7 Tech Stack Pitfalls to Avoid in 2026

Tech stacks are evolving faster than ever, thanks to the pace of technology innovation and business change. To keep the tech stack operating smoothly, CIOs should avoid common mistakes that cause waste. That isn't as easy as it sounds.

"There's so much proliferation of tech stacks, many of them competing almost on a weekly basis," said Ranjit Bawa, chief strategy and technology officer at Deloitte, citing the intense jockeying among AI stacks for dominance as an example.

"We're constantly seeing the churn in [choosing] the Gemini stack, Anthropic or Open AI," he said. Inevitably, CIOs have one stack they think is aligned to their strategy. As other AI models evolve, they feel compelled to hedge their bets, experimenting with the latest alternative for certain workloads with an eye to displacing the "champion stack," or adopting a multi-stack mode for more flexibility.

The issue is compounded by the broader cloud stack dilemma, Bawa added. Standardizing on a single cloud provider simplifies operations but comes at a cost of limited flexibility. "For every tech stack you pick, there are five reasons why you wouldn't pick that because it didn't integrate to your mainframe system, your database didn't talk to it, or you were beholden to the Microsoft stack," he said.

7 Tech Stack Challenges Facing CIOs in 2026

Balancing innovation and vendor lock-in is one of many tech stack challenges in today's AI landscape. As CIOs firm up their 2026 strategies, decisions about tech stacks have become increasingly complex -- and consequential, experts told InformationWeek. From addressing AI agent sprawl to navigating budget constraints and managing shadow AI, decisions made now will determine how successfully companies can innovate and scale in the coming years.

The following are some of the most pressing challenges CIOs must be aware of to prevent tech stack chaos in 2026:

Anand Nimkar, partner and applied AI practice leader at Deloitte Canada, said he sees the need to balance AI innovation and efficiency as a core tech stack challenge among Deloitte's enterprise clients. The issue of AI agent sprawl across multiple platforms encapsulates the dilemma. Each platform has its own way of building agents, but to be effective, the platforms need to be interoperable with systems of record and each other.

As a result, IT departments are discovering redundant investments across the enterprise.

"There are two major forces I see: One is segregation based on these silos of platforms, and the other is a lot of enterprises saying they want to centralize their AI capabilities because it's hard to manage this and benefit from it without all this redundant work," Nimkar said.

Meanwhile, companies are empowering nontechnical users -- citizen developers -- to create their own AI agents and automated workflows in a more organic way using tools like Microsoft Copilot, Google Agent space and other systems.

"Organizations want to achieve productivity gains by just making tools available, connecting them to data sources and letting use cases bubble up," Nimkar said. But without centralized oversight, you get chaos. CIOs must find ways to enable citizen developers, while implementing enough governance to identify redundancies and share learnings.

At the crux of this push and pull is the evolution of tech stacks from traditional architectures to agentic ones . This evolution is occurring simultaneously both at the infrastructure and developer levels, said Tim Lehnen, CTO of the nonprofit Drupal Association, which supports the Drupal open source project.

"On the infrastructure side, we're seeing continued abstraction of the management layers and down-stack software components, integration of AI analysis and workflows into analytics and monitoring, and bad-actor protection on the [system] level," Lehnen said.

On the development side, AI assistants are being deeply integrated with developer environments, with prompt engineering emerging as an important skill. The approach also introduces new risks created by "vibe coding," he said, referring to the use of generative AI tools to generate code from natural language prompts.

As a result of this tech evolution, traditional roles are being reshaped, he said. "We're seeing AI tools being developed to move certain tasks from an engineering role to a 'builder' role in editorial or marketing, while the engineering role is moving up from direct development into orchestration and systems oversight."

Sanjeev Vohra, chief technology and innovation officer at advanced technology services and solutions company Genpact, explained that tech stacks are evolving into modular architectures designed for scale, and, yes, with AI becoming the underlying linchpin powering enterprise systems.

"Today's stacks are increasingly cloud-native, API-first, and infused with AI agents that can learn, adapt, and act autonomously," Vohra said. But the real inflection point is the shift from traditional software development lifecycle-driven stacks to agentic architectures.

"Software is no longer the end product. AI agents orchestrating across layers are becoming the new architecture," Vohra explained. Enterprises are moving away from siloed applications toward living, learning systems that combine data, AI and domain expertise. "In short, it's not about making a faster horse. It's about adopting the newest EV car."

The democratization of AI tools , which has helped create AI agent sprawl, has also revived an old IT challenge in a new form: shadow AI.

"[One of our clients] has 85 different models in play across their enterprise. Most of them have been driven by the individual business units and product owners," said Deloitte's Bawa.

The upshot? The organization is now centralizing these efforts through a clearing house to determine the enterprise stacks by persona and by workload. "If you're the finance function, we are going to normalize on stack X. If you're the marketing function, we're going to do it on another stack," he said.

The aim is to use enterprise architecture standards and just good basic hygiene. According to Bawa, there has been a lot more central governance among Deloitte's "better clients" in the last 18 months, whether it's through an enterprise architecture function or a center of excellence that acts as a clearing house.

As always, CIOs are expected to achieve more while IT budgets decline, fall flat or grow incrementally. AI adoption doesn't make that easier, Bawa said. Though labor costs shrink with AI and automation, the cost of software continues to rise.

These AI and automation initiatives require new incremental budget that CIOs don't have readily available. "Maybe you can see productivity benefits in six months or 12 months, but there's a working capital issue," Bawa said. "How do you fund this until you find savings from some other portion of the business, [such as] through productivity or revenue enhancement?"

Deloitte Canada's Nimkar said business sponsors are increasingly helping to overcome IT budget limitations by directly committing to reduce their own budgets based on some of these [AI and automation] capabilities.

"It often takes some change management or proof of value," Nimkar said. "Organizations that have seen the value of AI have very large budgets because they know they can start to proliferate use cases much faster than before." Conversely, some organizations hesitate to scale up because they haven't seen proof yet.

Patrick Gilgour, managing director at global consulting firm Protiviti, pointed out that redundant platforms and applications create waste that drives up costs, which hinders ROI, flexibility, and innovation. "When you have three tools all [trying to] deliver the same solution, you end up with increased cost," he said.

And that is a problem for CIOs, Gilgour added, especially in today's economic climate. "ROI is significantly important as we have these economic headwinds, and we try to shorten the ROI cycle and really see the productivity boost from AI and other technologies," he said.

Vohra framed the budget challenge as an operating model issue, rather than a purely financial issue.

"Enterprises need to fund architectural change, not just incremental system upgrades," he said. This requires shifting from project-based to outcome-based spending that connects budgets directly to measurable business impact.

"Only when budgets align with new architectures [designed for AI-driven systems] will organizations escape the trap of legacy spending eating into innovation," he said.

The sheer amount of hype around AI integration into just about every tool and process is in itself a huge challenge for CIOs, Lehnen said.

"It's very healthy right now to be an AI skeptic, while still being open-minded," he said. Almost every vendor of analytics, telemetry, support, or infrastructure SaaS solutions has announced AI integrations and enhancements, and oftentimes with price hikes. But in many cases, they have yet to demonstrate the value of those changes.

Similarly, business leaders feel the urgency to adopt AI solutions as part of the tech stack, even though their understanding of how AI can be useful comes from vendors who try to sell them products rather than provide expertise.

His advice? IT and business leaders alike need to get educated on concrete use cases for AI that can deliver value. They must show a willingness to push vendors for concrete value over hype and remember to keep investing in core business functions that, while not new, are just as critical.

Though AI agents are growing in popularity, data access is still a significant problem for CIOs, Nimkar and Bawa said. Enterprises struggle with permissions and role-based access because they haven't established the governance frameworks for these new technologies.

"If an AI agent acting on behalf of the employee can access the data sources and the API owners of those sources, organizations haven't mapped any data usage policies against the roles that are within the upstream application, [like Salesforce]," Bawa said. They need to figure out how to expose this data for their employees.

There are also situations in which AI agents are acting on behalf of a group or functional unit. In this case, the AI agents need broad permissions, but they also require constant monitoring to confirm they're not arbitrarily changing data and breaking things.

"I've had clients ask me for a literal kill switch that monitors the tasks these AI agents are taking or not," Nimkar said.

The guardrails for AI agents need to be non-deterministic and LLM-based , he added, so that what's put in place can be easily centralized and managed -- which is itself a challenge. "Cloud companies are often not covering this [holistically], so you still need to involve third-party vendors," Nimkar said.

He cautioned, however, that LLMs are still relatively unreliable, as evidenced by the proliferation of disclaimers by model providers. The fact that enterprises are already challenged with data management doesn't help.

Technology leaders need to see the tech stack as an opportunity to reimagine and reinvent the business, versus an opportunity to automate, Bawa said.

"AI gives us that power to say, 'In an AI-native world, if intelligence was ubiquitous and available and scale didn't matter, how would I do fraud detection, payments or food supply chain optimization?" Bawa said.

For tech leaders, this means enabling people to really engage in this big conversation and to redefine the future. "If all you're doing is automating a process or trying to take out a couple of heads, I think you completely missed the boat on this and the opportunity," Bawa said.

Vohra said the biggest challenge is that most enterprises are focused on making processes incrementally faster instead of fundamentally rethinking them. Many organizations are waiting for external disruption instead of disrupting themselves, because internal transformation means reworking operating models, budgets and tooling, reskilling and creating new roles, and managing short-term confusion.

"CIOs now face the critical question: Can the current stack support agentic, AI-driven applications, or does the organization need to rearchitect?" Vohra said.

"Since a wholesale rebuild is rarely possible, leaders must identify gaps and augment toward agentic readiness by carefully picking the technologies and products that create future-ready agentic architecture," Vohra said.

To avoid these pitfalls, Genpact's Vohra offered the following advice for CIOS navigating tech stack chaos:

Get your data house in order. "Without usable, governed, and accessible data, AI falls flat."

Adopt agentic development lifecycle principles. "This isn't just a new way of coding. It changes how enterprises design, budget, and deploy systems."

Stop bolting AI features onto legacy systems. "This creates more tech and process debt. Instead, rethink workflows with agentic orchestration at the core, and let AI handle scale, while humans focus on judgment and complex exceptions."

Architect for resilience. "Volatility, whether in markets, supply chains, or regulation, is the new normal. Modular, agentic systems give enterprises the ability to adapt and thrive."