⚡ Quick Summary
Published by MIT Technology Review Insights and sponsored by Boomi, this report examines why organizations struggle to move generative AI from pilots and experiments to enterprise-wide deployment, and sets out a playbook for AI strategy. It draws on a global survey of 205 C-suite and senior data executives conducted in March 2024 — 88% at C-suite level, all at organizations with more than US $500 million in annual revenue — and on interviews with leaders from Charli AI, IDC, Boomi and Infosys.
The central argument is that scaling AI depends on strategic and organizational transitions in infrastructure, data governance and supplier ecosystems rather than on models alone. The findings show 95% of companies already using AI and 99% expecting to, but 76% deployed in only one to three use cases; nine in ten expect to increase AI readiness spending in 2024; half cite data quality as the most limiting data issue; and 45% (65% at firms above US $10 billion in revenue) name governance, security and privacy as the biggest brake on deployment speed. The report closes with four principles on foundations, use-case value, cost and partnerships, and risk-informed caution.
🧩 What’s Covered
The report moves from survey data to practitioner guidance across four chapters and a set of closing principles.
- Executive summary (pages 4–5): the gap between AI ambition and execution, with figures on current use, pilot-bound deployment, spending plans, data quality as the top limiting issue, and willingness to forgo first-mover advantage for safe delivery.
- Partnering for success (pages 6–7): why few firms should build their own large language models, Figure 1 on industry-specific versus business-unique use cases, vendor due diligence on data handling, multi-AI environments, and financial services tools that unlock data trapped in documents.
- Counting the cost (pages 8–10): compute and hardware figures (US $78 million for GPT-4, US $191 million for Gemini Ultra), GPU, energy and talent costs, Figures 2–4 on readiness spending, the "squeezed middle" of US $500 million to US $1 billion firms, and ROI measures such as Motorola's task-time framework and PwC's hard versus soft ROI.
- Building a data core (pages 11–13): data quality, infrastructure and governance requirements, Figure 5 on bottlenecks, four data management tactics on lineage, communication, contextualization and core capabilities, legacy systems and data lakes that become "data swamps", data liquidity, and metadata languages such as Darwin Core and VRA Core.
- Acceleration versus caution (pages 14–16): hallucinations, errors and bias; liability such as the Air Canada chatbot case; prompt injection and data poisoning; the regulatory tide (bills mentioning AI rose from 1 in 2016 to 37 in 2022 across 127 countries); the EU AI Act's risk-based obligations and outright bans; the October 2023 US executive order; compliance assurance and explainability.
- Conclusion (page 17): four principles on data foundations, business-specific use cases, cost and partnerships, and risk-informed caution.
💡 Why it matters?
For executives weighing enterprise AI investment, the report supplies benchmarks rather than assertions: where peers sit on deployment, what limits them and how much readiness spending is planned. Risk, security and compliance functions get a compact account of the pressures shaping deployment — bias and liability, prompt injection and data poisoning, privacy and the regulatory response. It also connects practice to named instruments: the EU AI Act's risk categories and conformity assessment, the October 2023 US executive order, algorithm impact assessments, bias audits and ISO guidance.
❓ What’s Missing
The survey covers only organizations with more than US $500 million in global annual revenue, so smaller firms, public bodies and third-sector organizations appear mainly as context. Results are self-reported percentages, with no methodology appendix on sampling, weighting or margins of error. The regulatory account is a snapshot from early 2024 — the EU AI Act is described as "currently being enacted" — so legal detail may have moved on. Sector-specific compliance obligations are not mapped, cost figures stay illustrative, and the recommendations remain principles rather than a step-by-step method.
👥 Best For
Best for strategy, transformation and data leaders building an enterprise AI roadmap, for chief data officers and data governance teams making the case for lineage, metadata and quality investment, and for risk, compliance and security functions that need a survey-based view of governance and regulatory pressure. Advisers can use the benchmarks to frame client conversations about scaling AI.
📄 Source Details
A playbook for crafting AI strategy, a report by MIT Technology Review Insights, sponsored by Boomi. Author: Adam Green; editor: Teresa Elsey; publisher: Nicola Crepaldi. The survey was conducted in March 2024 with 205 respondents; the copyright line reads 2024. Twenty pages, English, no series or reference number. No URL for the report itself is printed; the back cover gives www.technologyreview.com and insights@technologyreview.com. Text extraction of all 20 pages was available.
