A Blueprint for Success with Enterprise AI

One of the hottest topics today is the enterprise AI boom and its enormous potential for helping businesses do exponentially more with less. And the hype is warranted: research predicts that by 2030, the AI gold rush could contribute over $15 trillion to the global economy.

But for enterprises to realize this level of value, IT leaders need to find ways to integrate AI across their organizations. This involves working AI into processes and supporting it with strong data management strategies. Only then can they realize the full value of AI in the enterprise.

This article explores insights to help IT leaders jumpstart their enterprise AI strategies with examples, tips to get better outcomes from AI investments, and considerations and cautions for using AI in business.

Why do enterprises need to invest in AI? Because those that don’t will fall behind.

AI has already begun to change the way we work, and it’s not going to stop any time soon. Todd Lohr, Principal at KMPG LLP, put it this way:

[AI] will change every business and every industry. Wherever you have people working, AI will augment their work, change what they can do, and change the roles they play.

Enterprises who have adopted machine learning and AI as part of their product strategy or business strategy are already seeing bottom-line and top-line growth, according to Amazon Web Services’ Field CTO, Principal Solutions Architect Piyush Bothra. He says:

[These companies] are setting themselves up for that kind of trajectory against companies that don’t. This gap will increase even more in the coming years. The competitive landscape may change drastically. If the majority of companies don’t start thinking about AI now, we may see only a few companies running the show.

The consequences of failing to effectively implement AI extend beyond hindering immediate operational effectiveness—this oversight impacts long-term strategic viability. Companies slow to embrace enterprise AI risk becoming irrelevant.

Let's look at some use cases of AI for business and the enterprise AI applications that are transforming business operations today.

Invoice and purchase order (PO) processing is an essential part of business across many industries. As organizations scale, manually processing invoices and PO documents can quickly become a bottleneck for other workflows. Let's say your current invoice and PO applications support a rapidly growing finance department. You have a limited number of employees, and they increasingly spend an overwhelming amount of time processing invoices and POs daily.

Building a document extraction AI skill into your existing workflows can take the burden off your employees, freeing them up from repetitive tasks for more meaningful work. Invoice and purchase order document types are excellent for automatic document extraction. They contain semi-structured data with clearly labeled values that can be easily extracted into key-value pairs.

A company that specializes in supply management receives several invoice forms daily that must be audited and filed for processing. Since each form is structured in the same format and contains easily identified fields, you could delegate this task to AI-powered robots (learn more about AI and robotic process automation (RPA) trends) to extract the data from each field and store the results.

You can also design your processes to classify documents and extract data from them automatically. If you want to confirm that the data was extracted correctly before saving it to a database, you can have human workers validate the AI’s work, adding an extra layer of quality control and human oversight.

And that's just one document extraction use case. You could also apply this process to any application that requires a human to audit, review, or organize data from a document. Since document extraction focuses on fields, consider using these capabilities when processing forms like invoices, records, or applications.

Next, let's look at an insurance industry use case. Say your claims management application receives hundreds of insurance claims a day. If your team is overwhelmed by the amount of data involved, it could slow down the process and increase the risk of inaccurate decision-making. That's a scenario where AI can improve insurance processes. Here’s an example of how this could work:

• Use AI to quickly design a claims submission interface that allows users to process claims data in a fraction of the time it would otherwise take.
• Instead of having all of your claims go to one team to identify and sort through reams of supporting documentation manually, employ AI to delegate claims to different teams depending on where the damage occurred.
• Use custom machine learning (ML) models to identify the types of documentation that accompany each claim, and combine it with automation to notify or involve relevant teams.
• When an image accompanies each insurance claim, leverage computer vision to analyze it, determine where the damage occurred, and then route the claim to the proper team.

A large law firm has to manage case data for class action lawsuits. A single case can include thousands of plaintiffs, so it takes a long time to look for traits that the plaintiffs have in common. For example, law clerks could have to manually research how many plaintiffs reported a respiratory illness due to materials used to build apartments where they live in a class-action lawsuit.

To support this use case, the IT team could build an enterprise AI application that incorporates  an AI-powered tool like the records chat component of Appian. This would enable the law clerk to query related records using natural language processing without engineering a prompt. And this particular component of Appian uses AI to answer questions held within record data. So if the law clerk asks a question in the context of the case record, Appian looks within the related records to find the answers quickly.

AI will have a profound impact on process automation, but only if large organizations can successfully incorporate the technology into digital workflows. Traditional developer tools are insufficient: they’re resource-intensive and demand a very deep understanding of complex business processes that is just not feasible for most of us.

Hyperautomation offers a holistic, accessible approach. Hyperautomation combines AI, machine learning, and robotic process automation to help you make your enterprise more agile, efficient, and adaptable without needing to be a process expert.

Generative AI is also changing the game for rapid workflow generation. Imagine, for example, instructing an AI to construct a workflow that performs a series of tasks, leveraging the existing business process automation technologies within the platform, such as robotic process automation, business rules, and data fabric. Or envision empowering users throughout your organization with instant access to analytics by simply inputting their queries into an AI assistant using natural language.

As these capabilities and others like them come to market, they’ll begin to change organizational productivity as we know it.

As you make inroads into more AI use cases and tools via hyperautomation, keep these three considerations top of mind.

For any AI process automation venture to excel, it must be firmly grounded in robust data management. Why? Because the effectiveness of any AI implementation is intricately linked to the quality and accessibility of the data that fuels it. The most successful AI implementations prioritize not only making data instantly accessible but also readily usable for AI-driven process automation.

. . . Fundamental to every AI problem is data. When we talk about how prepared organizations are, a driving force will be the maturity of their data ecosystem. - Brendan McElrone, Managing Director, Deloitte Consulting LLP

To this end, organizations are pivoting from traditional data warehouses and lakes to more agile data management strategies, like data fabric. A data fabric is a virtualization data layer that enables you to connect all your enterprise data sources without moving the data from where it lives. This technology lets organizations give AI the data fuel it needs to enable transformation.

As you develop a data management strategy to support AI implementation, keep data privacy a priority. Recognize the risks of using AI models trained on public data sets (as opposed to those trained only on proprietary data). For instance, massive language models like ChatGPT incorporate the data they are prompted with into their public models, which limits how fully employees can engage with it and what information they can provide it with (see more in public AI vs. private AI).

Seek out fully private AI solutions, where your data remains within the confines of your cloud site and under your control. A private AI model is exclusively trained on your organization's data, ensuring that the resulting intelligence stays within your organization, preventing competitors from capitalizing on your data.

Depending on your business requirements, you can use AI for a wide range of actions: reviewing files, extracting critical data points, monitoring customer interactions, or collecting and digitizing handwritten information from contracts, just to name a few. Whatever your situation, start with pain points that AI can improve or solve (rather than investing in a tool first, and coming up with a use case later).

Top-performing organizations stay true to their business strategy and use AI as an accelerant. - Todd Lohr, Principal, KPMG LLP

Here’s the bottom line: AI will be a powerful enabler of competitive advantage for enterprises, but only for those that crack the code of AI adoption. AI has to be supported by a strong foundation of process and data if you want to see its value shine across your enterprise.