Artificial Intelligence is everywhere, but it’s critical for companies to take the time to research, analyze, and develop a strategic plan before deploying AI initiatives.
Well it seems this artificial intelligence thing has caught on after all, and looks like it’s here to stay. AI is already used across most industries and shows no signs of slowing down. It’s a rapidly growing technology that will impact nearly every product and business process over the next decade.
Even late adopters are ready to embrace it, because it’s clear that AI, along with its machine learning (ML) and deep learning (DL), is reshaping the way we do business. AI can help organizations reach their targeted business outcomes by:
◈ Increasing efficiency of internal applications
◈ Improving customer experience
◈ Increasing lead generation and customer acquisition
◈ Automating business and HR operations
◈ Improving ROI
Of course, with all the cool and exciting things AI can do, it’s temping for businesses to jump in and get started right away. That eagerness can be a good thing, but deploying AI without preparation can lead to a wide range of problems. As with most new business applications, it’s critical to analyze and think through your IT Transformation strategy first. Carefully research when and where it makes sense to use AI, so that you can be most effective and cost efficient in the long run.
IT leaders need to consider questions like:
◈ What projects make the most sense for our business and goals?
◈ What applications will give us the best ROI (both in the short and long-term)?
◈ What kind of shape is our current data in, and do we have the right data management technology?
◈ Do we have the proper infrastructure hardware to scale?
◈ Do we have the skills to implement AI initiatives? If not, should we train our current staff or hire new? Or both?
◈ How will we communicate across all our lines of business?
The problem is that too many companies, excited about the benefits of AI, cannonball in without a strategic plan. The want to make a big splash, but forget to check that there is water in the pool first.
Don’t be them.
Just as you have to crawl before you can run, you have to start small with AI. It’s best to dip your toes in the water and Once you have some initial success and a solid strategy in place, you can move on to more extensive applications.
Whether your organization has already embraced AI or plans to adopt it soon, consider the following before you go any further:
Evaluate Current Challenges and Opportunities
Take the time to research and evaluate your current opportunities. Chances are, there are multiple areas of your organization that can benefit from AI and ML. The different lines of business might be clamoring to get started, but it’s critical to do the research up front to ensure that your IT strategy aligns with your overall company objectives.
Prioritize Your List
After you’ve gathered the data, you’ll need to prioritize projects based on the scope, potential risks and ROI. This is often easier said than done, because different departments will have different priorities. It’s up to you as an IT leader to manage this and make recommendations based on the company’s best interests.
Examine Your Organization’s Current Data
What shape is your company’s data in? Again, be realistic in your current state (not in where you hope to be), so that you don’t get in over your head. You can always start small, perhaps using your Big Data analytics to deploy one or two ML applications. Measure the ROI of those initial projects, using the data to develop recommendations for future applications.
Focus on Staffing
AI initiatives require a different skill set. Automating operations can free up your staff to do other things, but you may find that they don’t have the right skills. You’ll need engineers and data scientists to manage the applications and analyze the data. Will you retrain current staff or recruit new employees? Or a mix of both? Now is the time to think about and budget for these staffing issues.
To succeed with AI initiatives, it’s critical that organizations have a comprehensive and prioritized execution strategy in place. Doing so will allow you to deploy the right technology and IT infrastructure for each specified use-case.
For additional information on how IT leaders can ensure successful ML and DL projects check out The Artificial Intelligence Starter Guide for IT Leaders. This white paper by Moor Insights & Strategy also covers Dell EMC’s hardware for classical machine learning, hardware for deep learning, pre-configured “Ready Bundles,” enterprise cloud services, and our consulting and training services.
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