Artificial intelligence is now ever-present in many businesses. But where’s the ROI? Many deployments stall in pilot mode, failing to drive transformation.
Over the past two years, businesses have rushed to deploy generative AI to try to boost operational efficiency, improve customer experiences, and achieve critical organizational objectives. But without a structured enterprise AI strategy, these efforts have failed to drive tangible business outcomes.
The problem? Most AI deployments are isolated experiments. Organizations must take a more strategic approach by putting AI inside a process.
A process is anything an organization does thousands of times. Processes are how insurers manage claims and banks validate money. They’re how the government operates procurement cycles and pharmaceutical companies run clinical trials. Processes are how organizations spend their money, drive customer engagement, comply with regulations, and build their reputations.
Embedding AI in processes gives it structure, repeatability, and transparency.
This post explores six key reasons why process-driven AI is the foundation of a strong enterprise AI strategy.
Enterprise AI strategy: 6 reasons to embed AI in process
Most AI deployments today are passive—they wait to be called. Useful? Yes. Transformative? Not on their own.
Digital transformation happens when AI moves off the sidelines and into business processes where:
Work gets done
Decisions are made at scale
Transactions are executed
Efficiency and cost savings are realized
Process is critical for effective agentic AI. AI agents can trigger actions, collaborate with digital and human coworkers, and escalate complex cases when needed. When embedded in workflows, AI doesn’t just assist—it drives execution on critical organizational goals.
1. Process makes it easy
Most businesses deploy AI tactically, leading to isolated pilots with no path to scale. This often requires custom applications and heavy infrastructure.
A process-driven approach eliminates these hurdles. With low-code automation, AI can be deployed in just a few clicks—assigning tasks within existing workflows, no complex infrastructure needed.
2. Process provides structure
Ease is just the start. To drive real impact, AI needs structure—clear objectives, defined workflows, and human oversight. Structure keeps AI aligned with strategic goals and prevents poor decisions. Process provides:
Purpose & guardrails to keep AI focused on outcomes.
Collaboration between AI, humans, and digital workers.
Predefined workflows ensuring AI operates within set steps.
Escalation paths for AI to hand off uncertain cases.
Audit logs tracking AI-driven actions for transparency.
Human oversight to ensure quality, compliance, and trust.
With structure, AI delivers real business outcomes—driving efficiency and scaling impact.
For example, Acclaim Autism set a clear goal: streamline patient intake so children receive care faster. Acclaim Autism provides ABA treatment for children with autism spectrum disorder. Prior to using Appian AI, patient intake could take up to 180 days. By embedding AI, they automated data extraction from unstructured medical documents, eliminating a key bottleneck. Intake times fell to 30 days.
With the right structure, AI delivers real impact, not just experimentation.
3. Make smarter decisions with data and context
AI is only as good as the data it uses. Without relevant high-quality data, AI lacks context—whether for training or real-time decisions.
Too often, critical data is trapped in silos—CRM records, ERP systems, and legacy databases—leaving AI with an incomplete picture for its decision-making processes.
Data fabric solves this. It enables enterprise-wide data access without migration, syncing live data across systems so AI operates with real-time, complete information.
It also simplifies security by centralizing permissions, preventing unauthorized access while ensuring compliance. With data fabric, AI makes smarter, more informed decisions.
For example, the University of South Florida improved student support using a combination of AI and data fabric. Data fabric provided access to student data, and then Appian’s AI copilot enabled counselors to gain personalized recommendations. This cut manual research time down by 50% for counselors for each, allowing their advisors to provide more personalized attention to USF’s nearly 50,000 students.
4. Process builds in safety and compliance
AI poses potential risks. Guardrails are essential for preventing harm and navigating ethical concerns. Whether reducing legal risks or protecting brand reputation, businesses must prioritize AI safety. Process makes this happen. It provides a counterbalance to AI power, offering the ability to:
Process makes this happen. It provides a counterbalance to AI power, offering the ability to:
Deploy AI strategically to reduce risk and align with strict compliance regulations and governance frameworks.
Keep humans in the loop by designing process models to pass work between human and digital workers.
Set clear escalation paths for when AI needs to uplevel information to humans.
Provide transparency and support compliance with AI activity logs.
Set user permissions to ensure AI doesn’t share data with the wrong users.
5. Process makes AI measurable
AI is often black box. It’s often impossible to know whether it’s making an impact. But business leaders need to know if their enterprise AI solutions are truly addressing their top business challenges.
By embedding AI in a process, every action gets logged, tracked, and analyzed. A strong process platform orchestrates routine tasks and complex tasks across enterprise systems, people, and AI. This orchestration layer makes everything trackable.
- Track your goals. Track success metrics like efficiency gains and reduced wait times on customer inquiries to measure AI’s impact and align with business goals.
- Find and fix bottlenecks. Get actionable insights to identify slowdowns caused by manual work or rework, then assign AI or other process tools to eliminate them.
- Improve workflows over time. Improve AI and processes with process intelligence. Choose actions to boost AI accuracy or pinpoint where human oversight enhances performance.
6. Process helps you scale
As mentioned earlier, tactical approaches keep AI siloed. This limits AI’s value and prevents you from accomplishing enterprise goals. Process expands AI enterprise-wide by providing:
End-to-end process orchestration with pre-built integrations, APIs, and connectors to scale AI value across the enterprise.
Scalability to handle high-throughput, demanding workloads.
Enterprise security with zero-trust architecture, role-based access, encryption, data loss prevention, and more.
Compliance certifications such as FedRAMP, HIPAA, and SOC II.
Reusable process components that simplify building new applications and accelerate development as you expand Appian to new processes.
Enterprise AI demands more than isolated wins. It needs enterprise-wide structure, security, and efficiency. Process delivers all three.
Business runs on process. So should AI.
AI alone won’t transform your business—but embedding AI in processes will. The companies that integrate AI in their workflows using enterprise AI platforms will see faster processes, cost reductions, and greater agility. Those that don’t will remain stuck in AI experimentation mode, never reaching enterprise scale.
AI isn’t the future of work—process is. But combining the two gives you the advantages to thrive in the AI economy.
