Any IT leader with a pulse already understands that AI will have an increasing impact on their business in 2019, as well as in their broader industry. The same goes for the related disciplines that tend to get lumped in under the AI umbrella, such as machine learning and robotic process automation (RPA).
Amy Hodler, AI and graph analytics program manager at Neo4j, says that the best first step for uncovering AI opportunities is to look for areas where things aren’t running optimally today.
“Identify which of your processes have measurable inefficiencies,” Hodler advises.
A mostly universal example would be to look for critical processes or tasks in your business that rely heavily (or entirely) on manual data entry.
AI depends upon the information you feed it. While that could ultimately come from all manner of sources, the early phases of identifying promising AI opportunities will likely be better served by considering areas in which you have robust, reliable, and accessible data.
Data is step two in Hodler’s holy trinity for uncovering good AI opportunities.
“Determine which processes you have the most information on, prioritizing those where you have the most relevant information about the elements and relationships involved,” Hodler advises.
That “relevant information” Hodler mentions is of fundamental importance. Without it, you might be pursuing problems rather than results.
As with other technology hype cycles, don’t make the mistake of force-fitting AI into the business rather than letting business needs and goals dictate sensible applications of AI.
The final step in Hodler’s trinity: Always align your AI and ML initiatives with your overall business strategies.
“It makes no sense to invest in an area that does not have a clear impact on business goals,” Hodler says. “If you ignore this, you will never make it to production and be stuck in proof-of-concept purgatory.”
Hodler points to example business goals or impacts such as new business opportunities, decreasing waste or fraud, or accelerating efficiencies. Again, you get to decide internally which goals and impacts matter most – and you should be sure to have more than one perspective in the room.
Read more at: https://enterprisersproject.com/article/2019/2/ai-opportunity-how-to-identify-5-questions-ask?page=1
Keywords: ai • AI / Machine Learning • Amy Hodler • artificial intelligence • graph algoritms • machine learning • Proof of concept