakes are high. Globant’s AI-native model may still be in its early innings, but its implications are profound. If successful, it could serve as a template for the entire services industry. The model promises faster turnaround, higher margins, and longer client retention—but only if executed well.
For legacy GSIs like Accenture, Infosys, and Deloitte, the path forward is clear but challenging. They must decide whether to proactively disrupt themselves or risk being leapfrogged by more agile, AI-native competitors.
As the billable hour era fades, the next few years will define which firms can truly evolve. The Innovator’s Dilemma is real—and this time, it’s powered by AI.
Salesforce and LIV Golf Announce Strategic Partnership
Salesforce and LIV Golf Announce Strategic Partnership
In a bold move that blends cutting-edge AI with live sports, Salesforce and LIV Golf have announced a strategic partnership that will see Agentforce, Salesforce’s AI-native automation platform, powering fan engagement, operations, and broadcast experiences across LIV’s global golf league.
This isn’t just a tech integration — it’s a blueprint for how AI is reshaping the sports entertainment industry from the ground up.
Why This Partnership Matters
As sports organizations race to meet evolving fan expectations and operational demands, LIV Golf’s alignment with Salesforce marks a significant shift toward autonomous digital labor in professional sports. This partnership isn’t about incremental enhancements — it’s about creating intelligent, scalable infrastructure to support the growth of a new-age league across continents.
LIV Golf, known for its disruptive approach to the traditional golf circuit, gains a powerful digital ally in Salesforce, whose Agentforce platform brings AI agents capable of handling tasks traditionally reserved for humans, but at machine speed, scale, and consistency.
Fan Engagement 2.0: Introducing “The Fan Caddie”
At the center of this transformation is The Fan Caddie, an AI-powered “second-screen” companion embedded in the LIV Golf mobile app. Imagine watching a tournament while receiving personalized, real-time updates tailored to your preferences: shot-by-shot insights, player stats, merchandise suggestions, location-based notifications, and curated content — all driven by Agentforce.
Fans no longer passively watch tournaments — they experience them in an interactive, hyper-personalized digital environment. The Fan Caddie transforms fan engagement into a two-way relationship, enabling real-time decision-making, emotion tracking, and content delivery tailored to individual tastes.
Real-Time Intelligence with “Agent Caddie”
For broadcasters and viewers, Agent Caddie is a game changer. It introduces predictive analytics into the live stream, offering insights on likely shot outcomes, optimal strategy suggestions, contextual statistics, and dynamic storytelling prompts for commentators.
The line between data and entertainment blurs. Agentforce injects intelligence directly into the broadcast, elevating the commentary, enriching storytelling, and keeping viewers more engaged, especially in a data-savvy, mobile-first generation.
Beyond the fan experience, Agentforce will automate LIV Golf’s complex back-office operations. From equipment logistics and staff coordination to sponsor reporting and audience segmentation, AI agents will manage processes that were once manual and fragmented.
LIV Golf can now scale its global tournaments more efficiently, optimizing travel, staffing, inventory, and marketing with real-time intelligence and autonomous workflows. It also enables deeper commercial partnerships, as sponsors gain clearer, data-driven insights into engagement and impact.
Global Growth at Speed and Scale
Operating tournaments across the Americas, Asia, Europe, and the Middle East demands agility. Agentforce’s ability to localize engagement strategies, coordinate international logistics, and drive decision-making with AI insights is exactly what LIV needs to grow smartly in competitive markets.
Expansion isn’t just about going global — it’s about adapting locally. Agentforce equips LIV Golf with the intelligence to meet fans and partners where they are, with relevant experiences and optimized operations.
Performance and Wellness Support
Salesforce and LIV are also exploring ways to personalize the athlete experience. With Agentforce, players could receive AI-driven performance analytics, wellness insights, and training suggestions tailored to their style and needs — all powered by data from past tournaments, health metrics, and equipment sensors.
This takes LIV’s athlete-first model to the next level. It’s not just about data — it’s about delivering real-time, contextual support to optimize performance and longevity for each player.
What This Signals for the Future of Sport
This partnership is about more than golf. It’s a signal of where the sports world is headed:
AI agents as digital teammates — working behind the scenes to orchestrate operations, content, and fan experiences.
Hyper-personalization at scale — delivering content that’s as unique as each fan or player.
Smarter monetization models — powered by deeper data on viewer preferences, sponsor impact, and player behavior.
While many leagues are still experimenting with digital transformation, LIV Golf is going all-in, and Salesforce’s Agentforce is the engine behind that leap.
This partnership between Salesforce and LIV Golf shows us that the future of sports is not just about better games — it’s about better experiences, smarter operations, and inclusive growth powered by AI.
Salesforce Projects, Done Smarter — and 10x Faster
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Salesforce Signs Definitive Agreement to Acquire Informatica
Salesforce Signs Definitive Agreement to Acquire Informatica
On May 27, 2025, Salesforce made waves across the enterprise tech landscape by announcing its definitive agreement to acquire Informatica in an all-cash transaction valued at approximately $8 billion. This acquisition isn’t just about expanding market share – it’s a strategic leap toward reshaping the future of artificial intelligence (AI) and data infrastructure in the enterprise software space.
Salesforce’s acquisition of Informatica signals a sharp focus on building AI agents that are not only intelligent but also trustworthy, scalable, and secure. At the heart of this deal is a vision: enabling agentic AI through data clarity, governance, and end-to-end orchestration.
Marc Benioff, Chair and CEO of Salesforce, described the merger as the convergence of Salesforce’s Einstein AI with Informatica’s CLAIRE AI—a fusion designed to unlock next-generation business intelligence and automation.
Core Reasons Behind the Acquisition
1. Powering Agentic AI with Trusted Data
Salesforce is betting big on agentic AI—autonomous systems capable of performing end-to-end business tasks with minimal human oversight. But these agents are only as good as the data that fuels them. Informatica’s strengths in data integration, master data management (MDM), data cataloging, and privacy controls make it the ideal backbone for building high-performance, low-risk AI systems.
2. Unifying the Salesforce Data Stack
Salesforce already owns a portfolio of data-driven platforms, including Data Cloud, Tableau, and MuleSoft. Adding Informatica fills a critical gap—deep metadata management and governance. This acquisition allows Salesforce to offer a comprehensive, unified data architecture where analytics, automation, and AI operate seamlessly across the enterprise.
3. Scaling Agentforce with Transparency and Governance
Salesforce’s Agentforce platform represents the company’s future-facing strategy for deploying autonomous agents across departments. Informatica’s data lineage, quality scoring, and auditability are key enablers for safe, governed AI agent operations, especially in regulated sectors like financial services, healthcare, and government.
4. Expanding Customer Base and Market Leadership
Informatica brings with it a blue-chip client list—over 5,000 enterprise customers, including more than 80% of the Fortune 100. This deal opens Salesforce to a broader set of industries and cements its leadership in the $150 billion+ enterprise data market.
Strategic Synergies and Expected Impacts
AI-First Transformation
With CLAIRE and Einstein combined, Salesforce is poised to lead in explainable and proactive AI. Expect faster product rollouts in AI-driven workflows, such as autonomous CRMs, self-healing integrations, and intelligent decision support.
Financial Upside
Salesforce projects the acquisition will be accretive to non-GAAP operating margin, EPS, and free cash flow within two years. The synergy benefits will come from:
Cross-selling AI and data products
Reduced customer churn via platform unification
Streamlined R&D and product alignment
Interoperability & Platform Integration
From MuleSoft’s APIs to Tableau’s dashboards and Data Cloud’s harmonized customer profiles—Informatica will become the glue tying these components together. Expect near-term innovations like:
AI agents that can explain decisions based on data lineage
Real-time MDM syncs across Salesforce objects
Unified governance policies across all customer data
Industry Reactions & Competitive Implications
The acquisition caught the attention of competitors and analysts alike. As Microsoft, Oracle, and AWS ramp up their AI-native offerings, Salesforce’s move is seen as a proactive defense and an offensive strike in the race for enterprise AI dominance.
Key Analyst Takeaways:
Gartner called it “a defining move in intelligent data orchestration.”
Forrester highlighted the deal’s potential to create a data-centric operating system for AI agents.
IDC predicts that the combined entity could dominate over 25% of the global AI-data integration market by 2027.
Potential Challenges Ahead
While the deal is strategic, it’s not without risks:
Integration Complexity
Informatica’s tools will need to harmonize with Salesforce’s existing ecosystem. Some functionality overlaps with MuleSoft and Data Cloud could cause temporary confusion in product roadmaps and client transitions.
Regulatory Scrutiny
Due to the size and scope of the deal, regulatory authorities across the U.S., E.U., and APAC may investigate for antitrust implications. However, both firms have expressed confidence in meeting compliance standards.
Customer Reassurance
Existing Informatica clients may be concerned about potential changes to the platform’s independence, pricing, and roadmap. Salesforce will need to offer strong post-acquisition guarantees and onboarding strategies.
What’s Next?
The acquisition is expected to close in early FY27 (post-February 2026), subject to regulatory and shareholder approvals. Until then, Informatica will continue to operate independently, but product teams across both companies have already begun planning for platform synergies.
Salesforce has made it clear: this is not just a buyout—it’s a bet on the future of autonomous enterprise software, where every business workflow is data-driven, agent-augmented, and insight-first.
Informatica is not merely an addition to Salesforce’s toolkit—it’s a foundational layer for the next era of AI. With this acquisition, Salesforce is no longer just a CRM company. It’s positioning itself as the enterprise AI + data powerhouse of the future.
As generative AI matures, only those platforms with trusted data pipelines, real-time orchestration, and traceable decisions will win the enterprise market. Salesforce just made a bold, $8 billion move to ensure it leads that race.
Agentforce for Employees: How Slack Is Becoming Your AI-Powered Teammate
Agentforce for Employees: How Slack Is Becoming Your AI-Powered Teammate
In 2025, the workplace is being reimagined—not just with smarter tools, but with intelligent digital teammates. With the rise of AI agents that can independently reason, act, and support employees in real time, Salesforce’s Agentforce for Employees marks a bold step into this future.
Seamlessly embedded in Slack, Agentforce enables organizations to deploy task-specific, context-aware AI agents that work side by side with teams—amplifying productivity, accelerating decisions, and minimizing manual work.
Redefining the Role of AI in Slack
Salesforce envisions a “Third Wave of AI”—a shift from basic chatbots and copilots to autonomous AI agents that act with intelligence, context, and accountability. In Slack, these agents aren’t passive tools. They actively collaborate within conversations, respond to mentions, and help move work forward like true teammates.
Unlike traditional bots, Agentforce AI agents understand Slack’s conversational dynamics. When summoned via @mentions, they can parse message context, reference related files, retrieve structured and unstructured data, and respond with relevant insights. They operate in the exact digital spaces where employees already communicate—Slack channels, threads, canvases—ensuring AI becomes part of the workflow, not an external detour.
Why Slack is the Natural Home for Agentforce
Slack’s role as a central work hub makes it the ideal launchpad for AI-powered agents. Here’s why Agentforce thrives in the Slack environment:
Native Access to Context: Agents can read and interpret messages, files, and workflows in real time.
User-Level Permissions: They only access what the requesting user is authorized to view.
Cross-Tool Collaboration: Agents operate alongside both human colleagues and integrated tools like Salesforce, Google Drive, and Asana.
Always-On Support: Agents are available 24/7 to handle routine tasks, freeing employees for higher-value work.
This tight integration ensures Agentforce doesn’t just deliver answers—it participates in work, making Slack more dynamic, responsive, and intelligent.
Introducing Slack Agent Templates (Launching June 12, 2025)
To make Agentforce deployment fast and scalable, Salesforce is introducing Slack Employee Agent Templates—prebuilt, customizable agent blueprints designed to serve common business needs right out of the box.
1. Customer Insights Agent
This agent accesses Salesforce data directly from within Slack conversations. Whether summarizing opportunities, updating CRM fields, or providing context during a sales huddle, it works like a CRM-savvy teammate embedded in your channel. It can even create Slack canvases combining Salesforce data and Slack conversations for easy sharing.
2. Onboarding Agent
Designed to streamline employee onboarding, this agent explains company policies, identifies subject matter experts, facilitates introductions, and accelerates new hire productivity. It leverages internal knowledge bases and Slack history, helping employees acclimate faster and with more confidence.
3. Employee Help Agent
A flexible support assistant for HR, IT, legal, or sales ops. This agent pulls answers from documentation, FAQs, and Slack’s message archives to answer questions like “How do I apply for leave?” or “What’s our Q2 target?”—reducing repetitive queries and freeing internal teams to focus on strategic tasks.
Each template is fully customizable—organizations can refine workflows, connect to proprietary data sources, and adapt interactions before rollout.
Advanced Capabilities That Power Agentforce in Slack
Agentforce is more than just a smart responder. It’s powered by a robust architecture that simulates enterprise-level reasoning and securely accesses work context from across the organization.
Enterprise Search
Agentforce integrates Slack Enterprise Search, allowing agents to look beyond Slack messages into platforms like Google Drive, Microsoft OneDrive, Asana, and more. Employees can issue prompts like:
“Search Google Drive and Slack for our Q3 planning deck.”
The agent will compile results, based on the user’s permissions, and share them in a Slack canvas or summary. This end-to-end knowledge retrieval ensures employees never have to leave Slack to find what they need.
General Slack Topic
With the General Slack Topic assigned in the Agent Builder, AI agents operate using the permissions and history of each individual user. That means an agent can act securely and contextually, referencing prior conversations and data to produce tailored responses. For instance:
“Summarize @amanda’s last 3 comments about the new pricing structure.”
Agentforce understands the command, retrieves the relevant messages, and generates an actionable summary within Slack, in seconds.
Scaling Adoption Through Shareable Prompts
To drive widespread usage, Agentforce supports shareable prompts—quick, reusable commands that employees can circulate across teams. For example:
“How do I create a customer onboarding canvas?”
Once an agent responds effectively, the prompt can be saved, shared, or embedded in Slack canvases to help others replicate the interaction. This democratizes access to AI and encourages self-service knowledge sharing across the organization.
Lowering Barriers with No-Cost Salesforce Identity Licenses
Not every Slack user has a full Salesforce license—and that’s okay. Salesforce now offers no-cost Salesforce Identity licenses that link Slack users to Agentforce without requiring full Salesforce access.
This enables:
Permission-based access to Salesforce and Data Cloud context
Seamless interaction with AI agents for all Slack users
Broader organizational rollout of Agentforce without increased licensing costs
Under the Hood: Agentforce’s Technological Backbone
At the heart of Agentforce is the Atlas Reasoning Engine—Salesforce’s proprietary AI brain built to mimic human planning and logic. The engine breaks down complex queries, evaluates decision branches, and iterates through potential actions until it completes the task.
This reasoning engine is tightly integrated with Salesforce Flow, Apex, MuleSoft, and Data Cloud, allowing Agentforce agents to not only analyze but also act. For example, an agent can:
Trigger approval workflows
Update Salesforce records
Generate deal handoff canvases
Summarize pipeline status in real-time
The Evolution: Agentforce 2.0 → Employee-Facing AI
The current wave of Agentforce for Employees builds on Agentforce 2.0, which debuted in December 2024. That release introduced the ability to:
Interact with agents in channels and DMs
Extend agents using pre-built Slack actions
Tap into conversational context through Slack’s APIs
With the new employee-facing experience, Agentforce has matured from a proof-of-concept into a full-fledged AI teammate platform ready to scale across global organizations.
Agentforce in Slack isn’t just about saving time. It’s about amplifying human potential by embedding intelligence into the systems where real work happens. Whether summarizing conversations, finding documents, onboarding new employees, or helping sales close deals, Agentforce empowers teams to move faster, work smarter, and focus on what matters most.
Curious how AI teammates could support your own Salesforce projects?
With GetGenerative.ai, you can plan, design, and deliver your entire implementation—step by step, with intelligent support along the way.
Salesforce has just rewritten the rules of AI adoption for enterprises. On May 15, 2025, the company announced a major overhaul to the pricing structure of Agentforce, its flagship AI agent platform, moving from a flat per-conversation model to a more scalable and value-aligned action-based pricing system powered by Flex Credits.
This shift represents a pivotal moment for Salesforce customers, especially those looking to scale their AI initiatives without being constrained by rigid licensing or unpredictable costs.
From Conversations to Actions: The Core of the New Model
Previously, Agentforce was priced at $2 per conversation, a straightforward but blunt tool for measuring AI usage. While predictable at small volumes, this model quickly became inefficient for teams deploying AI at scale across support, sales, and service use cases.
The new pricing model introduces “Flex Credits”, a unit-based system that charges per action rather than per conversation. Here’s how it works:
Each Agentforce action (like creating a case, updating a record, or generating a solution) costs 20 Flex Credits.
100,000 Flex Credits can be purchased for $500, translating to just $0.10 per action.
Customers now pay only for what AI agents do, not just for initiating conversations.
What this means for users
This model finally aligns costs with actual business value. A simple “hello” and a full case resolution are no longer priced the same. It encourages smarter use of AI by rewarding outcome-focused workflows.
Introducing the “Flex Agreement”
In a bold move to empower hybrid workforces of humans and AI, Salesforce has also launched a Flex Agreement that enables businesses to:
Convert user licenses into Flex Credits when roles are automated or offloaded to AI agents.
Convert Flex Credits back to licenses if human intervention becomes necessary for specialized or evolving roles.
Salesforce has also introduced Agentforce User Licenses, which follow a simple per-user-per-month (PUPM) pricing model. These licenses allow unlimited internal usage of Agentforce capabilities across departments.
What makes this offering powerful:
Unlimited interactions with internal AI agents.
Embedded seamlessly into Salesforce and Slack.
Enables wider AI adoption without worrying about cost per query.
For IT teams and digital transformation leaders, this means no more internal “AI rationing.” Teams can now build AI-enhanced workflows without hitting usage caps or hidden costs.
Salesforce Foundations Package: Try Before You Scale
To help businesses test and validate AI use cases before committing, Salesforce is rolling out a Salesforce Foundations starter package, which includes:
1,000 free Agentforce conversations
Access to critical features across Salesforce Clouds
Guided onboarding and sample use cases
This lowers the barrier to entry, especially for SMBs and mid-market firms that may not yet have mature AI workflows but want to explore use cases in customer service, pre-sales, and internal productivity.
A Strategic Shift in Enterprise AI Adoption
This new pricing structure is not just about affordability — it’s about scalability, predictability, and alignment with ROI. Salesforce is clearly positioning Agentforce as a core digital labor platform, not a nice-to-have add-on.
Salesforce’s new Agentforce pricing is not just a feature release — it’s a vision statement. As businesses blend human expertise with AI automation, pricing models must evolve too. With Flex Credits, dynamic agreements, and unlimited-use licenses, Salesforce has set a new industry standard in AI accessibility and affordability.
Stay ahead of every Salesforce AI update.
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Salesforce’s Acquisition of Convergence.ai Explained
Salesforce’s Acquisition of Convergence.ai Explained
Salesforce has just signed a definitive agreement to acquire Convergence.ai, a rising AI startup based in London, sending a clear message to the enterprise tech world: the age of autonomous AI agents in CRM is no longer a distant vision—it’s happening now.
This move is not just another acquisition—it is a strategic leap toward redefining how AI is embedded into enterprise software. Here’s everything you need to know about the deal and what it means for Salesforce users, developers, and decision-makers.
Who Is Convergence.ai?
Convergence.ai is a young but rapidly ascending startup founded in April 2024 by Marvin Purtorab and Andy Toulis, alumni of AI powerhouses like Cohere and Shopify. The company gained attention for developing autonomous AI agents capable of performing complex workflows across web interfaces, without needing structured APIs.
Their AI can adapt in real time to dynamic environments, handling pop-ups, UI changes, and unexpected system errors with the intelligence and flexibility of a human operator. It’s not just automation—it’s true AI orchestration that can learn and act within live systems.
In short, Convergence built the kind of agent technology Salesforce wanted for Agentforce—but faster, and with deep foundational AI expertise.
Why Salesforce Bought Convergence.ai
Salesforce’s Agentforce platform already aims to bring AI agents into its cloud ecosystem, but Convergence.ai brings something Salesforce lacked:
Real-time adaptability in chaotic environments
AI agents that function across third-party apps and systems
Top-tier AI talent from DeepMind, OpenAI, Meta, and Google
With this acquisition, Salesforce is looking to go beyond declarative AI, like Einstein GPT or Flow-based AI, and into true autonomous agent execution. This means AI that not only suggests actions but carries them out across apps, tools, and platforms.
This acquisition has huge implications for how Salesforce will operate—and what users can expect:
1. AI Agents Will Become Action-Oriented, Not Just Advisory
So far, most Salesforce AI tools like Einstein GPT assist with suggestions, recommendations, or predictive insights. With Convergence’s tech, Agentforce will evolve into an execution engine. Imagine AI agents that:
Log into external portals to fetch documents
Navigate complex HR or finance systems to complete tasks
Troubleshoot issues in real time, like a Tier 1 support agent
2. Wider App Integration—Without Relying on APIs
One of the major bottlenecks in enterprise AI adoption is integration complexity. Convergence.ai’s agents bypass that by learning to interact with web UIs like a human, opening the door to automation across legacy systems, partner tools, and third-party platforms without custom connectors.
Salesforce users in operations, finance, legal, and support can soon expect intelligent agents that:
Fill in procurement forms
Upload signed contracts
Generate compliance reports
— all autonomously.
3. Game-Changing for Admins, Consultants, and Developers
For Salesforce professionals, this acquisition could transform project delivery timelines and consulting models. AI agents could take over tasks like:
Data migration
Sandbox setup
Testing and UAT workflows
Even deployment verifications
This means more focus on high-value strategy work, and less manual configuration and validation.
Strategic UK Expansion
This deal also strengthens Salesforce’s UK footprint. With a $4 billion investment in the UK market and the launch of its first AI Center in London, the acquisition of a London-based AI startup aligns with its ambition to build a world-class AI talent hub in Europe.
This is not just about acquiring tech—it’s about acquiring vision and leadership in AI.
With this acquisition, Salesforce isn’t just extending Agentforce—it’s redefining it. Convergence.ai’s capabilities could help Salesforce leap ahead in the race to deliver truly adaptive enterprise AI agents.
In the coming months, users can expect:
Deep integration of Convergence’s AI into Agentforce workflows
Cross-platform automation with fewer dependencies on structured integrations
A faster roadmap for agent-based CRM use cases in service, sales, legal ops, and back-office functions
The acquisition is expected to close by Q2 of Salesforce’s fiscal year 2026.
For more in-depth insights, real-time updates, and expert analysis on Salesforce and AI innovations, connect with GetGenerative.ai.
