Mastering AI’s fast lane where speed thrills & ethics pay bills, Phani Tangirala MD & CEO, Expleo Solutions Ltd

By Gloria Methri

AI’s rapid evolution demands a delicate balance between speed and ethics, where trust, transparency, and smart governance pave the way for scalable, sustainable growth.

How can organisations balance the need for rapid AI adoption with the ethical considerations and potential risks associated with AI deployment?

“Organisations globally are integrating AI and redefining their business models because AI has the potential to transform decisionmaking, streamline operations, and improve customer experiences. However, in the rush to deploy AI, factors like technical feasibility, scalability, and ethics are sometimes overlooked. While AI capabilities, such as self-driving cars and advanced facial recognition, have opened up previously unimaginable opportunities, they also bring new challenges related to data privacy, bias, and security.

“Yes, fast AI adoption means faster results, but cutting corners on ethics can cost millions in fines and lost trust—and that, in my experience, is the biggest challenge—not developing the technology itself but developing an environment of trust.

“Trust starts with purpose-driven AI, designed to be transparent, secure, and scalable. It also means involving stakeholders at every level—from IT and compliance teams to end-users—ensuring alignment and readiness for change.

“Upskilling talent is equally crucial. Teams need the right skills to manage AI systems, validate outputs, and continuously optimise performance. Regulatory compliance must also be embedded early to safeguard data privacy and security.

“At Expleo, we believe effective AI adoption is about aligning people, processes, and governance. Our three-pronged framework focuses on:

Ethics First: We establish clear AI rules that address algorithmic bias, data privacy, and regulatory compliance. We also embed fairness checks and transparency tools into AI development pipelines, ensuring decisions are auditable and aligned with ethical standards.

Data Quality: AI models are only as effective as the data they process. Fix errors, remove duplicates, and connect systems so data flows without roadblocks. Better inputs mean smarter outputs.

Security Focused: AI-driven systems are also vulnerable to threats, making cybersecurity by design non-negotiable. Treat AI systems as high-value assets. With intelligent monitoring, encryption, and anomaly detection, businesses can protect data integrity and customer trust, mitigating cybersecurity risks.

“Adopting AI boldly and responsibly means integrating these principles from the ground up. Companies that integrate ethics with AI development set the stage for sustainable growth and customer confidence. Ethical AI should not be seen as a guardrail but as a growth enabler.”

What strategies can industries adopt to overcome the common barriers to scaling AI projects, such as data quality issues, lack of skilled talent, and integration challenges?

“Scaling AI isn’t a technology challenge—it’s a business one. Our report on ‘Integrating AI: Navigating the next wave of business transformation’ reveals companies want faster time-to-market (91%), financial gains (90%), and better quality (90%) from AI investments. Yet obstacles—like integrating AI with legacy systems (39%) and managing high costs (35%) often slow progress.

“At Expleo, we understand that scaling AI isn’t plug-and-play. It requires a combination of technology readiness, process optimisation, and people enablement.

Start Small, Scale Smart: AI adoption doesn’t have to be an all-ornothing approach. Many organisations are testing AI in one area before scaling further. Most BFSI companies adopt this phased approach—testing AI in segments, proving value, and scaling incrementally.

Build Talent: AI is all about smarter people using intelligent tools. While 60% of businesses are hiring external AI expertise, 55% are investing in employee training to develop internal capabilities. At Expleo, we invest in AI-focused upskilling programmes to build a future-ready workforce. Our AI-ready teams are equipped with the expertise and frameworks needed to seamlessly integrate into client environments, scaling AI initiatives without disruption.

Fix Data, Fix Outcomes: Again, AI is only as good as the data it uses. Fragmented data landscapes and low-quality inputs lead to flawed outputs. Automated data validation, governance frameworks, and API-driven integrations can clean, structure, and scale data pipelines, turning data into actionable insights

“Finally, integration challenges can be tackled by adopting modular AI architectures that enable interoperability between legacy systems and modern platforms.

“AI doesn’t eliminate jobs—it evolves them. ATMs once raised fears of job losses, yet they freed up bank tellers for higher-value tasks like customer service. AI follows the same trajectory, eliminating repetitive tasks while creating new roles.

“Look at chatbots in banking—they started as simple FAQ tools but have now evolved into AI-driven virtual assistants capable of handling loan approvals and fraud detection in real-time. Similarly, AI-powered credit scoring systems are helping banks improve accuracy while reducing manual errors. These are no longer experiments but bottom-line drivers. Companies that embrace this shift early position themselves for long-term success.

“Scaling AI doesn’t have to mean overhauling everything. AI, done right, delivers massive results, helping companies scale faster and win bigger.”

How will advancements in AI engineering, digital transformation, and cybersecurity shape the competitive landscape for engineering and technology service providers by 2025, and what strategic initiatives should companies prioritise to stay ahead? What are the key challenges and solutions in achieving successful digital transformation in the financial sector?

“AI is driving faster innovation, pushing companies toward the ‘agentic era,’ where autonomous systems deliver insights and decisions. To stay competitive, businesses must focus on AI-driven frameworks, tailored industry solutions, and strong cybersecurity, with AI playing a key role in fraud detection, risk mitigation, and ensuring compliance in finance.

“AI and machine learning enable hyper-personalised experiences, while microservices, cloud platforms, and zero-trust frameworks simplify integration and enhance security. Business transformation requires adaptable, secure systems, an AI-ready culture, and strong partnerships to foster trust and innovation.

“The agentic era will force us to think differently—not just about how we build technology, but about how we lead. For me, that means prioritising partnerships, building ecosystems of trust, and enabling teams to experiment without fear of failure.”

How is Expleo positioning itself to help organisations effectively integrate AI tools and drive industry transformation within the next 3 years?

“At Expleo, we’re doubling down on AI to future-proof businesses. We’re investing in AI and Generative AI capabilities through in-house accelerators, ready-to-deploy frameworks, and testing labs. These enable us to rapidly develop proof-of-concept (POC) models, test outcomes, and measure impact before full-scale implementation.

“Blending technical expertise with engineering precision, we’re creating cross-functional teams capable of tackling AI challenges head-on. We’re actively investing in upskilling our talent pool to cover AI, data governance, security, and compliance—empowering organisations to move beyond adoption hurdles.

“With years of experience, proven use cases, tested frameworks, and best practices—we help our clients accelerate AI adoption without reinventing the wheel. By making these insights accessible, we help them move from pilot projects to enterprise-wide implementation faster and with fewer risks.

“As AI evolves, so must leadership. While AI can enhance decision-making, humans will always be the driving force behind its purpose and direction. Appointing AI governance leads to overseeing programmes, unifying adoption strategies, and mitigating risks, combined with AI incubator teams to test models, set guidelines, and develop AI-first practices, will ensure businesses remain in control and ready for what’s next.