Gartner warns AI model advantage is shrinking

Will SaaS survive AI disruption?

Addressing the much-hyped death of software as a service (SaaS) in what has been dubbed “SaaSpocalypse”, Chandrasekaran acknowledged that SaaS is being disrupted by AI, but argued the short-term impact has been exaggerated.

SaaS providers retain critical advantages that enterprises rely on, including deep domain knowledge, tight workflow integration and compliance capabilities, he added, noting that for most IT departments, suddenly throwing away a trusted SaaS ecosystem seems far too risky.

However, pricing remains the Achilles’ heel of the SaaS sector. Traditional seat-based or user-based pricing models are under threat because AI agents enable fewer people to accomplish the same amount of work. Consequently, suppliers are struggling to balance stock market growth expectations with the shifting operational demands of their customers.

The changing economics of AI

The broader economics of AI are also facing intense scrutiny, particularly following reports of companies burning through their entire annual AI budgets in a single quarter.

Chandrasekaran’s take is that generative AI (GenAI) was adopted by users in their personal lives long before it became a staple of enterprise IT. While AI companies have an incentive to serve the consumer market for brand awareness and to establish a foothold inside enterprises, there is pressure to make business customers pay their way.

That’s partly because more businesses are incentivising employees to increase AI consumption – including through agents capable of making sequential or parallel requests, which drives up the total volume of model queries.

Since AI providers are constrained by compute capacity, their options for managing this load are limited: they must either reserve capacity for paying customers, throttle the number of requests a customer can make in a given timeframe, or raise prices.

In response, SaaS companies are already altering their billing structures. GitHub Copilot Pro, for example, has moved away from per-user pricing to a consumption-based model. That worries enterprise IT buyers who often lack the internal instrumentation to measure their token usage, leaving them unable to predict or monitor their expenditure until the monthly bills arrive.

Measuring AI maturity

Turning to how effectively organisations are deploying AI, Gartner vice-president Pieter den Hamer pointed to the analyst firm’s AI maturity model, which provides a comprehensive overview of the capabilities required to successfully scale AI.

Chandrasekaran described six dimensions of this maturity. Organisations must focus on value by measuring return on investment and using it as a primary metric for use cases. They must ensure data quality, as AI is only as useful as its contextual information.

They also need strict governance for risk mitigation, and strong engineering practices to ground systems in corporate data and automate deployments. Finally, businesses must balance top-down and bottom-up organisational innovation, while fostering a culture of psychological safety so employees feel empowered and secure in their long-term future.

A global Gartner survey found that just 17% of companies are at a high level of maturity, having successfully scaled AI pervasively across their business functions. Another 51% sit at a medium level, generating some value in isolated pockets but largely struggling to demonstrate clear returns on investment. The remaining 32% exhibit low maturity, limiting their efforts to ideation, experiments or pilots.

The importance of AI literacy

While generative AI and traditional AI remain the top two investment areas, agentic AI is showing significant growth. Yet, den Hamer noted that only about one in five companies currently has AI agents in production, indicating a clear gap between supplier marketing hype and the practical reality of enterprise adoption. Companies are routinely stumbling over seemingly simple hurdles, such as identifying the right use cases and grappling with poor internal data quality, which starves AI implementations of essential context.

Despite these challenges, clear best practices are emerging from the most mature organisations.

Many businesses initially deploy AI to boost employee efficiency, but these productivity gains are often modest. Den Hamer also noted that, despite AI being used as a convenient scapegoat for recent technology sector restructuring, only a small percentage of industry layoffs can be directly attributed to AI.

Instead of focusing purely on headcount and productivity, mature companies are using AI to accelerate research and development, enhance manufacturing quality, and improve overall business resilience. Furthermore, these organisations are embedding AI deeply into their business processes.

Den Hamer warned that the vast majority of current AI initiatives are acting merely as a band-aid. Organisations need to rethink their workflows entirely to maximise the technology’s potential, rather than accepting the over-optimistic narrative that AI is mature enough to fully replace human workers. The best recipe for success, Den Hamer noted, is seeking synergy between humans and AI, rather than trying to replace people completely with AI.

Ultimately, that success hinges on educating people. Den Hamer concluded that there is a surprisingly strong correlation between financial returns and actively fostering AI literacy, noting that training and guiding staff at all levels of the organisation is key for them to gain an understanding of AI.