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In this opinion piece, Neil Walker, Head of Product at intelligent process automation software provider TCG Process, outlines 6 key principles that organizations should consider to ensure that AI does not misinterpret data or even ‘hallucinate’ and disrupt workflows, but rather works for their IDP processes.
As organizations increasingly adopt artificial intelligence (AI) to enhance their intelligent document processing (IDP) workflows, the allure is undeniable. However, alongside this promise, there is a legitimate concern about potential risks, which in turn is driving a growing movement around explainable AI. This refers to systems where the internal workings can be understood by humans. For IDP, this means that the AI can provide clear reasons for its responses or decisions, making it easier to trust and verify the outputs.
What happens if the AI misinterprets crucial data or even ‘hallucinates’? How can businesses ensure AI doesn’t disrupt workflows, make biased decisions or worse – produce results that are either inaccurate or unreliable?
The answer lies in a thoughtful and strategic approach to AI integration. By focusing on having a flexible technology stack, human oversight and continuous improvement, businesses can mitigate these risks and ensure that AI enhances processes rather than complicates them.
Below, we’ll dive into several key principles that will help your organization ensure AI is continuing to work for your IDP processes, not against them.
1. Encourage Open and Adaptable Solutions
A significant risk businesses face when adopting AI is becoming dependent on a single vendor or technology, as the ‘lock-in’ can leave you with suboptimal performance or high costs as AI evolves. A technology-agnostic AI platform allows you to orchestrate and combine multiple AI services depending on the needs of the specific process or document. This allows organizations to switch between solutions without disruption to the underlying business process.
2. Use Human-in-the-Loop (HITL) for Critical Decisions or Validation
AI is powerful, but not perfect. That’s why the most effective implementations combine AI automation with human oversight. ‘Human-in-the-loop’ or HITL ensures that when AI encounters situations of uncertainty or low confidence, a person can step in to verify and correct as required. This hybrid approach improves efficiency and accelerates the AI learning process.
With explainable AI, insights into responses and the reasoning behind AI led decisions are transparent, enabling human reviewers to quickly identify and correct errors. In turn, this accelerates the validation process and improves overall reliability.
3. Employ Multi-Technology Redundancy as a Safeguard
Another way to mitigate risk is to use a multi-technology approach. This involves using different AI technologies to cross-check each other’s results. If two separate AI systems arrive at the same conclusion, you can confidently move forward without human intervention. However, if they disagree, a human may step in to make the final decision. This provides not only a safety net, but it also helps to build trust as organizations can feel more secure that a second service has validated the output or decisions made by the first, reducing the chances of errors that could disrupt workflows or harm customer relations.
When combining outputs from different AI systems, explainability allows teams to understand discrepancies in results, facilitating more informed decisions when they do arise.
4. Leverage AI as a Continuous Learner
AI is not a ‘set it and forget it’ technology – we must only look to just how fast more of the consumer-facing models have had to iterate to keep up with the pace of information to understand this. In document-heavy business processes, new types of documents and formats are constantly emerging. Continuous learning enables the AI to improve its accuracy, reducing the likelihood of hallucinations as it becomes more familiar with your specific documents and data format.
5. Build Trust with Validation and Verification
The final key to ensuring you can robustly adopt AI for your business-critical document-centric business processes is to build-in automated validation and verification. Embedding AI into a structured process with embedded validation steps ensures that decisions driven by the data from AI can be tracked, verified and corrected if necessary. Explainable AI enhances validation efforts by providing clear reasoning and justification, making it easier to verify accuracy. By validating and verifying AI outputs regularly, businesses enable continuous learning and build trust in their solutions, making it easier to scale across different departments and use cases.
6. Implement Explainable AI Practices
Adopting explainable AI practices will further safeguard your IDP processes. Explainable AI not only provides transparency, allowing your team to understand, trust and effectively manage the AI’s output, but also helps in diagnosing the root causes of hallucinations.
Conclusion
Following these steps to ensure you’re mitigating risk means that AI is working for your processes and becomes a reliable partner. Furthermore, by proactively addressing potential issues and prioritizing explainability, businesses not only mitigate risks but also foster a culture of transparency and trust, essential for the successful integration of AI into their business processes.
About the Author
Neil Walker, Head of Product at TCG Process, is a passionate product leader with over 20 years of experience in technology, spanning project delivery, sales, and product management. He drives the strategy and vision for TCG’s intelligent process automation solutions, bringing cutting-edge products to market. Outside of work, Neil and his wife enjoy exploring the local Yorkshire countryside with their dog.
Learn more about TCG Process or check out their latest webinar on Future Proofing your AI Strategy for IDP.
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