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.