LTIMindtree Executive Vice President and Chief Business Officer Srinivas Rao examines how AI will evolve across industries in the European market in 2025.
From financial services to retail, healthcare, and manufacturing, organisations are leveraging AI to reimagine operational efficiency, customer experiences, and innovation.
As businesses navigate this landscape, they face opportunities and challenges that could define the future of AI-driven growth.
Yet what shape will these challenges take for different industries? And how can they be overcome?
To explore the evolving role of AI and its practical applications, we spoke with Srinivas Rao, Executive Vice President and Chief Business Officer at LTIMindtree.
AI’s role in revolutionising industries
AI’s impact across industries is profound, with each sector experiencing unique benefits and challenges.
For instance, in the financial sector, AI-powered innovations are changing the game.
Srinivas explains, “Conversational systems are being refined to implement robotic advisory and personalised financial management, while AI is also being deployed for fraud detection through Zero Trust security frameworks.”
This shift highlights how institutions are embracing technology to deliver precision and trust in financial operations.
These changes are equally transformative in healthcare, where adaptive AI is improving diagnostics and enabling personalised treatment plans.
“Adaptive AI integrated with computer vision enhances diagnostic accuracy, while decision intelligence is accelerating drug repurposing by analysing vast datasets,” says Srinivas.
This approach not only improves patient outcomes but also optimises resources in a sector where efficiency is paramount.
Retail and manufacturing are similarly benefiting from AI innovations.
The retail industry is pushing boundaries with hyper-personalisation, as noted by Srinivas: “Self-adaptive hyper-personalisation is expected to reshape consumer experiences, with real-time data driving dynamic pricing and tailored content.”
Meanwhile, manufacturing is stepping into the era of autonomous factories, signalling Industry 5.0, where AI-powered systems will dominate smart production environments.
Overcoming operational challenges with AI
As businesses integrate AI to boost operational efficiencies, certain hurdles demand attention.
A significant trend is the adoption of small language models tailored for specific use cases, addressing the inefficiencies of larger, generic AI systems.
“Organisations are realising that pre-trained models often require extensive fine-tuning, leading to high operational costs,” Srinivas points out.
Compact AI solutions, therefore, are emerging as a practical alternative.
Operational integration also requires meticulous governance.
The rise of Chief AI Officers underscores the need for structured policies to guide AI deployment.
Srinivas observes, “AI governance frameworks compliant with GDPR and CCPA are vital to addressing ethical concerns and ensuring transparency in AI outputs.”
This regulatory alignment is crucial as organisations seek to balance innovation with accountability.
The challenge of integrating AI with existing systems, particularly Robotic Process Automation (RPA), also looms large.
“Ensuring seamless interoperability between AI and RPA requires significant investment and a rethinking of workflows, particularly in scalability, performance, and security,” says Srinivas.
Despite these obstacles, businesses that tackle them head-on will gain a competitive edge in operational efficiency.
Solutions for sustainable AI integration
For industries like manufacturing and retail, where human oversight is vital, sustainable AI integration is the key to success.
Clear objectives, robust governance, and incremental implementation are foundational steps.
Srinivas suggests, “Aligning AI objectives with responsible AI principles ensures human control while addressing compliance, security, and operational risks.”
These measures create a framework that keeps humans at the centre of decision-making.
Moreover, fostering cross-domain skills and sustainability roadmaps is critical. AI’s energy demands could disrupt carbon neutrality goals, making sustainability planning an imperative.
As Srinivas highlights, “Energy-efficient AI systems and comprehensive monitoring plans will maintain system effectiveness while meeting environmental aspirations.”
Organisations must also focus on post-deployment monitoring and maintenance, ensuring that AI systems remain effective and aligned with evolving goals.
By implementing these strategies, businesses can leverage AI responsibly while keeping innovation and human oversight balanced.
As AI continues to evolve, its potential to reshape industries remains undeniable. By addressing challenges head-on and adopting responsible practices, organisations can harness AI as a force for good, driving efficiencies, fostering innovation, and ensuring sustainable growth in an increasingly digital world.
