Top global enterprises are chasing three things with AI:
- Deploy agents and automations to streamline repetitive workflows
- Augment large teams with AI tools to improve output quality
- Build enterprise-specific AI models that understand the rationale behind work and can execute on it. Thinking about a world beyond basic AI agents.
However, AI transformation leaders are struggling to implement products that are widely adopted, secure and most importantly, actually capable of yielding ROI and improve business outcomes.
Enterprises are deploying AI blindly, often wasting millions on pilots and years on roadmaps that never ship.
The success of an AI transformation project boils down to 3 main questions:
1. How do I know where to deploy AI?
2. How do I optimize AI usage during deployments?
3. How do I prove ROI beyond basic adoption metrics to the board?
There are 5 common ways transformation executives try to solve this:
- Consultants (humans reconstructing workflows in-person)
- Celonis (systems reconstructing reality from event logs)
- Mimica (desktop-level task mining)
- Worktrace (agent-native workflow discovery + automation)
- Fluency (observation-first work ontology + world model)
Consultants
Consultants are the traditional form of mapping out an enterprise's operations and processes. While consultants are good at creating alignment around how work should happen, their output process document goes stale immediately.
Consultants shadowing employees, running workshops and interviews are merely a snapshot into how work was done 3 months prior to the document being released. Processes, behaviours and software literacy evolves over time.
Moreover, the process documents are only able to capture a standardised, point-in-time observation of how work was done. They miss the important context that matters the most: the handoffs, the communications, the skipped steps and variations that make work really function.
By the time leadership approves the AI roadmap based on consultant findings, the workflows have already changed. The baseline is fiction.
Celonis
Celonis leads the pack in process mining. It reads system logs - think purchase orders created, approvals routed, invoices processed. If a team's work happens inside Salesforce or SAP and left an event trail, Celonis sees it crystal clear.
What it cannot see:
The Excel reconciliation between systems. The Slack thread where the exception was decided. The three-hour email chain that unblocked the approval. The workaround your finance team built because the official process doesn't actually work.
Celonis excels at showing bottlenecks in transactional systems. But most enterprise work happens between those systems - in the copy-paste, the judgment calls, the tribal knowledge that keeps operations running.
When leadership asks where to deploy AI, Celonis shows them what systems logged. It doesn't show them how work actually moves. The baseline is incomplete.
Mimica
Mimica records what happens on employee desktops - clicks, keystrokes, application switches. It captures the "Excel plus email plus browser" reality that system logs miss entirely, then turns that desktop activity into process maps and automation opportunities.
The pattern: Record for a few weeks → generate the map → identify repetitive tasks → hand leadership an automation backlog → stop recording.
The problem: Work changes faster than procurement cycles. Tools get swapped, team structures shift, new exceptions become the norm. Task mining optimizes for repetition, surfacing what can be automated rather than what drives outcomes. The highest-value work - judgment, coordination, exception handling - gets flattened or ignored.
When leadership asks how teams are using the new AI tool three months after rollout, Mimica can't answer because the recording already ended. The baseline went stale.
Worktrace
Worktrace positions itself as agent-native workflow discovery. They capture real workflows from desktop activity, then convert them into AI agents that can execute those workflows autonomously. AI agents do need to understand real workflows, not documented ones. Worktrace gets this aspect of AI transformation right.
The key difference is what the product optimizes for. Worktrace optimizes for conversion: discover the workflow, turn it into an agent, deploy the automation. If a workflow can't be automated, it's deprioritized. Once agents deploy, Worktrace loses sight of how humans work alongside them - the handoffs, the corrections, the exceptions humans handle when agents fail.
When leadership asks whether AI is improving outcomes across the business, Worktrace can show which agents are running and how often. It can't show whether the work actually improved or how humans and agents collaborate in practice.
The baseline is built for one future: autonomous agents. The enterprise needs a baseline that works for any AI future.
Fluency
Fluency observes work at the point of execution, across every system, capturing the full context - not just what systems logged, but the decisions, handoffs, communications, and exceptions that make work function. This builds a work ontology: a structured representation of how work actually happens, not how it's documented.
This addresses all three transformation questions:
- Before deployment: Leadership understands where work happens and which variations drive better outcomes
- During deployment: Real-time visibility into how teams adopt new tools and which use cases work
- After deployment: ROI proven by comparing workflows before and after - actual work patterns and business outcomes, not adoption dashboards
Fluency isn't built for one AI future. The work ontology becomes a world model - a living, queryable representation of execution reality that supports whatever comes next. Autonomous agents that understand real workflows. Copilots that learn from top performers. Enterprise-specific AI trained on actual operations. Transformation approaches that don't exist yet.
The observation becomes infrastructure. The baseline stays current and compounds over time. While snapshots depreciate, the enterprise infrastructure that Fluency creates compounds.
The Foundation for AI Transformation
"The baseline either stays true or it doesn't. Infrastructure either appreciates or it depreciates."
Top enterprises are deploying agents, augmenting teams, and building enterprise-specific AI. Success comes down to three questions: where to deploy AI, how to optimize during rollout, and how to prove ROI.
The snapshot approaches:
- Consultants give you alignment but their baseline goes stale before implementation
- Process mining shows system logs but misses the work between systems
- Task mining captures desktop activity but stops recording after discovery
- Agent platforms optimize for automation but lose sight of everything else
The infrastructure approach:
Continuous observation solves all three questions. It shows where work happens before deployment, how teams adopt during rollout, and whether outcomes improved after. More importantly, it compounds - supporting agents, copilots, enterprise models, and whatever comes next.
AI transformation works when it's built on operational truth, not snapshots.
Find and measure AI use cases in your enterprise.
Fluency is the fastest way to get real-time insights into your operations.
