Built on Systemic AI, bringing end-to-end intelligence across workflows, systems, and decisions
India | May 06 — MathCo, a global enterprise AI leader, announced its collaboration with Google Cloud to help enterprises move toward workflow-native AI, a fundamental shift in how organizations build, scale, and realize value from artificial intelligence.
A Deloitte report, State of AI in Enterprise, 2026, states that 66% of organizations report productivity gains from AI, yet only 34% are truly reimagining their business with it. For nearly three-quarters of enterprises, revenue growth from AI remains aspirational. This highlights the growing gap between AI activity and real business outcomes. Anchored in MathCo’s proprietary concept of Systemic AI, the collaboration will leverage the full Gemini Enterprise ecosystem, including Gemini Enterprise Agent Platform and enterprise data connectivity, to help organizations build workflow-native AI systems.
Aakarsh Kishore, Chief Product Officer, MathCo, said, “We are excited that this collaboration comes at a stage when enterprises are truly looking at scaling. We are not just going to implement – we will advise our customers on the right use cases, how to build the right data and AI foundation, and how to sequence their journey to extract compounding value from every AI investment they make.”
Gemini Enterprise serves as the central AI platform for the enterprise, bringing together models, agents, data, and tools into a single, secure environment where workflows can be designed, executed, and scaled. MathCo extends this by embedding Gemini Enterprise layers into a systemic architecture, ensuring intelligence is not applied to isolated tasks but orchestrated across workflows to deliver measurable business outcomes.
From Tasks to Workflows: Operationalizing Systemic AI on Gemini Enterprise
MathCo’s Systemic AI framework enables enterprises to move from action-oriented AI to outcome-driven systems by embedding intelligence directly into end-to-end business workflows. By bringing together enterprise data, AI models, and intelligent agents, organizations can redesign processes rather than simply automate individual tasks.
This approach allows AI systems to reason, plan, and execute across multi-step workflows, while remaining grounded in business context through integrated data, KPIs, and rules. With built-in governance, observability, and feedback mechanisms, enterprises can ensure AI operates in alignment with business goals as it scales across the organization.
From AI Activity to Industry Outcomes
The collaboration will enable workflow-native transformation across industries and business functions, connecting intelligence across planning, decisioning, and execution.
In Retail, enterprises can build end-to-end merchandising intelligence where demand forecasting, assortment planning, pricing, and replenishment are orchestrated into a unified workflow, reducing stockouts and improving margins.
In CPG, trade promotion workflows move from fragmented planning to closed-loop systems by connecting promotion design, real-time sell-out monitoring, and ROI measurement, enabling dynamic optimization of trade spend.
In Pharma & Life Sciences, intelligent HCP engagement workflows connect content creation, medical-legal approval, deployment, and performance tracking by ensuring compliant, end-to-end engagement with continuous learning.
As enterprises navigate increasing AI complexity, with hundreds of tools, fragmented systems, and low adoption, the real challenge is no longer building AI, but making AI work cohesively within the enterprise and its people.
MathCo aims to address this by shifting the focus from isolated AI usage to AI that works across workflows, and works for people to augment decision-making while enabling teams to operate with intelligence at scale.
