In my role, I regularly engage with customers – listening to their ambitions, challenges and now increasingly about their expectations from Artificial Intelligence. From these conversations, one thing has become clear – that AI has captured the imagination of business leaders like no technology in recent memory. 

Be it CEOs exploring new revenue streams, or CIOs rethinking their operating models, AI dominates every boardroom conversation. Positioned not just as a critical driver of innovation, but as a potential growth engine for the global economy, according to FICCI and BCG, it could inject over $15 trillion to the global economy by 2030, a number that has captured the imagination of investors and executives alike. It promises to transform business operations, unlock new revenue streams, and reimagine customer value. Delivering a strategic advantage, AI is set to become the new operating fabric of enterprises. The scale and speed of its adoption will be unprecedented and unpredictable - but ubiquitous, accelerated, and deeply embedded across functions. 

Yet, the early adoption narrative paints a sobering picture. Many of the same leaders who speak with excitement about AI also share a common frustration: they are yet to see real returns. According to a BCG report, nearly three-quarters of companies (74%) struggle to capture value from AI, while MIT recently reported that 95% of generative AI projects are failing to achieve their desired Return on Investment (ROI) targets. 

Gulf between aspiration and outcome

What we are seeing currently with AI is hype without scale. And from my vantage point, it exists because too many enterprises approach AI as an exciting pilot, not as a strategic enabler. Without linking it to core business objectives, customer journeys, and data foundations, the technology risks becoming an experiment, not an engine of growth. 

AI is often positioned and sold as a plug-and-play solution, but like any new technology, it brings its own set of complexities. In my various customer conversations, four obstacles come up repeatedly:

  • Undefined strategy: In the last few years, I have come across several fragmented and siloed AI projects in the industry that are – as expected - failing to deliver measurable outcomes. This is because many companies jump into pilot projects, without integrating these experiments into the broader enterprise strategy. 
  • Data Governance and Security: AI models rely on massive data sets. Interestingly, most enterprises struggle not with data scarcity, but the complexity surrounding its consumption and governance, including privacy, compliance, and ethical use. This obstructs the effective usage of AI models, not allowing organisations to move from pilot to production-grade AI. 
  • Legacy Drag: Legacy architectures were never built for AI. They typically have complex, legacy IT environments with diverse platforms and technologies that further add to the complexity. This becomes even more pronounced with the rise of Agentic AI, which demands a fundamental re-engineering of digital infrastructure to support real-time data flows, dynamic orchestration, and scalable compute environments. Only then can autonomous agents continuously learn, adapt and deliver evolved decision-making at enterprise scale. 

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Move from pilot to scale

Despite this all, there are incidents where AI is demonstrating value. In my recent conversations, customers have reported success with Agentic AI, especially in sales and marketing. In sales, it autonomously qualifies and prioritises leads, helping teams focus on high-value prospects and shorten sales cycles. Post-sale, it enables intuitive, personalised customer care to drive long-term loyalty and repeat buy. In marketing, real-time campaign optimisation maximises ROI and reduces ad spend waste. It also segments audiences, offering personalised content based on shifting behaviours, leading to higher conversion and engagement. But how do organisations replicate this across the enterprise?  

"At Tata Communications, we are intentional about how we approach AI. It is not an add-on; it is becoming the foundation of how we build, deliver, and secure our services."  

We have developed an AI Framework where we evaluate ourselves on two dimensions - capabilities (strategy, talent and culture) and outcomes (cost savings, improved experiences for employees and customers, and revenues anticipated from the AI investments). This helps us understand the maturity of different business units. We are also encouraging our partners and customers to use the same framework to assess their own AI maturity. 

The organisations need to understand their position on the AI maturity - to prioritise investments, close capability gaps, and focus on initiatives that deliver measurable business value. A few principles stand out from our journey and from what I see resonating with customers: 

  • Anchor AI to Strategy: As a first step, clearly define goals for AI. Tie every AI initiative to clear business priorities, whether it’s revenue growth, customer experience, or efficiency gains. Success should not only be seen in terms of cost savings but evaluate how AI drives long-term differentiation and resilience in the organisation. Enterprises that approach AI intentionally will be the ones to capture real value.

  • Prioritise Data: From our strategic perspective as the communications technology provider, we’ve seen that data challenges are both at technical and organisational levels. The answer: A unified data strategy that spans the entire enterprise, and prioritises secure, clean, connected and contextually rich data sets. To realise the full potential of AI models, data needs to be considered as a strategic asset that requires continuous management, governance and alignment with real business value. 

  • Inherent Security: With the EU’s AI Act and US National Security memorandum on AI, regulation is making security inseparable from AI deployment. For enterprises, this means embedding security frameworks from the very first stage of AI development and implementation. It requires not only robust data protection at operational level, but also the deliberate design of building transparency, control, and resilience into AI systems. 

  • Onboard the Right Partner: Scaling AI is not a solo journey. Enterprises need a strategic partner - who can help them drive meaningful outcomes from AI investments, not just deliver a one-off implementation. One who can bridge infrastructure gaps, streamline data pipelines, safeguard networks and provide guidance on ethical AI use, governance, and long-term scalability. Dedicated Forward Deployed Engineers (FDEs) play a critical role as they work alongside customers, understanding their objectives, assessing real-time outcomes of AI applications, and – ultimately - tailoring AI solutions aligned with the business needs. 

    The right partner will help quickly identify and address deployment hurdles - whether it’s data quality issues, integration complexities, infrastructure challenges, or security barriers - accelerating the path from pilot to scale, ensuring that outcomes remain the central priority.  

 

Governments worldwide are accelerating AI adoption with various initiatives – whether its EU’s Digital innovation Hubs or India’s National AI Strategy. But policy alone will not drive impact. Businesses need to look beyond hype and excitement and approach AI through the strategic lens, aligning their vision, people and processes. At Tata Communications, we see AI as a multiplier of human creativity, enterprise resilience, and customer trust. The winners in the AI era will not be those who launch the most pilots, but those who weave AI into the fabric of their operations — turning hype into scale, and experiments into sustainable enterprise value.