Imagine this: A global retailer deploys an AI chatbot to cut customer service costs. Within weeks, the bot frustrates loyal customers by misrouting queries, breaking workflows, and undermining trust. Leaders scramble to fix the damage — realizing automation without orchestration isn’t transformation.
That’s the inflection point many enterprises face today as they move from AI pilots to production-scale impact. In this exclusive CXQuest.com conversation, G. Venkataramanan, President & Business Head of Intelligent Enterprise Operations at Mindsprint, unpacks how Agentic AI and domain-led intelligence are reshaping the journey from operational efficiency to business value covering intelligent operations.
With two decades of global technology leadership, Venkataramanan brings grounded expertise in scaling intelligent operations ecosystems. Under his leadership, Mindsprint — a Singapore-headquartered AI-first innovation partner — helps enterprises modernize with automation, analytics, and responsible engineering across industries like retail, manufacturing, healthcare, and life sciences covering their intelligent operations.
This conversation goes beyond buzzwords to explore how enterprise leaders can evolve from experimenting with AI to embedding intelligence into every customer and employee touchpoint.
Most Inspiring CX/EX Wins at Mindsprint?
Q1. What recent CX or EX win at Mindsprint surprised or inspired you the most?
GV: One win that truly stood out was Mindsprint securing a Finance Operations & Taxation managed services engagement with a global engineering solutions and manufacturing company in the oil & gas sector. What made it distinctive was how we combined platform-led services with a seamless transition of the client’s GCC talent into a unified operating model, anchored on a strong BPM foundation and powered by agentic AI execution.
What made this win remarkable was the level of trust the client placed in us – the confidence that we could transform their operations while strengthening workforce effectiveness and organizational resilience amid an evolving business and technology landscape. We fundamentally reimagined how work gets done, shifting the model from being people-dependent to platform-driven intelligent operations, with AI at the core of execution allowing humans to focus on higher-value strategic activities, judgments, stakeholder engagement, and solving complex problems.
What is AI-first Transformation?
Q2. When leaders talk about “AI-first transformation,” what does that mean in operational terms at the enterprise level?
GV: AI-first transformation is best understood through four integrated levers that fundamentally rewire enterprise operations
Process Intelligence
1• AI-enabled digital value stream mapping to eliminate dark data that undermines AI outcomes
2• A domain interpretation layer that gives AI agents real operational context, not abstract instructions
3• KPI evolution—from short-term cost savings to long-term, AI-led value creation
Data Readiness
• Well-governed, domain-specific data pipelines that ground AI in accurate, contextual intelligence
Technology Modernization
• State-of-the-art frameworks, architectures, and protocols for responsible deployment of advanced AI technologies like GenAI and Agentic AI at scale
People Empowerment
1• Redefined roles enabling effective human–AI collaboration
2• Talent that blends functional depth with AI fluency
3• Humans at the helm focused on outcome supervision and AI guardrails, not just people management
When done right, AI-first transformation converts traditional back-office processes into autonomous operations. The result is not just cost efficiency, but faster cycle times through
touchless execution with minimal handoffs, superior customer & employee experience, and operations that actively drive growth rather than simply support it.
Evolution of Customer Expectations with Automation
Q3. How have customer expectations evolved as automation reshapes back-end operations?
GV: What customers now expect from service partners is not automation in isolation, but operational ownership, and measurable business impact delivered reliably at scale in increasingly complex, global environments.
Customers expect ownership of outcomes, not task execution. From day zero, they demand visible operational uplift, end-to-end transparency, and continuous innovation embedded into execution, not deferred as roadmaps.
As AI moves into core operations, customers expect partners with skin in the game. Faster ROI, accountability, and measurable outcomes now matter more than pilots or proof points. With customer experience becoming a core metric, siloed operations and slow response times are unacceptable.
Enterprises are increasingly demanding solutions that protect data, reduce bias, and maintain transparency at every level.
Co-innovation
Innovation is expected to be user-aligned, with solutions co-created alongside customer business teams to ensure relevance, adoption, impact, and seamless change management.
Scaling AI Impact
Q4. Many organizations still hover between pilots and production — what’s holding them back from scaling AI impact?
GV: Many organizations struggle to move AI from pilots to production because the foundations are not ready.
First, AI is added without rewiring processes.
Enterprises often automate existing workflows as they are, carrying forward legacy bottlenecks. Without end-to-end process visibility, AI is applied in the wrong places, limiting ROI and real impact.
Second, success is measured using the wrong metrics.
Most teams still rely on SLA-based KPIs that reward activity, not outcomes. Without metrics designed for AI-led operations, scaling value becomes difficult.
Third, technology and operations remain fragmented.
Point solutions and disconnected systems slow change. Without a platform-led approach that unifies data, workflows, and domain expertise, AI cannot scale consistently across the enterprise.
Fourth, trust and governance are not built in.
Limited oversight of how AI uses data weakens auditability and explainability. This erodes trust and increases compliance risk, especially in regulated environments.Finally, there is a talent gap. Organizations lack teams that combine deep domain knowledge with AI fluency, making it hard to design, run, and continuously improve AI-driven operating models.
Enterprise Readiness for Intelligent Operations
Q5. How do you build enterprise readiness for intelligent operations without overwhelming legacy systems or teams?
GV: We build enterprise readiness for intelligent operations through a structured approach that minimizes disruption to legacy systems and teams. This is anchored in our proprietary 4E transformation framework, applied in tandem with our signature change management and operational platform, Fulcrum, to ensure controlled, scalable adoption.
Our 4E transformation framework includes:
First Step: Evaluate existing processes and assess process maturity, technology readiness, and organizational capability
Second Step: Embark on AI-enabled digital value stream mapping to create true end-to-end visibility, eliminate dark data, and identify where intelligent automation will deliver the maximum impact — creating a clear blueprint to re-engineer business processes for AI-driven execution with humans in control of outcomes.
Third Step: Execute or deploy fit-for-purpose solutions ranging from simple automation, classical AI/ML to Agentic AI powered autonomous solutions guided by technological readiness, integration feasibility and ROI to deliver measurable business impact without overwhelming legacy systems of team.
Fourth Step: Elevate or scale the usage of AI in stages, expanding only when outcomes are proven and teams are ready; Continuous feedback loops ensure operations evolve in line with business priorities, performance expectations, and real-world process dynamics.
Governance Frameworks for AI Automation
Q6. What governance frameworks ensure that AI automation aligns with ethical and responsible innovation principles?
GV: As classical AI evolves into Agentic AI that operates with greater autonomy effective governance frameworks for AI-driven operations become paramount:
Context grounding
AI systems must be grounded in reality through trusted, well-governed, domain-specific data pipelines. Techniques such as Retrieval-Augmented Generation (RAG) ensure AI operates on refined, relevant, and context-rich intelligence rather than generic or probabilistic knowledge.
Behavioural boundaries
Robust system prompts explicitly define an AI agent’s role, goals, tasks, tools it may use, and—critically—what it must not do. These prompts translate policy and intent into enforceable operational behaviour.
Error handling and fallback mechanisms
Resilient AI systems are designed not only to act, but to recover. This includes confidence thresholds for high-risk actions, fallback responses when uncertainty is detected, and retry or escalation paths—often handing control back to humans.
Observability and feedback loops
Continuous monitoring of AI performance across accuracy, relevance, latency, task completion, and hallucination rates enables real-time corrections through human-in-the-loop and exception-handling architectures.
Why Agentic AI is Pivotal?
Q7. How do you define “Agentic AI,” and why is it pivotal to enterprise modernization in 2026?
GV: Agentic AI refers to goal-driven, autonomous systems that can sense context, reason across multiple steps, make decisions, and execute actions across enterprise processes, while operating within clearly defined human, policy, and ethical guardrails. Unlike traditional AI or task-level agents, Agentic AI is designed to own outcomes, not just perform actions. It combines planning, memory, tool orchestration, and continuous learning to operate across complex, end-to-end workflows.
In 2025, agentic AI marked a clear shift – from generating insights to autonomously orchestrating end-to-end business processes. It also forced leaders to confront questions around governance, trust, performance, and human–AI collaboration.
In 2026, the focus will decisively shift beyond abstract “AI” conversations to scaled adoption of agentic systems built with strong governance, humans in control, and measurable business impact. Successful deployment of AI Agents will be enabled through a tight fusion of domain knowledge, process intelligence, and optimized workflows effectively rewiring traditional operations into Intelligent Operations.
Agentic AI Transforming Real-World Process
Q8. Could you share a real-world example where Agentic AI transformed a process or outcome for a client?
GV: For organizations operating complex global supply chains, Accounts Payable (AP) is both a critical control function and a driver of stakeholder experience. Variations in tax regimes, currencies, languages, and regulations make manual or fragmented AP a source of risk, delays, employee strain, and supplier dissatisfaction—ultimately impacting end-customer experience.
By deploying our IP led Agentic AI–powered platform to autonomously orchestrate end to end Accounts Payable operations for a global food ingredients major processing hundreds of thousands of invoices annually we were able to achieve for the client a 50% improvement in processing efficiency, over 70% reduction in AP cycle time, 100% audit traceability, and stronger supplier relationships—directly supporting reliable, on-time delivery to its end customers.
Quantifying ROI of Intelligent Operations
Q9. How do you quantify the ROI of intelligent operations powered by autonomous or semi-autonomous AI systems?
GV: Quantifying ROI from intelligent operations requires moving beyond traditional metrics like FTE reduction or task throughput. The true measure is the quality, speed, and adaptability of decisions, and how effectively intelligence compounds across enterprise operations over time.
For example, in finance operations ROI shows in Latency-to-insight. Month-end close cycles compressed by more than 60% through continuous real-time reconciliation & consolidation versus batch processing. CFOs gain P&L visibility 5 days earlier will be strategic advantage, not just efficiency.
Similarly, in supply chains, value is driven by multi-echelon forecast reliability. Autonomous systems anticipate supplier disruptions, downstream impacts, and mitigation actions with full value-chain visibility, enabling On Time and In Full (OTIF) improvements exceeding 5% even under volatility.
Agentic AI–powered procurement platforms further demonstrate ROI by reducing procurement cycles by 30–50% through autonomous orchestration of Vendor Management, Source to Contract an Purchase Request to Purchase Order cycles. Faster, more reliable procurement directly improves service levels and elevates customer experience.
For people operations, AI enabled support for resolution completeness matters more along with speed to enhance employee experience
Taken together, these examples illustrate how ROI from autonomous systems is realized through faster decisions, greater resilience, and sustained performance improvement, not isolated efficiency gains.
Most Common Misconceptions While Adopting Intelligent Automation
Q10. What are the most common misconceptions leaders have when adopting intelligent automation?
GV: Leaders often approach intelligent automation with the right intent but flawed assumptions. The most common misconceptions tend to fall into a few clear patterns:
1. Automation equals cost takeout
Many leaders still frame intelligent automation as a productivity or FTE-reduction play. This misses the real value: improved decision quality, faster responsiveness, better risk control, and superior customer and employee experiences. Cost efficiency emerges as a natural outcome of better decisions and smarter operations, not the central objective.
2. We can automate as-is processes
There is a belief that existing processes can simply be digitized or augmented with AI. In reality, automating broken or opaque processes is nothing but GI-GO and only accelerates inefficiencies. Without process intelligence and redesign with right workflows, AI creates more complexity, not value.
3. Technology maturity alone guarantees success.
Leaders often overestimate tools and underestimate operating models. Intelligent automation fails when roles, KPIs, governance, and accountability are not redefined for human–AI collaboration. Technology without organizational change rarely delivers sustained impact.
4. Pilots prove readiness for scale
A successful pilot does not mean an enterprise is ready for production-scale autonomy. Scaling requires data discipline, interoperability, observability, security, and governance—capabilities often absent in pilot environments.
5. AI replaces expertise
The most damaging misconception is viewing AI as a substitute for domain knowledge. In practice, intelligent automation amplifies expertise. The strongest results come when AI systems are shaped, supervised, and continuously improved by experienced practitioners.
6. Governance slows innovation
Responsible AI frameworks are often seen as constraints. In reality, governance enables speed at scale by building trust, auditability, and control—especially as autonomy increases.
Correcting these misconceptions shifts automation from experimentation to true intelligent operations.
Cultivating a Culture Embracing AI Augmentation
Q11. How do you cultivate a culture that embraces AI augmentation rather than fears job disruption?
GV: It requires adeliberate blend of top-down leadership and bottom-up ownership.
From the top, leadership sets a clear narrative: AI is a force multiplier for human expertise, not a replacement. This intent is institutionalized through cross-functional AI Councils that guide responsible adoption, ensure ethical use, and embed AI thinking consistently across internal operations as well as GTM solutions and products. In parallel, AI Centers of Excellence (CoEs) act as scaling engines – building reusable accelerators, reference architectures, and an internal AI marketplace that gives employees access to approved tools, platforms, and components. This enables rapid experimentation and PoC’s without reinventing the wheel.
From the bottom up, culture is built through periodical AI workshops and innovation forums where employees themselves identify opportunities for AI to augment their roles. This creates shared ownership and turns curiosity into contribution. Adoption accelerates when teams co-create AI agents – finance professionals defining decision rules, supply chain experts training agents on supplier nuances, and HR specialists encoding policy interpretation logic. When an agent reflects 15 years of lived expertise, the perception shifts from “being replaced” to scaling personal impact.
This is reinforced through transparent transition architectures that clearly map what AI agents handle autonomously, where human–AI collaboration applies, and which responsibilities elevate to human judgment. Targeted L&D programs then build skills in supervising AI, interpreting outcomes, and owning business results, not just executing tasks.
Enhancing Success of AI Productization and Enterprise Integration
Q12. How does domain expertise enhance the success of AI productization and enterprise integration?
GV: Domain expertise is the difference between AI that works in theory and AI that delivers outcomes in production. In enterprise environments, AI productization succeeds only when technology is deeply fused with how the business actually operates.
First, domain expertise provides contextual grounding. Finance, supply chain, HR, or risk processes are governed by nuanced rules, exceptions, and regulatory constraints that are rarely explicit in data. Domain knowledge ensures AI systems interpret signals correctly by distinguishing true anomalies from acceptable variance, prioritizing actions based on business impact, and applying policies as practitioners would. Without this, AI generates noise, not decisions.
Second, it enables product-grade design rather than bespoke automation. Domain experts help identify repeatable patterns, standard decision archetypes, and scalable operating models that can be embedded into platforms. This is what allows AI to move from one-off use cases to reusable products that integrate cleanly across clients, geographies, and regulations.
Third, domain expertise is critical for enterprise integration and trust. AI must align with existing controls, approval hierarchies, audit requirements, and handoff points. When domain experts shape agent behaviors, escalation logic, and human-in-the-loop thresholds, AI fits naturally into operating models—accelerating adoption instead of disrupting it.
Finally, domain expertise accelerates value realization. It ensures AI is applied where it materially improves outcomes like cycle time, accuracy, resilience, and experience rather than where it is merely technically feasible. That alignment is what turns AI investments into sustainable business impact.
Reconciling the Tension Between CX Ambition and Cost Optimization
Q13. As AI scales, how do you reconcile the tension between CX ambition and cost optimization?
GV: As AI scales, reconciling customer experience ambition with cost optimization requires engineering CX into the operating model, not layering cost on top of it. We do this by tightly linking where autonomy is deployed, how value is measured, and how spend is governed.
First, we deploy Agentic capabilities incrementally based on business impact, not technological novelty. We prioritize CX breakpoints— manual transaction processing, close-cycle delays in finance, OTIF misses in supply chains, unresolved employee intents in HR—and introduce autonomy only where it measurably improves experience outcomes. This avoids blanket automation that inflates cost without improving CX.
Because these CX improvements are clearly measurable and should become the basis for outcome-linked commercial models. In other words, improving customer experience and improving commercial outcomes to be driven by the same levers.
Second, we maintain explicit clarity on which technology addresses which customer pain point, aligned to process maturity. Deterministic automation is used where stability matters, cognitive AI where interpretation is needed, and Agentic AI only where adaptive decision-making improves outcomes. This precision prevents over-engineering and keeps cost aligned to value delivered.
Third, cost is governed architecturally. API-first designs, usage-based AI services, and infrastructure throttles (API limits, compute and model-usage controls) ensure spend scales in proportion to realized CX impact—not experimentation volume.
At scale, CX and cost are reconciled through measured autonomy, disciplined technology choice, and outcome-linked economics. The result is experiencing improvement that is both commercially sustainable and operationally efficient.
How to Measure Agentic AI Improvement?
Q14. What metrics best demonstrate that Agentic AI improves both business and customer outcomes?
GV: Agentic AI improves both business and customer outcomes by restructuring how back-end operations sense, adapt, and execute at scale. Rather than optimizing isolated tasks, it enables enterprises to operate as cohesive, intelligent systems. The following examples illustrate the kinds of impact Agentic AI delivers across horizontal offerings such as finance operations, supply chain & procurement, HR, risk & compliance.
First, Agentic AI breaks traditional delivery constraints. By embedding domain knowledge, multilingual capability, and decision logic into agents, enterprises reduce dependence on location-specific talent. This allows flexible global delivery, and consistent service quality while customers benefit from quicker ramp-ups and locally precise outcomes without operational handoffs.
Second, traditional back-end operations rely on multiple, disconnected automations and data silos. Agentic AI replaces this with multi-agent systems operating on a shared domain model, equipped with tools and context to orchestrate entire processes end to end. For businesses, this reduces integration debt and operational fragility. For customers, it delivers seamless execution with fewer handoffs, fewer errors, and clearer accountability for outcomes.
Third, Agentic AI enables a true Service as Software operating model. AI agents handle execution across the full process lifecycle, supported by platforms that integrate data, analytics, and tools seamlessly. Humans focus on guiding outcomes and managing exceptions rather than coordinating work. For customers, this means fewer handoffs, greater transparency, and reliable delivery of end-to-end outcomes moving beyond isolated efficiency gains to consistently better experiences.
These examples reflect a broader pattern: Agentic AI enables intelligent operations delivering durable business value alongside superior customer experience.
AI Agents Becoming Proactive Decision-makers
Q15. Looking ahead, how will enterprise operations evolve as AI agents become proactive decision-makers rather than reactive systems?
GV: As AI agents shift from reactive execution to proactive decision-making, enterprise operations will move from process efficiency to operational intelligence.
First, operations will become anticipatory rather than responsive. Rather than responding to visible exceptions—late payments, supply disruptions, or compliance breaches—AI agents will continuously detect weak signals across operational data, workflows, and external events, acting before risk or delay materializes. Month-end close will no longer be a periodic event but a continuously governed state. Supply chains will move beyond reacting to disruptions, dynamically rebalancing inventory, sourcing, and logistics ahead of downstream impact.
Second, enterprises will adopt a services-as-software operating model. AI agents will act as primary executors of end-to-end outcomes, orchestrating tasks across systems, teams, and partners. Humans will shift from transaction handlers to supervisors of intent, policy, and judgment—steering outcomes rather than executing steps.
Third, decision-making will become distributed yet governed. Instead of centralized control rooms, fleets of specialized agents will make local decisions within clearly defined risk, financial, and ethical boundaries. Governance will move upstream—into policy encoding, context grounding, and continuous assurance, rather than downstream audits.
Finally, organizations will unlock compounding advantage. Proactive agents learn from every decision, every exception, and every outcome, improving performance over time. Enterprises that embed process intelligence, domain expertise, and feedback loops will see operations evolve into adaptive systems that not only support the business, but actively shape strategy, resilience, and growth.
Real AI Impact Happens When…
As Venkataramanan emphasizes, real AI impact happens when enterprises stop thinking of automation as a project and start treating it as an evolving capability — where intelligence powers agility, resilience, and a measurable uplift in experience. Mindsprint’s vision of responsibly engineered operations captures this new enterprise ethos: a shift from experimentation to transformation.
For readers exploring AI in CX, EX innovation, or intelligent automation frameworks, explore more insights on our CXQuest.com hubs — and see how next-generation operational intelligence is reshaping the business experience landscape in 2026.
