Enterprises yearn for the competitive advantage that ML and AI can offer their business, but often prioritize technology strategically over people to unlock the value of their data. The hype about AI and ML, and the ease of access to it through cloud tooling, belies the complexity of effectively leveraging these capabilities. Why are AI and ML critical capabilities to your business and how will pushing their introduction or expanding their use impact your data strategy?
Unfortunately, many leaders also misinterpret the desire for AI/ML capabilities as a proxy for “we need a better data strategy” and underestimate the effort required to take on this change. It’s imperative that leaders define their data ambitions clearly and align them with the business outcomes sought. This is because the key to effectively unlocking the value of your data starts with aligning your people to this business outcome-driven data strategy. Don’t get me wrong, technology is essential for a modern data strategy, but too often organizations over-rotate on technology and forget about the critical strategic and human elements.
Establishing a data strategy that successfully supports AI/ML adoption requires 3 foundational elements:
1) Data strategy aligned to business goals
Define the “Why” and the “What” first. Start by defining what concrete business goals can be achieved through their use, in what timeframe, at what cost, and at the expense of what other organizational priorities. Is your organization looking to drive down product defects, increase client satisfaction, or innovate new products? Understanding the product or service drivers of your new data strategy will highlight how your existing data strategy will need to change.
Continue reading: https://www.cio.com/article/303947/artificial-intelligence-needs-people-intelligence.html
Unfortunately, many leaders also misinterpret the desire for AI/ML capabilities as a proxy for “we need a better data strategy” and underestimate the effort required to take on this change. It’s imperative that leaders define their data ambitions clearly and align them with the business outcomes sought. This is because the key to effectively unlocking the value of your data starts with aligning your people to this business outcome-driven data strategy. Don’t get me wrong, technology is essential for a modern data strategy, but too often organizations over-rotate on technology and forget about the critical strategic and human elements.
Establishing a data strategy that successfully supports AI/ML adoption requires 3 foundational elements:
1) Data strategy aligned to business goals
Define the “Why” and the “What” first. Start by defining what concrete business goals can be achieved through their use, in what timeframe, at what cost, and at the expense of what other organizational priorities. Is your organization looking to drive down product defects, increase client satisfaction, or innovate new products? Understanding the product or service drivers of your new data strategy will highlight how your existing data strategy will need to change.
Continue reading: https://www.cio.com/article/303947/artificial-intelligence-needs-people-intelligence.html
