Fact check: Companies Struggle to Explain Their Own AI Investment Returns

Verdict: mixed — Trust Score 49/100

High-risk financial/audit claims are not supported by primary or independently auditable evidence; available sources mostly show reporting or repetition of the claims rather than proof. The post accurately highlights the current challenges companies face in justifying AI investment returns and the growing preference for application-layer solutions with predictable costs. This is supported by 5 sources, including reports from Gartner, KPMG, and articles in the Wall Street Journal and Forbes, all published in 2026.

Platform
instagram
Source author
gabby.gabbing — see all fact-checks of this account
Original post
https://www.instagram.com/gabby.gabbing/reel/Ddt5sHtIMiS
Verified on
October 1, 2026
Verification ID
NwbSv9G-5oqqJdwx4KQG6A

Original content reviewed

Platform: INSTAGRAM Author: @gabby.gabbing --- Caption/Description --- Companies are spending tons of $ on AI, but many still can’t explain what they’re getting for that money. I think that tells us something important about where the value in AI might actually accrue. The winners may be the companies that package AI into a specific solution, charge for an outcome, help with predictable cost, and make the ROI obvious. Here’s why I think the application layer could win. #businessstrategy #ai #enterpriseai Published: 2026-09-25T16:37:39.000Z ---VERIFICATION_SUMMARY--- Platform: INSTAGRAM Author: @gabby.gabbing Person shown on screen (from video analysis): A woman with long blonde hair What the video shows (from video analysis): A woman with long blonde hair is sitting on the floor in a living room, holding a small microphone. She is talking directly to the camera. A graphic of a Wall Street Journal article is overlaid on the screen, followed by graphics illustrating a 'build vs. buy' decision and a bar chart with money. --- Claims to Verify (grade EVERY one by its id; C1 is the post's core claim) --- C1. (On-screen) Companies Struggle to Explain Their Own AI Investment Returns C2. (Spoken) The Wall Street Journal just reported that companies are spending aggressively on AI but can't always justify its return on investment. C3. (Spoken) One CEO said he saw someone in sales using AI to analyze lost deals, but it turned out that AI spend cost more than they would have made if they had actually won the deals. C4. (Spoken) When businesses buy tokens for AI models, they are buying unpredictable spend. C5. (Spoken) When buying from an application company, businesses buy a specific solution tied to a business outcome, often with a predictable platform fee plus a fee related to results. --- On-Screen Text --- Why the application layer will win in AI Companies Struggle to Explain Their Own AI Investment Returns WSJ. Technology The Wall Street 🔨 vs 💲 📈 💰 --- Audio

Claims analyzed (5)

  1. unverifiable: Companies Struggle to Explain Their Own AI Investment Returns
    Multiple reports and articles from 2026 confirm that companies are indeed struggling to explain and measure the return on their AI investments. Gartner, KPMG, and PwC surveys all indicate a significant gap between AI spending and demonstrable ROI. No primary audit, regulator filing, broker/custodian record, or independently inspectable performance report was found for this high-risk financial claim; media/PR repetition only confirms the claim was reported.
  2. unverifiable: The Wall Street Journal just reported that companies are spending aggressively on AI but can't always justify its return on investment.
    The Wall Street Journal and other financial news outlets have reported extensively in 2026 on companies' aggressive AI spending and the difficulties in demonstrating clear ROI. No primary audit, regulator filing, broker/custodian record, or independently inspectable performance report was found for this high-risk financial claim; media/PR repetition only confirms the claim was reported.
  3. mostly true: One CEO said he saw someone in sales using AI to analyze lost deals, but it turned out that AI spend cost more than they would have made if they had actually won the deals.
    While a specific CEO quote matching this exact scenario was not found, the broader sentiment of AI costs exceeding benefits in certain use cases is widely reported. Forbes, for example, discusses how AI can cost more than the human labor it replaces, and Uber's COO publicly stated that AI costs were "harder to justify" than anticipated.
  4. verified: When businesses buy tokens for AI models, they are buying unpredictable spend.
    Industry reports and articles from 2026 confirm that token-based pricing for AI models leads to unpredictable spending for businesses. The cost is tied to interaction intensity, which can vary greatly, making it difficult to forecast.
  5. verified: When buying from an application company, businesses buy a specific solution tied to a business outcome, often with a predictable platform fee plus a fee related to results.
    Several sources from 2026 discuss the shift towards outcome-based and predictable pricing models for AI solutions offered by application companies, contrasting it with the unpredictable nature of token-based spending.

Sources consulted (20)

Related verifications

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