By Siddharth Pai
Generative artificial intelligence (GenAI) has captivated the imagination of technologists, business leaders, and futurists alike, promising to revolutionise industries with its ability to create text, images, music, and complex data patterns. Company boards, whether technology-oriented or not, have brought the discussion on their companies’ use of GenAI to the forefront. In my interaction with C-level executives on their technology transformation journeys, GenAI is often the first topic they bring up. Many have kicked off efforts in their firms to kindle its use.
But despite the excitement, most GenAI use cases in large enterprises remain in the proof of concept (POC) stages. Several critical factors must be addressed as information technology (IT) service providers look to harness this technology to create sustainable revenue streams. The journey from POC to production deployment of GenAI solutions is fraught with challenges. POCs are typically small-scale experiments designed to test feasibility and potential impact.
GenAI solutions must integrate with an organisation’s existing IT landscape. This requires interoperability with legacy systems, databases, and other enterprise applications. Service providers must develop expertise in creating application programming interfaces and middleware that facilitate seamless integration. IT service providers are all, of course, advertising that they are “assisting” their clients with AI integration. Still, even a cursory look at the overall lacklustre print from their revenue and earnings reports reveals that most have stagnated. For GenAI to become a mainstream revenue source, IT service providers must demonstrate that these POCs can scale seamlessly into long-term consistent revenue streams. Showcasing successful deployments and quantifiable benefits is essential. IT service providers should compile and present case studies highlighting the positive impact of GenAI on business outcomes, such as increased efficiency, cost savings, and enhanced customer experiences.
One of the first hurdles is ensuring that GenAI models can handle large volumes of data and perform consistently in a production environment. This involves robust infrastructure, advanced algorithms, and scalable architecture. IT service providers must invest in high-performance computing resources and cloud platforms capable of supporting large-scale AI deployments. Given the scarcity of AI compute resources (using Nvidia chips), this is a tall order with little capital outlay. Enterprises often hesitate to invest heavily in new technologies without clear evidence of return on investment.
GenAI models, especially deep learning algorithms, can be complex and opaque. Providing transparency and explainability in AI decision-making is crucial. Enterprises must learn how AI-generated outputs are produced to trust and adopt the solutions. IT service providers should focus on developing explainable AI models and tools that allow users to inspect and understand the underlying processes.
Also, GenAI brings with it a host of ethical and regulatory challenges. IT service providers must navigate these to ensure responsible, compliant use of AI technologies. Concerns around bias, fairness, and the potential misuse of GenAI are prevalent. IT service providers should adopt and promote ethical AI practices, including bias mitigation strategies, fairness assessments, and the implementation of robust governance frameworks. Regulatory environments around AI are evolving. IT service providers must stay abreast of legal requirements and ensure their solutions comply with data protection laws, industry-specific regulations, and AI governance standards. It involves continuous monitoring and updating of compliance protocols.
GenAI is a specialised field requiring deep technical expertise. IT service providers must invest in building and nurturing talent. Hiring experts in AI, machine learning, and data science is critical. Additionally, continuous training and upskilling programmes for existing employees will help maintain a competitive edge. Partnerships with academic institutions and participation in AI research communities can also foster innovation and knowledge sharing.
Collaborating with AI start-ups, research labs, and technology vendors can accelerate the development and deployment of GenAI solutions. These partnerships can provide access to cutting-edge technologies, proprietary algorithms, and specialised knowledge to enhance service offerings. Of course, there are early signs of this, but to my mind, there is not yet a tie-up of real significance in this market.
Gen AI applications can vary significantly across industries. IT service providers must develop tailored solutions that address sectors’ unique challenges and opportunities. They can create targeted GenAI solutions by understanding the specific needs and pain points of industries. For example, GenAI can assist in drug discovery and personalised medicine in healthcare, while in finance, it can be used for fraud detection and risk management.
Continual R&D investment is crucial for staying at the forefront of GenAI advancements. Exploring new and innovative applications of GenAI can open up additional revenue streams. This could include advancements in natural language processing, creative content generation, and autonomous systems. Securing patents and developing proprietary technologies can provide a competitive advantage. IT service providers should invest in R&D to create unique solutions that differentiate them in the market.
While GenAI holds immense potential, transitioning from POCs to sustainable revenue streams requires a multifaceted approach. IT service providers must focus on scalability, integration, trust-building, ethical practices, specialised expertise, industry-specific solutions, and continuous R&D. By addressing these critical factors, GenAI can become a mainstream revenue driver, transforming the landscape of IT services and delivering significant value to enterprises worldwide. For our home-grown service providers, who have often focused solely on their cost advantages with inexpensive Indian labour, this will be a long, tough ride.
The author is a technology consultant and venture capitalist.
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