The A.I. ROI Starting Point: Information Management & Document Processing
Every organization today recognizes they need to implement A.I. in some way, shape or form. There are no shortage of sensational headlines that would make any company want to tread carefully into the world of A.I., but what if there were a reasonable, sane, smart area in a company’s operations to take the plunge? Well, there is and it’s an area where organizations can realize measurable A.I. ROI fairly quickly – their information, document and enterprise content management (ECM) systems.
It’s important to remember that A.I. has been with us for a long time, delivering benefits to consumers and businesses alike – from smartphone voice-activated assistants to powerful software platforms that can rapidly create efficiencies and predict outcomes in drug clinical trials. Likewise, it’s also important to understand that A.I. is a technology that is continually evolving just like the Internet, social media, and cybersecurity. Like all of these technologies, A.I. will experience hiccups, even setbacks, which will self-correct and become more efficient over time.
The Evolution and Varieties of AI Technologies
In its current evolutionary stage, there are a number of varieties and categories of A.I., some of which have higher implementation risks than others. Therefore, businesses need to understand which are ready to deploy now versus which should be considered for later when they are further developed and refined. Generative A.I., predictive A.I.machine learning (ML), natural language processing (NLP), large language models (LLM), robotic process automation (RPA), and digital process automation (DPA) are just some of the types on the market today. Organizations may be tempted to experiment with any or all of these because of market pressures to employ the most cutting-edge technology. However, a scattershot approach like this could prove costly without realizing any measurable returns.
Strategic Entry Points for AI Integration
Whatever stage a business may be in developing or implementing a master A.I. strategy in its operations, reassessing an A.I. entry point or pivoting to one where near-term tangible outcomes can be realized is a good place to take a step back and refocus. That entry point should be an optimal center for achieving new process efficiencies as well as learning new operational insights that can impact future top and bottom line forecasting.
In fact, there is a logical AI entry point that is often overlooked but is actually right in front of a company’s “nose” where A.I. can produce near-immediate value. It is the digital and physical repository hubs containing an organization’s aggregate body of information and knowledge in the form of its documents, data and other content types and formats.
Evaluating Current Information Management Systems
Companies must first do a deep dive evaluation of the state of their information management processes and procedures and their current enterprise document management technologies that support content processing within their organization. When that’s completed, they are ready to look at A.I. tools that can exponentially improve both functional features and intelligence mining in their entire content collection and organization systems.
AI Tools for Enhanced Information Processing
These are tools that range from streamlined AI technologies like GPT and Large Language Models (LLM) based on instruction “prompts” for extracting the meaning of specific document text to more sophisticated, engineered machine learning (ML)-based algorithms for mining data. Whatever tool is utilized, it is imperative that organizations reach consensus on instructions, rules, and parameters inputted for AI document management systems to work effectively.
Over time, these AI-driven content management systems self-teach themselves and can suggest further adjustments based on analysis of cross-organization document workflows. As long as document processing and operational teams closely monitor whether the A.I. technology is producing the desired outcomes and/or does not fundamentally diverge from the original programmed instructions, significant A.I. ROI and value can be achieved in a relatively short period of time.
Long-term AI Strategy and Flexibility
Beyond document management, over the longer-term, organizations should take a patient, measured approach for how they implement A.I. technologies in other areas of their operations and business processes. Companies should maintain a posture of flexibility and openness in their master A.I. strategy that will enable them to build A.I. enterprise content services platforms (CSPs) that can scale as the technology continues to evolve and the business grows.
The Future of AI in Business: AI 2.0
A.I. has been a positive and potent force for consumers and businesses alike for nearly a decade. We are still a long way off from the promise of A.I. enabling businesses to auto-operate or self-operate aided by A.I. skillset-enabled work teams. However, the next phase of A.I. will focus on eradicating the negative perceptions of the artificiality of its intelligence. A.I. 2.0 will self-strengthen its capabilities as a true augmented (vs. artificial) engine for human-interfacing intelligence that will produce new models and formats for what information and content will look like.