At this week's CDW Executive SummitIT on “Creating More Agile and Secure Digital Experiences” in Chicago, IT leaders learned that building a competitive digital ecosystem requires behind-the-scenes technology, people, and processes. I learned that I have to calibrate carefully.
For Paul Zajdel, vice president of data and analytics at CDW, the definition of agility is “anti-fragility.” He said innovation must not come at the expense of efficiency, so we need to fail fast and adapt quickly. This means zero downtime during technology updates.
But updating the technology stack is difficult. “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 multi-layered strategy, IT leaders must implement change management tactics to empower team members, streamline policies, and build a culture of data literacy and governance.
Here are some best practices for companies to consider as they navigate the data-driven world of AI.
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Foster a culture of data literacy
To empower the entire organization to make insight-based decisions, IT leaders must treat data as a strategic asset essential to business strategy and operational decisions. “Before you add AI, you need to implement a modern data ecosystem,” he says.
In the rush to implement AI, many organizations skip this critical step. However, investing in flexible data infrastructure can support the storage, processing, and analysis of AI applications.
Related: Why data literacy and data quality are important to your business.
Next, establish good standards for your data. “Build a culture through training and literacy programs,” Seidel said. This establishes a clear, unified vision and motivates employees to be more successful.
Asking your team to let go of old habits and embrace change is difficult. This is where change management comes into play. “As leaders, we have a responsibility to set the conditions to develop rather than punish our employees 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. It is important to develop a robust data governance framework that includes policies for data access, privacy, quality, and security. This framework must align with global data regulatory obligations (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 Chris Wayman, senior manager of sales engineering and managed detection and response at Sophos, organizations need to do their research. Learn more; ask your vendor if the data collected from their AI apps is private or if users can opt out.
“What I want from you is to be transparent about your data and privacy policies when implementing AI. That will save you a lot of heartache,” he said. . Then, “Be prepared to subject these changes to rapid and rigorous scrutiny. Think about what you will do if they violate our Terms of Use.”
Invest in scalable infrastructure and agile AI development practices
Finally, think of AI development as an iterative and continuous process. “Choose a scalable architecture that supports analytics that can grow over time, like a hybrid cloud,” he says. This makes it easier to test, prototype, and refine different AI projects over time.
SummIT experts also encouraged companies to be selective and target where they deploy AI within their organizations. That means supply. He prioritizes areas with the greatest return on investment, such as chain, predictive analytics, and fraud detection. Zajder describes this as exploiting “convexity,” where returns relative to the benchmark curve upward.
read more: AI and data analytics solutions that can transform your business.
Asking for guidance can also speed up the process. For Michaels, entering into a strategic technology partnership with CDW will help retailers identify the right tipping point for incorporating his AI and what the cost of ownership will be over the next three to five years. I did.
“CDW helped us determine whether what we wanted to accomplish was realistic or unexpected, and also helped us assess whether we met our criteria for success.”Michaels Infrastructure said Niraj Gupta, Director of Structures.
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