As the dust settles on the Artificial Intelligence (AI) gold rush of the past few years, 2026 marks a pivotal moment for
the hi-tech industry. The sector has evolved from chasing operational efficiency and process excellence to delivering services with an AI-first mindset, as everyone, from deep tech giants and Software-as-a-Service (SaaS) players to consumer tech platforms, begins to operationalize their AI experimentations. Importantly, research shows that AI-first businesses derive significantly more value from their AI initiatives, achieving 1.7 times revenue growth and 2.7 times return on investment compared to their peers.¹

As these deployments mature, we are witnessing a seismic shift in how products are built, how services are delivered and how hi-tech organizations structure themselves. The next test is clear: Translate AI maturity into measurable business outcomes, consistently and at scale. This means shifting from human-led service delivery to AI-led delivery with humans in the loop, ensuring high-quality experiences, personalization at scale and uncompromising compliance.

At the center of this transformation lies scaled operations. They are becoming the heart of change, powering key initiatives, shaping differentiated end-products, elevating customer experience and strengthening regulatory / contractual compliance and trust frameworks. With the advent of Agentic AI and automated decisioning systems, companies are re-wiring their operating models and re-defining their partner ecosystems to build leaner, smarter and more resilient AI-native structures.

This evolution is not optional. While nearly nine in ten companies report using AI in at least one business function today, only about four in ten say they’ve seen any meaningful enterprise-level EBIT impact, underscoring that most organizations haven’t yet realized significant bottom-line benefits.² In 2026, however, the impact will begin to show at scale, as the hi-tech industry shifts focus toward AI model enhancement (e.g., localization, STEM domain adaptation), product innovation and go-to-market acceleration, all anchored by next-generation operational frameworks.

This paper examines six operationally focused trends that will define how the global hi-tech industry transforms in 2026 and beyond — with scaled, AI-powered operations at the core of the story.

1. Re-architecting the Hi-Tech Operating Model

The most progressive hi-tech enterprises are realizing that scaling AI isn’t just about deploying new tools; it’s about re-defining the operating model itself. They are embedding intelligence across every layer of execution, connecting engineering, product, customer and partner ecosystems through continuous learning and real-time feedback orchestration for ongoing enhancements.

Many firms are now re-thinking their data and process blueprints to lay the groundwork for AI-led execution. In one instance, a leading technology company partnered with WNS to co-create an enterprise-wide data foundation that unified fragmented systems, standardized governance and enabled seamless collaboration between human expertise and machine intelligence. This shift not only accelerated insight generation but also set the stage for sustainable AI adoption, illustrating how future-ready operating models must prioritize readiness with a well-defined execution roadmap and associated success criteria.

These AI-native models move beyond siloed digital initiatives, allowing decisions, data and design to flow dynamically across functions. In this paradigm, AI becomes the connecting fabric that powers agility, foresight and innovation.

2. Accelerating Autonomous Operations with Agentic AI

Agentic AI is poised to become a standout transformative force in the hi-tech industry in 2026, as the second wave of AI adoption gets underway. Agentic AI systems will shift focus away from optimizing processes to achieving business goals, proactively solving challenges across complex workflows. Deloitte predicts that as we enter 2026, 25 percent of enterprises using Generative AI (Gen AI) will have launched Agentic AI pilots, rising to 50 percent by 2027.³

Hi-tech enterprises are rapidly moving from task-specific AI-led automation to orchestrated workflows powered by Agentic AI. Rather than relying on AI solutions that execute pre-defined tasks, organizations are now deploying intelligent agents that can coordinate end-to-end processes — analyzing context, prioritizing actions and executing outcomes autonomously.

Human oversight, in this new model, shifts from routine monitoring to managing complex, nuanced scenarios where judgment and contextual understanding are critical. These human interventions, in turn, feed back into the system, helping AI agents refine their decision models and improve continuously. By 2027, Agentic AI will enable enterprises to run intelligent, self-orchestrating operations, with humans guiding strategy and handling the exceptions that require deeper insight.

To cite a real-world example, WNS leveraged Agentic AI combined with human intelligence to power end-to-end lead qualification and prioritization processes as part of its Revenue Operations (RevOps) program for a leading client, resulting in a one-third reduction in the “cost of acquisition” for new accounts.

3. Harnessing Human-in-the-Loop AI Models

AI’s growing role within decision-making will see governance emerge as an even more critical concern in 2026. Transparency will represent a business imperative, with future-facing organizations embedding the right levels of human oversight into the AI lifecycle to ensure accuracy and combat bias.

Reinforcement Learning with Human Feedback (RLHF) is one example of how organizations can tackle these issues — and we can expect further innovation within this space in the year ahead. Almost 72 percent of businesses are concerned about ethical AI decision-making, while 78 percent of leaders agree that greater regulation is needed when it comes to AI implementation.⁴

Teams will also be tasked with curating local expressions and technical nuance in 2026 to ensure that models are both globally scalable yet locally or generationally relevant. The need for this level of nuance is evident in the limitations of existing models. One recent study, for instance, found that four leading AI models (GPT-4, Claude, Gemini and Llama 3) all struggled to fully understand slang from Gen Alpha, defined as young folks born between 2010 and 2024.⁵

As another example, a leading AI platform for Identity Verification (IDV) and Know Your Customer (KYC) services partnered with WNS to embed a human-in-the-loop framework, where expert evaluators reviewed nuanced edge cases and fed insights back into model training. This continuous feedback loop between humans and AI reduced false acceptance rates to below 2 percent, significantly improving accuracy and trust in automated verification.

4. Leveraging Next-gen Data Annotation

As hi-tech firms generate and consume massive volumes of data, the need to validate, structure and synthesize that data has become critical to powering the next generation of AI and Machine Learning (ML) models. To ensure these models learn accurately and perform reliably, enterprises are increasing their investments in high-quality data annotation and labeling. This shift is giving rise to next-generation data labeling and annotation services that combine scalability, automation and domain expertise, ensuring data from varied sources is tagged consistently and with precision.

In addition, enterprises increasingly require annotation ecosystems that can scale dynamically with shifting training and testing needs, responding quickly to changing product priorities and model evolution cycles. To address this complexity, leading organizations are adopting configurable platforms that can support diverse use cases, metadata structures and annotation taxonomies. Equally important are contextualized quality workflows — multi-layered quality assurance steps tailored to the specific demands and nuances of each use case, ensuring accuracy that is both scalable and dependable.

Forward-looking organizations recognize that well-labeled data is the foundation of ethical, explainable and high-performing AI systems. As a result, the global data annotation market is projected to reach nearly USD 8.2 Billion by 2028,⁶ reflecting the growing realization that smarter AI begins with smarter data preparation. This trend is evident in multiple clients seeking WNS’ services in curating and annotating large datasets across multiple use cases and locales.

For example, a leading fleet and video telematics company collaborated with WNS to enhance its driver monitoring AI systems through high-quality computer vision annotation services, upholding high safety standards across its clients’ transportation network.

5. Shaping the AI-ready Workforce

The shift toward AI-led operations is accelerating demand for contingent and elastic workforce models. Hi-tech enterprises are increasingly favoring flexible talent ecosystems that can scale dynamically with engineering priorities, product roadmaps and AI experimentation cycles.

Industry studies suggest that nearly 38 percent of today’s workforce is already contingent, and this figure could rise to ~50 percent within the next decade as organizations seek agility, cost efficiency and access to specialized AI and data skills.⁷ This trend is particularly pronounced in hi-tech, where companies need rapid access to niche capabilities such as data labeling, model evaluation and AI quality assurance.

To meet this evolving need, WNS is investing in platforms such as Open Talent, designed to help AI-driven enterprises tap into a pay-as-you-go talent model. These curated talent ecosystems provide domain-ready professionals and crowdsourced experts to support use cases such as localization, STEM content development, model testing and workflow optimization — all aligned with client-specific engineering priorities and product Objectives and Key Results (OKRs).

6. Winning the CX, Trust & Safety Battleground

Customer Experience (CX) will emerge as a critical battleground for the hi-tech sector in 2026 and beyond, as enterprises face mounting pressure to deliver culturally attuned, high-quality interactions at scale. As the market becomes hypercompetitive, the true differentiator will lie in driving CX excellence without inflating cost, delivering personalization and localization at scale while preserving efficiency and trust.

Beyond frontline experience, RevOps is fast becoming a central lever for growth, particularly across cloud, internet and social media platforms where customer expansion, renewals and retention hinge on precision engagement and data-driven insights. At the same time, Risk and Compliance Operations (RCOps), within the umbrella of Trust & Safety, are gaining prominence in scaled digital marketplaces, where ensuring platform integrity, safety and regulatory compliance is integral to customer confidence and brand reputation.

Many hi-tech firms are already building the foundations for this evolution, using unified data fabrics and AI-powered Customer Experience Management (CXM) and Risk & Compliance (R&C) systems to enable real-time personalization, predictive engagement, platform safety and seamless service delivery.

For example, a leading ride-hailing company partnered with WNS to enhance the safety of its platform as part of risk and compliance services spanning identity verification and anti-fraud operations. Similarly, a leading technology client partnered with WNS to manage its end-to-end RevOps value chain for one of its key products, leading to a 7-10 percent increase in revenues beyond original baselines.

Another client, a leading payment infrastructure provider, collaborated with WNS to design a centralized crypto investigations model by establishing a dedicated Crypto Center of Excellence, powering end-to-end investigative coverage across IDV, KYC and complex transaction monitoring.

Partnering to Accelerate Transformation

The pursuit of AI-first operations will define the hi-tech industry in 2026. From product development to customer experience, AI-first models will re-shape how technology firms compete, grow, innovate and ultimately deliver value.

However, as the pace of change accelerates, so does the complexity of execution, with many future-facing organizations exploring partnerships to help them navigate this terrain. For instance, research from Everest Group, in collaboration with WNS, shows that 62 percent of organizations are seeking third-party assistance when it comes to training Gen AI models on enterprise data.

By doing so, leaders can accelerate transformation, manage risk and scale responsibly – with seamless access to the right tools, expertise and strategies that turn disruption into differentiation.

The next leap in Hi-Tech isn’t about more AI; it’s about smarter orchestration. Learn how we are helping leading firms make that shift.

References

1. The Widening AI Value Gap | BCG

2. The State of AI in 2025: Agents, Innovation, and Transformation | McKinsey

3. Autonomous Generative AI Agents: Under Development | Deloitte

4. Now Decides Next: Moving from Potential to Performance | Deloitte

5. AI Models Don’t Understand Gen Alpha Slang, Study Reveals | MSN

6. Data Collection and Labeling Market Size Worth $8.22 Billion by 2028 | PR Newswire

7. How Contingent Talent Data is Changing the Game for HR Leaders | HR Executive