If you’ve been following this series for a while, then you know how crucial it is for organizations of all digital maturities to build upon the basics with their innovative technologies. To recap, the foundational technologies that drive innovation are cloud, business intelligence and analytics, and application and data integration. Once companies build upon these foundational technologies, they are able to move into more advanced technologies such as artificial intelligence and machine learning, IoT, open source, and blockchain.
In our last post, we discussed how businesses have adopted AI and ML for various use cases in their organizations. In this post, we are going to talk about the technology, business, and financial drivers behind adoption.
Adoption of advanced innovative technology like AI and ML is threefold; support must come from stakeholders in the technology, finance, and business groups of a company. No one team should have more influence than another—all three should work in unison to make adoption a reality. While a single team may want to place emphasis on one of the three drivers, all must have a voice in order to create a solid foundation for implementation.
AI and ML have opened up a whole new world of possibilities for business use cases, with new capabilities and reasons to deploy them. Companies are looking at AI and ML to gain certain features and benefits for their products, such as next-best offer, price optimization, security, and more. More companies are utilizing AI and ML as a sophisticated, value-oriented solution to drive innovation across the enterprise.
According to Forbes, the top 10 use cases for AI and ML are the following:
1. Data Security
2. Personal Security
3. Financial Trading
5. Marketing Personalization
6. Fraud Detection
8. Online Search
9. Natural Language Processing (NLP)
10. Smart Cars
As you can see from the list above, these use cases widely vary by industry, proving that AI and ML isn’t just a technology implemented on the IT front; there is plenty of business- and finance-related use cases companies are implementing. This further reinforces the point that technology, business, and finance teams need to work together to make this implementation happen.
According to survey data from the 2018 TIBCO CXO Innovation Survey, the primary technical drivers behind the adoption of AI and ML are moving from predictive to prescriptive analytic insights and actions, apply AI to data management, preparation, profiling, discovering insights and actions in growing business data, deploying algorithms and models to the edge (IoT), and meeting analytic demands of growing consumer community (data scientist, power users, citizen data science).
Primary business drivers behind AI and ML adoption include deeper business learning and understanding, R&D to create added value in organizations products and services by embedding AI/ML, Faster data-driven business decisions, empowering users with AI/ML powered solutions (AI/ML driven software), and utilizing AI/ML to identify competitive advantage.
Last but not least, the primary financial drivers behind AI and ML implementation are reducing the cost of goods and production, raising product quality, optimizing insights for higher margin/profit, faster time to market with new products and services, and enhancing customer service and satisfaction.
As you can see from the survey results, these three teams are discovering more and more use cases regarding AI and ML. AI and ML can benefit all areas of the business, providing more value in order to optimize operations, improve customer experiences, and create digitally connected innovative products. Through adopting AI and ML for business, technology, and financial use cases, you are one step closer to truly innovating and disrupting in your industry.