🚨 Why AI Spend ≠ AI Return (YET) 🚨

What the 2025 IBM CEO Study really tells us—and how to fix it.

https://newsroom.ibm.com/2025-05-06-ibm-study-ceos-double-down-on-ai-while-navigating-enterprise-hurdles

🔎 Executive Snapshot

• AI budgets will 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐝𝐨𝐮𝐛𝐥𝐞 in the next 24 months, yet only 𝟐𝟓% of initiatives hit ROI and 𝟏𝟔% scale enterprise-wide.

• 𝟓𝟎% of CEOs say rapid AI investment left them with a “piecemeal” tech stack.

• 𝟔𝟖% call integrated data architecture critical, but silos still rule.

• 𝟓𝟒% are hiring for AI roles that didn’t exist a year ago—skills gap alert.

Five Root-Cause Failure Modes

1️⃣ Fuzzy Business Model & Weak Governance

Pilots aren’t tied to clear capabilities or P&L owners, so “cool demos” never become revenue engines.

2️⃣ Master-Data Mayhem

LIMS, MES, ERP all speak different dialects. Garbage in → GPT hallucinations out.

3️⃣ Security & Compliance Blind Spots

Shiny GPU clusters, but no validated pipeline or GxP controls. QA waves the red flag.

4️⃣ Portfolio & Budget Chaos

Funding gets yanked mid-stream; CFO labels AI “expensive science project.”

5️⃣ Legacy-Centric Architecture

2010-era SOA + nightly ETL can’t feed real-time agents; value stays local.

👉 Casual Chain: Undefined capabilities ➡ scattered funding ➡ siloed data/infrastructure ➡ low model accuracy & security concerns ➡ stalled rollout ➡ poor ROI ➡ executive skepticism.

Six-Step Playbook

1. Map capabilities before chasing shiny use cases
   Document Level-0→4 processes and the master-data objects they touch.
   
   

2. Stand up an AI governance board
   Give it purse strings and stage-gate power (Idea ➡ Proof-of-Value ➡ Pilot ➡ Scale).
   
   

3. Fix master data first; buy GPUs later
   Golden IDs, ontologies, lineage tracking, quality scorecards—unsexy but essential.
   
   

4. Modernize the backbone via a “strangler-fig” pattern
   Wrap legacy in APIs & event streams, layer in vector search, and sunset monoliths piecemeal.
   
   

5. Split budgets
   Experiment fund: small, time-boxed.
   Scale fund: released only after proof of value, security & change-management sign-off.
   
   

6. Measure ROI like a VC
   Clear Objectives/Key Results, adoption metrics, “graduate or sunset” discipline—no zombie projects.
   
   

7. Coach the culture before you code the future
   Map current ↔ needed skills (data wranglers → prompt engineers, model validators, translation product owners).
   Launch bite-sized AI-literacy sprints, peer-mentor pods, learning KPIs.
   Celebrate wins and noble failures so teams crawl → walk → run without face-planting.

💡 Bottom Line: The technology is ready. It’s the plumbing, people, portfolios, and processes that leak value. Patch those, and the next CEO survey will headline AI payoffs—not disappointments. 🚀

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