According to Appian, AI should be able to perform even better. Currently, many companies have completed pilots and implementations, but the project failure rate is high. What is the solution? An approach where AI does not exist on its own, but works with data and processes. This allows businesses to automate back-office processes and get tangible results.
The numbers don't lie, Appian explained during a visit to the company in London. An MIT study last year showed that 95% of generative AI implementations fail. They create little or no business value. There can be various reasons for this. Think about poor integration, little focus on solving real problems, and unrealistic expectations.
As far as Appian CEO Matt Calkins is concerned, that's an unreasonable percentage for what is considered a breakthrough technology of this generation. What is the crux of the problem? AI is often thought of as a standalone technology. “People thought AI could work on its own,” Calkins explains. “But that’s not the case. AI needs other technologies to deliver real value.”
Calkins therefore advocates for “full-fledged AI,” where technology is primarily used for critical business functions. This approach combines three elements: AI, data, and process. According to the company, this triangle is the basis for successful implementation.
Data and processes as the foundation
It's no surprise that data is the powerhouse of AI. At the end of the day, a model is only as good as the data used to train it. AI agents need access to all relevant information in order to draw conclusions or learn from mistakes. Without data in place, your entire AI project will fall apart and become one of many failed projects.
However, as a player in process automation, Appian also places great emphasis on connecting processes to AI success. That's because an AI's capabilities are the same as the jobs you give it, Calkins argues. According to him, process is the missing link that connects AI to meaningful work. These are tasks that involve multiple employees and steps. In fact, processes provide structure and measurability for AI, components that are often missing.
Full-scale AI will not ultimately be directly realized in the expected areas. For example, AI can help with high-volume back-office processes that add important new skills to existing teams. Think procurement, case management, contract management, and compliance. Calkins acknowledges that this may be boring AI, especially when compared to the industry's AI promises. “But the results are anything but boring,” Calkins says.
tips: Appian raises the bar in the battle for the process automation crown
Potential applications of full-scale AI
Appian has spent years investing in multiple tools designed to enable full-scale AI. Data fabric and process modeling are the cornerstones of full-fledged AI concepts. The first of these two, data fabric, enables you to link AI to data across your organization. Process modeling connects AI to large-scale collaboration between people and the digital workforce.
An example of how Appian is enabling full-fledged AI in practice is the platform's DocCenter functionality. DocCenter handles the large amount of documents that large organizations must process. Registrations, upgrades, compliance documents, complaints, and questions come in all types of formats. Digital, paper, audio recording, or handwritten. AI reads documents, determines their meaning, and extracts important information. That information is written to the appropriate database. AI also directs tasks to the right people. For urgent or emotional messages, the system increases the urgency even further.
To achieve reliable output, Appian sends the same document to multiple AIs to verify the results. If they agree, action will be taken. A human must check if there are any differences. In this way, you can achieve a success rate of up to 99%. Here, it is not only important to read the documents, but also to integrate the process. Documentation is a stimulus that requires action. Therefore, DocCenter is integrated with all follow-up actions.
Appian shares DocCenter customer examples. A large Australian insurance company was performing commercial acceptance assessments manually. Using DocCenter, the process was reduced from 4 days to 90 minutes with 99% accuracy. Appian said the savings amount to more than $1 million per month. Meanwhile, 70% of all customers reportedly use AI in production in this way.
results are leading the way
Going forward, Appian will also be paying particular attention to agent AI. Appian 25.4 marks the end of Agent Studio's long beta program. Announced in April, the tool allows developers to build AI agents that orchestrate entire business processes. Agents have access to all tools, records, rules, and documents. Independently determine the best path to the desired outcome. They also adapt to changing circumstances.
According to Calkins, this feature differs from other AI agents in three ways. Agents can access all data in your organization through the data fabric. They perform actions by starting processes. And we learn by extensively tracking everything that happens on the platform.
With this agentic AI step, Appian, like other artificial intelligence companies, clearly understands that technology is only valuable when embedded in business-critical processes with clear objectives and metrics. It's not just about technology, it's also about business outcomes. Over the next few years, Appian expects more and more organizations to follow this path, from experimenting with AI to structurally embedding it in processes that really matter.
tips: Appian 25.4 uses Agent Studio to control AI agents
