Workflow and opportunity audit
Map recurring work, friction, handoffs, decision points, and risks. Prioritize AI use cases by value, feasibility, and the cost of being wrong.
AI adoption consulting for IT teams
Turn AI experiments into a repeatable way of working.
I help business analysts, systems analysts, product managers, and technical product managers redesign team workflows with AI — so the result is faster, more consistent work with clear human ownership.
- Workflow-first, not tool-first
- Built for real delivery teams
- Human review stays explicit
01 · Team workMap the current processTasks, handoffs, bottlenecks, risks, and quality checks
02 · AI layerDesign reusable workflows and agentsContext, tools, prompts, skills, guardrails, and escalation paths
03 · Operating modelEmbed, measure, and improveTeam practices, human review, adoption metrics, and iteration
The gap I help close
Your team uses AI. The process still has not changed.
A few strong prompts do not create an operating model. Sustainable adoption needs the right use cases, reliable context, review points, ownership, and a way to measure whether the work actually improved.
AI use is individual and inconsistent
Everyone experiments differently, so quality and productivity gains depend on the person.
High-value work is still manual
Requirements, research, analysis, documentation, and reviews remain slow despite access to AI tools.
Security and quality concerns block progress
The team lacks clear rules for data, validation, human approval, and responsible use.
There is no measurable adoption plan
Tools are purchased, but there is no prioritized pilot, baseline, owner, or success metric.
What we can build together
AI adoption designed around the work your team already owns.
AI workflows, copilots, and agents
Design repeatable flows with the right context, prompts, reusable skills, integrations, tools, guardrails, and human review.
Team practices and capability building
Create role-specific playbooks, examples, review standards, and hands-on enablement so the team can use and improve the workflows independently.
Governance and rollout model
Define ownership, approved patterns, risk controls, feedback loops, and outcome metrics that support adoption beyond a single pilot.
Designed for cross-functional product delivery
One operating model across roles — not four disconnected AI playbooks.
The best opportunities often sit between roles: research becoming requirements, requirements becoming system design, decisions becoming backlog items, and delivery feedback improving the next iteration.
Business analystsSystems analystsProduct managersTechnical product managersSolution architectsEngineering leads
A practical engagement
Start with one valuable workflow. Prove it. Then scale.
Understand
Clarify business goals, current workflows, constraints, stakeholders, available tools, and risk boundaries.
Prioritize
Select use cases with meaningful upside and manageable risk. Define a baseline and what success will look like.
Pilot
Design and test the workflow with real examples, explicit human review, and the people who will use it.
Embed
Document the working pattern, enable the team, assign ownership, measure outcomes, and improve through feedback.
An example from my own work
From a long manual task to an agent-assisted workflow.
A complex analysis and requirements task was redesigned so an AI agent could prepare the first structured result, while an experienced analyst retained review and approval.
24h → 2.5hAbout 1.5 hours of agent work plus roughly 1 hour of expert review in this workflow.
Structured contextStandards, examples, domain knowledge, and a clear task definition
Reusable AI skillsSpecialized instructions and outputs instead of one oversized prompt
Agent workflowMultiple steps, checks, and artifacts assembled into a consistent process
Human quality gateAn expert verifies completeness, accuracy, tradeoffs, and final accountability
Typical deliverables
Artifacts your team can keep using after the engagement.
Workflow opportunity mapPrioritized use cases, expected value, constraints, and risks
Pilot blueprintScope, roles, inputs, outputs, review points, and success metrics
AI workflow or agent designPrompts, reusable skills, tools, context, and escalation paths
Quality and guardrail frameworkValidation rules, human approval, data boundaries, and failure handling
Team playbookRole-specific practices, examples, ownership, and operating standards
Rollout roadmapAdoption sequence, enablement plan, metrics, and improvement loop
Your consultant
Systems thinking, product context, and applied AI — together.
I am Ekaterina Ananyeva, founder of GetAnalyst and The Digital Unicorn, systems analyst, solution architect, educator, and technology founder based in San Diego.
I approach AI adoption as a systems-design problem: understand the work, model the decisions, design the flow, define failure handling, and make accountability visible. That lets teams move beyond demos without losing quality or control.
Systems & solution architectureTechnical depth across APIs, data, integrations, and distributed systems
Business and product translationConnecting operational goals with implementable team workflows
AI education and agentsHands-on work with prompt systems, reusable skills, and agentic workflows
Enablement that sticksTeaching teams how to reason, review, and improve — not just copy prompts
Questions teams often ask
Practical answers before we start.
Do we need to know exactly what to automate?
No. Identifying the right workflow is part of the work. We start with business value, repeated effort, decision complexity, available data, and the consequences of an incorrect result.
Will you recommend a specific AI platform?
Only when it fits your environment and goals. The work is vendor-aware but not vendor-led: process, context, security, integration needs, and operating constraints come first.
Can you work with our security or compliance constraints?
Yes. Data boundaries, permitted tools, access, retention, validation, human approval, logging, and escalation should be explicit parts of the workflow design.
Is this about replacing analysts or product managers?
No. The goal is to remove avoidable manual effort, strengthen consistency, and give people more time for judgment, stakeholder work, and complex decisions.
Can the engagement begin with a small pilot?
Yes — and that is usually the strongest starting point. A focused workflow creates evidence, reveals constraints, and gives the team a pattern it can improve and scale.
Start with a conversation
Let’s identify where AI can create real value for your team.
Send a short note about your team or current challenge. I will reply by email so we can decide whether a consultation makes sense.
- No generic AI transformation pitch
- A practical conversation about your workflow
- Clear next steps, even if a larger engagement is not needed