AI & Automation

It’s nearly impossible to miss the hype around generative Artificial Intelligence (AI) and automation, but it can seem like technology is advancing too quickly to keep up with. If you find yourself wanting fewer complicated techy words and more practical advice, you’ve come to the right place.

The FlexPoint team is passionate about deploying technology in the service of people. So, while we are indeed keeping up with the latest generative AI models, we’re far more concerned with how these advances in technology can serve you and your team. We’ve been through many hype cycles before, and we know how to understand your business needs, match those to potential technology enablers, and chart an achievable path forward.

How do we thread the AI needle between hype and meh? We hold these five principles in mind with every AI-related engagement.

We’re aiming for the Goldilocks of AI and automation initiatives.

1

Not so big that we get bogged down in what-ifs. Not so small that value is difficult to see. We’re aiming for practical, achievable, and exciting — with the expectation that we’re on an upward trajectory.

To make this tangible, we’ve developed tailored recommendations based on your team’s unique combination of:

  • Culture & Risk Tolerance

  • AI Experience & Comfort Level

  • Readiness to Act

  • AI Roadmap Stage

  • Technical Foundation

  • Budget & Investment Evaluation

Take the assessment for coaching on your best next steps in AI.

We want to address challenges and opportunities already in your sights.

2

It’s enticing to dream up the best first AI initiative for a team or organization. But we can miss out on value by veering too far outside of your current objectives when identifying a starter AI initiative.

So, let’s look at your goals, objectives, and challenges in the next quarter or two: where can AI and automation fit in to drive success?

See these AI and automation use cases to spark your thinking.

This is a people puzzle as much as it is a technology puzzle.

3

The most successful AI roll-outs take place within a culture of experimentation and psychological safety.

Knowing that AI is new to the vast majority of us, we want to set up AI experiments clear success criteria around outcomes, processes, and/or learning objectives. Igniting curiosity about which challenges we can bring to the AI punchlist is one of the results we’re seeking! And, ultimately, we’re in search of strategic adaptability as companies and individuals navigate the age of AI.

For more, listen to our podcast with Tony Peleska, CIO of Kraus-Anderson, about the People Side of the AI Revolution.

This is about more than Generative AI and large language models.

4

Yes, LLMs are awesome. We use and love them. Additionally, machine learning and AI-powered automation have been driving real results for over fifteen years, so we look to the entire landscape of AI when considering how to meet business needs.

For instance, OCR (optical character recognition) and purposeful machine learning can dramatically increase the efficiency of processing invoices. We can go from manually entering each data element to simply verifying that the invoice was processed and coded correctly, with some time spent setting up the vendor’s invoice template and directing/checking the automatic processing three to five times. Now that’s real progress!

Expect the FlexPoint team to suggest options across the breadth of AI offerings, always aiming to meet the challenge or opportunity in front of us with the best set of tools and techniques we can.

Data is getting more important, not less.

5

We hear some people say, “just throw all the data in and figure it out.” While AI can help us mitigate some data quality issues (finding patterns where they weren’t obvious to us, or speeding up profiling efforts, for instance), we still want clean, organized data to feed into our AI projects as much as possible. This will help us draw the right conclusions and build the team’s confidence in continued reliability.

And of course, we want to be very careful about security and privacy. This is where the guardrails are needed around our experimentation, so can drive meaningful progress in mastering AI within our team or organization while managing risks and costs well.

In fact, AI initiatives can magnify existing challenges. In “AI Adoption Demands a New Approach to Data Security and Governance” (April 2024), the Forrester team shared that the risk of ungoverned enterprise data challenges “are magnified as organizations accelerate AI adoption.” Because of this, “proactive data discovery, access controls, and lifecycle management” are critical to address before adopting AI technologies.

For more, listen to our podcast with Justin Nguyen, data science leader at The Home Depot. He described the importance of and some steps to accomplish Maintaining a Strong Data Foundation to Drive Business Results.

Interested in learning more?