Text Analytics Is Leading AI Adoption - But Are You Doing It Right?
According to new data from Statistics Canada, text analytics has officially become the most common use case for artificial intelligence in Canadian businesses. Among companies that reported using AI between April 2024 and May 2025, over one-third (35.7%) said they were applying it to analyse text data. From customer reviews and survey responses to internal communications.
It’s a milestone moment. But it also raises a critical question:
What do we actually mean when we say “text analytics”?
And are we doing it right?
Surface-level tools are everywhere. Insight isn’t.
For many businesses, “text analytics” still means keyword counting, sentiment labelling, or some form of automated classification. These approaches can be useful for dashboard metrics or initial scanning, but they tend to flatten nuance. They don’t reveal how ideas are linked, how concepts evolve, or where contradictions lie.
Put differently, they tell you what words are being used, but not why they matter.
That’s a problem. Because if AI adoption is meant to help businesses “respond to changing conditions” (as the report puts it), then surface-level insights just won’t cut it. You can’t innovate with shallow understanding.
It’s no surprise then that so many AI tools function as black boxes. Fast, opaque, and ultimately unaccountable.
If you don’t know how a result was generated, can you really act on it with confidence?
Investment ≠ insight
According to the same Canadian survey, over 28% of businesses now consider AI investment “somewhat” or “very important” to their operations. But the jump in usage doesn’t always reflect a deeper understanding of how to make AI work for them.
Throwing money at automation won’t give you meaning.
Buying AI isn’t the same as building insight.
If your team is spending time collecting valuable text data, it deserves more than shallow dashboards and one-click summaries. It deserves tools that help you think.
What if your AI tool could explain itself?
With Leximancer, it can.
Rather than outsourcing meaning to a black box, Leximancer invites you into the process to see how ideas connect, explore changes over time, and make evidence-based decisions that reflect the full richness of your data.
Want to see what your data is really saying?
Whether you’ve got internal interviews, survey feedback, or public reviews, Leximancer lets you run advanced text analytics without needing to code or define categories in advance. Just upload your data and explore the themes your audience is actually talking about.
It’s one of the few AI tools that shows you the forest and the trees.
Curious?
Try it with your own transcripts or open-text data - and see what emerges.
