Skan AI
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About
Skan AI is an enterprise-grade work intelligence platform designed to provide a comprehensive Digital Twin of organizational operations. Unlike traditional process mining that relies on sanitizing event logs or manual time-and-motion studies, Skan uses AI-powered observation to capture how work actually happens in real-time. It continuously monitors activity across every application—including legacy systems, spreadsheets, and non-integrated tools—to map out workflows, identify variants, and pinpoint bottlenecks that other tools often miss. By creating a high-fidelity Enterprise Work Graph, the platform allows leadership to move beyond guesswork and base their transformation strategies on the messy reality of daily operations. The platform’s core functionality revolves around its Observation to Agent (O2A) framework, which focuses on training AI agents using real-world enterprise data. Most AI implementations fail because they are trained on incomplete or idealized data; Skan solves this by capturing every action, exception, and workaround performed by human workers. This rich context ensures that when AI agents are deployed, they are already equipped to handle the complexities of the specific enterprise environment. Beyond agent training, the platform enables continuous benchmarking, governance, and the establishment of guardrails to orchestrate work across both human teams and automated agents. Skan is primarily built for large-scale enterprises in highly regulated or complex industries such as insurance, banking, healthcare, and manufacturing. It serves senior leaders in operations, customer success, and digital transformation who need to reduce operational costs, improve efficiency, and scale automation reliably. For example, in contact center environments, Skan helps identify the workflows of top-performing agents to reduce average handle times and after-call work. It is particularly effective for organizations with thousands of knowledge workers where manual oversight is impossible and system-wide visibility is obscured by fragmented technology stacks. What sets Skan AI apart is its non-intrusive, zero-integration approach to process discovery. While traditional task mining tools struggle to scale or capture cross-app movement, Skan observes the entire desktop environment without requiring API access or back-end logs. This allows for rapid deployment—often providing actionable insights in as little as four to eight weeks. By quantifying the dollar impact of specific tasks and surfacing automation gold mines, Skan provides a data-driven foundation for building a self-healing, autonomous enterprise that can adapt to changing operational demands with confidence.
Pros & Cons
Requires zero IT integration or back-end event logs to start capturing workflows.
Monitors all desktop activity across any application, including legacy and non-integrated systems.
Delivers deep data-driven insights in 3 months that would take manual teams years to gather.
Quantifies the precise dollar impact of specific tasks and automation opportunities.
Scales effectively to monitor thousands of knowledge workers simultaneously.
Pricing is not publicly listed and requires a custom quote through a demo request.
The 2-8 week implementation period may be too long for small businesses seeking instant results.
Focused exclusively on large enterprise environments rather than individual or small team use.
Requires initial setup for data masking and local storage to satisfy compliance requirements.
Use Cases
Operations leaders at Fortune 500 banks can identify hidden workarounds in legacy systems to reduce cost per case and improve decision-making.
Contact center managers can analyze the workflows of top-performing agents to reduce Average Handle Time (AHT) by up to 30%.
Digital transformation officers can use 'Observation to Agent' to train AI agents on real-world exceptions for more reliable automation.
Healthcare payers can increase workforce utilization capacity by identifying repetitive tasks in non-integrated apps suitable for automation.
Insurance providers can reduce call processing times by up to 45% by eliminating excessive application switching patterns.
Platform
Features
• automation opportunity identification
• context-driven ai agent training
• automatic playbook generation
• enterprise work graph analytics
• digital twin of operations generation
• zero-integration deployment model
• continuous cross-application monitoring
• observation to agent training framework
FAQs
How does Skan AI protect sensitive customer data?
The platform observes process flows without capturing actual sensitive data content. All captured images are masked and stored locally to ensure privacy while still providing the context of how work is completed.
How is Skan AI different from traditional process mining tools?
Unlike traditional tools that rely on system event logs, Skan AI captures work across all applications, including Excel, legacy mainframes, and non-integrated apps, without needing any system integrations.
How long does implementation typically take?
Implementation for large enterprises generally takes between 2 to 8 weeks from kickoff to delivering initial insights. This speed is possible because the technology is non-intrusive and requires minimal IT resources.
Will our agents feel like they are being monitored for performance?
Skan AI focuses on process improvement rather than individual performance evaluation by anonymizing data. Employees often welcome the initiative as it aims to eliminate frustrating inefficiencies and workarounds.
Can Skan AI handle global teams and different systems?
Yes, the platform is designed to scale to thousands of users across any geographic location. Because it does not require system integration, it can observe work consistently across diverse global teams.
Pricing Plans
Custom Enterprise
Unknown Price• Process Intelligence Platform
• Observation to Agent (O2A) training
• Zero-integration deployment
• Digital Twin of Operations
• Enterprise Work Graph
• Automation ROI Calculator
• Application switching patterns
• Top vs average performance benchmarking
Job Opportunities
Customer Success Manager
Optimize enterprise operations and train reliable AI agents by capturing real-time work context across all applications without complex system integrations.
Education Requirements:
Bachelor’s degree in Computer Science, Engineering, Business, or a related field
Experience Requirements:
12+ Years
5+ years of experience in pre-sales, solutions delivery, product consulting, or technical project management
Other Requirements:
Experience with B2B platforms, process intelligence tools
Domain knowledge in fintech, insurance, healthcare
PMP, Scrum Master, or other project management certification is a plus
Responsibilities:
Lead the planning, execution, and delivery of POCs across target customer accounts
Collaborate with the Sales and Solutioning team to define success criteria
Deliver insights and readouts to key stakeholders and executives
Partner with process data analysts to explore product configurations
Own communication and stakeholder management
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Director- Customer Success (Healthcare Vertical)
Optimize enterprise operations and train reliable AI agents by capturing real-time work context across all applications without complex system integrations.
Education Requirements:
Bachelor’s degree in business administration, Engineering, Computer Science, or a related field required
Master’s degree (MBA or equivalent) preferred
Experience Requirements:
17-22 Years
12+ years of progressive experience in customer success or consulting
At least 5 years in a senior leadership or client-facing executive role
Other Requirements:
Deep industry expertise in BFSI
Experience with process intelligence, automation, AI/ML
Responsibilities:
Lead account planning initiatives aligned with customer business goals
Establish, maintain, and deepen executive relationships
Collaborate with customer and partner stakeholders to define transformation roadmaps
Lead value management initiatives to ensure measurable ROI
Coach and enable a team of CSMs
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Customer Success Manager
Optimize enterprise operations and train reliable AI agents by capturing real-time work context across all applications without complex system integrations.
Education Requirements:
Bachelor’s Degree in technology or management
Experience Requirements:
8+ Years
4+ years’ experience in the banking and financial services, Insurance or healthcare verticals
Other Requirements:
Experience in business process design or intelligent automation
Industry knowledge in Digital Transformation Initiatives
Responsibilities:
Lead Program Management across all phases of engagement
Implement Skan customer success model and value management methodologies
Own Skan ROI and business outcome reporting
Manage all aspects of project delivery and risk management
Develop deep expertise in Skan product technology
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Ratings & Reviews
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