Responsible applied AI: start with the problem, not the tool

Conversations about artificial intelligence tend to start with the tool. Initiatives that create value tend to start elsewhere: a concrete business problem, with a clear owner and an agreed way to measure whether it improved.

Start with the problem

Where does your team lose time? Which decisions are made with incomplete information? Which repetitive tasks take attention away from customers or patients? The answers to these questions are a better starting point than any technology catalog.

Govern from the start

Responsible AI isn't a brake: it's what allows you to scale with confidence. The NIST AI Risk Management Framework (AI RMF 1.0), released in January 2023, is voluntary guidance that organizes this work into four functions: govern, map, measure and manage^[1]. Its practical value is a common language for technology, legal, compliance and business teams.

The same framework describes the characteristics of trustworthy AI: valid and reliable; safe; secure and resilient; accountable and transparent; explainable and interpretable; privacy-enhanced; and fair, with harmful bias managed^[2].

Take care of the data

In regulated sectors such as banking, insurance, healthcare or government, questions about where data resides and who can see it must be answered before any pilot. AI should operate with the same level of control as every other critical system.

Measure in business terms

Time recovered, errors avoided, revenue generated, customer experience. If an initiative can't be expressed in these terms, it's probably not ready to scale.

Three questions before you start

1. What concrete problem are we solving, and who owns it?
2. What data do we need, and under which controls?
3. How will we know, within a defined timeframe, whether it worked?

At Shervacom Inc., we integrate AI where it saves time or drives revenue, with security and control of your data. If you'd like to explore where to start, let's talk.

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