Markets like India offer a demanding environment for enterprise AI, where multilingual user bases push localisation and natural language processing (NLP) systems to their limits.
Startups across South Asia are entering a new phase of maturity, shifting focus from early traction to building enterprise-grade AI and automation designed to scale globally. This evolution is reflected not just in products, but in where confidence and capital are flowing.
India Newsroom spoke with Kartik Gupta of Salesforce Ventures to understand the signals that matter most today and what it takes to build for enterprise scale.
Q. From the Salesforce Ventures perspective, what signals indicate that startups are building for true enterprise scale today, rather than just early traction?
The clearest signals we look for are depth of integration within an organisation and repeatability of deployment.
Early traction often comes from a compelling demo or a single champion within an organisation, but true enterprise scale requires solving for the messy realities of production environments – think systems integrations, security and compliance, audit logging, and the ability to operate within complex data governance frameworks.
We pay close attention to whether customers are expanding usage across departments and geographies (not just renewing contracts), and whether the startup has developed a systematic implementation playbook that doesn’t rely on heroic efforts from the founding team for every new logo.
True enterprise scale requires solving for the messy realities of production environments.
Q. Across global markets, what patterns are emerging in how AI and automation startups are approaching enterprise readiness?
We’re seeing a convergence around “trust by design” as a founding principle rather than an afterthought. The most sophisticated startups – whether in Tel Aviv, Bangalore, or Berlin – are building with compliance, explainability, and data residency requirements baked in from day one, recognizing that these are prerequisites for growth.
There’s also a notable shift toward vertical specialisation. Rather than building horizontal AI tools and hoping enterprises will figure out the use cases, the strongest companies are going deep into specific workflows – healthcare, financial services, or supply chain – where they can demonstrate measurable ROI against clearly defined benchmarks that procurement teams understand.
Q. Why is the shift from AI as a feature to agent-driven systems and workflows such an important inflection point for enterprise software?
The feature-level integration of AI – smarter search, better recommendations, predictive analytics – delivered incremental improvements to existing workflows. Agent-driven systems represent a fundamentally different value proposition: the automation of entire processes end-to-end with human oversight at decision points rather than at every step.
This matters enormously for enterprises because it finally addresses the persistent gap between software’s theoretical capabilities and the operational bandwidth required to realize them. When AI can autonomously handle exception processing, coordinate across systems, and escalate intelligently, you’re not just making knowledge workers slightly more efficient, you’re unlocking capacity that was previously constrained by the limits of human attention.
Agent-driven systems unlock capacity that was previously constrained by the limits of human attention.
Q. How do trust, governance, and responsible AI factor into investment decisions when evaluating long-term enterprise potential?
Trust and responsibility have always been foundational to how Salesforce Ventures operates – it’s core to our DNA and has long been central to our investment criteria. What’s encouraging is that we’re now seeing this mindset expand across the broader market. These considerations have moved from nice-to-have to core diligence criteria for investors and enterprises alike.
We actively assess whether a company has thought through data provenance, model transparency, and bias mitigation – not because we’re checking boxes, but because enterprises increasingly require these assurances before procurement can proceed. Regulatory frameworks – from the EU AI Act to India’s forthcoming digital regulations – are making them mandatory. The companies that treat responsible AI as a competitive moat, rather than a compliance burden, tend to win larger contracts, face shorter sales cycles with regulated industries, and build more defensible businesses over time.
Q. From an operating-context lens, how do factors like scale, diversity of users, or regulatory complexity in markets such as India and South Asia influence how enterprise AI solutions are stress-tested or matured before global deployment?
Markets like India offer a demanding environment for stress-testing core technology and product capabilities. Multi-lingual user bases push localisation and NLP systems to their limits, vast variance in connectivity forces architectural decisions around edge computing and graceful degradation, and price sensitivity drives genuine efficiency in compute and infrastructure rather than brute-force scaling. Products that achieve technical robustness in these conditions often emerge with more resilient, cost-effective architectures than those built solely for homogeneous developed markets.
That said, founders need to be clear-eyed about where their primary market lies. The US remains the largest enterprise software market by a significant margin and B2B SaaS companies that spend too long perfecting product in South Asia risk being distant from the customers who will ultimately determine their success.
Enterprise sales cycles, procurement processes, and buyer expectations in North America have their own nuances that can only be learned through proximity, and there’s no substitute for that direct exposure when building a globally scalable business.
Q. Looking ahead, what should founders building enterprise-grade AI prioritise to scale sustainably and globally?
First, obsess over time-to-value. Enterprises have limited patience for lengthy implementations, so the companies that can demonstrate impact within hours and days, rather than weeks and months, will win.
Second, build your security and compliance posture early. Retrofitting Service Organization Control 2 (SOC 2), General Data Protection Regulation (GDPR), and industry-specific certifications is painful and expensive – having them ready opens doors that remain closed to competitors.
Third, invest in customer success infrastructure before you think you need it. The difference between a churned customer and an expansion opportunity often comes down to proactive engagement in the first ninety days.
Finally, stay genuinely close to the evolving needs of your customers rather than falling in love with your own roadmap. The AI landscape is moving fast enough that the best founders are the ones who remain students of their market.
