AI is Rewriting Rules of MarTech and Solving Core Problems

Enterprise technology is undergoing a major transformation, courtesy AI. And the rules of the game are changing. For the longest period of time, marketing-tech (MarTech) revolved around a few specialised tools, including WhatsApp, SMS, emails, analytics, and CRM. Stakeholders worked with these tools despite the limitations caused by fragmentations – until AI happened. 

With AI, there’s a clear shift in the approach to MarTech, with making everything available under one roof being the primary necessity. The shift is also reflected in the recent Boston Consulting Group (BCG) Global CMO Survey which said that 96% of CMOs it surveyed acknowledged that AI is now driving the need for a transition to end-to-end solutions. 

The findings also suggest that India is emerging as one of the world’s most marketing-led AI transformation markets: 57% of Indian CMOs report that AI investments are owned by the marketing function itself, compared with 47% globally. The report adds that India leads globally in agentic commerce adoption, with 73% of Indian CMOs ranking it among their top three strategic priorities, versus a global average of 63%.

About 43% of Indian CMOs say accelerating AI and digital transformation is their CEO’s top priority, underscoring the growing strategic importance of marketing in enterprise AI agendas. 52% of Indian CMOs expect GenAI to have a significant positive impact on personalization, the highest across all regions surveyed.

“Ninety percent of the CMOs in our survey agreed that GenAI is already reshaping how consumers discover and evaluate brands. But most marketing organizations are not yet built to compete in that environment,” said Mark Abraham, Global Leader, Marketing, Sales and Pricing Practice, Boston Consulting Group, and coauthor of the report, in a statement.

“Investment must now move beyond individual AI tools, and towards fully connected agentic operating systems built on strong data foundations, brand intelligence layers, multi-agent orchestration, and the right talent. If established brands don’t build this first, new agentic-native attacker brands will do so.”

The Tipping Point

The legacy MarTech has worked but the fragmented structure has remained a challenge, creating a few vacuums and silos. This often leads to duplication and even friction in communications now and then. 

Nishant Das, Founder and CEO of Cheerio AI, captures this exact tipping point. 

Speaking to Entrepreneur India, Das says: “The tipping point comes when the cost of coordination exceeds the value of specialization. Most enterprises today have separate systems for engagement, support, analytics, CRM, loyalty, and communication channels. Each tool performs well in isolation, but customer journeys don’t happen in isolation. The result is fragmented customer data, duplicated communication, inconsistent attribution, and rising operational costs. We’ve seen enterprises where multiple teams unknowingly contact the same customer across different channels, leading to poor experiences and wasted marketing spend.”

It’s worth highlighting that there’s also a significant operational burden that often gets overlooked. Every additional tool increases implementation effort, integration complexity, employee training requirements, and the context-switching overhead for teams trying to manage customer experiences across multiple systems. Instead of focusing on customers, teams spend more time learning, maintaining, and coordinating between platforms. Another challenge emerges when something breaks. 

Moreover, in a fragmented stack, diagnosing which tool, integration, or data pipeline caused the issue can take days or even weeks, with teams passing ownership between vendors and internal stakeholders. The more tools involved, the harder it becomes to identify root causes and restore normal operations quickly. 

Das further says, “The real problem isn’t the number of tools—it’s the number of disconnected decisions being made across those tools. Once customer context becomes fragmented, AI models, analytics systems, and business teams all operate on incomplete information. That’s when enterprises start seeing declining ROI despite increasing technology investments. The future isn’t adding more tools; it’s creating a unified intelligence layer that can coordinate them.” 

AI Plus MarTech

This is also in line with the above-mentioned BCG survey which also talks about the role of AI (generative and agentic) reshaping key fundamentals like discovery. And this is also trickling down to the Indian market, which anyway has become one of the largest AI users in the world.

“On agentic commerce, India leads globally, with 73% of CMOs ranking it among their top three strategic priorities, against a global average of 63% and well ahead of the EMESA rate of 54%. India’s quick commerce density and digital-native consumer base make agentic commerce an immediate go-to-market priority rather than a future-state concept for Indian marketing leaders. On personalisation, 52% of Indian CMOs expect GenAI to have a significant positive impact, the highest of any region, against 47% globally. A further 88% of Indian CMOs agree that GenAI will help their teams sense signals and act in near real time, underscoring the speed imperative that defines India’s AI marketing agenda,” Parul Bajaj, India Leader, Marketing, Sales and Pricing Practice, Boston Consulting Group, and coauthor of the report, added. 

Das further explains how an AI-native infrastructure can be refreshingly different from what the legacy platforms have to offer. “Most legacy platforms are essentially collections of acquired products connected through integrations. While they appear unified on the surface, the underlying data models, workflows, and decision engines often remain fragmented. An AI-native orchestrator takes a fundamentally different approach. Instead of connecting tools, it connects context. Every interaction, customer signal, business rule, and organizational objective feeds into a shared intelligence layer that continuously learns and optimizes outcomes. At Cheerio AI, we think of orchestration as moving from workflow automation to decision automation. The platform doesn’t just execute campaigns—it decides the best channel, timing, message, and next action based on real-time context. Whether the customer interacts through WhatsApp, email, voice, or a support channel, the AI understands the full journey. The difference is that legacy platforms unify software. AI orchestrators unify intelligence,” he said.

Another important merit of the AI integration is the reduction of dependence on what may seem an overstretched engineering teams of basic execution. 

Das notes: “Historically, every new customer journey, integration, segmentation rule, or campaign required engineering involvement. That created bottlenecks where business teams had ideas, but technology teams controlled execution capacity. Applied AI changes this dynamic by becoming the abstraction layer between business intent and technical implementation. Instead of writing workflows, teams describe objectives. Instead of configuring dozens of rules, they define outcomes. At Cheerio AI, business users can ask the platform to identify churn risks, launch retention journeys, create audience segments, or optimize outreach strategies without navigating complex integrations or technical dependencies. The result is a significant reduction in time-to-execution. Initiatives that previously took weeks of cross-functional coordination can often be launched in hours. As AI becomes the interface, organizations move from ticket-driven operations to outcome-driven execution.”