Q. What are the most immediate AI use cases you see driving CX and revenue impact today?
AI has evolved from a productivity tool into a strategic enabler across four key enterprise priorities: growth, optimization, experience and compliance. It drives intelligence-led decision-making by combining internal data, customer feedback and industry insights to eliminate bias and reliance on intuition. For profitability, AI streamlines processes across the employee lifecycle and financial operations, such as reducing procure-to-pay turnaround time, boosting efficiency and resource utilization. It enhances experience by improving onboarding, predicting actions and enabling proactive engagement for both employees and customers. On the compliance front, AI helps organizations scale responsibly while staying aligned with regulatory requirements.
AI is revolutionizing CX even further by enabling hyper-personalized experiences and seamless interactions across all touchpoints, not only predicting customer needs but also anticipating potential issues and resolving them proactively.
For instance, our AssistX framework powers our AI-driven services across multiple functions to redefine enterprise efficiency. Part of which is ITAssIst, our GenAI-powered IT assistant, that has delivered a 70% improvement in self-service IT ticket management efficiency.
In short, AI has moved beyond being just an efficiency booster; it’s now a strong growth engine offering a competitive edge. The real game-changer happens when businesses stop treating AI as a set of isolated tools and start embedding it into core strategies, workflows and decision-making across the enterprise.
Q. Marketing now depends deeply on data, automation and stack orchestration. How do you see the CIO-CMO relationship evolving in enterprises as MarTech stacks mature and AI layers become central?
The CIO-CMO relationship has matured from a tactical collaboration to a strategic enterprise-wide collaboration. Today, CXOs across sales, delivery, finance and HR are working closely with technology leaders to align AI capabilities with business priorities. AI-driven insights from both open and internal data sources now empower marketing and sales teams to act faster and with greater precision. On the creative front, contextual AI models are generating domain-specific strategies tailored to geographies and audience segments, redefining how campaigns are conceived and executed.
As MarTech stacks mature and AI layers become central, CIOs and CMOs are co-owning platform evolution, data governance and outcome-based orchestration. While the CIOs manage the stack and governance, CMOs own the narrative and personalization. The enterprise is no longer structured around silos - it’s architected around intelligence. At Persistent, our internal CXO signal intelligence platform tracks leadership movements across BFSI, healthcare and hi-tech, linking them to solution opportunities and enabling real-time marketing orchestration. This convergence is no longer about technology enabling marketing; it’s about AI steering business-wide outcomes.
Q.What foundational steps must enterprises take to become truly AI-ready on the data side, beyond the buzz around LLMs and other AI-powered tools?
Becoming AI-ready is fundamentally a data infrastructure transformation journey and not just a technology selection exercise. At Persistent, we recognized early on that the effectiveness of any AI initiative hinges on having enterprise-grade, contextual and secure data. That meant re-architecting our data systems to move away from fragmented sources and toward a unified, intelligence-driven core.
We built an internal ecosystem that integrates historical enterprise data with real-time external signals, including public domain insights and industry benchmarks. This eliminates guesswork and brings contextual intelligence to the fingertips of business leaders, whether it’s shaping go-to-market strategies or driving precision engagement.
Additionally, our enterprise data and AI readiness framework plays a pivotal role. It ensures data is discoverable, secured with enterprise-grade governance and primed for scale. It also enables orchestration across multiple LLMs, with observability, prompt control, agentic workflows and responsible AI guardrails built in. This isn’t a one-time initiative; it’s a deliberate transformation, designed to future-proof our infrastructure for an AI-first world.
Q. Which enterprise functions will adopt agentic workflows fastest and what governance questions must be addressed early?
Agentic AI is transforming all the functions such as finance, customer service, sales, marketing and software development. AI models are also consumers of data. If you allow sensitive internal IP-like pricing models, customer intelligence, or business logic to flow into public LLMs without ring-fencing, that data can be learned and reused by others globally. On the other hand, if you restrict all data within your enterprise perimeter, you risk losing access to the innovation and capabilities of global AI models; that’s a trade-off.
Additionally, mitigating AI bias requires a human-centric approach: conducting data audits, re-evaluating algorithms and grounding decisions in societal contexts. Long-term solutions include continuous monitoring, interdisciplinary collaboration and aligning AI outcomes with ethical principles and societal wellbeing.
At Persistent, we are embedding agentic workflows into platforms like SASVA™ and iAURA to enable autonomous decision-making in data engineering and release management. As these systems move from pilot to performance, governance becomes critical. Enterprises must define boundaries for autonomous actions, ensure auditability, implement escalation protocols and enforce role-based access. Our deployments emphasize explainability and compliance, ensuring agentic systems remain aligned with business priorities and regulatory expectations.
Q. Enterprises face a strategic choice on whether to adopt platform AI, build their own models, or co-create. What framework do you recommend CIOs use to make that decision?
Buy vs build is a long-standing debate and it’s not going away anytime soon. There’s no universal answer, because the decision hinges on an enterprise’s industry needs, strategic priorities, risk appetite and maturity curve. In most cases, a hybrid strategy tends to serve CIOs best: buy proven, ready-made components where they offer clear value and build or wrap custom layers only where differentiation, integration with legacy processes, or domain-specific intelligence is required. Increasingly, enterprises evaluate the question through a simple lens: “What is the business outcome I’m driving toward and how can I deliver intelligence to decision-makers in real time so they can act faster and more effectively?” This outcome-first mindset helps CIOs balance speed, cost and long-term scalability.
More importantly, the choice is rarely binary. Many organizations adopt a modular approach, taking off-the-shelf components that need minimal customization, then stitching them together with internally built sub-processes or intelligence layers to create the desired result.
At Persistent, for example, we see this play out across industries and geographies. As an IT services and digital engineering partner, we increasingly co-create with clients and technology partners to embed domain-specific intelligence into reusable frameworks, allowing enterprises to accelerate innovation without compromising on differentiation.
Q. As AI becomes embedded across functions, what new technology and leadership competencies are essential for technology teams? How are CIOs balancing tech skills with domain fluency?
Business understanding is no longer a soft skill; it’s a strategic requirement. Technology teams are moving beyond functional delivery to understand business priorities, customer journeys, regulatory needs and competitive pressures. Domain fluency helps teams anticipate needs and design outcomes. At Persistent, we embed domain specialists within engineering pods to ensure every solution is rooted in a real-world context.
Technical expertise must also be applied with precision. Deep skills in AI, cloud, security and data engineering matter, but only when aligned to the problem at hand. Our work on SASVA™ and iAURA, for example, goes beyond deploying GenAI. We tailor it to regulate environments, integrate with legacy systems and align with client KPIs. Turning technical depth into business impact is what differentiates high-performing teams.
CIOs are balancing domain and technical strength by embedding governance into AI programs. Prompt engineering controls limit who can run sensitive commands, role-based access controls determine who can see enterprise data and principles like least privilege and zero trust ensure no one gets more access than they need. Each enterprise adapts these guardrails to its priorities.
This model creates teams that are both technically strong and business-aware, engineers who understand outcomes and business leaders who grasp AI’s implications. The result is a culture where domain fluency and technical excellence go hand in hand, enabling secure and effective AI adoption.
Q. AI introduces new risks from prompt injection to data leakage. What should CIOs put in place first to build secure, compliant and explainable AI systems?
Beyond risk mitigation, internal audits have a unique opportunity to drive value and enable transformation with the responsible use of AI by shaping governance frameworks that drive innovation.
AI models can absorb sensitive IP, exposing proprietary data to external reuse. The balanced solution is to deploy virtual isolation environments using enterprise-grade AI models that analyze data without learning from it. At Persistent, we use such ring-fenced models to ensure privacy, compliance and innovation coexist. Our Responsible AI framework ensures that our AI objectives align with values and goals that enable responsible and purposeful AI adoption, centered on core principles including fairness, transparency, accountability, security and privacy.
Our Managed Security Service Provider model integrates Zero Trust architectures and AI-led threat modeling to secure hybrid environments, enabling clients to scale securely, making trust-by-design non-negotiable.
Q. What guardrails should enterprises implement to ring-fence data in AI systems and prevent misuse or unintended exposure?
Within enterprise boundaries, AI systems can surface sensitive internal data if not properly governed. Enterprises must implement prompt engineering controls to restrict sensitive queries, enforce role-based access to limit visibility and apply Zero Trust principles to ensure no implicit access.
In implementing necessary data privacy controls or achieving compliance, the first step is to identify the amount of sensitive data being stored, processed and used by respective application systems. Updated and accurate inventory of sensitive data helps in conducting privacy impact assessments, defining and applying data classification policies, responding to any data subject access request and maintaining required ‘records of processing activity.’ Some of the tools available today allow us not only to detect sensitive data but also to implement protection controls such as Anonymization-Pseudonymization and to apply retention or deletion policies in an automated way, reducing manual effort while increasing the accuracy and efficiency of the process.
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