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What's really holding enterprise AI back?

MIT Technology Review Insights polled 250 executives on where their AI stands and what is holding it back. 68% of organizations rank governance, business understanding, or trustworthy outputs as their biggest AI barrier. Sign up now and read it first.

Who answered

Executives at organizations like yours

Senior leaders

C-suite, EVPs and SVPs, directors, and technology department heads

Interviewed for the report. Meet them ↓

4 regions

$100M to $5B+

In annual revenue

12 industries

From banking and healthcare to aerospace and retail

  • Banking and finance
  • Technology
  • Healthcare
  • Life sciences and pharma
  • Retail
  • Consumer goods
  • Media and telecom
  • Energy and utilities
  • Automotive
  • Aerospace and defense
  • Transportation
  • Construction

Who we interviewed

The pioneers of enterprise AI

Vidyut Naware

Vice President, AI Architect

Unless AI is grounded in your enterprise knowledge and in your enterprise's way in which it operates, it is still going to be very prone to hallucination, very prone to not acting like Verizon would.

Joe DosSantos

Vice President, Enterprise Data and Analytics

Context gives AI “the strategy for finding the right answer.”

Prukalpa Sankar

Co-founder

This cannot be a two-year-long ontology project. This needs to be something that you can get to business value in the next three months.

What the poll found

What 250 executives told
MIT Technology Review Insights

What's inside

What the report answers

  1. How far most organizations have really gone with AI, and how returns change at each stage
  2. The top three challenges blocking organizations from making progress with AI
  3. How you can start building your organization's context
  4. Who owns AI at the organizations that have agents in production
  5. How Verizon and Workday build, test, and own the context their agents run on
Context Conference

Watch the report come to life.

The report quotes Verizon and Workday. At Context Conference, they walked through the context their agents run on.

  • The Post-AI Data Stack: A blueprint for analytics agents. Zach Michel and Austin Kronz, Atlan; Joe DosSantos, Workday; Piyush Bhargava, DocuSign; Sowmya Parthan, Upwork.

    Joe DosSantos of Workday, quoted in the report, joins DocuSign and Upwork on what analytics agents need before the business trusts their answers.

    Watch the Session
  • Inside Verizon’s context architecture. Steven Huang, Verizon.

    Verizon walks through its context architecture: what it built, bought and standardized, and what it would redo.

    Watch the Session
  • Enterprise AI is missing a context layer. Prukalpa Sankar, Atlan.

    Prukalpa Sankar on why AI needs context to get answers right, and where context platforms are heading.

    Watch the Session
Produced by MIT Technology Review Insights, the custom content arm of MIT Technology Review, in association with Atlan.
About Atlan. Atlan is the AI context platform. Organizations use Atlan to turn the knowledge, expertise, and rules that run their business into context their AI agents can use, so a copilot, an analytics agent, or an operational agent reasons the way their best employee would. Atlan calls this contextual intelligence: the quality of reasoning that emerges when AI understands a business the way its best employee does. Atlan partners with hundreds of AI-forward companies, including Mastercard, Fox, Workday, Virgin Media O2, and General Motors, to help AI understand their business. Learn more at atlan.com.
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