A launch is a multiplication problem for a lean analytics team: more stakeholders, more questions, and a same-day clock replacing a comfortable one. Agios saw it coming a year out. Rather than add headcount, the commercial analytics team put a governed virtual analyst on the warehouse it already trusted — live in weeks, and now running pre-call planning, market mapping, and root-cause analysis for the whole commercial organization.
Static Excel and PowerPoint reporting gave way to live apps and dashboards that can be changed in natural language.
And days to hours for ad hoc questions — answered live in the session instead of over rounds of vendor back-and-forth.
The same handful of analysts covered market access, field, and leadership through the launch and past it.
Highlights from the PMSA session on how an AI-native commercial team prepared for launch scale — what they evaluated, what they built, and what they'd tell a team starting now.
A live example of how a rep gets a physician 360 ahead of a conversation — and how the same governed answer becomes something the rest of the team can use without filing a ticket.
Do not assume the AI will automatically know your organization. You have to train it like a new employee. Do that groundwork diligently, because it is going to serve you for a long time to come.
Nothing was ripped out. The existing warehouse stayed the single source of truth, and Tellius went on top as the semantic, reasoning, and agentic worker layer. That is what makes the speed safe: every answer runs on the same definitions the business already trusts, with the SQL exposed underneath.
Metric definitions and business rules, validated metric by metric against trusted reports. The same answer for every user, with the SQL exposed.
The most-asked questions run on a cadence, delivering scorecards and briefs to email, Slack, or Teams.
Dashboard-like apps built and changed in natural language, with deeper exploration sitting underneath the default answer.
Row-level access so a rep sees only their own territory, defined workflows, and a validation agent checking output against source data.
The objection Deepanshu hears most is that this has to be a multi-year project. It wasn't — and the reason is unglamorous. Agios had already done the data work. "AI has skills. What it lacks is context, and that's where you come in."
The warehouse was already analytics-ready and structured as the single source of truth. Tellius connected to the data lake directly. His rule of thumb: if a person can manage the data, AI will be able to manage it too.
Business context, KPI definitions, and guardrails set up in a few weeks — including row-level access, so a rep sees only their own territory from the first login.
The step most teams skip. The team compiled the questions market access, field, and home office actually ask, then tested against that list until the answers came back right every time.
Existing dashboards ran in parallel first so the business could check the new answers against the old ones. Then a pilot group. Then everyone.
People who want to interrogate the data directly — including an RVP of Sales who asks his own questions and builds his own custom reports.
For the questions many people ask, the team encodes the steps to follow and the structure of the output, so a familiar question is handled identically each run.
Agentic apps that behave like dashboards on the surface — default answers up front, deeper exploration underneath. Users don't care what's beneath. The analytics team cares a great deal.
Agios started about a year ahead of its launch. "If you start when the pressure hits, panic kicks in and you are not diligent enough." The diligence that makes this work is a luxury of teams that aren't already underwater.
Commercial numbers need the same right answer every run. General-purpose AI covers the rest — email, slides, general analysis. Don't cross the streams.
Analytics-ready data and the semantic layer were most of the effort, and they're what make the speed safe. Define your success metric before the pilot starts — for Agios it was turnaround time on the most frequently asked questions.
Building dashboards from a prompt wasn't on the radar at kickoff. The idea arrived months into production and landed on a foundation that was ready for it. "AI is evolving every day whether we like it or not."
Tellius gives commercial teams an AI worker that shows up with the work done. It connects structured and unstructured data, applies your business rules through a governed semantic and reasoning layer, and delivers finished work where your team already works.
Live in weeks on the stack you already own.