Despite a shortage of internal expertise in generative AI (GenAI), enterprises are often reluctant to engage with third parties for help with their GenAI deployments. They note a lack of confidence in their abilities, claiming that consultancies lack expertise in the new technology. Instead, they prefer to build internal skills and avoid paying steep prices for consultants who don’t deliver. Furthermore, enterprises also note that high turnover at large IT service providers inhibits continuity and delay projects.
To counter these concerns, IT services provider HCLTech is enticing customers by offering outcomes-based pricing. The IT services provider is committing to clients that they will only charge for their services once projects are in production. The company is being forward-looking with the strategy and proactively offering the pricing model to customers and tying outcomes to timelines. The engagements are often AI-led deals that also include application modernisation and data transformation components.
According to Rena Bhattacharyya, chief analyst and practice lead for Enterprise Technology & Services at GlobalData: “HCLTech is wise to demonstrate to customers that it is committed to seeing projects come to fruition. Over and over again, customers note the challenges of implementing GenAI but express hesitation when it comes to working with third-party vendors. Additionally, the market is not committed to any one type of vendor for professional services – enterprises are open to working with hyperscale cloud providers, telecom services providers, software vendors, small consultancies, and large IT services providers.”
GenAI is considered the most disruptive technology in the market today. Momentum is strong, with the market opportunity expected to grow from $2.8bn in 2023 to $75.7bn in 2028, a CAGR of 94%, as projected by GlobalData’s latest forecast. Enterprises across a range of industries are eager to harness the benefits of GenAI in a wide variety of use cases. The technology can be used to support customer service and marketing initiatives, improve operational efficiency, enhance security and fraud prevention measures, modernise applications, and much more.
However, challenges abound. Many projects don’t move past the experimentation phase to full deployment. And despite enthusiasm for GenAI initiatives, which often comes from lines of business teams, projects take longer than expected and require more resources than originally anticipated. The need for integration with existing systems, a lack of a strong foundational data management structure, and a shortage of internal expertise can hinder project momentum. Furthermore, the complexities around emerging trends such as the adoption of RAG, customisations of guardrails, incorporation of on-premises deployments, interest in agentic workflows, and security concerns create numerous challenges.
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