ChatGPT for enterprise is an opportunity for IT sector, say analysts

ChatGPT, launched just nine months ago, has seen teams adopt it in over 80 per cent of Fortune 500 companies, said the company on its website regarding the enterprise offering

On Tuesday, OpenAI, the firm that owns the artificial intelligence-powered (AI) tool ChatGPT, announced a product aimed at the enterprise segment.

ChatGPT Enterprise will offer enterprise-grade security and privacy, and unlimited high-speed ChatGPT 4 access among various features.

The obvious question that arises is the impact of this on the information technology (IT) services players. Analysts believe this is an opportunity for the sector.

ChatGPT, launched just nine months ago, has seen IT personnel adopt it in over 80 per cent of Fortune 500 companies, said the company on its website as it unveiled the enterprise tool.

The launch of ChatGPT for enterprise comes at a time when IT services players are building their own versions of a similar platform for their clients. Accenture, TCS, Infosys and HCLTech are only a few of a long list of companies that have declared their intention to have a significant presence in generative AI (GenAI) capabilities.

Jim Hare, distinguished VP analyst, analytics and AI, Gartner, says that ChatGPT Enterprise provides an additional opportunity to companies. They could engage enterprises offering services to organisations, who want to use an out-of-the box GenAI tool, and connect it with their internal data, he explained.

“I don’t see it disrupting the IT services ecosystem. OpenAI also hasn’t shared their pricing and licensing model for ChatGPT Enterprise. It’s unclear if there is better value in this new tool versus using Microsoft’s versions or custom-built ones,” Hare said.

Even if ChatGPT Enterprise is an opportunity, analysts pointed out that enterprises would need to take a calibrated approach.

Phil Fersht, chief executive officer and chief analyst of HfS Research, says that the enterprise world is still absorbing the generative AI overload and there are issues that need to be crystallised.

“Enterprises are still struggling to adopt the cloud! And we should remember that progress with GenAI is only possible when you fix your data infrastructure and integrate cloud and your other AI tools,” he said.

“With that, you have to digest all those surveys with data on adoption rates with a big pinch of salt as consultants and tech firms vie to lead the GenAI narrative. For example, many traditional NLP (natural language processing) projects are getting relabelled as GenAI to make them sound more appealing among many other initiatives using older AI tech,” added Fersht.

He also said that the path to incorporate Gen AI into the enterprise segment was fraught with challenges. Anything touching customer or employee data is more scrutinised than ever, and GenAI opens up a can of worms when it comes to immersing it into the enterprise.

He added: “Most GenAI use cases use public data today. Getting enterprises to share private data will be challenging, if not impossible. We hear about approaches for data anonymisation and data impact assessments. But as we saw with GDPR (General Data Protection Regulation), in the end the courts will be the arbiters of the effectiveness of those approaches.”

It’s this complexity that may give an edge to IT services and consulting players.

“Enterprises will likely ask IT service providers which approach is better based on the business value and use cases. Service providers have an additional opportunity to create/deliver services for those enterprises that want to use ChatGPT Enterprise for certain use cases but without the data privacy/security concerns,” said Hare of Gartner.

Indian IT services firms as well as global services and consulting players like Accenture and Lenovo have announced plans to invest billions in creating expertise in the GenAI space. Indian firms such as Tata Consultancy Services (TCS), Infosys, HCLTech and Wipro are also working with hyperscalers (large cloud service providers) like Google and Microsoft.

Fersht pointed out that GenAI is not free. “To attract talent for data management, the rare breed of prompt engineers, or even to run your foundational model, it requires deep pockets. And that is before the debate around the carbon footprint of AI is getting started,” he observed.

“Besides, getting access to the IT infrastructure to build these language models becomes expensive, and building business cases and longer-term viable cost models is going to dominate sourcing discussions in the coming months,” cautions Fersht, who recommended enterprises to start building business cases first.