Infosys has embraced AI at an enterprise level, combining generative AI with traditional AI and digital services to become an AI-first organization. Our interaction with the technology has taught us what works well and what we need to be careful about. Coming directly from the CTO office at Infosys and designed for CXOs grappling with how to get the most out of AI, we have put together the top 10 learnings from our own journey to help you adopt AI ethically, quickly, and profitably.
AI Imperatives for 2025
We have curated the top 10 learnings and takeaways from our own experience into Infosys Topaz, our AI-first set of services, solutions, and platforms using generative AI technologies. With 12,000+ AI assets, 150+ pre-trained AI models, 10+ AI platforms steered by AI-first specialists and data strategists, and a 'responsible by design' approach, Infosys Topaz helps enterprises accelerate growth, unlock efficiencies at scale, and build connected ecosystems.
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infosystopaz@infosys.comSmaller and more focused models
As we move into 2025, models will get smaller, offering enhanced performance, efficiency, and cost-effectiveness. These models, with fewer than 7 billion parameters, excel in task-specific applications, providing a foundation for companies to tune to their specific requirements. They also consume far less energy and compute than their larger siblings. Lower costs, lower risk, data containment, and greener energy all stack up to make this imperative a growing force in business.
Agentic AI as the future of enterprise
Workforces will be increasingly guided by AI agents, with human supervision and oversight. Agentic AI refers to artificial intelligence systems that have a degree of autonomy and act on their own to achieve specific goals. For example, in software development, agentic AI can write, test, and critique code, acting as a behind-the-scenes workhorse. Leaders should use agentic AI to increase productivity, transform processes, and ensure agents make complex workflows faster and more efficient through a natural language interface.
Evolving processes from static to adaptive
2025 will see leading enterprises embed AI, deeply into business operations, preparing the ground for more valuable AI implementations. Embedding AI in this way is the bedrock of large-scale AI adoption, where workflows evolve from static processes to adaptive AI systems. To succeed, businesses must be prepared to view AI not as a tool but as an integral part of their long-term strategy.
Flexibility and leverage through poly AI
Enterprises should look to a poly AI architecture, which provides an interface layer between users and AI tools and services. This reduces the issues of vendor lock-in and provides the flexibility to use the best-fit option and cross-leverage AI capabilities across user experiences and business processes. A poly AI architecture reduces fragmentation, increases efficiencies, and ensures consistent performance across deployments.
Innovation through an AI foundry/factory
Businesses should use a foundry/factory approach. The foundry experiments with, and incubates, new technologies. It enables companies to develop new patterns and try on different use cases. The factory then industrializes these learnings at enterprise scale. We’ve found that this approach helps balance and manage the risks associated with AI while scaling its adoption with the enterprise.
Greener, lower-cost hardware and models
Sustainability is a crucial element of responsible AI. Carbon footprints can be reduced through use of smaller, more efficient models and greener data centers that optimize power consumption without reneging on service level agreements. By integrating sustainability into AI design, not only do companies keep their eye on net zero, but differentiate their brand in a crowded market.
Fingerprinted data for better governance
Good, clean, accessible data is at the heart of good AI. Cleansing and fingerprinting data makes sure data is transparent, and companies know what is present and where in each piece of data. This information can then be used to establish data correlations across the enterprise and conduct governance activities that are compliant with ever tighter regulations like the EU AI Act.
Value-chain-to-AI capability mapping
A strategic AI value map identifies high-impact business areas across the organization rather than pursuing siloed use cases. The choice of capability rests on how much automation is required, whether a simple generative AI assistant or a full-on AI agent. Strategic mapping and deployment of AI across the organization will take the business to the next level.
AI competency through upskilling and product-centricity
To bring AI technology to life, every organization needs a talent strategy, a change management strategy, and a target operating model. The most effective operating model for AI is product-centric, where AI products and services are created by cross-functional teams, grouped to orchestrate an end-to-end customer journey, which increases business velocity and experimentation.
AI for business growth
AI shouldn’t be viewed simply as a cost-cutter or productivity enhancer. Rather, it can be used to do things that weren’t possible before, leading to new ways of operating and doing business. AI can process vast amounts of data to identify potential new growth markets. It can also identify new channels that might not have been considered, especially in an international context. In research-intensive fields, it accelerates progress by enabling rapid exploration of complex problems.
