The analytics landscape is undergoing a seismic shift. Artificial intelligence (AI) has well and truly arrived, unlocking new levels of growth, innovation, productivity, agility and resilience while accelerating value creation. While most enterprises have yet to move beyond the pilot phase, 2025 marks the transition from experimentation to deployment at scale.

McKinsey research reveals that while 92% of companies plan to increase AI investment over the next three years, only 1% consider themselves mature in its adoption. In 2025, we can expect the focus of this investment to fundamentally shift as leading enterprises begin the journey to deploy game-changing technologies at scale.

This shift will require organizations to re-think foundational frameworks – from data privacy and ethical AI adoption to bridging the talent gap. With further disruptions in the Large Language Model (LLM) market, opportunities to innovate have accelerated while compelling businesses to stay agile and adaptable. 

Organizations that successfully navigate this transition will gain a competitive edge, enable new business models, enhance operational efficiencies and harness next-generation decision intelligence. Here’s what to expect from the defining AI and analytics trends in 2025.

1.  Generative AI deployment at scale

From experimentation to enterprise-wide impact

The intersection of GenAI and data analytics is revolutionizing business decision-making, making strategic growth more achievable than ever. Initial experiments in Gen AI are now evolving into enterprise-wide integration, driven by enhanced customer engagement, cost reduction, operational efficiency gains and opportunities to drive growth.

While scaling these capabilities will take different forms – like expanding from one market to many or rolling out Gen AI-powered solutions to an entire function – the essential elements required to do so will be consistent. Deloitte research indicates that while companies feel prepared in technology infrastructure (45%) and data management (41%), risk management (23%) and talent development (20%) remain significant challenges.

A platform-centric approach offers scalability and customization, making it a reliable path to seamless, end-to-end integration of GenAI capabilities. The diminishing cost of building LLM models enables organizations to leverage unstructured data across various formats – images, audio and video – in addition to text. This will enable the acceleration of Gen AI deployments across organizations. By leveraging such frameworks, organizations can maximize the reach and impact of GenAI, driving data-led growth and transformation across the enterprise.

2. Agentic AI in business

The advent of a new class of autonomous decision-makers

Beyond scale, the scope of AI applications is expanding. Agentic AI frameworks enable AI agents to autonomously execute tasks based on defined goals set by human experts or other systems. 

Deloitte predicts that by 2025, 25% of enterprises using GenAI will launch agentic AI pilots, rising to 50 % by 2027. These autonomous AI systems will shift the focus from optimizing processes to achieving business goals, enabling AI to proactively solve challenges across complex workflows. 

Encouragingly, barriers to adoption are rapidly diminishing with the innovative development of cost-efficient LLMs. A successful agentic AI approach will leverage super-specialized agents to address granular use cases. With the help of multi-agent systems, it can deliver value across increasingly complex business processes. A core feature of agentic AI is its ability to comprehend natural language questions, break down complex tasks, efficiently delegate to AI agents and generate human-like responses.

3. Data privacy and AI security

Balancing innovation with enterprise protection

As AI becomes deeply embedded in enterprise operations and agentic capabilities are unlocked, concerns around data privacy, security and governance will take center stage. With emerging technologies evolving at speed, a mindset of continuous adaptation will be required to ensure requisite data privacy, combat cyber risks and successfully achieve digital resilience.

As organizations expand their global footprint, understanding the implications of evolving AI regulations across regions will be crucial. While unifying data is essential for maximizing value, ensuring compliance with diverse regulatory frameworks is mandatory. A nuanced approach to regional regulations will be key for organizations navigating this dynamic landscape.

Data security isn’t just a regulatory necessity – it’s a strategic advantage. Given that compliance concerns, risk management and governance gaps rank among the top barriers to AI deployment, businesses that embed security into their AI frameworks will gain resilience and agility, enabling sustained innovation.

4. Competency transformation

Addressing the AI talent and skills gap

As the rapid advancement of AI transforms the analytics landscape, the skills and the talent required to harness new capabilities will also evolve in response. According to Gartner, 80% of engineers will need to upskill by 2027 due to the impact of GenAI, while data from the World Economic Forum shows that AI and big data are the fastest-growing skills across the entire workforce.

Future-facing organizations are already working to bridge the AI talent and skills gap, upskilling their workforce to ensure that AI initiatives are fully realized. IKEA exemplifies this approach by equipping its workers and managers with AI literacy training. Microsoft has launched an AI skills program to prepare its workforce in Australia and New Zealand for the evolving digital workplace.

Expect further acceleration in 2025. A massive 93 % of HR leaders say sought-after job description skills are evolving at breakneck speed and 97 % are witnessing the need for different skill sets in new hires.

As the technology landscape evolves, continuous learning becomes essential. Professionals must stay updated on the latest technologies while letting go of outdated practices. Tech talent responsible for building AI systems must be upskilled in evolving AI technologies. At the same time, employees across the organization need training to collaborate effectively with AI, ensuring seamless integration and success. Whether through internal upskilling or embarking on skills-focused partnerships, investment in talent management will prove crucial to winning the tech-talent gold rush and thriving in 2025 and beyond.

5. Responsible AI adoption

The ethics of intelligent decision-making

Embedding trust could prove a competitive differentiator, particularly in the realm of LLMs, where concerns about bias, transparency and misinformation persist. As technology accelerates the analytics landscape, we’ll see a growing demand for robust ethical frameworks to guide the development and use of new tools. 

A recent investigation into the UK government’s AI system for detecting welfare fraud uncovered biases against individuals based on age, disability, marital status, and nationality. This finding underscores the critical need for ethical oversight to ensure fairness and accuracy in AI applications. In another survey, 72% of businesses expressed concerns about bias and opacity in AI-driven decision-making, highlighting the urgency of responsible governance.

Businesses must take a comprehensive approach to navigate this evolving landscape, ensuring the responsible and sustainable deployment of AI technologies, with 78% of leaders agreeing that greater governmental regulation is needed. Prioritizing data quality, collaborating wisely and addressing ethical concerns head-on can help businesses unlock AI’s true potential, ensuring transparency, fairness and accountability across the enterprise.

6. Value re-defined

The shift to cost-effective AI innovation

As the LLM market undergoes rapid evolution, advanced AI and analytics capabilities will become more accessible than ever to a wider range of enterprises in 2025. Disruptors like DeepSeek offer potential opportunities to reduce operational expenditures. Their innovative approach to building LLMs, fueled by algorithmic innovation, hardware optimization and streamlined architecture, is enabling the accelerated adoption of Gen AI.

Emerging cost-effective solutions require less computational power – and there is a strong appetite among organizations to explore and experiment with technology stacks. As the barriers to innovation continue to fall, organizations can accelerate their adoption of such solutions to gain a competitive edge. McKinsey reports that 76% of technology leaders expect their organizations to increase the use of open-source AI technologies over the next several years, with multi-model approaches set to emerge in response.

This marks the emergence of a dynamic and expansive AI ecosystem, where technological capabilities and evolving use cases will drive differentiation. With Gartner forecasting that the average price of GenAI APIs in 2027 will drop to less than 1% of today’s average, enterprises can anticipate new waves of AI-driven innovation, unlocking broader applications and creating fresh opportunities for competitive advantage.

Seizing the AI-driven future

A convergence of technological breakthroughs and process re-invention is unlocking new avenues for business transformation through analytics. Enterprises that leverage AI-powered data intelligence will gain the agility, resilience and competitive edge required to thrive in 2025 and beyond.

To accelerate this journey, identifying the right partners is critical. Organizations must embrace the optimal mix of digital tools, AI-powered insights and domain expertise, balancing human intelligence and AI to unlock data-driven growth.

In a rapidly evolving business landscape, success lies in not just adopting AI but mastering its strategic application – ensuring enterprises are equipped for the next era of AI-driven analytics.

Explore how AI-driven analytics can transform your business to unlock intelligent insights and drive enterprise-wide impact.

About the authors:

Jasmine Samant, Head GTM and Strategic Partnerships, Corporate Vice President, WNS Analytics, is a seasoned leader in data, analytics and AI. As head of Go-to-Market & partnerships at WNS Analytics, she enables strategies and fosters impactful alliances to offer comprehensive solutions for the organization’s global clientele. Samant’s expertise spans building successful AI-driven products, strategic business planning, and championing successful digital transformations.

Rajesh K., Strategy/GTM, Senior Director, WNS Analytics, leads various strategic programs and enables go-to-market for WNS Analytics. He builds compelling brand positioning strategies that differentiate WNS Analytics offerings in the market along with building impactful content and driving thought leadership to positioning WNS Analytics as a leader in the data, analytics and AI services space.

---

About WNS Analytics

WNS is a digital-led business transformation and services company with  64,505 professionals across 64 delivery centers worldwide, including facilities in 13 countries. WNS combines deep industry knowledge with technology, analytics and process expertise to co-create innovative, digitally led transformational solutions with over 700 clients across various industries.

WNS Analytics, the Data, Analytics and AI practice of WNS, enables business decision intelligence for 250+ global companies through award-winning, industry-specific productized services that combine Artificial Intelligence and Human Intelligence (AI+HI).

At the core of these productized services are our proprietary AI utilities hub, AI lab, strategic partnerships and a team of seasoned domain, data, analytics and AI experts. We collaborate with leading global enterprises, unlocking transformative decision-making and differentiated outcomes backed by data-led intelligence. As an end-to-end partner, WNS Analytics supports clients from consulting to implementation, driving business success through outcome-based engagement models.

To know more, visit https://www.wns.com/capabilities/analytics