Hi, I'm Nishtha.

I lead design and research at Workato, where I'm building the platform that gives a flock of unpredictable AI agents their rules and shape.

Platform strategy

Shaping an integrated AI platform strategy

Used research and prototypes to win support for an integrated platform over a standalone product, then guided its launch and what came next.

Product incubation

Translating the MCP standard into a product

I understood the emerging MCP standard, translated it into a product model, and guided its implementation from prototype through release.

Design org leadership

Scaling a design org from seed-stage to a $5.7B company

Built Design and Research from one to 22 over ten years. Created shared practices and design leaders to keep the quality bar high.

Design direction

Supervising AI agents without leaving your work

Led a product-wide model for monitoring and steering parallel AI agents without interrupting people’s current work.

Hands-on design

Designing workflow primitives that scaled into a platform

Designed composable workflow primitives that let customers build almost anything and later supported copilots, agent skills, and MCP.

Co-inventor

Patenting a turn-taking cue for streaming AI

I co-invented a patented conversational cue that shows when streaming AI is still responding and when the user can take the next turn.

Some tinkering. A few musings.

Trust in human-robot interaction

5 levels of AI maturity in design

Design your own bird swarm

Nishtha presenting on stage at a Workato conference

I've been leading Product Design and Research at Workato for the last 10 years.

With two master's and a bachelor's degree, my formal training spans physical product design, visual communication, psychology, and human factors.

Before Workato, I co-founded MyNewsStudio. We built a good product, but the business model was not viable. We also tried to do too much at once. The experience showed me that design alone cannot create success. Product strategy, revenue model, sales, distribution, and marketing all have to work together. Focus is a business decision. We eventually wound down the company, so knowing when to walk away was my hardest lesson.

Before software, I designed tabletop accessories for Episode, an Indian design brand. Sixteen years later, elements of that work are still sold and used to market the collection. I've also created art installations, designed luxury retail spaces, and worked as a design merchandiser.

I'm looking to build with companies, in software or hardware, where the hard part is making powerful systems people can trust.

LinkedIn comment from Vijay Tella, Co-founder and CEO of Workato: “Nishtha Mehrotra, I'm incredibly grateful. We're so lucky to have you leading the intuitive product experience we're known for. I've spent 30 years in integration middleware, and this tech is complex and whack under the hood. Refactoring six complex platform categories into one while making it seamless for both pro-code integration experts and business teams? No joke. What you and your team have built is one of the great feats of enterprise software design. So much so, you have created a marketing challenge as Workato confounds people. How can something that looks so easy be so powerful? What blows me away is your first-principles thinking and relentless drive to keep attacking the complexity. We don't have real competition for our product experience, productivity, and speed; our biggest challenger is ourselves. You and your team are awesome role models of our core value to keep raising our game. Thank you.”

From Vijay Tella, Workato’s CEO

Credentials

M.S. in Human Factors and Ergonomics

Department of Industrial and Systems Engineering San José State University

M.Design

Indian Institute of Technology Guwahati, India

B.Design

National Institute of Fashion Technology, Gandhinagar, India

5 levels of AI maturity in design

This five-level model follows AI's role in design from completing bounded production tasks to independently researching, evaluating, and executing a brief. Each level changes what AI can own and where human judgment remains essential.

Overview of five levels of AI maturity in design, progressing from mechanical and generative work through evaluated, intentional, and autonomous design

Level 1: Mechanical

At the mechanical level, AI is a fast pair of hands. It completes bounded tasks with clear instructions and limited product context.

Examples include:

  • Writing error messages, button labels, and placeholder copy
  • Removing image backgrounds
  • Documenting a component
  • Creating dark mode or responsive variants
  • Summarizing research transcripts
Level 1, Mechanical: AI executes bounded production tasks while the designer defines each task, provides context, and reviews the output

The designer still decides what needs to happen, breaks the work into steps, supplies the context, and reviews every output. AI speeds up the workflow while leaving its structure unchanged.

Most design teams already use AI at this level, even if they don't describe it as an AI workflow.

Level 2: Generative

At the generative level, AI receives a problem and produces a landscape of possible directions. For an interface that manages multiple agents, it might research comparable patterns, explain tradeoffs, and prototype four interaction models:

  • A mission-control dashboard showing every active agent
  • A dock for switching between agents
  • A spotlight-style command interface
  • An inbox that organizes requests, updates, and approvals
Level 2, Generative: AI researches existing patterns and generates a landscape of possible directions while the designer judges, challenges, and eliminates options

The designer supplies the problem, constraints, customer context, and product goals. AI expands the solution space.

The most important design action at this level is selection. Generating ten plausible concepts is easy for AI to do. Identifying the two that reveal something useful requires strong product understanding and restraint.

Level 3: Evaluated

At the evaluated level, AI generates directions, gathers evidence, and challenges early assumptions. In a study about managing multiple agents, it might notice that participants treat the agent as a command executor even when the product presents it as a collaborator, then propose several responses:

  • Change the study to investigate expectations of agency and control
  • Test language that makes collaboration more explicit
  • Compare a conversational interface with a task-management interface
  • Introduce moments where the agent asks for clarification
  • Examine whether users want collaboration at all
Level 3, Evaluated: AI runs tests, identifies patterns, and adapts a study within defined boundaries while humans set the rubric and approve important changes

Level 3 requires an explicit evaluation system. The team defines success, evidence, recruitment, and the decisions AI may make during a study. A designer or researcher interprets the findings and decides whether the work should change direction.

AI can surface a pattern. The design team determines its product significance.
For many organizations, Level 3 is a practical stretch goal. It depends on good research operations, reliable product context, clear approval boundaries, and traceable evidence.

Level 4: Intentional

At the intentional level, AI can execute toward a specific experiential quality.

This level concerns the details that shape how a product feels:

  • The difference between optical and mathematical corner radii
  • The speed and curve of an animation
  • The rhythm of a multi-step interaction
  • The amount of friction before a consequential action
  • The tone of an interruption
Level 4, Intentional: AI executes toward a specific experiential quality while the designer guides it with lived experience, taste, critique, and emotional intent

A team may want an approval interaction to feel deliberate without feeling slow, or a healthcare alert to feel urgent without creating panic. AI can generate, test, and refine several executions, but qualities such as “calm,” “confident,” or “playful” still allow many technically valid interpretations.

AI can chase the ceiling, but human judgment keeps moving it.

The designer guides AI using references, critiques, customer knowledge, lived experience, and taste. AI can already produce polished work, but consistently creating the right emotional effect across an experience requires an evolving sense of what good looks and feels like.

Level 5: Autonomous

At the autonomous level, AI operates as an independent designer across the full process.

A designer or product lead might write:

The brief
Users need to monitor multiple agents working in parallel.
Level 5, Autonomous: AI independently researches a design brief, generates and evaluates directions, and produces a specification while a human writes the brief and gives final approval

From that brief, AI would:

  • Research users and their current workflows.
  • Review existing product patterns and technical constraints.
  • Identify the problem and generate several product directions.
  • Build prototypes, recruit participants, and run studies.
  • Revise or eliminate weak directions.
  • Select the strongest solution.
  • Produce an annotated, production-ready specification.
The initial brief and final approval still have clear owners. The work between those points is conducted by AI.

Level 5 requires every preceding capability to work together reliably, from research and evaluation through interaction design, accessibility, edge cases, and documentation.

This level sits beyond the current planning horizon for most design teams. Individual steps already exist. Reliable end-to-end performance remains the central challenge.

Using the model

A team can operate at several levels at once. AI might handle production tasks at Level 1, concept exploration at Level 2, and a narrowly defined research program at Level 3.

The model is useful because it makes each working relationship concrete. For any AI-assisted design activity, teams can ask:

  • What context does AI receive?
  • What work can it complete independently?
  • How is the output evaluated?
  • Which decisions require approval?
  • What evidence supports the final direction?
  • Who owns the outcome?
The goal is to choose the appropriate level for the work and make the responsibilities at that level explicit.