Customer and employee service
Connect requests with relevant information, permitted actions, and human support.
Services
Turn business priorities into operational AI
Engage Normatic around a defined transformation need or bring our capabilities together through the Fractional AI Transformation Office. Each engagement connects its scope to business outcomes and the path into operational use.
AI Transformation Strategy
Set a clear AI agenda and a practical roadmap from business priorities to implementation.
AI Transformation Strategy
- Business need
- Many potential AI initiatives compete for investment, with limited agreement on where to begin.
- Typical outputs
- An opportunity map, prioritization criteria, outcome measures, and an implementation roadmap with owners and dependencies.
- Intended outcome
- A shared investment agenda that connects selected opportunities to business value and delivery readiness.
AI Operating Model
Define how AI-enabled work is owned, governed, funded, and managed across the organization.
AI Operating Model
- Business need
- AI initiatives need clear decision rights, funding, governance, and coordination across business and technology teams.
- Typical outputs
- A target operating model, accountability map, governance mechanisms, capability priorities, and a transition plan.
- Intended outcome
- Clear ownership of AI-enabled work and a practical way to manage it across the organization.
AI Workflow Transformation
Redesign a priority workflow across people, information, and systems, then make it operational.
AI Workflow Transformation
- Business need
- A consequential workflow is slowed by fragmented information, manual handoffs, or repeated rework.
- Typical outputs
- A redesigned workflow, human and AI responsibilities, acceptance measures, an integrated solution, and operating procedures.
- Intended outcome
- Better workflow performance, assessed against agreed measures such as cycle time, quality, and exception handling.
Applied AI Engineering
Build and integrate enterprise AI applications with evaluation, monitoring, and operational support in mind.
Applied AI Engineering
- Business need
- A defined business requirement or promising prototype needs to become a dependable operational system.
- Typical outputs
- Integrated software, evaluation results, deployment configuration, monitoring, and technical and operational documentation.
- Intended outcome
- A solution that meets agreed acceptance criteria and can be operated, maintained, and improved.
Adoption and Scale
Embed AI into everyday working practices and extend successful capabilities where evidence supports expansion.
Adoption and Scale
- Business need
- A deployed capability needs to become part of everyday work before wider rollout can create value.
- Typical outputs
- An adoption plan, learning materials, usage and outcome measures, an improvement backlog, and scaling priorities.
- Intended outcome
- Sustained use, prepared internal owners, and evidence to guide where the capability should expand.
These outputs guide engagement design. The specific deliverables, acceptance measures, and responsibilities are agreed around your priorities and operating context.
Illustrative applications
Where transformation can begin
Three illustrative scenarios show how AI, people, and business systems can work together. They describe possible applications across industries, not client projects or achieved results.
Enterprise knowledge and research
Find authorized information and prepare source-linked answers and briefs.
- Business problem
- An employee preparing a briefing searches several repositories, reconciles conflicting documents, and asks colleagues which information is current. The effort repeats whenever a similar question arises.
- Redesigned workflow
- The employee submits a question. The system searches permitted sources and prepares an answer linked to supporting passages. Missing evidence or conflicting material is routed to a subject expert. Reviewed answers and source corrections feed back into the knowledge base.
- AI responsibilities
- Retrieve relevant material, summarize it, and draft an answer that distinguishes supported findings from gaps. Access permissions are enforced by the surrounding system.
- Human judgment and accountability
- Employees validate consequential conclusions before using them. Subject experts resolve conflicts; a named knowledge owner maintains source quality and reviews recurring gaps.
- Success measures
- Time to a verified answer, accuracy of supporting references, correction and escalation rates, and repeat use. Assess speed alongside answer quality.
Document processing
Classify inputs, extract information, and route exceptions.
- Business problem
- A team receives forms and attachments in different formats. Staff copy information into business systems, check for missing fields, and chase corrections before the next activity can begin.
- Redesigned workflow
- Incoming documents are classified and required fields extracted. Validation rules check completeness and consistency. Routine records move to the next permitted activity; ambiguous or incomplete records enter a review queue. Approved corrections update the connected system with a traceable record.
- AI responsibilities
- Classify varied documents, extract information, and flag ambiguity. Conventional validation rules and integrations govern which records can proceed automatically.
- Human judgment and accountability
- Reviewers resolve ambiguous inputs and make decisions requiring judgment or approval. The process owner sets acceptance rules, assigns exception handling, and maintains operating guidance.
- Success measures
- Processing time, field-level extraction accuracy, manual correction effort, rework, and exception backlog. Track results by document type to expose weaknesses hidden by averages.
Internal requests and approvals
Gather information, coordinate approvals, and update connected systems.
- Business problem
- Requests arrive through email and forms with incomplete information. Employees chase status, approvers reconstruct the context, and completed decisions are manually copied into other systems.
- Redesigned workflow
- A requester describes the need. The system gathers missing information, prepares a request summary, and routes it under agreed approval rules. Named approvers record their decisions. Authorized integrations update the relevant systems and return a clear status to the requester.
- AI responsibilities
- Interpret the request, identify missing information, summarize supporting context, and draft follow-up messages. Routing and system updates follow defined rules and permissions.
- Human judgment and accountability
- Approvers authorize commitments and resolve exceptions to policy. A process owner maintains decision rights and escalation routes, and reviews stalled or incorrectly routed requests.
- Success measures
- End-to-end cycle time, first-submission completeness, handoff count, overdue requests, and completion accuracy. Monitor unauthorized or incorrectly executed actions alongside efficiency.
Other applications
Management analysis
Assemble evidence and prepare decision briefs for leaders.
Proposal and deliverable development
Assemble approved content and coordinate quality review.
Assess each opportunity against business value, data and system readiness, operating requirements, and adoption needs. Establish a baseline and agree success measures and targets for the workflow before delivery.