Businesses often consider using AI in order to access cutting-edge transformational change.
Less exciting uses of artificial intelligence get overlooked, but have the potential to save money and reduce errors – as well as freeing up time spent on labour-intensive, repetitive work.
Information extraction with natural language processing is a ‘boring’ but immensely worthwhile use of AI.
We all know about the paradigm-changing use of AI for Netflix recommendations, chatbots that impersonate customer service agents online, and the dynamic pricing of hotel rooms. Such efforts are the value creation engines of countless large, successful companies. But organisations can also adopt a decidedly less splashy and, at face value, more pedestrian use of AI—to process documents faster and simplify operational procedures.
Although this use is aimed at reducing costs rather than transforming industries, ‘boring AI’ is actually quite exciting—because it confronts issues that all companies wrestle with, and because the gains in productivity are real. Recent research by PwC on automating analytics found that even the most rudimentary AI-based extraction techniques can save businesses 30–40% of the hours typically spent on such processes.
For example, the evidence used for audits usually appears in PDF form—invoices, account statements, receipts—and it can run into the thousands of pages. Information must then be manually entered into spreadsheets. For a midsized company that processes 100,000 pages of documents annually at three minutes per page, it would take approximately 5,000 person-hours to complete the task; at US$50 per hour, that’s $250,000.
Consider the results if the company used AI to “read” text on each of the invoices and use relational data search to quickly identify supporting documentation that the organisation had previously tagged as being important. Even though paper invoices can be unique to each supplier, AI techniques can identify important fields in the different invoices, such as unit cost and quantity, and calculate ledger balances automatically. By implementing an AI solution and assuming the 40% estimate above, the example midsized company could save 2,000 hours for every 100,000 pages processed.
AI-driven information extraction can tackle inefficiencies, but doing so requires a slew of complex data science techniques involving multiple dynamic components. For example, optical character recognition (OCR) is the ability to read printed characters on a page, regardless of font, size, orientation, and brightness—even handwritten characters. It is older technology but is still essential as the first step in the process that gathers the relevant data from the documents in question.
AI-enabled information extraction can also take advantage of some of the recent advances in natural language processing to identify the true ‘meaning’ of a document, through identification of contextual words, parts of speech, and so on. The AI itself does not understand what it is saying (although it might appear that way), but algorithms are able to generate summaries of documents; identify topics; judge the sentiment (positive or negative) of prose; identify key terms, provisions, or clauses within documents; and identify clusters of documents requiring similar actions.
AI tools tend to be highly accurate, but when they do make errors, they can be nonsensical and downright bizarre. Maintaining human oversight during the implementation of these AI techniques is crucial to ensuring quality. Successful implementation requires more than the right tools. Companies will also need to take the following actions:
Create a new platform (or reconfigure an existing one) that combines data management, automation tools, and AI applications, but also keeps people in the loop. This platform could be a central enterprise-level portal, wherein data could be stored and exchanged, applications uploaded and downloaded, and collaboration and joint development encouraged through a communication interface. This platform should be accessible to everyone in the organisation and allow employee-led innovations and applications as well as those from professional developers. Of course, such democratisation of these powerful technologies ought to proceed responsibly; leaders must stay vigilant about the potential risks and cognisant of the need for proper training and corporate governance.
Develop an enterprise-wide training program focused on digital and analytic understanding and awareness. Everyone will need to be upskilled, from the CEO to the newest entry-level hire, across all functions. Companies should consider training many of these employees not only in the use of these time-saving information extraction tools, but also in the fundamentals of the AI technologies behind them. With a better understanding of the capabilities, risks, limitations, and assumptions of the AI, employees will better understand how to use the tools responsibly and effectively. Every organisation should ensure that its employees are conversant with current technologies, and this transformation will take hold only if the entire workforce is brought along.
Pay special attention to the impact on middle managers for whom a substantial portion of daily tasks will essentially be eliminated. That is a reality of automation—it creates efficiencies by taking over some tasks that are currently done by humans. The important message to communicate to managers is that, in so doing, AI will free them to focus on harder-to-solve problems, and to work on issues that demand human judgment or creativity—to do more managing and fewer mind-numbing repetitive tasks.
Enthusiastically offer incentives for those at the tactical level to use these tools and the new platform, beyond simply citing facts regarding the potential ROI. These incentives are dependent on the corporate culture, but could include KPIs for performance reviews, real-time bonuses, entry into a lottery for a large prize, and so forth. Incentivising initial use of these tools will likely accelerate their acceptance. People will be won over when they start to see how the tools enhance their productivity.
Promote culture change by designating top-down champions who consistently and frequently communicate the benefits of AI implementation. The message that using these tools is on-strategy, is viewed favorably, and is good not only for the organisation’s customers but also for the organisation’s health and growth will accelerate adoption and make the technical and cultural changes stick.
With automated or augmented solutions, businesses have the potential to energise processes that have traditionally been time-consuming and error-prone, identify opportunities to add speed and efficiency, and unlock new insights that contribute to long-term growth. Boring has never seemed so exciting.
This is an abridged version of an article previously published in strategy+business on 24 June, 2021.
Robert N. Bernard is director of the intelligent enterprise at PwC Labs. He has more than 25 years of experience developing advanced analytics and artificial intelligence, and formulating analytic strategy in multiple industries, including national security and algorithmic investing. Based in northern New Jersey, he is a director with PwC US.
Anand Rao is PwC’s global leader for artificial intelligence and innovation lead for the US analytics practice. He holds a Ph.D. in artificial intelligence from the University of Sydney and was formerly chief research scientist at the Australian Artificial Intelligence Institute. Based in Boston, he is a principal with PwC US.
Also contributing to this article were PwC US principals Jacob T. Wilson and Joe Harrington. They lead the US AI Lab within PwC Labs, and have helped lead the creation of the US firm’s information extraction platform.
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