Enterprise Transformation Architect

Role Overview

Aivar Innovations is an AI-native services and software company and AWS Preferred Partner. Aivar operates three accelerator platforms — Convogent (voice and agent AI automation), Velogent (governed agentic process automation for regulated industries), and Kubogent (Kubernetes-native AIOps) — serving enterprises across fintech, healthcare, and technology verticals.

The Change Management Consultant plays a pivotal role in ensuring that enterprise customers of Aivar's AI accelerator platforms — Convogent, Velogent, and Kubogent — successfully adopt, embed, and champion AI-driven process transformation within their organizations. This is not a theoretical change role. The consultant operates at the intersection of AI product delivery, organizational readiness, and customer success, guiding enterprises from early discovery through sustainable behavioural adoption of automation and AI workflows.

This role sits within Aivar's Customer Success and Delivery practice, reporting to the Head of Customer Enablement, and works in close partnership with implementation leads, solution architects, AI engineers, and product managers. The successful candidate will combine structured change methodology with a genuine appreciation for how AI agents, automated workflows, and governed agentic systems redefine how people work — and use that understanding to reduce friction, accelerate value realization, and convert organizational resistance into advocacy.

Why This Role Exists

Enterprise AI adoption fails not because the technology is wrong — it fails because organizations are not prepared for the shift in how people, processes, and systems must operate together. Aivar's change management function exists to close that gap, ensuring that every deployment of Convogent, Velogent, or Kubogent delivers measurable business value, not just technical installation.

Purpose of the Role

The Change Management Consultant at Aivar is responsible for driving the human side of AI transformation within enterprise accounts. While Aivar's engineering and solution teams deploy and configure the technical platform, this role ensures that the people — executives, process owners, end users, and IT teams — are ready, willing, and able to work within an AI-augmented environment.

The role spans the full adoption lifecycle: from pre-deployment stakeholder alignment and organizational readiness, through go-live communications and training, to post-deployment reinforcement, feedback loops, and sustained usage governance. It requires equal fluency in change methodology, enterprise dynamics, and Aivar's AI platform capabilities.

Key Responsibilities

1. Adoption Strategy & Change Planning

• Design structured change management plans tailored to each customer's AI transformation journey — spanning process automation, agentic workflow deployment, and AI-augmented operations.
• Conduct organizational readiness assessments to identify adoption risks, resistance patterns, communication gaps, and training needs before go-live.
• Define change journeys for diverse stakeholder segments: C-suite sponsors, process owners, business analysts, IT operations teams, and end users.
• Establish measurable adoption KPIs — usage rates, training completion, workflow adherence, incident volumes — and track them across program phases.
• Build and maintain a living change roadmap that evolves with the product deployment timeline and customer feedback.

2. Stakeholder Engagement & Executive Alignment

• Map customer power structures, decision pathways, influencers, and blockers to inform stakeholder engagement strategy.
• Secure and maintain executive sponsorship by translating AI deployment outcomes into business value narratives relevant to senior leadership.
• Build trusted relationships with customer program directors, operations leads, technology heads, and business transformation owners.
• Facilitate leadership alignment sessions to ensure sponsorship is active, visible, and sustained throughout the deployment.
• Navigate political complexity, competing priorities, and organizational inertia with maturity, patience, and structured diplomacy.

3. Training Design & Enablement

• Design and deliver role-based training programs that equip users to work confidently within Convogent, Velogent, and Kubogent environments.
• Develop practical enablement materials — user guides, quick-reference cards, workflow simulations, video walkthroughs, and adoption playbooks.
• Simplify complex AI and automation concepts — agent orchestration, supervised automation, self-healing workflows, multi-modal interaction — into accessible language for non-technical audiences.
• Run hands-on workshops, process rehearsals, and scenario-based training aligned to each customer's live workflows and environments.
• Coordinate train-the-trainer programs where customers build internal change capability for sustained self-sufficiency post-engagement.

4. Communication & Narrative Development

• Develop compelling change communications — announcements, transition narratives, FAQ documents, and leadership messages — that explain the why, what, and how of AI transformation.
• Leverage digital channels (intranet, collaboration tools, in-app messages, email campaigns) to sustain engagement momentum throughout the program.
• Craft storytelling frameworks that connect Aivar's AI capabilities to each customer's strategic imperatives and operational realities.
• Run adoption roadshows, impact showcases, and success-story campaigns to reinforce positive behavioural change and build internal champions.
• Ensure communications are consistent, timely, and audience-calibrated — reducing uncertainty and resistance at each program milestone.

5. Resistance Management & Adoption Reinforcement

• Proactively identify resistance signals — passive non-usage, workaround behaviour, negative sentiment, escalation patterns — and design targeted interventions.
• Partner with process leads and team managers to embed AI-assisted workflows into daily operational routines and performance expectations.
• Prevent adoption decay post-deployment by establishing reinforcement mechanisms: usage reviews, coaching sessions, peer champion networks, and feedback integration loops.
• Translate customer concerns about AI reliability, job impact, and process disruption into constructive dialogue that builds confidence and trust.
• Ensure that adoption standards — correct usage patterns, governed automation practices, audit-ready behaviour — are maintained and not diluted over time.

6. Cross-Functional Collaboration & Feedback Integration

• Work closely with Aivar's implementation, solution architecture, QA, and product teams to ensure change workstreams are aligned with technical delivery milestones.
• Capture structured field feedback on user experience, adoption friction, training gaps, and usability issues during live deployments.
• Translate customer insights into actionable product improvement recommendations for Aivar's product and engineering teams.
• Contribute to Aivar's change management practice by developing reusable frameworks, templates, and playbooks from engagement learnings.
• Participate in pre-sales activities — discovery sessions, proposal responses, customer workshops — where change capability is a differentiating factor.

The Aivar Context — What Makes This Role Unique

Change management at Aivar is not about generic digital transformation. It is about helping enterprise customers understand, accept, and operationalize AI agents — autonomous systems that make decisions, execute workflows, and interact with customers, data, and other systems on behalf of human operators. This requires a change consultant who can:

• Explain the difference between traditional automation and AI-driven agentic automation — and why that difference matters to users and managers.
• Address concerns about AI reliability, human oversight, error handling, and accountability in regulated industries such as fintech and healthcare.
• Help organizations redefine roles and responsibilities when AI handles tasks that were previously performed by people.
• Build trust in AI-augmented workflows through demonstration, evidence, and structured governance — not persuasion alone.

Aivar's Accelerators — What You Will Be Enabling

Convogent: Voice and agent AI automation for customer-facing and internal workflows. Velogent: Governed agentic process automation for regulated industries, with audit trails and human-in-the-loop controls. Kubogent: Kubernetes-native AIOps for infrastructure and operations teams. Datagent: AI-powered data intelligence and analytics workflows.

Required Qualifications

Education

A bachelor's degree in Information Technology, Computer Science, Business Administration, Organizational Development, Management, Psychology, Human Resources, or a related discipline is required. A postgraduate qualification — MBA, Master's in Organizational Behaviour, Technology Management, or Digital Transformation — is preferred for candidates managing large, multi-stakeholder enterprise programs.

Formal certification or structured training in change management (Prosci ADKAR, APMG Change Management, Kotter), project management (PMP, PRINCE2), agile delivery (SAFe, Scrum), or enterprise technology adoption will strengthen suitability, provided it is supported by demonstrated work accomplishments.

Experience

Dimension

Requirement

Total Experience 6 to 12 years of overall professional experience

Change Leadership Demonstrated ownership of change workstreams in enterprise technology programs

AI / Automation Context Exposure to AI, RPA, automation, or digital transformation programs is highly preferred

Customer Facing Direct experience working with enterprise customers in consulting, implementation, or CSM roles

Regulated Industries Experience in fintech, healthcare, BFSI, or similarly regulated environments is an advantage

Stakeholder Management Proven ability to influence executives and manage multi-level stakeholder ecosystems

Training Delivery Hands-on design and delivery of role-based training programs for diverse user groups

Skills & Competencies

Technical & Functional Skills

• Working knowledge of AI agents, agentic workflows, conversational AI, process automation, and AI governance principles relevant to enterprise environments.
• Familiarity with cloud-native platforms, API-led integrations, multi-system automation, and AIOps concepts at a functional — not engineering — level.
• Competence in change management methodologies: stakeholder analysis, impact assessment, readiness measurement, communication planning, training strategy, and adoption tracking.
• Ability to translate platform capabilities into practical adoption behaviours for operations teams, process owners, IT administrators, and business leaders.
• Proficiency with enterprise collaboration, documentation, workshop facilitation, and program management tools (Confluence, Jira, Miro, PowerPoint, MS Teams, Slack).
• Understanding of governance, audit requirements, and compliance implications in regulated industries — and how they shape change strategy.

Soft Skills & Behavioural Competencies

• Exceptional communication, storytelling, facilitation, and executive presence — able to present to C-suite and coach frontline users with equal clarity.
• High emotional intelligence, political awareness, empathy, and patience — especially when navigating organizational resistance to AI adoption.
• Ability to build credibility without relying on authority, operate in ambiguous fast-moving environments, and guide change through trust-building.
• Strong analytical thinking, structured problem solving, and ownership mindset — able to see both the systemic and individual dimensions of adoption challenges.
• Collaborative by instinct — comfortable partnering with engineers, architects.