Whether you’re a program manager at a government agency or an executive in the private sector, implementing AI is generally a daunting task.

At water coolers across the country and around the world, important conversations are happening between analysts, program managers, CIOs, and others:

We have tons of data – but it’s difficult and time consuming to understand…

I spend most of my time digging through information that doesn’t help make decisions…

I feel like I’ll miss something if I don’t keep digging…

Big doesn’t mean better.

Many agencies believe that enterprise-level implementation is the best way to start using AI. This time-consuming and expensive approach can build more barriers than it removes.

With large agencies, alignment of dozens (or even hundreds) of stakeholders is rare and causes launch failures of even the most straightforward initiatives (Office365 transitions, for instance). When the same method is used to adopt more advanced technologies (AI, cloud computing, etc.), it is often met with delays, frustration, and constant changes in scope, budget, and timelines.

Artificial Intelligence has huge upsides if the downsides can be mitigated. The first step into solving problems with artificial intelligence doesn’t need to be difficult – it needs to be targeted. Using AI to solve smaller problems requires fewer stakeholders to approve, costs far less than enterprise solutions, and works within a smaller scope.

Choosing the right problem.

Think about the biggest hurdles your team must overcome on a day to day basis. They can be sources of frustration, extremely time-consuming tasks, problems that are never really solved – just worked-around. Ask yourself and your team these questions:

Do any of the hurdles have large amounts of data associated with them?

Are they hurdles based on that fact that there is simply too much data to ever analyze in a comprehensive way?

If the answer to either of these questions is yes, then you likely have a good use case for AI at the program level.

Hold AI accountable.

Once the use case has been identified, make a list of desired outcomes and impacts of using AI.

Use non-AI benchmarks as a basis for comparison. For instance, an analyst currently takes 50 hours to collect, extract, and analyze 25 documents. With artificial intelligence integration, however, the goal may be to process twice as many documents in the same time – or the same amount of documents in half the time.

Choose a goal that will provide meaningful improvement in workflow, process, quality, or efficiency. An added bonus of program-level AI rollouts is overlap with other projects, enabling wider agency AI implementation and cost-cutting.

Find a Partner, not a Vendor.

It’s important to remember that implementing an artificial intelligence solution is much more than installing a piece of software and pointing it at a database.

Many vendors offer “AI” tools but most are enhanced analytics on the data you provide. It’s the next step that brings so much more added value – discovering and using the data you didn’t know about.

Successful AI implementation starts with a company that listens, asks questions, and really understands the problems and inefficiencies you currently have with data – and develops a tailored solution to meet your goals. Working with a company that provides a scalable solution built for your purpose maximizes ROI and ensures the highest quality results.

Interested in learning about using data to your advantage? Curious how artificial intelligence can impact your program?

ALEX Velocity has a wide array of use cases for your program.

Visit alexinc.com/velocity for more information.

Featured Photo by Calvin Ma on Unsplash