Natural Habitat.
AI Consulting and Selecting the Right Solution
Understanding the need makes it possible to separate organizational problems from technical ones and decide whether a generative model is necessary at all.
The essential questions are concrete: what data enters the process, what output is expected, who verifies its accuracy and what happens if the result is wrong.
Assess the need before selecting technology
For repetitive tasks, establish case volumes, exception rates and data sources. For one-off analyses, speed, verifiability and clarity may be more important. The same technology can be justified in one scenario and unnecessary in another.
The scope of artificial intelligence consulting can be defined by comparing the current workflow with the expected outcome. This reveals where human expertise is needed and where software can safely take over.
Acceptance criteria instead of impressions
For data extraction, a “good answer” is not a sufficient requirement. Specify mandatory fields, permitted omissions, uncertainty indicators and conditions for rejecting an output. For text generation, criteria may involve fidelity to documents and inclusion of required elements.
An AI use-case audit is more meaningful when it examines actual errors, processing times and restrictions on information access. Fast generation should not be confused with the time needed to obtain an approved result.
Risk as part of the decision
NIST AI RMF 1.0 identifies four functions: GOVERN, MAP, MEASURE and MANAGE. In practice, these establish accountability, context of use, evaluation methods and risk responses. They are not a rigid sequence for every project.
When designing AI solutions, evaluate data sensitivity, the reversibility of actions and the consequences of an incorrect output separately. A workflow that merely suggests a reply carries different risks from one that automatically updates a customer database.
Choosing the next step
After testing a representative sample, the next step may be an off-the-shelf product, a simple rule-based script, an API integration or a data preparation project. The largest solution is not necessarily the right one. Quality, cost and long-term maintenance should determine the choice.
Cost-effectiveness calculations need to include verification, exceptions, errors and maintenance following changes to the model or data source. Otherwise, the apparent savings may be misleading.
Sources
- Elham Tabassi, “Artificial Intelligence Risk Management Framework (AI RMF 1.0)”, NIST AI 100-1, 26 January 2023, chapter 5.