By Shishir Saxena, Executive Vice President & India Head, Innover Digital
Over the last few decades, fewer technologies have demanded a more urgent action from the executive leadership teams across enterprises big or small, as has Generative AI (Gen AI). The clamor for leaders to act intentionally on first crafting their strategy and then committing to seeing it through execution has only grown in the past 12+ months or so. Consider this for a proof-point, the global Gen AI market, valued at USD 36.06 billion in 2024, is projected to skyrocket to USD 356.10 billion by 2030, with an explosive CAGR of 46.47%, according to Statista.
Yet, real world examples of organisations truly committing to the idea of making Gen AI a cornerstone of their operating model are few and far between. Right off the starting block, organisations have to deal with getting the right people in the organisation – across seniority levels and functions – to build a sincere appreciation of the possibilities that Gen AI offers, as well as the rate of change at which this disruption will have an impact on everything. Then there are operational challenges such as achieving contextual accuracy, integrating AI into domain-specific applications, and navigating evolving regulatory and ethical landscapes underscore the need for a thoughtful, strategic approach.
So where does one start?
Building the foundation for GenAI success
Gen AI success requires more than just acquiring technology; organisations must invest in skilled talent, resources, and foresight to align AI efforts with business goals. Effective Gen AI implementation demands cross-functional collaboration among data scientists, machine learning engineers, and domain experts, working closely with business departments, compliance, and legal to guide AI initiatives from concept to execution.
One approach that we’ve seen organisations adopt for effective Gen AI deployment is the establishment of an AI Centre of Excellence (CoE). This team oversees AI adoption, monitors industry trends, curates’ best practices, and manages compliance requirements. The CoE fosters a collaborative environment for training and experimentation, enabling organisations to remain adaptable and compliant in a rapidly evolving AI landscape.
A Strategic framework for effective GenAI deployment
To navigate the complexities of Gen AI adoption, organisations can benefit from a structured framework that assesses needs, sets clear objectives, and defines a roadmap for implementation. This strategy includes deciding on the model development scale and balancing the potential benefits of large-scale language models against the efficiency of tailored models.
- Needs assessment and goal setting: The framework can begin with assessing an organisation’s specific needs and goals. By analysing data capabilities, desired outcomes, and key pain points, teams can determine whether a large language model (LLM) is essential or if a smaller, more targeted model will meet requirements. A thorough ‘needs assessment’ aligns AI initiatives with business objectives, preventing costly misalignments later.
- Evaluating custom vs. off-the-shelf solutions: Not every application requires a generalised AI model. Sometimes, custom solutions trained with industry-specific data yield better results, such as healthcare models prioritising HIPAA compliance or e-commerce models focusing on customer sentiment.
- Implementation roadmap and continuous optimisation: A roadmap outlines deployment steps, assigns roles, and sets realistic timelines, reducing scope creep. Ongoing optimisation ensures the Gen AI system evolves with organisational needs and industry standards.
Through this structured approach, organisations minimise overspending and ensure that Gen AI is directly contributing to measurable business outcomes.
Domain-specific GenAI solutions: Enhancing accuracy and relevance
Domain-specific Generative AI models deliver precision by leveraging specialised datasets and context-specific training. Techniques such as Retrieval-Augmented Generation (RAG) and Domain Data Encapsulation (DDE) elevate these models further.
RAG integrates external, relevant data during the generative process, enhancing real-time accuracy.
DDE builds on RAG by combining it with knowledge graphs—structured, semantic representations of domain knowledge. This synergy enables models to handle complex relationships, provide explainable insights, and deliver actionable outcomes.
For instance, in the BFSI sector, domain-specific models enhance fraud detection by incorporating specialised training data and contextual insights. This precision reduces false positives, conserving valuable resources while strengthening safeguards. Across industries, such models can address nuanced challenges with accuracy and relevance that standard solutions often lack.
Responsible AI: Guardrails for ethical and compliant Use
As Gen AI adoption grows, a responsible approach to AI development and usage becomes paramount. Responsible AI includes embedding ethical principles, ensuring data privacy, and achieving regulatory compliance, which mitigates risks and builds stakeholder trust.
- Data privacy and transparency: Responsible AI ensures compliant data handling and transparency, fostering trust among customers and stakeholders.
- Bias mitigation and fairness: Implementing Responsible AI involves developing protocols to monitor and mitigate biases. For instance, Gen AI models for recruitment must be tested for biases against gender, age, or ethnicity to ensure fair treatment.
Through Responsible AI practices, companies can uphold ethical standards, maintain regulatory compliance, and build customer trust positioning themselves as leaders in responsible AI usage.
Strategic GenAI adoption as a pathway to competitive advantage
Gen AI has emerged as a top strategic priority for business leaders worldwide, and organisations are investing substantially in its potential. However, positioning oneself well to gain from its tremendous potential demands a comprehensive approach encompassing leadership, mindset, technical, ethical, and operational dimensions.
Organisations must assess their current state of readiness, set AI objectives with clearly defined interim milestones, and establish responsible AI guardrails, all coming together to foster a culture that gives the teams a fair shot at realising tangible returns on their AI investments. Gen AI as an exponentially disruptive force is here to stay, and companies that act thoughtfully, strategically, and responsibly will lead the way, delivering both measurable ROI and sustainable innovation.
