Curious about what keeps experts, CEOs and other decision-makers in the Intelligent Document Processing (IDP) space on their toes? Get food for thought on IDP-related topics from the industry’s leading minds.
In this opinion piece, Harald Collet, Co-Founder and CEO of IDP vendor Alkymi, explores what should be considered when adopting document processing solutions.
Companies often try to enforce standardized document formats, yet this method struggles with external compliance, scalability, and unstructured data. Template-based automation systems offer a solution, but many can’t handle complex documents and business processes.
Generative AI and machine learning can revolutionize document processing and eliminate the need for standardization by shifting focus from documents to data. Advanced AI can comprehend context, categorize, extract, and transform information from unstructured data, regardless of format. Companies can set parameters for the information they need, and allow the technology to take over the process.
Productivity gains from transforming your data workflows can dramatically change a company’s growth prospects. The McKinsey Global Institute estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually to revenues, which is more than Brazil’s annual GDP.
Create a Scalable Process
An AI-driven approach provides scalability and efficiency. It reduces errors and process deviations, which are common as firms expand. Teams can then concentrate on leveraging data insights rather than fixing inconsistencies.
We recommend three key steps for effective data processing and transformation using AI:
- Select the right partner: Look for teams with deep domain experience in your industry, who can understand both the technology and your business’ use cases.
- Implement new technology strategically: Begin with a detailed plan for a specific use case, then expand based on initial successes to build momentum.
- Focus on flexibility: Being aware of how your data is used is critical. A one-size-fits-all approach to AI is not the answer, whether because of data security or due to specific use cases. Companies that adopt an AI platform tied to a single provider or strategy could limit their flexibility.
By taking these steps, companies can craft an AI-powered technology stack that grows alongside their needs, ensuring stability and continuity for the long haul.
Build Trust With Traceability
Companies can enhance and transform their workflows using AI, but it’s vital that any data be validated and verified. When using any AI-powered platform for document processing, your extracted data needs to be auditable and traceable back to the source by default.
Traceability is essential in document processing, especially financial document processing, where getting accurate data into downstream systems is critical. AI-driven systems should link extracted data to its source in the document, enhancing user trust and facilitating independent validation.
Easily Find the Data You Need
Utilizing an AI-powered platform for document processing will also ensure that your data is discoverable and you can easily find what you’re looking for, regardless of source formatting or differing terminology.
Traditional search relies on a data structure that maps words to the documents containing them, enabling efficient retrieval based on keyword matching. While effective for exact matches, this method struggles with semantic equivalence—the ability to recognize words or phrases with similar meanings but different forms. For example, “asset manager” and “fund manager” would be treated as distinct queries despite their semantic overlap. Generative AI enables platforms to utilize semantic search, which addresses this limitation by using sophisticated AI techniques to understand and match the intent and meaning behind user queries, not just the specific words used.
Semantic search represents a significant shift from traditional keyword-based methods to a more nuanced understanding of language’s semantic structures, leveraging the actual meaning behind words and phrases rather than relying solely on their presence within a document. Tools that can interpret and respond to queries with a deep understanding of context can support more efficient information retrieval.
Make Unstructured Data Work for You
Research firm Gartner finds that unstructured data, which includes PDFs, emails, images, and slide decks, constitutes 80% of incoming business information.
Companies need actionable insights from all that unstructured data. An AI-powered intelligent document processing solution, not one based on templates, can automatically respond to ever-changing information coming into the business.
Sophisticated machine learning and AI can boost operational efficiency and propel companies forward. This AI-driven approach can scale as the amount of unstructured data grows. Solutions that navigate the data complexities with scalability, traceability, and discoverability are pivotal in gaining a strategic advantage and achieving operational excellence.
About the Author
Harald Collet is the CEO and co-founder of Alkymi, which turns unstructured investment documents into actionable data for financial services companies. Before Alkymi, Harald built businesses and products at Bloomberg, Autonomy, and Oracle.
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