The AI hype is hitting IT leaders, particularly CIOs, like a relentless sales pitch that won’t quit, and it’s driving them up the wall in 2025. IT suppliers are plastering “AI-powered” or “intelligent” on every product from cloud platforms, security tools, even basic infrastructure - promising game-changing efficiency, predictive insights, or automation that’ll slash headcount. But when CIOs get these tools in hand, reality often feels like a bait-and-switch, and that gap between expectation and delivery is where the irritation starts.
One big issue is the lack of tangible value. IT suppliers hype AI as a magic bullet - say, an ERP system that predicts supply chain snags or a helpdesk tool that resolves tickets without human input. Yet, after deployment, CIOs find:
- The AI either needs mountains of clean data they don’t have
- Requires constant tuning by expensive specialists
- Delivers results so incremental, they barely move the needle
For example, an IT supplier might claim their AI-driven analytics will cut downtime by 30%, but in practice, it’s more like 5% and only after months of tweaking. This leaves CIOs stuck explaining to the CEO why the seven-figure investment hasn’t paid off.
Even Microsoft’s CEO, Satya Nadella, recently said on podcaster Dwarkesh Patel’s show, that the industry’s obsession with artificial general intelligence (AGI) and flashy benchmarks, like AI solving tricky math problems, is getting ahead of itself. Instead, he emphasized that AI’s real worth should be judged by its tangible economic impact, like driving measurable GDP growth, not just by tech breakthroughs that sound cool but don’t yet pay off broadly.
Then there’s the “AI washing” trend, where IT suppliers slap an AI label on features that aren’t truly intelligent. Think basic rules-based automation rebranded as “machine learning” or a chatbot that’s just a fancier decision tree. IT leaders see through this fast, they’ve got teams who know the difference, but it wastes their time sifting through marketing fluff to figure out what’s real. Worse, some IT suppliers bundle these half-baked AI add-ons into higher pricing tiers, forcing CIOs to pay for capabilities they don’t need or can’t use effectively.
Integration headaches are another thorn. IT suppliers pitch AI as plug-and-play, but in messy, real-world IT environments with legacy systems, siloed data, hybrid clouds - it’s rarely that simple. CIOs report spending more on consultants or internal resources to get the AI working than they did on the tool itself. And when it doesn’t mesh with existing workflows, like a security AI that flags everything as a threat and overwhelms the team, it’s not just useless, it’s a liability.
The pressure from above doesn’t help. Boards and CEOs, dazzled by AI headlines, push CIOs to “get on the train,” often based on IT supplier demos that overpromise. When the results disappoint—like an AI CRM that can’t handle industry-specific nuances, CIOs take the heat, not the IT supplier. Add in the noise about generative AI, where IT suppliers tout content creation or code generation but deliver outputs that need heavy human cleanup, and it’s no wonder IT leaders are fed up. They’re not anti-AI; they just want practical, proven solutions, not a shiny toy that turns into a science project.
Here are six best practices the IT C-Suite can and should consider with any AI initiatives:
- Tie to Clear Business Outcomes: Anchor every AI project to specific, measurable goals (e.g., 20% faster processing, higher sales conversions). Demand IT suppliers quantify how the AI delivers, linked to existing KPIs.
- Pilot Small, Validate Fast: Start with a limited proof-of-concept (e.g., one department, 60-90 days). Measure against a baseline; cut bait or renegotiate if it underperforms. Expose hidden costs early.
- Check Data Readiness: Audit internal data quality first—clean, accessible data is a must. Prioritize projects where data’s solid or cleanup is cheap; avoid disguised data overhauls.
- Scrutinize the AI Reality: Question IT suppliers: Is it true ML or just automation? What’s the training data? How much tuning is needed? Test with your own data, not theirs, and demand detailed case studies with ROI proof.
- Demand Cost Transparency: Pin down total cost—licenses, compute, support, integration—versus projected return. Push for kill clauses or phased deals to limit lock-in if it flops.
- Focus on Human Enablement: Target AI that boosts staff (e.g., faster insights) over full automation. Train teams, track adoption—ensure it’s used, not shelved.
When eyeing AI solutions from IT suppliers long-term, CIOs should focus on forging IT partnerships that deliver enduring value over fleeting promises, treating IT suppliers as collaborators in a marathon, not a sprint. This means favoring those with a track record of adaptability, offering scalable, interoperable tools that evolve with the enterprise’s needs over rigid, hype-driven products that might strand them later.
Look for IT suppliers who prioritize transparency on costs, data demands, and ROI timelines, while committing to ongoing support and co-innovation as AI matures. The aim is to lock in solutions that amplify strategic goals, like resilience and efficiency, over years, not just months, while staying wary of overblown claims (e.g., AGI-like breakthroughs as Satya Nadella warned of) and leaning toward those who prove value through practical, incremental gains aligned with the company’s roadmap.
NET(net) can help you navigate these waters via RFP support, rigorous review of supplier Ts & Cs, SLAs, ROI, benchmarking and negotiation and more. Contact us to have a supplier conversation.
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