The era of AI experimentation is over. Organizations want to see ROI. And they will—as long as they understand that the competitive edge isn’t in AI itself. With AI evolving rapidly, businesses need a clear strategy that cuts through the noise and generates ROI. This key strategy is to embed AI into core business processes.
This post will cover five enterprise AI trends for the new era of AI and why process is the key to ROI.
1. AI agents will be embedded in processes
The most talked-about trend today is agentic AI. Early AI required human interaction, like an employee asking a chatbot to write an email. But AI agents act autonomously. An AI agent could write an email without being asked and notify a human to review and edit it.
But organizations can’t let autonomous agents run amok. How do you control AI at scale? With process.
Process gives AI agents clear guidelines and structure. Process defines goals and sets clear limits. Process gives agents a clear escalation path, adding human oversight when needed. It also enables secure, enterprise-wide data access through data fabric.
Businesses need AI to be low-risk and reliable. Process lets you deploy AI agents safely by keeping them in your control.
2. AI models will adopt expanded context windows
Feeding information to AI algorithms raises security risks. Without the right safeguards in place, sensitive data could be improperly stored, shared, or accessed. That’s why the new era of AI models will adopt “extended context windows.”
A “context window” is the amount of text AI can process at once. Extending this window allows the AI to handle more information in a single session, reducing the need to store or repeatedly transfer data. In some cases, this means AI can process user input and then "forget" it after the session ends, lowering the risk of data exposure.
3. RAG will improve AI accuracy
More organizations will use retrieval augmented generation (RAG) to improve AI accuracy. With RAG, models reference a knowledge base before responding to users as a way of fact checking themselves. This leads to more accurate information and fewer AI hallucinations.
For example, The Texas Department of Public Safety optimized its award management process with Appian’s enterprise copilot. The copilot they deployed references a 2000+ page knowledge base to understand thousands of regulations and policies. Staff use the copilot to get real-time answers to compliance questions, streamlining the procurement process and speeding up approvals.
4. AI trust will grow
48% of business leaders find security an impediment to AI adoption, according to Workday. Global markets haven’t regulated AI as well as they should, leading to foundation models that sacrifice privacy and security for speed-to-market.
Expect companies to prioritize safety, increasing trust and driving further enterprise adoption. The data bears this out: Forrester found that spending on AI governance software will quadruple by 2030, reaching $15.8 billion. Businesses will turn to software or process platforms for AI governance. As this builds trust, we’ll see AI expand to more use cases.
Additionally, businesses will adopt private AI. They’ll train their own purpose-built AI models that operate within company boundaries. Private AI safeguards sensitive data and prevents employees from exposing proprietary information to public models. Even organizations using public LLMs will gain more privacy by integrating them into a process platform with built-in security safeguards and compliance certifications.
Over time, a virtuous cycle of trust will develop. Organizations will gain the confidence to scale their AI from isolated pilots to enterprise-wide solutions.
5. Personalized AI will become more popular
As trust grows, expect more personalized AI interactions. Current large language models are generic. They’re trained on vast sets of static content. But people aren’t generic. They have jobs with unique responsibilities. They have writing styles and brand guidelines to follow. Businesses need AI models and copilots built to handle unique workflows, preferences, and knowledge.
Appian enables businesses to train custom AI models for document and email classification, delivering more precise results to businesses. For example, a major Australian financial services firm streamlined case creation and sped up issue resolution times with Appian AI. They trained an email classification model to classify emails and route cases to the right teams. This model was trained on 220 examples and reached a 98% accuracy rate in just minutes.
Businesses will also deploy AI models and copilots that adapt to individual users. For example, an assistant could remember a user’s writing style and preferences. A financial institution could offer personalized investment advice when a user logs in to check their portfolio. All they need is a login to track and remember the user.
A strong process platform lets you choose how to deploy AI. It lets you decide when to use personalized AI and when to use a more generic model.
Putting AI to work in process
Businesses are no longer satisfied with isolated AI use cases or one-off deployments. They need AI to be scalable, reliable, and embedded in their core operations.
Process makes this happen. AI’s future isn’t about standalone tools—it’s about integration, orchestration, and control. By embedding AI within structured processes, organizations get safety, accuracy, and real business impact.
The AI winners will be those who act now. Businesses that fail to integrate AI into their core workflows will be left behind. The next step is clear: Assess your AI strategy. Close gaps. Scale where it matters. And embed AI into your processes.
