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Top 4 AI Challenges and How to Overcome Them
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.
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Top 4 AI Challenges and How to Overcome Them
- 1. Top 4 AI Challenges andHow to Overcome Them
- 2. Corporate AI spendingis on the rise ($, in billion)
- 3. Yet, just 11%of all AI deployments eventually yield a positive ROI
- 4. Discover what challenges companiesface when creating and implementing AI solutions — and what YOUR company can do to avoid these scenarios
- 5.
- 6. Poor architecture choicesthat 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.
- 8. Determine a businessuse 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 AIEngineering
- 10. Only 53% ofenterprise 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 AIwith 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 solidDevOps, MLOps, and DataOps skills, which may be acquired through close collaboration with your AI vendor
- 15.
- 16. Companies invest inAI to... (%)
- 17. AI is capableof all that — but is not smart enough to replace human workers once and for all
- 18. Tip: Treat AIas an enhancement, not a replacement
- 19. Companies that succeedat Intelligent Automation appoint qualified employees to supervise and teach algorithms
- 20. The approach willalso help increase AI acceptance levels among your employees
- 21.
- 22. AI systems inheritbias (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 employeesembrace the change
- 24. Educate workers aboutthe 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 companiesthat tweak AI solutions based on the lessons learned from failures eventually see a sizable ROI
- 26. If you needhelp 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 AIImplementation 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