Designing enterprise AI for repeatable consulting work with human-in-the-loop checkpoints.

I led the product strategy and design that moved Kenley from one-off AI interactions toward repeatable consulting workflows. I designed the editor, runtime, and output experience that enabled teams to create, run, monitor, and reuse structured work under enterprise constraints.

The central challenge was defining how autonomy, human judgment, observability, reuse, and client-ready outputs should work together as one product system.

Kenley Workflows turns a team’s way of working (WoW) into an explicit sequence of inputs, tasks, sources, and outputs that teams could reuse.

What I led

• Defined the product model for reusable, AI-assisted consulting work.
• Designed the end-to-end experience across workflow authoring, discovery, execution, supervision, and output.
• Established the human-checkpoint model and led its delivery with engineering under role-based access control, GDPR, accessibility, and AI-reliability constraints.

Impact

• Workflows reached approximately 4,200 runs in a peak week, with 1,000–3,000 weekly runs typical at one enterprise tenant.
• Approximately 450 people used Workflows each week at that deployment.
• The deployment expanded more than 6x after launch.
• The wider product experience contributed to an approximately 95% reduction in deck-creation time and an 80% reduction in precedent-search time.

Why Workflows mattered

Kenley already helped consulting teams search institutional knowledge, synthesize evidence, and create deliverables through chat. Chat worked well for open-ended exploration, but it was a poor home for important work that followed a recurring sequence.

Research across multiple customer firms showed that most users stored certain prompts outside Kenley and copied them back into chat whenever they needed to repeat a task. The working method existed, but the product could not preserve its complete structure: which inputs mattered, which steps were required, where judgment was needed, or what the run was expected to produce.

Consulting work also rarely arrives as a clean template. Different firms use their own frameworks and methodologies, while individual projects combine procedural tasks, professional judgment, and AI-assisted reasoning.

The design challenge was to make that repeatable work visible, editable, and reusable without concealing consequential decisions inside an autonomous run.

Before Workflows

[Prompt stored elsewhere]
            |
            v
[Copied into chat]
            |
            v
[Opaque AI run]
            |
            v
[Deliverable rebuilt manually]

Context, review points, and decisions are lost between runs.