AI industry analysts state that AI projects and their real-world success metric is less than 8%. This is a staggeringly low number given the billions of investment and big bets being made.
Unlike other traditional software implementations, AI has always been vague and it has been accepted that machines are correct in making their decisions. However, what business leaders are concerned about is real world applicability and business relevance.
Here are some of the AI showstoppers we see out in the wild:
- Scientific ideation and experimentation do not guarantee success
- Scientists are rarely business focused; business leaders are rarely science focused and that disconnect is problematic across industries
- AI projects often go live without understanding business relevancy
- Conversely, many projects sit idle without going live due to risk averse leadership
- Enterprises should continue to invest in tools, platforms and remain vendor neutral. AI advancements are so fast that diversification is key
- Existing AI regulation is only going to increase. GDPR is only the beginning…..
Why Explainability is critical to AI advancement:
- The process of engineering, productization and scaling are critical facets of commercial AI exploitation
- Building an XAI product that business leaders can use & understand bridges the business gap
- Explainability highlights business relevancy and identifies AI failures
- Explaining AI outcomes prior to any go live breeds confidence
- Any XAI software needs to seamlessly work with any existing assets or future investments
- XAI can explain automated individual decisions and can protect enterprise assets
Should you be experiencing AI hurdles and underwhelming ROI then implementing XAI offers significant upside.
