See you this week CDW Executive Summit on “Create a more agile and secure digital experience,” held in Chicago, IT leaders learned that creating a competitive digital ecosystem requires careful orchestration of technology, people, and behind-the-scenes processes.
For Paul Zajdel, vice president of data and analytics at CDW, the definition of agility is to be “anti-fragile”. Fail fast and adapt quickly, he said, because at no time should innovation compromise efficiency. This means no downtime during technical updates.
But updating any tech stack is a challenge. “It’s never smooth. So, you always have to have a plan B and a plan C,” said Suresh Sreeramulu, vice president of infrastructure at Michaels. To achieve this strategy on several levels, IT managers should implement change management tactics to empower team members, streamline policies, and create a culture of data literacy and governance.
Here are some best practices to consider as businesses navigate the data-driven world of AI.
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Foster a culture of data mastery
For an entire organization to make information-driven decisions, IT leaders must treat data as a strategic asset, an integral part of business strategy and operational decisions. “We must implement a modern data ecosystem before adding AI to the mix,” Zajdel said.
In the rush to deploy AI, many organizations skip this critical step. But investing in flexible data infrastructure can support the storage, processing and analysis of AI applications.
RELATED: Why data literacy and data quality are crucial to your business.
Following, cultivate a standard of excellence for data. “Building a culture through training and literacy programs,” Zajdel said. This establishes a clear and united vision and also inspires employees to succeed.
Asking teams to unlearn old habits and embrace change is difficult. That’s where change management comes in. “As leaders, we have a responsibility to create the conditions to cultivate and not punish people when mistakes are made,” said Susan Hardy, head of organizational change management at CDW.
Prioritize data security and ethical AI practices
Transparency and trust are also part of building a data-driven AI organization. It is important to develop a robust data governance framework including data access, privacy, quality and security policies. This framework should align with global data regulatory mandates (such as the General Data Protection Regulation and the California Consumer Privacy Law) to ensure compliance and protect against data breaches and misuse.
According to Kris Wayman, senior director of business engineering and managed detection and response at Sophos, organizations need to do their research. Read the fine print; ask providers if data collected from AI applications is private or if users can opt out.
“I hope that when you implement AI, you will be transparent with your data and privacy policies. It will save you a lot of heartache,” he said. Then, “be prepared to examine these changes quickly and drastically.” Determine what you are going to do if it goes against your terms of service.
WATCH: Susan Hardy, CDW leader, offers advice on managing change.
Invest in scalable infrastructure and agile AI development practices
Finally, think of AI development as an iterative, ongoing process. “Choose a scalable architecture, like hybrid cloud, that can grow over time and supports analytics,” Zajdel said. 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 about where they deploy AI within their organization. In short, prioritize areas that will yield the greatest ROI, such as supply chain, predictive analytics And fraud detection. Zajdel describes this as a way to take advantage of “convexity,” where the gain relative to its benchmark is curved upward.
LEARN MORE: AI and data analytics solutions that can transform your business.
Asking for advice can also speed up the process. For Michaels, engaging in a strategic technology partnership with CDW helped the retailer identify the right inflection points for AI integration and what that would mean. cost of ownership that would be over the next three to five years.
“CDW helped us determine whether what we want to accomplish is realistic or out of the ordinary, and helped us evaluate whether we are meeting our success criteria,” said Niraj Gupta, director of infrastructure at Michaels.
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