Artificial Intelligence is no longer merely a technology initiative

Reflections from SCIT's 27th National Seminar 2026

Artificial Intelligence is no longer merely a technology initiative. It is a strategic choice, an operating-model challenge and, increasingly, a boardroom responsibility.

That was the central conviction I carried into the panel discussion at the 27th National Seminar 2026, presented by the Quality Improvement Cell of the Symbiosis Centre for Information Technology (SCIT). The theme, “AI Momentum: Fueling Enterprise Reinvention Through Data, Security and AI,” captured an important moment for business and society. AI has moved beyond curiosity. The question is no longer whether organisations will use it, but whether they will use it with purpose, discipline and accountability.

Participating as a panelist gave me an opportunity to engage with fellow leaders, faculty members and students on the choices that now confront enterprises. Which emerging technologies deserve serious investment? Why do promising pilots fail to become enterprise capabilities? How should organisations integrate AI, cloud, analytics and automation without creating more complexity? And as systems become increasingly autonomous, who remains accountable for their decisions and consequences?

The inaugural ceremony of SCIT's 27th National Seminar 2026, centred on enterprise reinvention through data, security and AI.

Momentum is valuable only when it has direction

“AI momentum” is an apt description of the present environment. New models, platforms, copilots and autonomous agents appear at extraordinary speed. Capital is flowing into AI ventures. Boards are asking management teams for AI strategies. Business units are experimenting with automation, knowledge assistants, prediction and decision support. Yet movement must not be mistaken for progress.

An organisation can run dozens of pilots and still remain fundamentally unchanged. It can subscribe to leading AI platforms and still possess fragmented data, unclear ownership and weak adoption. It can demonstrate an impressive proof of concept while lacking the controls needed to deploy it responsibly. The real measure of momentum is therefore not the number of experiments launched. It is the amount of durable business value created.

The biggest constraint on enterprise AI is no longer access to technology. It is the quality of judgement applied to selecting, integrating, governing and scaling it.

That judgement must connect technological possibility with customer value, economics, organisational capacity, risk and long-term strategy. It cannot reside only within the technology function. It must become a shared leadership capability.

AI has become a board agenda

Boards do not need to become model developers or data scientists. They do, however, need sufficient technological literacy to govern consequential decisions. When AI influences credit, hiring, pricing, customer treatment, cybersecurity, healthcare, collections, supply chains or strategic planning, it directly affects enterprise value and stakeholder trust.

From a board and governance perspective, five questions have become essential:

1. What business outcome are we pursuing? AI must begin with a meaningful problem or opportunity, not with pressure to follow a trend.
2. Who owns the outcome? Every initiative needs accountable business leadership, not merely technical sponsorship.
3. What data and operating conditions does it depend upon? A compelling model cannot compensate for unreliable data or a broken process.
4. What could go wrong, and how would we know? Risk must be designed into the initiative through testing, monitoring, escalation and human oversight.
5. How will value be measured? Productivity claims must eventually translate into cycle-time reduction, revenue, cost, quality, risk reduction or a better stakeholder experience.

This is why AI governance should not be treated as a late-stage compliance gate. Good governance improves decision quality from the beginning. It clarifies acceptable use, assigns accountability, creates evidence and enables organisations to scale with confidence.

On the expert panel at NATSEM 2026, discussing how enterprises can convert technological possibility into governed, measurable value.

Separating durable opportunity from technological fashion

Leaders often ask how they can distinguish a technology worth investing in from one that is largely fashionable. There is no perfect forecast, but there is a disciplined way to think.

First, begin with the problem. Is it material, recurring and sufficiently important to justify change? Second, identify the advantage. Does the technology create a meaningful improvement over existing methods? Third, test readiness. Are the data, architecture, skills and processes capable of supporting it? Fourth, evaluate risk and reversibility. Can the organisation contain failure, protect stakeholders and change course without disproportionate damage? Finally, assess scalability. Can a successful experiment be integrated into real workflows and adopted by the people who must use it?

This framework shifts the conversation away from excitement and towards evidence. It also reminds us that early adoption is not automatically an advantage. Early adopters create value when they learn faster than competitors, not when they merely buy sooner.

From experimentation to enterprise transformation

Many organisations are now caught in what I call the pilot paradox: they can prove that AI works in a controlled setting, yet struggle to make it work reliably across the enterprise. The gap is rarely caused by the model alone. It is created by fragmented ownership, inaccessible data, legacy integration, unclear process design, inadequate controls and resistance to new ways of working.

Moving beyond experimentation requires a portfolio approach:

• Prioritise use cases by value and feasibility, not novelty.
• Assign an accountable business owner with authority over the affected workflow.
• Create reusable foundations for data access, model evaluation, security, identity, orchestration and monitoring.
• Redesign work instead of placing AI on top of an unchanged process.
• Measure adoption and outcomes alongside technical performance.
• Retire weak initiatives so that capital and attention remain focused on those capable of delivering value.

The strongest AI programmes create a repeatable path from discovery to controlled deployment and then to scale. They balance speed with evidence. They allow teams to experiment, but they also define the standards an initiative must meet before it is allowed to influence important decisions.

Data, security and architecture are not background issues

The seminar theme rightly placed data and security alongside AI. AI cannot be separated from the environment in which it operates. If data is incomplete, biased, poorly governed or disconnected from its business meaning, the system will amplify those weaknesses. If identity, access and monitoring are inadequate, an intelligent interface can become a new route to sensitive information or unintended action.

Organisations therefore need more than a data lake or a policy document. They need clarity about authoritative sources, ownership, lineage, quality, access rights, retention and permitted use. Security must extend from infrastructure to prompts, models, tools, integrations and outputs. Architecture must allow AI to work with enterprise systems without creating uncontrolled dependencies or yet another generation of silos.

This becomes particularly important when companies adopt multiple technologies simultaneously. Cloud, AI, analytics, automation, CRM and digital platforms may each be valuable, but value is lost when they are purchased and implemented as disconnected layers. The objective should be a coherent capability architecture in which data flows securely, responsibilities are clear and components can evolve without destabilising the enterprise.

Agentic AI raises the accountability threshold

Generative AI largely changed how people access and create information. Agentic AI goes further: systems can plan, select tools, coordinate actions and complete multi-step workflows with a degree of autonomy. This can unlock significant productivity and new service models, but it also raises the consequences of error.

When an AI system recommends, a human can choose whether to act. When an AI agent acts across applications, communicates with customers, alters records or initiates a transaction, governance must become more explicit. Organisations need defined authority boundaries, role-based access, approval thresholds, logs, monitoring, exception handling and reliable ways for people to intervene.

Autonomy does not transfer accountability from the organisation to the machine. It increases the organisation's obligation to define, observe and control how that autonomy is exercised.

The right design is not “human versus AI.” It is a thoughtful allocation of responsibility between people and machines. AI can process scale, detect patterns, maintain consistency and execute routine actions. Humans must remain responsible for purpose, values, ambiguity, exceptional circumstances and consequences.

With fellow panelists and academic leaders following a wide-ranging conversation on technology, transformation and responsible leadership.

Different industries, common transformation disciplines

Technology priorities differ significantly by industry. A bank must give particular attention to explainability, fraud, customer protection and regulation. A manufacturer may focus on predictive maintenance, computer vision, supply-chain resilience and quality. A telecom organisation may emphasise network intelligence and service assurance. A customer-experience operation may concentrate on conversational AI, agent assistance and workflow automation.

Yet the disciplines that separate successful transformations from unsuccessful ones are remarkably consistent: a clear business problem, engaged leadership, trusted data, integrated architecture, accountable ownership, adoption by frontline teams and sustained measurement of results.

Technology does not transform an organisation by itself. Transformation occurs when strategy, process, people, data and governance change together. This is why organisations that treat AI as a software installation often underperform those that treat it as a redesign of how work is conceived, decisions are made and value is delivered.

The entrepreneurial opportunity

For entrepreneurs, the present moment offers enormous opportunity—but not simply because AI is fashionable. The strongest ventures will solve specific, high-friction problems and combine domain understanding with intelligent technology. They will build trust into the product, demonstrate measurable economics and integrate with how customers actually work.

There is substantial room for AI-native solutions in financial services, customer operations, healthcare, education, manufacturing, logistics, governance and professional services. There is equal opportunity in the enabling layers: data quality, security, model monitoring, workflow orchestration, evaluation, compliance and human oversight.

The enduring advantage will not come from attaching “AI” to a conventional offering. It will come from reimagining an outcome and designing a better system to deliver it.

A message for students and future technology leaders

One of the most energising aspects of NATSEM 2026 was the presence of students preparing to enter a workplace that will be very different from the one many of us first encountered. Their careers will develop alongside increasingly capable AI systems. That reality should inspire preparation, not fear.

My message to them was to build interdisciplinary strength. Technical knowledge is valuable, but the leaders of the AI era will also need to understand business models, customers, processes, communication, ethics, risk and governance. They must learn to ask good questions, challenge assumptions, interpret evidence and make decisions when the answer is not obvious.

The most defensible human capabilities will include judgement, empathy, creativity, contextual understanding, collaboration and moral responsibility. These should not be viewed as “soft” skills. In an environment of abundant machine intelligence, they become core leadership infrastructure.

Speakers, faculty members, organisers and students at NATSEM 2026—a reminder that enterprise reinvention is ultimately a human and institutional endeavour.

A practical leadership agenda

For organisations seeking to convert AI momentum into responsible progress, I would suggest six immediate actions:

1. Establish a shared AI ambition. Define where AI should create value and where it should not be used.
2. Create a prioritised use-case portfolio. Balance near-term gains with strategic capability building.
3. Assess enterprise readiness. Examine data, architecture, talent, process maturity, security and change capacity.
4. Set governance before scale. Define accountability, risk classification, controls, evaluation and escalation.
5. Redesign workflows around human-AI collaboration. Specify which decisions are automated, assisted, reviewed or reserved for people.
6. Measure outcomes continuously. Track business value, adoption, model behaviour, risk and stakeholder impact.

This agenda is not about slowing innovation. It is about ensuring that speed produces progress rather than unmanaged exposure. Organisations that build these disciplines now will be better positioned to absorb future advances in AI without having to rebuild their foundations each time the technology changes.

From intelligence to judgement

The discussion at NATSEM 2026 reinforced my belief that the next phase of enterprise AI will not be defined only by more capable models. It will be defined by the ability of institutions to combine machine intelligence with human judgement, strategic clarity and responsible governance.

The future will belong neither to technology alone nor to leadership that remains distant from it. It will belong to leaders who understand enough technology to ask the right questions, enough business to identify real value, and enough governance to remain accountable for the outcomes.

My sincere appreciation to the leadership and organising team at SCIT, my fellow panelists, the faculty members and the students who made the seminar thoughtful, relevant and forward-looking. Conversations such as these matter because the choices we make today will shape not only how enterprises compete, but also how responsibly intelligence is embedded into the systems that influence our lives.

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About the author: Rinoo Rajesh is the Founder and Director of Enrich Technology Services, an enterprise AI and digital transformation leader, author, board advisor, angel investor, IICA Certified Independent Director and President of the PMI Pune-Deccan India Chapter. His work focuses on Generative AI, Agentic AI, intelligent automation, enterprise architecture, governance and responsible transformation.

Where CX meets Technology - Intelligent Experiences

As the pandemic forced us to change our very way of living, consumer behavior also changed drastically. Overnight, many people who were “fence-sitters” when it came to transacting “online” were literally forced to online since in-person contact became the least preferred way of doing commerce. This online commerce forced organizations to really take a deep look at their customer journeys both online and offline. Digital Technologies, earlier often relegated to “coming in future” or “nice to have” became “essential” to survival. Retail was the hardest hit with this realization.

However, we find that now almost every area of the customer experience is being transformed by new technologies. Customers have evolved to anticipate new levels of perfection, performance, and just about everything in between as the possibilities of digital technology improve. Customers' expectations are continuously evolving; thus, the current customer experience must go above and beyond old customer care practices. Companies that give a meaningful but relevant experience that meets or exceeds their consumers' expectations will be successful.

To stay on top of these advancements and trends, we must grasp these new technologies and use them to communicate, interact, and promote in novel ways. Today's organizations need to discover the proper digital tools to provide a great customer experience, build brand loyalty, and drive sales growth. CX is taking advantage of today's technology and data to improve the entire customer experience. This involves sharing data amongst teams such as marketing and customer support, developing new roles to act on the data, and using new technologies such as AI, resulting in the implementation of a class of digital practices that leverage data to improve the whole digital trip for customers.

Leading firms in customer experience management are already utilizing these new technologies to achieve a competitive advantage. This ranking is topped by the utilization of artificial intelligence. According to one Research, by 2022, consumers would probably be handling 85 percent or more of their interactions with the organization without the need to be engaging with a human, at least for simple interactions. Many of the technological breakthroughs accessible to CX delivery teams are determined by how artificial intelligence, conversational interfaces, and robotic automation are implemented. There are two aspects to this. One is related to productivity and efficiency. By automating frequent and repetitive processes, these solutions save expenses and boost agent productivity. The other aspect, perhaps more important, is to do with delivering superior experiences through quick, efficient, and empathetic engagements during various segments of customer journeys. Conversational Chatbots powered by artificial intelligence are another method for automating simple chores. Today's industry-standard chatbots offer an incredible step forward for organizations looking to automate and outsource easy, regular inquiries and interactions to an intelligent system, thanks to an algorithmic learning feature that allows it to learn from past discussions and adapt replies. Another widely used option, IoT, may boost a company's bottom line by cutting operational expenses, improving productivity, and assisting companies in expanding into new markets or developing new goods.

One technique, which seems to be gaining tremendous traction in understanding and delivering empathetic engagements, is speech analytics, which is being increasingly used to augment existing AI processes enabled processes by listening for heightened voice pitches and other emotional indications, analyzing the customer's sentiments, further contextualizing with backend order data and transferring them to an agent when appropriate. It also gives fresh sources of feedback by identifying emotional drivers and focusing on terms or themes that regularly provoke specific feelings.

Armed with this sentiment analysis technology and with comprehensive dashboards, agents can quickly acquire a full image of each client, their purchase history, and their expected goals for each engagement – and then take on a consultative role, alleviating emergent and even projected requirements as well instantly acquire a thorough understanding of each client. These new and continually growing technologies are assisting businesses not only to extend their client base but also to get a macro-level understanding of their customers. Adopting such technologies for one’s organization is providing a more competitive advantage over competitors that have yet to connect technology with customer experience.

For a few years, a huge focus of most of the organizations and service providers was to deflect a transaction or engagement towards a non-voice channel – with the hope that customers would find these easier and such channels also afford a lower cost to serve. However, as seen, most of the customers are not very comfortable using non-voice channels especially with an AI-powered bot behind the channel during a time of crisis or during a buying journey. In a country like India, literacy rates while climbing has also proved to be a challenge for a largely English-dominated non-voice channel scene. Hence, we see that voice is making a comeback, albeit in a different avatar for service providers! And Speech Analytics and related technologies are at the heart of this revolution.

This is not limited to service providers or call centers only! General use of such virtual assistants is happening on an increasingly wide scale. For example, Taxi and cab drivers in India are routinely seen using some form of voice-powered virtual assistant for doing routine things – hands-free! Mostly the interface is maps or google assistant. The next frontier in Speech-enabled virtual assistants would be real-time language translation capabilities, which would allow two humans to converse with each other even though they might speak different languages. 5G and increasingly powerful speech engines are the key. While we are far away from Star Trek’s Universal Translator, but such technologies, on a simpler scale can revolutionize the way we interact with each other and transact online.

The next frontier in CX and EX definitely involves a significant role for such technologies available through a variety of interface formats!

Virtual Assistants: Their Role in Superior CX & EX

The Digital era compels many companies to maintain continuous connections with their customers until the resolution of their queries or issues. As a result, with customer service as a top priority, the ideal customer experience allows consumers to communicate via the channel of their choosing while keeping the context of interactions intact. A limitless firm has a new operating system that incorporates a shift in thinking as well as the increased embrace of emerging technology. The conventional tethered and physical workflow is morphing into interactive customer communications through various digital channels.

The role of Virtual assistants is developing as businesses adapt to the Omni Channel experience which is increasingly being expected by their end customers. Though virtual assistants first appeared in the 1960s with ELIZA and IBM's Shoebox voice-activated calculators, this technology is now emerging as the go-to solution in the realm of customer experience.

A traditional virtual assistant may be conceived of as an application that responds to a user's voice or text command and does the work at hand. Emerging virtual assistants, on the other hand, are programs that integrate the use of artificial intelligence, robotic process automation, natural language processing, and machine learning to extract information and complicated data from conversations in order to comprehend and process them.

Structured data with standardized rules can easily adapt to RPA solutions whereas unstructured data with less application of standardization utilized Cognitive automation, a virtual assistant is a next-level combo that can extract information and complicated data from a conversation using a voice and text-based interface.

Virtual Assistant algorithms develop the capacity to construct data models that detect and adapt behavior patterns by merging past data. With the inclusion of historical data, preferences, and other user information on a regular basis, a virtual assistant can answer difficult queries, offer suggestions and predictions, and even start a discussion.

Pre-programmed virtual assistants, which are more rule-based, with logic mapped out with a logical next step for various answers users might ask, are giving way to more complex and dynamic AI virtual assistants, which can be divided into two categories: NLP virtual assistants and machine learning virtual assistants. As a result, virtual assistants are becoming smarter by ditching automated chats and becoming more participatory, as seen by one of the most popular products on the market, Google Home, which knows each user's voice and issues a secure ID to each. The trend is also emerging to integrate different Virtual assistants, which can provide enhanced functionality to consumers and end-users.

However, there are certain privacy issues with AI-enabled virtual assistants that gather and retain user data indefinitely for better upgrades and user experience; this may be mitigated with strict data privacy rules from the corporations that provide this technology. After all, technology is a double-edged weapon that must be handled with caution.

Virtual Assistants have a chance to build a new digital workforce that will supplement human talents while also benefiting B2C and B2B end customers. Virtual assistant technology may be efficiently applied to external customer-facing use-cases using Conversational AI and cloud-enabled services, assisting enterprises in meeting the demands of digital natives and growing omnichannel digital contact with Omnichannel digital virtual assistants. At the same time, with employee experience also becoming increasingly important in the delivery of holistic Total Stakeholder Experience, virtual assistants are playing an increasingly important role in enabling employees to perform their tasks with much more accuracy, speed, and efficiency. These assistants are used in a variety of formats including conversational text, guidance coaches, and even as “co-listeners” to a voice conversation and then providing guidance based on inferences derived from a vast database of previous interactions and persona-based profiling.

The next frontier in CX and EX definitely involves a significant role for virtual assistants available through a variety of interface formats!