Notes from the practice, written by hand
Essays, working papers, and research dispatches from the studio’s own engagements. No frameworks, no thought-leadership tax — only what we have learned, and would defend on a Tuesday.
More from the studio
Page 1 of 11
DispatchWhat AI Transformation Actually Costs in 2026, and Why Almost Everyone Budgets It Wrong
AI transformation in 2026 is no longer a single software purchase it is a business-wide investment spanning strategy, data, infrastructure, AI systems, integration, governance, talent, and ongoing adoption. This article breaks down what AI transformation actually costs, the hidden expenses most organizations overlook, and why traditional technology budgets often underestimate the true cost of moving AI from pilot to production.
DispatchThe First 90 Days of AI Transformation: A Week-by-Week Plan That Holds Up
The first 90 days can determine whether an AI transformation becomes a scalable business capability or another stalled technology initiative. This week-by-week guide outlines what organisations should prioritise during the first three months from assessing AI readiness and selecting high-value use cases to building governance, aligning teams, launching pilots, and measuring early business outcomes.
DispatchAI Strategy, Adoption and Transformation: What Boards Actually Need to Understand
AI is no longer just a technology decision it is a board-level business priority. Leaders need to understand where AI can create measurable value, what risks it introduces, and how it affects people, processes, data, and long-term strategy. This blog explains the key aspects of AI strategy, adoption, and transformation that boards need to evaluate before making enterprise AI investments.
DispatchThe 30-Question AI Readiness Test: Where Does Your Business Actually Stand?
AI adoption starts with readiness, not technology. Before investing in enterprise AI, organizations need to assess their data, infrastructure, people, processes, governance, and business goals. This 30-question AI readiness checklist helps enterprises identify gaps, measure their preparedness, and determine the practical steps needed to move from AI experimentation to scalable, business-driven implementation.
DispatchWhy Enterprise AI Projects Fail: 5 Failure Modes to Avoid?
Enterprise AI projects rarely fail because the technology does not work. They fail because organizations underestimate the complexity of deploying AI at scale. From unclear business goals and poor data quality to weak adoption and unrealistic expectations, these challenges can quickly derail even promising initiatives. This blog explores five common enterprise AI failure modes and the practical strategies businesses can use to avoid them.
-1200x675.png)
DispatchAI Readiness Assessment: Is Your Business Actually Ready for AI?
AI readiness is not just about having the latest technology. It requires the right data, architecture, governance, processes, and people to turn AI into measurable business value. This guide explores the key areas of an AI readiness assessment and how enterprises can identify gaps, prioritize use cases, and build a practical path from AI experimentation to successful implementation.
DispatchEnterprise AI Governance: How to Build Guardrails Without Slowing Innovation
Enterprise AI governance is no longer just a policy exercise. As AI systems become more autonomous, businesses need practical guardrails across data, security, risk, human oversight, architecture, and accountability. This guide explains how to build an AI governance framework that manages risk while giving teams the freedom to innovate and scale AI responsibly.
ResearchMVP Development Cost: A Realistic Budget Breakdown for Startups
Two founders walk in with the same idea on paper and leave with quotes three or four times apart. The reason is never the idea; it is the scope. This guide breaks down what an MVP actually costs in India, from lean to AI-heavy builds, where every rupee of your budget really goes, and why the cheapest quote is rarely the cheapest outcome. The goal is not the cheapest MVP. It is the smallest useful one that gives you a real answer from real users.
ResearchAgentic AI for the enterprise — where it pays off, and where it doesn't
The question that decides an agentic programme is not whether you can build the agent — it is whether the workflow should be agentic at all. A field map of the ones that pay off, the ones that don't, and how to tell them apart before you spend.
One email a quarter No frameworks
The studio’s reading list, three pieces we are working on, and one number we are watching. Sent only when there’s something worth saying — typically four times a year.