This document outlines 4 key challenges companies face when implementing AI solutions: 1) poor architecture choices that prevent scaling, 2) insufficient or flawed training data, 3) the trade-off between algorithm accuracy and explainability, and 4) a lack of AI engineering skills. It provides tips on how to overcome these challenges such as carefully planning use cases, starting with a proof of concept, and treating AI as an enhancement to - rather than replacement for - human workers. The document stresses the importance of continuous monitoring and adjustment of algorithms to help employees embrace changes from AI.

1. Top 4 AI
Challenges
and How to
Overcome Them

2. Corporate AI spending is on the rise
($, in billion)

3. Yet, just 11% of all AI
deployments eventually
yield a positive ROI

4. Discover what challenges
companies face when creating and
implementing AI solutions — and
what YOUR company can do to
avoid these scenarios

5. Technology Roadblocks

6. Poor architecture choices that make it difficult to deploy AI at scale
Insufficient or flawed training data, which prevents algorithms from making
accurate predictions
The trade-off between algorithm accuracy and explainability, which stems from
inherent differences between white-box and black-box AI
TECH
CHALLENGES

7. Tip: Plan before
you act

8. Determine a business use case for your AI solution
Evaluate the environment where the system will be used
Predict the expected number of users
Figure out what skills your company will need to create a highly accurate AI
solution that makes decisions in a transparent manner
Start your AI project with a Discovery
Phase and Proof of Concept (POC)

9. Lack of AI Engineering

10. Only 53% of enterprise
AI projects move from
prototypes to
production

11. According to Gartner, companies’
limited AI engineering skills
might be to blame for the
lackluster results

12. Tip: Merge AI with
DevOps

13. To create scalable, accurate, reliable,
and high-performing AI solutions, you
should treat AI systems as an
essential part of your IT
infrastructure

14. This requires solid DevOps, MLOps,
and DataOps skills, which may be
acquired through close collaboration
with your AI vendor

15. False Expectations

16. Companies invest in AI to...
(%)

17. AI is capable of all that —
but is not smart enough to
replace human workers
once and for all

18. Tip: Treat AI as an
enhancement, not a
replacement

19. Companies that succeed at Intelligent
Automation appoint qualified
employees to supervise and teach
algorithms

20. The approach will also help
increase AI acceptance levels
among your employees

21. Ethical Challenges

22. AI systems inherit bias (i.e., privileging one group of users over others) from
human engineers
Algorithms are prone to mistakes
Robots constitute a highly productive, always-on workforce that doesn’t need
employee benefits
ETHICAL
CHALLENGES

23. Tip: Help your
employees embrace
the change

24. Educate workers about the importance of data-driven decision making and
optimization opportunities created by AI
Prepare high-quality, diverse data for algorithm training
Continuously monitor and adjust algorithms’ performance
Experiment with AI — even if your pilot project fails
ADOPT AI
CONSCIOUSLY

25. 73% of companies that tweak
AI solutions based on the
lessons learned from failures
eventually see a sizable ROI

26. If you need help building, scaling, or tuning an
AI solution, feel free to contact the ITRex AI
development team, and we’ll connect you with
the right expert!
hello@itrexgroup.com
+1-213-436-7785 itrexgroup.com

27. Sources:
Top 5 AI Implementation Challenges and How to Overcome Them —
ITRex
3 Things AI Can Already Do for Your Company
Companies Are Rushing to Use AI—but Few See a Payoff
Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial
Intelligence
Global AI Survey: AI proves its worth, but few scale impact