AI disruption: How CIOs should prepare for a new economy

What the Citrini scenario portrayed

The scenario imagined a rapid, AI-driven disruption cycle in which AI systems dramatically boost productivity for organizations by 2028. This reduces the need for human labor across knowledge work, which, in turn, leads to mass unemployment and decreased consumer spending. This then triggers a negative feedback loop, pushing companies to automate further.

Simultaneously, AI disrupts the SaaS ecosystem, as enterprises reassess vendor value and use vibe coding to replicate software internally. The combined economic and financial effects compress massive technological and economic change into a few years rather than decades.

What the scenario got right: AI will reshape the economy

Experts generally agree that AI will create major change within the world economy. While the exact timing and scale remain uncertain, the Citrini scenario correctly highlights that widespread automation and productivity shifts will have profound effects.

"[The scenario] is between plausible and probable, with some things in there being less plausible," Jackson said.

While not every detail may unfold exactly as imagined, the overall disruptive potential to business, labor and technology markets are credible and worth planning for.

Another area the scenario got right is the pressure AI could place on SaaS business models, often referred to as the 'SaaSpocalypse.' AI is already enabling new approaches to internal workflows and software deployment, and organizations may begin to rethink long-standing SaaS business models as a result.

The big ERP systems are dead in the next five to 10 years.

While the most dramatic visions and timelines are hypothetical, current AI tools have begun to reshape how organizations approach ERP and SaaS systems, said Joe Locandro, CIO at Rimini Street, a third-party support provider of enterprise software. AI today handles simpler, repeatable tasks, but within a few years, low-code and no-code tools could accelerate this shift and level the playing field.

"The big ERP systems are dead in the next five to 10 years, and they've hit a technological peak … Currently, the AI ones are very uncomplex. They do singular, repeatable and not too complex tasks. But they will in the future, and within five years, AI with low-code/no-code will be a great leveler," Locandro said.

Taken together, these perspectives affirm that the scenario got it right in identifying AI's transformative potential and its likely pressures on enterprise software markets, even if the pace and full scale of change remain uncertain.

What the scenario got wrong: Speed and scale of disruption

The Citrini scenario's dramatic timeline for AI-driven change grabbed attention, but experts say that timeline likely overshoots reality. While AI is transformative, widespread replacement of knowledge workers and a full-blown SaaSpocalypse are unlikely by 2028.

For example, the scenario portrays rapid displacement of roles such as real estate agents and other social or professional positions.

"That's not where we're going to see [rapid displacement] in the next three years," Jackson said.

The really big transformations happen when they get deployed at scale, and that takes a lot of time.

A key reason sweeping job displacement is unlikely to unfold so quickly is the difference between technological capability and large-scale deployment. Even when AI tools exist, the truly transformative effects come only when organizations integrate them across workflows, create new business models, reskill employees and develop new products and services.

"There is a big difference between creating the technology … and deploying it at scale across the economy. The really big transformations happen when they get deployed at scale, and that takes a lot of time," said Wladawsky-Berger.

In the enterprise context, AI's near-term effect will likely focus on repetitive, rules-based tasks rather than replacing entire job categories.

"Within 24 months, you will see AI automate repetitive things like claims handling … but you can't say there's going to be massive redundancies on a global scale," Locandro said.

Trust, reliability and workflow complexity remain additional barriers. Organizations are not yet ready to fully hand over end-to-end processes to AI. Therefore, transformation will likely unfold gradually, unevenly and sector by sector -- rather than as the sudden, universal upheaval the scenario portrays.

7 steps CIOs can take to prepare for AI disruption

Even if AI disruption unfolds gradually, CIOs should prepare now to shape outcomes rather than react later. The following actions give leaders practical ways to manage risk and stay ahead.

1. Re-evaluate procurement and long-term SaaS commitments

As AI capabilities quickly evolve, locking into long-term software agreements can limit flexibility, Jackson said. CIOs must assess whether existing and future SaaS contracts let their organizations adapt quickly to new AI tools, or whether alternative arrangements -- such as shorter-term or more modular tools -- might better position them for agility and cost efficiency.

2. Rethink workforce strategy and operating models

AI adoption will inevitably shift roles, responsibilities and required skills across the organization. CIOs must consider how these changes affect their operating model, what roles become more strategic, what processes teams can automate and what new skills will be critical to success, Jackson said. CIOs who plan for these shifts now can prevent disruption later.

3. Adopt scenario-based foresight for risk mitigation

CIOs should consider a range of plausible scenarios to prepare for AI disruption. This can help them develop contingency plans, identify risks before they materialize and take actions to prevent negative scenarios from occurring. This proactive approach strengthens resilience and strategic decision-making.

"The point of foresight is to put forth hypotheticals and be proactive about plausible scenarios. You then consider how to prepare or prevent them from happening," Jackson said.

4. Take a cross-functional approach to governance

AI affects nearly every function of an organization. CIOs can't evaluate technology, risk or cost in isolation. Instead, they should bring together finance, operations, security and other stakeholders to understand total organizational risk, identify previously unseen vulnerabilities and develop governance strategies that align across departments. This integrated perspective also highlights opportunities to use AI more effectively.

"You must get everybody at the same table and put your points of view together … that will reveal new insights that you hadn't thought about before," Jackson said.

5. Consider the business model implications of AI-driven automation

AI-driven automation can create a feedback loop: reducing labor, compressing consumer spending and prompting further automation. CIOs must think beyond efficiency gains and ask how AI affects their broader business model. Will AI enable the company to grow market share and open new opportunities? Or will it only optimize existing processes without expanding potential? Strategic foresight here is critical.

"You have to think, 'If this is the business model I'm leaning toward … am I going to be in a position where my company is growing, or am I just … trying to raise the floor instead of the ceiling?'" Jackson said.

6. Plan for regulatory changes

As with other transformative technologies, AI adoption is likely to trigger regulatory oversight. CIOs should anticipate potential compliance requirements -- whether around data privacy, workplace effects or algorithmic transparency -- and build flexible strategies that permit innovation while meeting emerging rules. Early awareness can prevent costly disruptions and fines.

"Society eventually introduces regulations for all technologies that are this transformative," Wladawsky-Berger said.

7. Watch competitors and market signals closely

Market pressures offer the clearest indication of where AI adoption is critical, Locandro said. CIOs can observe competitors' speed, innovative approaches and operational changes to identify where they must invest and benchmark their own progress. These signals help leaders make more informed, strategic decisions about internal AI adoption.

Key takeaways

The Citrini scenario resonated with people because it reflects a real possibility: AI will drive significant disruption across the economy and enterprise IT. However, experts agree the timeline is likely overstated. Large-scale transformation depends on technology in addition to deployment, integration and organizational change -- all of which take time and vary by industry.

For CIOs, the scenario is less about prediction and more about preparation. It highlights the need to reassess technology investments, workforce strategy and risk in the face of accelerating AI capabilities. The organizations that benefit most from AI disruption will not be those that react to it, but those that plan for it early and adapt as signals emerge.

Tim Murphy is a site editor for Informa TechTarget's IT Strategy group.