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Transformation Discourse: CIOs Pondering Over the Implication of Being AI-Enabled

Various applications exist for AI, yet to ensure a profitable implementation, it's crucial to initially pinpoint the model's intended purpose and the intended user base.

Professional associates convening in contemporary meeting space
Professional associates convening in contemporary meeting space

Transformation Discourse: CIOs Pondering Over the Implication of Being AI-Enabled

Elise London serves as the CTO at Lakeside Software, where she handles the development and delivery of its digital employee experience platform.

Many Chief Information Officers (CIOs) find themselves in a tricky situation. According to Lenovo's yearly survey of CIOs, approximately half of them consider AI/ML as an urgent matter that needs immediate attention, equaling the significance of cybersecurity. However, the survey revealed that a substantial portion of their organizations lack the necessary readiness for AI. So, what should a CIO do in this predicament? Given their role in bridging business innovation and technology knowledge, CIOs need to back, teach, and negotiate on AI-related matters.

To ensure your business is AI-ready, address these three fundamental questions:

1. What are the primary use cases?

AI presents numerous applications, but building a successful deployment primarily requires identifying the model's intended usage and users. Diversifying internal tools to improve efficiency or integrating with your product for external users calls for distinct strategies.

Engage various team leaders in discussing how AI can improve their performance. Encourage each leader to present a solid business case outlining their intended use of AI along with the reasons behind it:

  • What current processes are used and how will AI improvement affect them?
  • What level of precision is required for the AI model? What are the consequences of an incorrect decision based on the model's output?
  • How will they measure success through improvements like efficiency or productivity?
  • Is the investment in the new tool justified by the potential increases in efficiency and productivity?

For internal use cases, you might also encounter leaders with AI enhancement ideas for the product offerings. The fear of missing out (FOMO) is strong; however, integrating AI blindly without assessing its alignment with your business's core value is merely money and time wasted.

To maximize success, ensure that any AI features or offerings directly connect to your business's value proposition by solving a specific problem for your customers. For example, in developing an AI model tailored to IT, our focus was on digital employee experience. We aim to augment employee satisfaction by providing devices requiring minimal IT support. By collecting data from endpoints, we created a machine-learning model capable of predicting IT issues before they disrupt operations. Using predictive analytics, the IT team can analyze data insights, prioritize tasks, and take preventive measures to avoid small issues escalating into widespread IT outages.

2. Are you adopting a build or buy approach?

Once you have determined that AI can contribute to your company's core offerings, the next decision to make is whether to create your AI model or acquire existing software. The key factor rests on whether you have the necessary data and strategy to support your AI development.

Most machine learning models need not just vast volumes of data but well-structured and labeled data as well. If your data isn’t sufficient or appropriately formatted, you are essentially putting the cart before the horse.

My company was fortunate to have a founder, Mike Schumacher, who in his latest article named "Will every company become an AI company? Why my answer is no," discusses the importance of historical data for AI success. He explains that companies with an extensive collection and organization of data over the years have a significant advantage, having already accumulated an immense amount of information. The use of quality data is crucial for ensuring the model's accuracy by providing it with a wide range of contextual information to feed the AI model like a gourmet meal.

However, it is essential to note that a significant portion of IT leaders (31%) cited "limited availability of quality data" as their primary challenge while implementing AI. If you belong to this group, it might be advisable to purchase software instead of constructing your own AI model. There are countless companies offering AI solutions tailored to specific problems ideal for product integration.

3. How will you implement AI?

Now, you've defined your AI users and their intended usage, determined your business value from AI, and made a decision between building and buying. But that's not enough to stamp your business as AI-ready – successful deployment is what guarantees the tool's success. The final step to achieving AI-readiness is change, release, and deployment management.

As the CTO of a rapidly growing software company, I have witnessed firsthand how the final implementation step can make or break the integration of new technologies. Before rolling out AI at full-scale, data should be collected to analyze the effects of an IT change positively or negatively. Analyze device and system health and performance before starting AI integration or building. Finally, plan a phased implementation, as outlined in the e-book "7 Ways to Build IT Resilience in 2025." A best practice is to initiate the rollout with a low-risk user group, allowing for easy troubleshooting before escalating to high-risk groups.

In my personal encounters, utilizing a practical application that resolves an existing dilemma, an excellent and reliable dataset, and data-driven system administration, you can transform your organization into a true "AI-capable" entity. The objective is to make AI unnoticeable; interestingly, AI becomes apparent when it's not present at all.

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Elise London, being the CTO at Lakeside Software, could share her insights on the challenges and opportunities of integrating AI into their digital employee experience platform, given her role in driving its development and delivery.

Given her expertise in implementing AI solutions at Lakeside Software, Elise London could provide valuable advice on how to approach AI adoption in organizations that are grappling with limited availability of quality data, a common challenge cited by IT leaders.

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