At this week’s CDW Executive SummIT on “Creating a More Agile and Secure Digital Experience,” hosted in Chicago, IT leaders learned that creating a competitive digital ecosystem requires the careful orchestration of tech, people and processes behind the scenes.
For Paul Zajdel, vice president of data and analytics at CDW, the definition of agility is being “anti-fragile.” Fail fast and adapt quickly, he said, because at no point should innovation compromise efficiency. This means zero downtime during technical updates.
But updating any tech stack is a challenge. “It is never smooth. So, you always need to have a Plan B and Plan C,” said Suresh Sreeramulu, vice president of infrastructure at Michaels. To achieve that multilayered strategy, IT leaders should implement change management tactics to empower team members, streamline policies and create a culture of data literacy and governance.
Here are a few best practices to consider as businesses navigate the data-driven world of AI.
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Foster a Culture of Data Literacy
For an entire organization to make decisions based on insights, IT leaders need to treat data as a strategic asset, integral to business strategy and operational decisions. “You need to implement a modern data ecosystem before you add AI into the mix,” said Zajdel.
In the rush to deploy AI, many organizations skip this critical step. But investing in a flexible data infrastructure can support the storage, processing and analysis of AI applications.
RELATED: Why data literacy and data quality are crucial for your business.
Next, cultivate a standard of excellence for data. “Build a culture through training and literacy programs,” Zajdel said. This establishes a clear, united vision and also incentivizes employees to be more successful.
Asking teams to unlearn old habits and accept change is difficult. This where change management comes in. “As leaders we have the responsibility of setting the condition to cultivate and not punish people when mistakes are made,” said Susan Hardy, organizational change management leader at CDW.
Prioritize Data Security and Ethical AI Practices
Transparency and trust are also part of building a data-driven AI organization. Developing a robust data governance framework that includes policies for data access, privacy, quality and security is important. This framework should align with global data regulatory mandates (such as the General Data Protection Regulation and the California Consumer Privacy Act) to ensure compliance and protect against data breaches and misuse.
According to Kris Wayman, senior manager of sales engineering and managed detection and response at Sophos, organizations need to do their research. Read the fine print; ask vendors if the data collected from AI apps is private or if users can opt out.
“My hope for you is when you implement AI, you are transparent with your data and privacy policies. That will save you a whole heck of a lot of heartache,” he said. Next, “be ready to vet those changes quickly and draconicly. Figure out what you’re going to do if it’s against your terms of use.”
WATCH: CDW leader Susan Hardy as she offers advice on change management.
Invest in Scalable Infrastructure and Agile AI Development Practices
Finally, think of AI development as an iterative, continual process. “Choose a scalable architecture, like hybrid cloud, that can grow overtime and that supports analytics,” said Zajdel. This will also make it easier to test, prototype and refine different AI projects over time.
Experts at the SummIT also encouraged businesses to be selective and targeted in where they deploy AI within their organization. In short, prioritize the areas that will yield the greatest return on investment, such as supply chain, predictive analytics and fraud detection. Zajdel describes this as leveraging “convexity,” where the payoff relative to its benchmark is curved upward.
READ MORE: The AI and data analytics solutions that can transform your business.
Seeking guidance can also accelerate the process. For Michaels, engaging in a strategic tech partnership with CDW helped the retailer identify the right inflection points for incorporating AI, and what the cost of ownership would be over the next three to five years.
“CDW helped us figure out if what we want to accomplish is realistic or out of the ballpark, and helped us evaluate if we are meeting our metrics of success,” said Niraj Gupta, director of infrastructure at Michaels.
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