Top 12 Challenges

The Top 12 Challenges for AI Scaled Adoption – We have identified twelve top challenges for the adoption of AI at scale that we see many firms experiencing at some point in the early years of AI adoption. Many occur when the organisation moves from the innovation lab to production ready live applications.

Top 12 Challenges for AI Adoption

For each of these challenges we explore the underlying reasons for each one and highlight methods to help manage and mitigate them to ensure you can progress with your adoption without these problems causing impediments.

Team Conflicts

Dealing with team conflicts across the organisation

Multiple Teams

Learn how to deal with multiple data science teams

Middle Management

Overcoming middle management power struggles

User Trust

How to build user trust to adopt AI and ML applications

Business Use-cases

How to determine the right business use-cases and properly prioritise

Return on Investment

Determining the ROI on AI Projects is a complex situation

Tech Landscape

Often the technology landscape is diverse and frequently changing

Ethical Issues

Various ethical considerations come into play with even the most simple models

Governance Model

Having a well defined governance model and process is essential

Culture & Org Structure

The human factor has a major role to play in the successful adoption

Skills & Training

It is essential to make sure your team has the right skills and training

Vision & Strategy

Without a clear vision and strategy it is likely that scaled deployments will be problematic

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It will provide valuable insights and suggestions on how to deal with the top challenges of AI adoption.