Machine learning models return biased results when the datasets used to train them contain bias. Instances of social bias, skewed model results, and outputs that don't represent the full scope of a business problem for a specific domain are some of the caveats when employing this technology.
Jelani Harper //
16 Jul 2025
AI stands beyond experimental status—it is a founding power for corporate productivity delivery. From automating repetitive workflows to enhancing complex decision making, AI transforms how work gets done.
Srinivas Sandiri //
06 Aug 2025
The integration of AI into customer service and support is by no means a futuristic vision—it is a present-day reality. AI is reshaping how organizations engage with their customers.
Ross Smith, Mayte Cubino, and Emily McKeon //
27 Aug 2025
Synthetic data applications are at the intersection of some of the most meaningful developments in enterprise AI. Synthetic data techniques represent some of the earliest manifestations of generative AI and predate the widespread adoption of language models. In fact, employing synthetic data is one of the foremost methods of building—and fine-tuning—language models and foundation models in general.
Jelani Harper //
22 Apr 2025