‘One of the biggest problems with AI today is that enthusiasm for it is practical and that people do AI projects because of AI.”
This observation comes from Marc WilsonAppian founder and Chief Executive ambassador. Dynamic company sits down with Wilson to discuss the risk that organizations invest in AI, automation and low code platforms, but with the prospect of one critical element: process.
Business modernization is based on structured processes
“When you take a step back and think about what you want, most organizations want more efficiency, more cost savings, a better component or customer experience,” says Wilson. “And then you ask the question, which process is required and how can I place AI in that process to make it better?”
Wilson states that AI investments often get stuck before they deliver ROI, because organizations do not first consider how they will use the technology.
“Thematic is one of the things that Appian drives on the market the idea that AI process needs, and I would expand that a little further, AI needs a process to show value,” he explains.
“The reasons for this are quite clear: you can make an AI algorithm or agent that magically X, Y, Z does. But if there is no way to close X, Y, Z in how the company works, it is not much.”
Seventy percent of Appian customer base currently uses AI, and Wilson says that in a considerable number of cases it is not applied in a new Greenfield project, but rather integrated with processes that have already been automated.
“One of the most common useful examples of AI that we see is in e -mail extraction, classification and routing. We work with many organizations that still receive a substantive number of their customer or supplier data that come in via e -mail. And they have people who have been assigned to read those E -mails and perhaps they have those who are the older direction.
“And there are high failure rates, because e -mails come in all different forms and sizes. Apply AI skills that are not aimed at rules sets, but on a generative approach to understand the context of the e -mail, is the context of the document that most of the most companies want?
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AI makes companies smarter and more responsibly
As a technology partner, Appian’s focus is on helping customers to implement AI and automation within structured processes to ensure that they are not disconnected with a limited impact.
Such a company is fast-growing Australian asset management company Netwealth, which uses the Appian platform and AI to automate its e-mail intake process for customer and advisory case management systems.
The National Insurance Scheme (NIISQ) of Queensland is another Appian customer that uses AI for data extraction from incoming documents. Wilson says that accelerating the processes of NIISQ is particularly important because participants have experienced catastrophic accidents and endured some of the worst days of their lives. Instead of people having to go through documents and have to work out certain data points, the introduction of agentic possibilities for data extraction has accelerated the application process and achieved almost 100 percent accuracy.
AI facilitates a richer human experience
Including AI in process workflows results in better case management, faster compliance and more responsive services without endangering the privacy of data or workforce.
Wilson says that many organizations, especially very regulated institutions such as banks and insurance companies, integrate AI into their customer journey to offer a richer human experience.
In an academic setting, the University of South Florida has a pioneer in the use of generative AI to improve the ability of its advisor community to offer support to the student body through the orchestic ration.
“Process orchestration is a situation for us in which many different activities are going on around a certain subject, in this case a student,” Wilson explains. “Process orchestration helps the student journey to bind and bind, and advisory services are part of it. This is where the AI components are about bringing a rich set of tools to improve the AI.
“This not only gives the consultant a consolidation of the data, but also offers a chance for advice on what can be discussed, perhaps to call certain care areas or optimism.
“What AI does is to make the preparation time easier and more valuable to enter the advice session. The consultant can be better, more personal and more involved. They can be more involved because they are better prepared for what they do.”
Building trust in AI through human supervision and administration
Despite his exciting possibilities, the concern about AI technology remain. “Most organizations are not willing to let an AI work,” says Wilson. “The people, the management team will run the organization; they are looking for AI to help them.
“Process creates a safety net. Horror stories have come up and we will see more examples of AI running Amok. They do things that they should not delete: delete data here, do an order there, do something that the company does not want to do here. This happens because the AI has not been told: it is not told what it was possible and not to do it a safety layer to do it.
“AI also offers organizations the opportunity to make an audit trail of what was asked of an AI and what an AI did. Because you can tell an AI to make a decision, but then use this process to do what you want, everything becomes a line item in an audit trail that can be easier to diagnose and see what to understand. And perhaps even more importantly, at the moment, what it should not do.
How private ai protects data
Wilson weighs in the public AI vs Private AI debate by emphasizing some important benefits for the Private AI option: one that uses a model as a starting point but is then trained by the data and information from an organization.
“First of all, I think that privacy delivery is of the utmost importance. If the big model that everyone uses, your customer information, your constituent information, the health file of your patient becomes, there is a leap of trust that I think don’t want to take many organizations.
“I still have to meet an organization in my travels around the world, which is comfortable to push its data into a public cloud environment. And much of it starts with privacy problems. It starts with worries about the rules and regulations that can exist in a certain state or at national level.”
Wilson says that companies have legitimate doubts about sharing data with competitors. “Most banks do not want to train a model to help their competitor. They want to train the best model for themselves. Governments do not want to be in a position where the personal information of their voters is mixed with the constituent personal information of another country.”
He believes that most AI infrastructure moves to a private model. “The best analogy I can give is that you go to a store, choose your toys from the shelf and choosing your AI that is trained to do something off the shelf. You take it home, and you start adding your own information. And you make it better. But you make it better based on your own information, your own likes and dislikes.”
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