As AI continues to reshape how businesses operate, we saw an opportunity to apply it where it mattered most: within the workflows that power the insights we deliver to our clients. We provide social listening services for one of the largest banks in the region, monitoring and analysing their online presence across platforms through Sprinklr. A core part of this engagement involves producing daily alerts: reviewing mentions, correcting sentiment tags where the platform's native classification falls short, and writing up summaries of emerging topics, with a focus on negative sentiment and potential risk areas.

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Mentions and sentiment sit at the heart of social listening. They are the raw material from which every insight, trend, and recommendation is built. Getting them right, both accurately and efficiently, isn't just an operational task. It's the foundation that determines the quality of everything that follows. This made it the natural starting point for our AI integration: a contained, repetitive process with a direct line to the value we deliver.

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We developed an LLM-based system to automate sentiment retagging and structure the daily writeup by grouping relevant mentions and key topics together. On paper, the scope was straightforward. In practice, it surfaced challenges that only become apparent through implementation. Consistency proved to be the most persistent obstacle, as LLMs don't always classify sentiment uniformly, particularly with edge cases where tone is ambiguous. Achieving reliable output required sustained iteration: refining how data was ingested, how the model processed it, and how results were structured for the team. Data quality emerged as a defining factor, with cleaner inputs producing markedly more dependable outputs. Equally important was designing for adoption, ensuring the system's output was intuitive enough for the team to integrate seamlessly into their daily rhythm.

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Central to the process was establishing trust. We implemented a confidence scoring mechanism where the model rated its certainty on each sentiment suggestion from 0 to 100, while the team continued to manually review and correct every output. This created a feedback loop that allowed us to track accuracy empirically and improve the system over time. Once it consistently achieved 95% accuracy, we transitioned to autonomous operation, allowing the system to retag mentions directly back into Sprinklr without manual intervention.

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The impact was immediate. Time spent on the daily workflow was cut in half, with the most significant gains on high-volume days where a surge in mentions previously demanded a proportional increase in manual effort. The system now absorbs that volume, allowing the team to redirect their focus from processing to analysis. The quality of insights today remains consistent with what we were delivering before this integration. The value lies not in a single dramatic improvement, but in the operational foundation now in place, one that positions us to apply AI more ambitiously across deeper, more strategic layers of analysis.

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We see this as the first few pieces of a larger jigsaw puzzle. The daily alerts workflow was a deliberate starting point, and the lessons it surfaced around data integrity, model governance, and workflow design now inform how we approach what comes next. This is just the beginning.

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