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Top 10 Challenges for AI adoption at Scale

From Innovation Labs to Live at Scale.

Our latest whitepaper explores the various challenges of deploying AI at scale across your entire organisation, dealing with a range of issues that spans not only the underlying technology but people, culture, process, and governance.

Two Fundamental Differences 

  • It’s an Art as much as a Science …. Works in a very different way to normal technology implementations
  • Managing Expectations with the stakeholders of how AI works and performs.

Top 10 Challenges for AI Adoption

  1. Conflicts between IT Departments & Data Science Teams (sometimes the Data Science work is done initially within the business lines / business users)
  2. Scaling AI Challenges with Multiple Data Science Teams
  3. Middle Management Fighting over ownership of Data Science
  4. Business Users not trusting the technology (taking 6 months to approve)
  5. Confusion with the number of vendors / products / tools and platforms
  6. Data Bias & Ethical Issues slowing acceptance and adoption
  7. Correctly defining the Governance model for monitoring and control
  8. Changes in Culture and Organisational Structure needed across the organisation with the democratisation of data (information & knowledge)
  9. Need to train existing teams to better understand how AI/ML works
  10. Need for a top-level Vision and Strategy for how the business will become AI First

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