AI automation is almost a motto for this decade — every business opts to optimise their workflow, cutting costs in the process. On paper, it seems quite straightforward: use AI-powered tools to optimise employees’ work and cut unnecessary expenses. The reality, however, isn’t as shiny.
The road to AI automation could be bumpy if not done correctly. To understand what could go wrong and what you can do about it, we should ask ourselves: why do businesses opt for automation in the first place?
Learn more about what can be done with AI automation here: https://www.altamira.ai/artificial-intelligence/ai-process-automation/
Why choose AI automation?
Since most companies have to deal with a lot of data, AI becomes a perfect technology to adopt. That’s because artificial intelligence can analyse, spot patterns, make decisions, and even create predictions based on given information.
Done by humans, these tasks will be done in days, if not weeks. AI, on the other hand, will finish them in minutes.
It’s not surprising that companies opt for AI automation. However, it’s not as straightforward as just booting up the model and letting it do its work. The integration process is challenging, and some issues are really easy to overlook if you’re in a hurry.
Common pitfalls in AI automation and how to avoid them
AI automation has a lot of implications for business. It affects both individual employees’ workflows and the decision-making process on the company scale. It also affects the performance of already implemented systems and how they interact with each other.
We have gathered the most common issues businesses might encounter during their journey to AI automation.
Problem: Poor data quality leads to bad outcomes
Solution:
AI systems rely heavily on the data they’re trained on or configured with. Biased, corrupted, or incomplete data will lead to the model reflecting those traits.
To avoid issues with the model after its deployment, assess the data quality. It should be clean, standardised, and properly labelled. AI can’t fix low-quality data, but low-quality data can harm an AI model.
Problem: Automating the wrong tasks
Solution:
Some processes aren’t worth automating due to how unpredictable or rare they are. Automating such tasks would be a waste of resources that could’ve been spent on automating rule-based tasks with clear inputs and outputs.
It’s better to start small, measure impact, and gradually expand the automation based on its outcomes.
Problem: Ignoring explainability
Solution:
AI’s decisions are really hard to explain. This can create discontent and impact trust between the business and customers. Using explainable AI (XAI) models can improve transparency and help interpret the decision-making process.
Problem: Overestimating short-term gains
Solution:
AI automation often delivers incremental improvements over time — not dramatic change overnight. Set realistic goals, measure impact carefully, and expect a learning curve. Involve the right people early — IT, operations, and the teams who actually use the tools — so expectations are aligned from the start.
Final words
AI automation has clear benefits, but the road to getting them might be covered with pitfalls for those underprepared. Most of the challenges boil down to businesses rushing AI implementation and skipping steps that serve as a foundation.
If your business carefully prepares and plans out AI automation adoption process, you will most likely avoid these common pitfalls and get results sooner.
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