How Data and AI Relates to Business Strategy
A 2018 PwC report predicted that global GDP would rise 14% as a result of AI, and each industry would gain at least 10% in GDP by 2030. An Accenture report was even more optimistic, believing that AI could double the annual GDP growth rates by 2035 and increase productivity by up to 40%.
Unfortunately, these predictions won’t come true without businesses knowing their must-win battles or where they need to succeed in the future. Data and AI can only help make more informed decisions, obtain information faster, automate processes and enable faster delivery. But they will not solve issues in business models, products and services, and construct or replace the lack of business vision and ideas.
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Aligning AI and Data Priorities With Business Goals
How business leaders prioritize their enterprise AI projects depends on their business goals. Leaders should consider a business case for each business area when considering AI projects, based on the company’s data and AI efforts and the relative importance of the case.
For example, leveraging data and implementing AI in sales and marketing will likely lead to quicker results than deploying them in product development. But the results from a product development project may be slow, but can be very impactful.
Yet it may make sense for enterprises to start their AI projects with optimization cases as calculating cost savings is often easier than assessing business opportunities. Also, early wins will help get buy-in from the whole organization and provide a better understanding of how AI and data projects work while demonstrating their benefits.
Business executives need to calculate a baseline internal rate of return (IRR) on planned investments and compare that to the IRR of the investments in data and AI as they predict will happen after implementation.
Harnessing Internal and External Data
With the potential for improvement in every business area, it’s not always obvious where’s the best place to initiate AI projects.
Often, businesses decide to start by optimizing their current business processes, such as using internal data sources to augment business models, products, services, internal processes, and functions like production, marketing, supply chain, HR. Leveraging external data sources, along with internal, will yield even more accurate AI models and results.
Once business leaders leverage data and AI to optimize use cases for their current business, they should consider new data-driven business opportunities.
For instance, businesses can use their data in the following ways:
Internal Data
- Business optimization: Combine internal data across verticals to optimize existing business processes and enable new services and products.
- Data as a business: Provide third parties access to data assets and insights, such as in an API ecosystem.
External Data
- Business optimization: Use external data sets to improve internal data assets and optimize business processes.
- Form data partnerships: Collaborate with external partners to exchange data to enable new products and services that wouldn't be possible alone.
Finnish mutual pension company, Elo, for example, had an airtight actuary unit, but data was not used in optimizing the customer experience. The company started by defining its target state and road map.
It then worked on implementation, which included cloud-based infrastructure, an analytics environment, data integrations and modeling from various sources, the development of dashboarding tools and analytical scores for various customer-interfacing actions. Resultantly, Elo was able to provide more personalized services and now has a dedicated data team focusing on customer experience.
AI projects work best when they leverage data from across the company, silos are broken down, everyone in the organization is on board with the projects, and the process of AI automation encapsulates business goals.
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