What are the hurdles faced by organizations in reaping the benefits from AI/Machine Learning? Most data scientists and AI researchers will agree with below list:
- Technology and Implementation complexity
- Data availability and quality
- Lack of Skills
- Lack of clarity of return on investments
In my opinion, there is a greater hurdle that needs to be crossed even before we deal with data, skill, and technology challenges.
How to identify the right opportunities to be addressed by AI/Machine Learning?
If an organization does not have a solid use case, it’ll struggle to create a solution that delivers business value.
Problem: Business and Technology silos
AI and Data Science teams do not understand the business focus and intricacies of operations. These teams may comprise of best of the brains and skills, but the success of initiatives addressed by these teams is limited by the problem definition provided.
On the other hand, business teams lack understanding of:
– Addressable use cases
– The complexity involved in AI initiatives
– Interdependencies between data format, data quality, and outcome
Solution: Multi-disciplinary team for AI initiatives
It is imperative for a team working on AI initiatives to include data scientists, AI engineers, as well as business SMEs from functions that will adopt AI use cases. Business SMEs working on AI initiatives should be educated about AI technologies and possible business benefits. Additionally, AI engineers and data scientists should be made aware of business processes and information flows.
Through a collaborative approach and effective knowledge sharing, these teams can identify and address the right opportunity and derive value for the organization.
Amresh is a technology leader with 17+ years of experience having expertise in designing and delivering IT platforms and solutions in Healthcare, Insurance and Financial industries. Amresh is lead analyst with DecadeForward and focuses his research on applications of AI across healthcare and insurance industries.