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By 2026, the way AI helps us manage and integrate data is expected to change significantly, especially for traditional IT roles such as data integration developers and data engineers. But do organizations understand how they are supposed to get started with generative AI?
As many companies have discovered, leveraging generative AI is not as easy as first thought. The technology itself is revolutionary—stunningly powerful and full of potential, but as research by the Washington Post shows, a whopping 90% of generative AI proof of concepts failed. Why? Your organization needs to have the right level of data maturity to assuage some of the side effects inherent to generative technologies.
Large language models (LLMs) work best when the data they learn from is rich and well-organized. For most organizations today, this means LLMs have minimal understanding of the specific needs and operations they are expected to support. Retrieval augmented generation (RAG), introduced by Meta in 2020, is re-emerging to bridge this gap. RAG is a cost-effective and secure method to enrich LLMs with organizational data, thus significantly enhancing their utility. But what are all the different puzzle pieces that make up your company's data?
When you’re getting started with this massive undertaking of sorting out your company’s data puzzle, it’s almost like you’re writing the script for a blockbuster action movie. Every single piece is a powerful artifact – “data” infinity stones anyone? – whose power you need to harness. But any good action movie needs heroes, so who could they be in this race to master the infinity “data” gauntlet?
In the vast expanse of modern enterprises, countless data artifacts lie scattered across disparate silos, isolated by the boundaries of incompatible technologies. While the common view of data integration focuses on accessibility, the most valuable data types—documents and processes—are frequently overlooked.
Documents—both paper and digital—are crucial, covering everything from contracts to emails, and are treasure troves of underutilized data. Our 35 years of experience have shown they involve 90% of the invaluable data often ignored in business processes. These documents are often accessible even from within their silos, but the true value lies in understanding what they mean. Document AI takes center stage, acting like a superhero in our action movie, extracting crucial information and turning raw content into valuable data insights, much like Indiana Jones solving a temple’s puzzle to retrieve long-lost relics, ensuring that no piece of knowledge is left behind.
Document AI, or intelligent document processing (IDP), transforms the way businesses manage and extract value from their documents. Utilizing a blend of AI technologies such as machine learning, natural language processing, and optical character recognition, IDP automates the extraction and interpretation of data from a wide range of content. This speeds up and streamlines how we handle all kinds of document-driven processes, which again are 90% of business processes. By automating these processes, businesses can reduce manual errors, lower operational costs, and focus human resources on more strategic tasks. This technology is especially crucial in industries like finance, healthcare, and legal, where document processing is voluminous and complex, requiring high levels of accuracy and compliance.
The challenge isn't just about gathering and getting knowledge from all these artifacts; it's about understanding the intricate processes that connect them, which are often overlooked yet vital for any meaningful insights. Process intelligence (PI) emerges as the strategist by lighting the path on the complex web of processes, bringing visibility where there was opacity, and ensuring every operational insight is leveraged. Process intelligence maps the terrain by connecting to every single one of your data silos, analyzing workflows and processes to identify patterns, efficiencies, and bottlenecks.
Process intelligence, especially when it comes to predicting future trends, leverages advanced analytics and machine learning to foresee future behaviors within business processes. This predictive capability allows organizations to anticipate operational issues, manage risks, and optimize outcomes by analyzing historical data to forecast future performance. For instance, in supply chain management, predictive process intelligence can identify potential delays or inefficiencies before they occur to enable proactive adjustments. Similarly, in customer service, it can predict high inquiry volumes to allow for better staff allocation. This foresight not only improves efficiency and effectiveness but also enhances decision-making, leading to more agile and responsive business operations.
With every piece in place, the full power of integrated data comes to life. Businesses that embrace this approach can see beyond the horizon. Decisions are no longer just reactive, but predictive, powered by AI's ability to analyze the integrated data tapestry.
To illustrate how data integration and AI are being applied in real-world scenarios, consider these examples:
1. Healthcare: Cancer treatment center LifeHouse used IDP to eliminate 80% of its paper records which accelerated and improved access to critical patient
information, significantly reducing waiting times and improving patient care. The
automation resulted in $50,000 a year in savings.
2. Transport and Logistics: Global brewery giant Carlsberg leveraged IDP to automate the capture of data from incoming orders, achieving a touchless order
processing rate of 92% and saving over 140 hours of manual data entry per
month. This drastically accelerated customer deliveries, subsequently improving
client satisfaction and loyalty.
3. Manufacturing: Global Fortune 500 manufacturing company Emerson used process intelligence to standardize its procedures worldwide and ensure
compliance. It involved real time analysis of processes conducted by over 70,000
staff in 130 locations across 28 countries to devise the best path for optimization,
resulting in increased efficiency, reduced costs and a better experience for
employees.
4. Government: The U.S. Food and Drug Administration (FDA) used IDP to
accelerate its public health initiatives. The agency was able to digitize and
process mass amounts of data within documents for monitoring public health,
including its 30-year archive of forms used to report adverse events, and current
documents to ensure that critical reporting is captured accurately and quickly.
With the outbreak of a worldwide pandemic, the technology was also used by the
FDA to help aggregate safety data in clinical trials for COVID – speeding up the vaccine process.
I believe the journey towards data integration and AI isn't just about technology—it's about transforming how we understand and use our data. By leveraging advanced AI solutions, organizations can unlock unprecedented insights and drive innovation.
Industry statistics further support the potential of data integration and AI. According to a report by McKinsey, companies that fully integrate AI into their operations can increase profitability by up to 40%. Additionally, Gartner predicts that 75% of organizations will shift from piloting to operationalizing AI, driving a five-fold increase in streaming data and analytics infrastructures.
An analogy I like to use is to think of your business data as an iceberg. The visible tip, your structured data, is just the beginning. Below the surface lies a vast, hidden goldmine of unstructured data – emails, PDFs, images, and more – accounting for 80% of your data's true potential. Don't be like the Titanic, blindsided by what lies beneath. Embrace AI to navigate these uncharted waters, uncover hidden insights, and steer your business toward transformative success.
As our blockbuster draws to a close, the message is clear: integrating your data with the help of purpose-built AI isn't just an operational upgrade; it's a transformative journey that opens up an infinite number of new possibilities, pun intended.
Looking forward, we see emerging trends like increased adoption of explainable, personalized and purpose-built AI, as well as the expansion of AI-driven data governance and Internet of Things (IoT) integration. Innovation leaders who adapt to these trends will be more successful in setting a trajectory of compliance, success and value for AI use in their organizations.
Like the best adventures, the road ahead has its challenges, yet the potential rewards promise to be extraordinary.
By incorporating these elements, we can ensure that developers are not only informed but also inspired to take action and leverage the transformative power of data integration and AI.
Author bio: Maxime Vermeir is Senior Director of AI Strategy at global intelligent automation company ABBYY. With a decade of experience in product and tech, Maxime is passionate about driving higher customer value with emerging technologies across various industries. His expertise from the forefront of artificial intelligence enables powerful business solutions and transformation initiatives through large language models (LLMs) and other advanced applications of AI.
