At the Agentforce World Tour in Singapore, Salesforce presented their vision for Agentic AI – showcasing how they’re helping customers stay ahead of rapid technological change and unlock stronger business outcomes with speed, trust, and agility.
Ecosystm Advisors, Ullrich Loeffler, Sash Mukherjee, Achim Granzen, and Manish Goenka share their take on Salesforce’s announcements, demos, and messaging, highlighting what resonated, what stood out, and what it means for the future.
Click here to download “Agentforce World Tour: Highlights from Singapore” as a PDF.
What truly stood out in Salesforce’s messaging?
ULLRICH LOEFFLER, CEO & Co-Founder
What stood out at the Salesforce event was their pragmatic, integrated approach to scaling AI. They made it clear AI isn’t plug-and-play, emphasising the complexity and cost involved in what they call ‘self-plumbing’ AI – spanning infrastructure, data management, model development, governance, and application integration. Their answer is a unified platform that lowers costs, accelerates time to market, and reduces risk by removing the need to manage multiple disconnected tools. This seamless environment tackles the real challenge of building and running a layered AI stack.
Equally notable is their view of Agentic AI as a capability refined through iteration, not a sudden overhaul. By urging businesses to start with the right use cases for faster adoption, less disruption, and tangible impact, they show a realistic grasp of enterprise change.
Salesforce offers a clear, practical path to AI: simplifying complexity through integration and driving adoption with measured, value-focused steps.
SASH MUKHERJEE, VP Industry Insights
What truly stood out at the Salesforce event was their unwavering commitment to Trust. They understand that AI agents are only as reliable as the data they use, and they’ve built their platform to address this head-on. Salesforce emphasises that building trusted AI means more than just powerful models; it requires a secure and well-governed data foundation. They highlighted how their platform, with 25 years of embedded security, ensures data resilience, protects sensitive information during development and testing, and provides robust visibility into how AI interacts with your data.
A key assurance is their Trust Layer, a unique innovation that safeguards your data when interacting with AI models. This layer automatically masks sensitive data, ensures zero data retention by LLM providers, and detects harmful language. This means organisations can leverage GenAI’s power without compromising sensitive information.
Ultimately, Salesforce is empowering organisations to confidently deploy AI by making trust non-negotiable, ensuring organisational data is used responsibly and securely to drive real business value.
How does Salesforce differentiate their approach to Agentic AI?
ACHIM GRANZEN, Principal Advisor
Salesforce’s focus on Agentic AI focus stands out for its clarity and depth. The Agentforce platform takes centre stage, demonstrating how clients can now build Agentic AI with little or no code and deploy agents seamlessly across the Salesforce environment.
But beyond the polished demos and compelling customer stories, the most critical takeaway risked being overlooked: Agentforce is not a standalone capability. It’s tightly integrated with Data Cloud and the broader Salesforce platform. That layered architecture is more than just a technical decision; it’s what ensures every AI agent is governed, auditable, and constrained to what’s been provisioned in Data Cloud. It’s the foundational safeguard that makes Agentic AI viable in the enterprise.
And that’s the message that needs greater emphasis. As organisations move from experimentation to real-world deployment, trust and control become just as vital as ease of use. Salesforce’s architecture delivers both – and that balance is a key differentiator in the crowded enterprise AI space.
MANISH GOENKA, Principal Advisor
Salesforce has moved beyond passive AI assistance to autonomous agents that can take meaningful action within trusted boundaries. Rather than focusing solely on chat-based copilots, Salesforce emphasises intelligent agents embedded into business workflows, capable of executing tasks like claims processing or personalised service without human intervention.
What sets Salesforce apart is how deeply this vision is integrated into their platform. With Einstein Copilot and Copilot Studio, customers can build their own cross-system agents, not just those limited to Salesforce apps. And by enabling partners to create and monetise agents via AppExchange, Salesforce is building a full-fledged AI ecosystem, positioning themselves as a platform for enterprise AI, not just a CRM.
Trust is a cornerstone of this approach. Salesforce’s focus on governance, auditability, and ethical AI ensures that Agentic AI is not only powerful, but also secure and accountable – key concerns as agents become more autonomous.
In a crowded AI space, Salesforce stands out by offering a grounded, scalable vision of Agentic AI, anchored in real use cases, platform extensibility, and responsible innovation.
Where are Salesforce’s biggest growth opportunities in APAC?
MANISH GOENKA
Salesforce has significant growth opportunities across Asia Pacific, with Singapore playing a pivotal role in its regional strategy. The company’s USD 1 billion investment and the launch of their first overseas AI research hub firmly position Singapore as more than just a sales market. It becomes a core engine for product innovation and a key driver of Salesforce’s long-term AI leadership.
Across the region, public sector transformation and SME digitisation represent major areas of opportunity. Salesforce’s secure and compliant Government Cloud is well suited to support Smart Nation goals and modernise public digital services. At the same time, governments are actively pushing SME digitisation, creating demand for scalable, modular platforms that can grow from basic CRM solutions to AI-enabled automation.
Sustainability is also emerging as a strong growth vector. As ESG reporting becomes commonplace in more markets, tools like Net Zero Cloud are well positioned to help businesses meet compliance requirements and improve data transparency.
Finally, the rapidly expanding ecosystem of certified professionals and ISV partners across Asia Pacific is enabling faster, more localised implementations. This grounds Salesforce’s capabilities in local context, accelerating time to value and delivering business outcomes that are tailored to the region’s diverse needs.
What does the Informatica acquisition mean for Salesforce’s AI strategy?
ACHIM GRANZEN
The planned acquisition of Informatica is a strategically important move that completes Salesforce’s Agentforce narrative. At the World Tour, Agentforce was positioned as the future of enterprise AI, allowing organisations to build and deploy autonomous agents across the Salesforce ecosystem. But some lingering concerns remained around how deeply Data Cloud could handle governance, especially as AI agents begin making decisions and executing tasks without human oversight.
Informatica answers that question. With proven tools for data quality, lineage, and policy enforcement, Informatica brings a level of governance maturity that complements Salesforce’s ambition. Its integration into Data Cloud strengthens the trust layer that underpins Agentforce and reinforces Salesforce’s positioning as an enterprise-grade AI platform.
Of course, there are broader implications too. Salesforce will gain access to Informatica’s installed base, potentially opening up cross-sell opportunities. And there are questions to resolve, such as how Informatica will operate as a product line within the larger Salesforce ecosystem.
But the core value of the deal is clear: by bringing Informatica’s governance expertise into the fold, Salesforce can significantly accelerate its ability to deliver trusted, production-ready AI at scale. From a risk and compliance standpoint, that governance capability may prove to be the most valuable part of the acquisition.
What will define Salesforce’s next chapter of growth in APAC?
SASH MUKHERJEE
Just as Salesforce is driving an integrated enterprise platform from the CRM and customer experience lens, competitors (and partners) are taking a similar platform-centric approach from other functional vantage points – whether it’s HR (like Workday), Finance (like Oracle), or IT (like ServiceNow). In fast-growing, cost-sensitive markets across APAC, competing on price alone won’t be sustainable, especially with strong regional players offering leaner, localised alternatives.
To win, Salesforce must adopt a nuanced strategy that goes beyond product breadth. This means addressing local economic realities – offering right-sized solutions for businesses at different stages of digital maturity – while consistently reinforcing the long-term value, resilience, and global standards that set Salesforce apart. Their differentiators in data security, compliance, and ecosystem depth must be positioned not as add-ons, but as essential foundations for future-ready growth.
More flexible entry points – whether modular offerings, usage-based pricing, or vertical-specific bundles – can reduce friction and make the platform more accessible. At the same time, strengthening local partnerships with ISVs, system integrators, and government bodies can help tailor offerings to market-specific needs, ensuring relevance and faster implementation.
Ultimately, Salesforce’s growth across APAC will depend on their ability to balance global strengths with local agility.
ULLRICH LOEFFLER
Salesforce is well positioned to lead in AI-driven transformation, but doing so will require evolving their sales approach to match the complexity and expectations of today’s enterprise buyers. With a strong foundation selling to marketing and customer leaders, the company now has an opportunity to deepen engagement with CIOs and CTOs, reframing themselves not just as a CRM provider, but as a full-spectrum enterprise platform.
Traditional sales reps who excel at pitching features to business users are no longer enough. Selling AI – particularly agentic, autonomous AI – demands sales professionals who can link technical capabilities to strategic outcomes and lead conversations around risk, compliance, and long-term value.
To sustain their leadership, Salesforce will need to invest in a new generation of sales talent: domain-fluent, consultative, and able to navigate complex, cross-functional buying journeys.
At a recently held Ecosystm roundtable, in partnership with Qlik and 121Connects, Ecosystm Principal Advisor Manoj Chugh, moderated a conversation where Indian tech and data leaders discussed building trust in data strategies. They explored ways to automate data pipelines and improve governance to drive better decisions and business outcomes. Here are the key takeaways from the session.
Data isn’t just a byproduct anymore; it’s the lifeblood of modern businesses, fuelling informed decisions and strategic growth. But with vast amounts of data, the challenge isn’t just managing it; it’s building trust. AI, once a beacon of hope, is now at risk without a reliable data foundation. Ecosystm research reveals that a staggering 66% of Indian tech leaders doubt their organisation’s data quality, and the problem of data silos is exacerbating this trust crisis.
At the Leaders Roundtable in Mumbai, I had the opportunity to moderate a discussion among data and digital leaders on the critical components of building trust in data and leveraging it to drive business value. The consensus was that building trust requires a comprehensive strategy that addresses the complexities of data management and positions the organisation for future success. Here are the key strategies that are essential for achieving these goals.
1. Adopting a Unified Data Approach
Organisations are facing a growing wave of complex workloads and business initiatives. To manage this expansion, IT teams are turning to multi-cloud, SaaS, and hybrid environments. However, this diverse landscape introduces new challenges, such as data silos, security vulnerabilities, and difficulties in ensuring interoperability between systems.
A unified data strategy is crucial to overcome these challenges. By ensuring platform consistency, robust security, and seamless data integration, organisations can simplify data management, enhance security, and align with business goals – driving informed decisions, innovation, and long-term success.
Real-time data integration is essential for timely data availability, enabling organisations to make data-driven decisions quickly and effectively. By integrating data from various sources in real-time, businesses can gain valuable insights into their operations, identify trends, and respond to changing market conditions.
Organisations that are able to integrate their IT and operational technology (OT) systems find their data accuracy increasing. By combining IT’s digital data management expertise with OT’s real-time operational insights, organisations can ensure more accurate, timely, and actionable data. This integration enables continuous monitoring and analysis of operational data, leading to faster identification of errors, more precise decision-making, and optimised processes.
2. Enhancing Data Quality with Automation and Collaboration
As the volume and complexity of data continue to grow, ensuring high data quality is essential for organisations to make accurate decisions and to drive trust in data-driven solutions. Automated data quality tools are useful for cleansing and standardising data to eliminate errors and inconsistencies.
As mentioned earlier, integrating IT and OT systems can help organisations improve operational efficiency and resilience. By leveraging data-driven insights, businesses can identify bottlenecks, optimise workflows, and proactively address potential issues before they escalate. This can lead to cost savings, increased productivity, and improved customer satisfaction.
However, while automation technologies can help, organisations must also invest in training employees in data management, data visualisation, and data governance.
3. Modernising Data Infrastructure for Agility and Innovation
In today’s fast-paced business landscape, agility is paramount. Modernising data infrastructure is essential to remain competitive – the right digital infrastructure focuses on optimising costs, boosting capacity and agility, and maximising data leverage, all while safeguarding the organisation from cyber threats. This involves migrating data lakes and warehouses to cloud platforms and adopting advanced analytics tools. However, modernisation efforts must be aligned with specific business goals, such as enhancing customer experiences, optimising operations, or driving innovation. A well-modernised data environment not only improves agility but also lays the foundation for future innovations.
Technology leaders must assess whether their data architecture supports the organisation’s evolving data requirements, considering factors such as data flows, necessary management systems, processing operations, and AI applications. The ideal data architecture should be tailored to the organisation’s specific needs, considering current and future data demands, available skills, costs, and scalability.
4. Strengthening Data Governance with a Structured Approach
Data governance is crucial for establishing trust in data, and providing a framework to manage its quality, integrity, and security throughout its lifecycle. By setting clear policies and processes, organisations can build confidence in their data, support informed decision-making, and foster stakeholder trust.
A key component of data governance is data lineage – the ability to trace the history and transformation of data from its source to its final use. Understanding this journey helps organisations verify data accuracy and integrity, ensure compliance with regulatory requirements and internal policies, improve data quality by proactively addressing issues, and enhance decision-making through context and transparency.
A tiered data governance structure, with strategic oversight at the executive level and operational tasks managed by dedicated data governance councils, ensures that data governance aligns with broader organisational goals and is implemented effectively.
Are You Ready for the Future of AI?
The ultimate goal of your data management and discovery mechanisms is to ensure that you are advancing at pace with the industry. The analytics landscape is undergoing a profound transformation, promising to revolutionise how organisations interact with data. A key innovation, the data fabric, is enabling organisations to analyse unstructured data, where the true value often lies, resulting in cleaner and more reliable data models.
GenAI has emerged as another game-changer, empowering employees across the organisation to become citizen data scientists. This democratisation of data analytics allows for a broader range of insights and fosters a more data-driven culture. Organisations can leverage GenAI to automate tasks, generate new ideas, and uncover hidden patterns in their data.
The shift from traditional dashboards to real-time conversational tools is also reshaping how data insights are delivered and acted upon. These tools enable users to ask questions in natural language, receiving immediate and relevant answers based on the underlying data. This conversational approach makes data more accessible and actionable, empowering employees to make data-driven decisions at all levels of the organisation.
To fully capitalise on these advancements, organisations need to reassess their AI/ML strategies. By ensuring that their tech initiatives align with their broader business objectives and deliver tangible returns on investment, organisations can unlock the full potential of data-driven insights and gain a competitive edge. It is equally important to build trust in AI initiatives, through a strong data foundation. This involves ensuring data quality, accuracy, and consistency, as well as implementing robust data governance practices. A solid data foundation provides the necessary groundwork for AI and GenAI models to deliver reliable and valuable insights.
