Fearing an AI Bubble? CIOs Have Answers
While there may be a lot of hysteria surrounding AI right now, some CIOs are not overly concerned about a potential AI bubble. For instance, Jim Palermo, CIO of Trimble, a $3.7 billion platform company serving the engineering, construction, and transportation industries, believes that despite the noise, Trimble will continue investing in AI technology to drive innovation and improve productivity.
Palermo acknowledges that while some CIOs view an AI bubble as a possibility, they are adopting a measured approach to technology adoption. He notes that the level of concern “depends on how much you’ve drunk the Kool-Aid.” Other IT leaders argue that an AI bubble wouldn’t imply that the technology lacks a future, but rather that inflated expectations might collide with operational realities. They emphasize that the real risk lies not in investing in AI itself but in betting on unproven models, vendors, or single-use platforms.
De-risking AI Commitments
To navigate these concerns, IT leaders advise making more disciplined and informed decisions, including considering shorter contracts to mitigate risks if the technology or market shifts. They recommend tightening governance and conducting small proofs of concept (PoCs) before committing to full-scale initiatives.
Regarding the future of the AI industry, Palermo predicts that many point AI solutions will disappear in the coming years. He believes that larger platform ecosystems are starting to develop a solid AI foundation, which allows them to articulate how to leverage AI effectively. CIOs are expected to focus on these ecosystems, as they invest millions in relevant software.
Strategies for Resilience
One strategy CIOs are implementing is to separate capability from hype. Shawn Jahromi, a principal advisor, notes that CIOs are funding narrowly scoped AI use cases linked to operational metrics like cycle time reduction and cost containment, which limits exposure to shifts in vendor viability or valuations. CIOs are also treating AI as an operating model change rather than simply a technology purchase. This includes establishing governance, accountability, and human override structures, making them less vulnerable to bubble-like conditions.
Another critical strategy involves retaining architectural control by prioritizing data ownership, model portability, and vendor exit options. This approach aims for resilience, ensuring that if an AI vendor fails or pricing collapses, the organization maintains decision rights and operational continuity.
Pragmatic Approaches to AI Investments
Allegra Driscoll, CTO of Bread Financial, employs a pragmatic approach to AI investments, focusing not on being the first to market but on building capabilities that create value while ensuring resilience. She emphasizes the importance of high-value, proven use cases and spends considerable time evaluating risks associated with all tech investments. This careful scrutiny builds confidence that the chosen use cases will continue providing value.
Staying focused on desired outcomes is essential for CIOs, as Palermo states, working collaboratively with business leaders to address pain points. He emphasizes the need for governance to transition from innovation and ideas to actual production effectively.
Managing Tool Sprawl
Many enterprises face challenges with tool sprawl. In response, CIOs are tightening their approach to software tools, particularly in the AI realm. Trimble is rationalizing its tools and creating metadata around software to ensure rigorous vetting. This method reduces complexity and minimizes risks associated with relying on potentially ineffective tools.
Driscoll agrees, noting that excessive tool acquisition can lead to complicated architectures that may not yield significant value. Therefore, enterprises are advised to focus on tools that meet specific needs within the AI space.
Investing in High-Value Use Cases
Looking ahead, Bread Financial intends to invest in high-value AI use cases, such as knowledge management capabilities for customer care agents. They adopt a philosophy of slowing down to proceed faster, ensuring that consumer trust remains paramount by establishing a robust and controlled environment.
As CIOs anticipate continued investments in AI, their strategies will be influenced by evolving landscapes and specific business needs. The objective remains to leverage AI where it can have a meaningful impact on operations and customer experience, ensuring that organizations are adaptable and financially disciplined.
Vendor Consolidation Risks
As the AI landscape evolves, some experts, like Benjamin Hori, cofounder of Spotlite, observe signs of an impending AI correction that could impact startups and create risks. When dominant players begin bundling capabilities, it can lead to instability for smaller vendors, affecting security teams that rely on those tools.
To mitigate risks associated with AI volatility, organizations prioritize partnerships with vendors that demonstrate strong governance practices and distinct data advantages. Building flexibility into technology stacks ensures that institutions are not dependent on any single model provider, especially in a rapidly changing environment.
