An SME’s guide to unlocking the value of AI with historical data
Today, many small and medium-sized enterprises (SMEs) are still dependent on paper-based and offline workflows, with data from Inside Government revealing that 55% of businesses across West Europe and North America are still completely reliant on paper. This means that without existing digital systems and a centralized database of historical data, the transition to AI-powered workflows can seem completely out of reach.
Balancing the integration of new technology while maintaining regular operations is the key to digital transformation. This has been a challenge for each transition period, but with the move to AI, the balance is even harder to find. Implementing AI solutions without consideration for existing systems and workflows can negatively impact the employee experience, with employees needing to double-check and correct inaccurate AI outcomes. That’s why companies must strategically plan for AI adoption, understanding where AI will be the most effective at improving workflows and how to unlock the greatest value for employees.
AI has the power to transform the way we work. Through the automation of routine tasks, such as searching and retrieving files or summarising large, complex documents, it can free up time for professionals to focus on creativity and innovation.
For SMEs to unlock the full potential of AI, they need AI systems that are fully tailored to their business, their operations, and their industry – and they need tools that become more specialized to their business with use. However, this level of personalization is achieved through leveraging historical data – a key challenge for many smaller businesses. Research from the World Economic Forum (WEF) shows that 64% of SMEs find it challenging to effectively use the data from their systems and 74% struggle to maximize the value of their company’s data investments. This is where digital document management is key to making the most out of your company’s data.
Proper documenting and labeling of historical data are critical to ensure AI tools have the right context to learn how to automate workflows and provide insights that are optimized for the unique characteristics of the business.
Without the right tools, translating paper-based records into a digital format that can be acted upon by AI systems can be incredibly slow and labor-intensive. This is especially true for SMEs that may lack the additional resources required to take on the mammoth task of digitizing their entire operational history.
Cloud-based document management tools can help SMEs lay the groundwork for AI adoption through improved data capture and data management:
Ensuring the quality of data captured is especially challenging with paper-based workflows. Paper documents require manual input from employees, which takes up valuable time as well as leaving the process open to the risk of human error and missing records, where data has not been recorded correctly or at all.
Employees need a system that simplifies the data input process and reduces the level of manual intervention required to accurately update records. Here, cloud-based document management tools can streamline the data capture process by automatically translating one form of data into another format. For example, the ability of document management tools to convert basic smartphone photos of documents into PDFs allows employees to record data in seconds and ensures data is captured and stored in one central database.
Taking automation one step further with the power of natural language processing, AI-powered transcription can now automatically generate transcripts from audio-visual content. This significantly streamlines the data capture process and even allows users to search audio and video files by phrases and quotes.
Without a central source of truth, version control becomes a significant challenge for paper-based workflows. Gaps in records, as well as a lack of a standardized process and improper labeling significantly limit the value of historical data.
A streamlined and centralized database to store all digital content is essential to boost the value of historical data, by enabling the data to easily be searched and retrieved across different document formats. For example, the ability to search within audio-visual documents, including object and optical character recognition inside images, means that as you search for images, you’ll not only search the image metadata that is included in each file, but also the contents of the images. Therefore, boosting the data accessible for analysis and business insights.
And with further developments in workflow-productivity AI tools, centralized cloud databases will be able to automatically sort and file documents based on the standard organization practices set out by the business.
Embracing AI technology shouldn’t just be about ticking a box and using the latest new tool. It's about the impact it can have on the business and the value it brings for employees, not just in saved hours on a single task a week, but in the seconds saved in every action taken throughout the working day. In order to achieve these benefits, AI algorithms require quality data to optimize workflows to suit the unique characteristics of each business and their employees’ needs. Now is the time for businesses to start laying the groundwork for AI-powered digital transformation by setting up processes to effectively capture and manage their digital data.
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