Build on the Stack You Have: How Anthropic’s Head of Industries, Atlassian’s Head of AI, and Scale’s Rory O’Driscoll Landed on the Same AI Playbook

Three of the strongest sessions at SaaStr AI 2026 came from three very different seats. Sharif Mansour runs AI and the product management craft across Atlassian’s 20+ apps and 450 product managers. Eleanor Dorfman leads the commercial and industries sales team at Anthropic. Rory O’Driscoll has been a software investor at Scale Venture Partners for 30+ years.

Different companies. Different jobs. Different stages. Almost the exact same lesson.

Every popular claim about AI right now comes packaged as a binary. Chat is the universal interface, so kill your UI. No, build dedicated experiences and skip chat. Reimagine your product from scratch. No, bolt AI onto what you have. Hire 10x AI builders. No, you can’t afford juniors anymore. Software is dead. No, software is fine.

The binary is the trap. The teams winning right now are running both sides of every one of these at once. And underneath the both/and framing, the same concrete primitive surfaced in talk after talk. Skills. The harness. The thin layer of software that turns a raw model into something a business can depend on.

Here is what each session said.

Session 1: Sharif Mansour, Head of AI and Product Management Craft at Atlassian, on the Three Contradictions of Shipping AI Into Real Products

Sharif Mansour opened with a line that set up the entire conference: for every claim he has read about how to implement AI, there is an equal and opposite claim that is also true. So instead of picking sides, Atlassian went looking for where the answer sits in between. The context matters. This is not a startup with one app and a clean slate. This is 20+ apps, most of them years old, six of them AI-native, with more than 5 million users on the AI features alone.

Contradiction one: chat as the universal interface vs. dedicated UI.

Two years ago, adding chat to the products was controversial inside Atlassian. One camp said chat is a terrible experience for most tasks, so just build the features people want and skip it. The other camp said nobody knows what people will do with these models, so ship chat and watch. Atlassian had to build a chat backend to power AI across the portfolio anyway, so they shipped it (it is called Rover) and learned from it.

The analogy Sharif used: the command line never died. DOS was the universal interface to the operating system, and over the years specific use cases got pulled out into dedicated apps for spreadsheets, documents, and games. The terminal is still here. Both layers coexist. Chat is that universal interface for AI, the infinite use case, and you pull specific workflows out of it into dedicated UI.

In Confluence Whiteboards, they watched users type things chat could not yet do. “Group all my sticky notes into common themes.” So they built it as a feature: select the cards, group them. Then they watched users try to push those notes into a Jira backlog through elaborate prompts that failed, and they built that as a workflow. Three patterns came out of this:

• Automate the prompt. A repeated prompt becomes a button.
• Prompt to workflow. A prompt that crosses systems becomes a multi-step capability.
• Conversation to UI and back. Some flows start in chat (“pull all my Salesforce and Google Drive feedback onto a whiteboard”), move into a dedicated experience, then return to chat.

On chat usage, they assumed it would fade once people standardized on outside tools. It did not. Millions use Rover chat every day, even though those same users also run Gemini and Claude Code and everything else. The lesson Sharif drew is workflow proximity. People reach for the AI closest to where they already work.

Contradiction two: reimagine from scratch vs. bolt AI onto existing workflows.

“Bolt-on” already sounds like an insult. Nobody wants to admit they did it. Atlassian did it on purpose. Two and a half years ago they were one of the first B2B vendors to put agents into the platform, and they added an agent step into an existing Jira automation workflow. Arguably the best thing they did, because they had no idea where the market was going and bolting on let them learn fast.

What they learned: customers immediately went past single agents into branching, conditions, and multi-agent chains. A ticket fires, an agent picks it up, a marketing agent talks to Canva to generate assets, a social agent posts them. Customers were automating workflows they already had. That pushed Atlassian to a principle that now governs the whole portfolio: every problem you solve for humans, you solve for agents. Humans need tools, context, goals, accountability, and awareness of what teammates are doing. So do agents. The primitives are nearly identical. Anything you can do with a human, you should be able to do with an agent, and if a design breaks that rule, the team has to justify why.

For new products, reimagine. For existing products with users and workflows, bolt on and evolve. Both.

Contradiction three: hire the 10x AI builder vs. build the 10x team.

Atlassian ran about 10 projects staffed with AI builders, people whose main job is shipping code with AI tooling, drawn from PM, design, and engineering. Early results felt incredible. Then after a few weeks they slowed down badly. Everyone was rowing and no one was steering. The PMs and designers organically drifted back to their old jobs, giving the team customer context, vision, and decisions. The takeaway is not that AI builders are fake. It is that “everyone becomes an AI builder” is the misconception. As engineers churn through far more work, the PM and design ratio that used to feel like 1:10 or 1:20 now feels like 1:30 or 1:40, which makes the people steering more important, not less. If your steerer is heads-down live coding, nobody is directing the ship.

The hiring twist worth stealing: Atlassian flipped its talent pyramid. They now hire more juniors and more seniors, fewer in the middle. Their internal research found juniors are 19 to 30% more likely to use AI and almost twice as likely to experiment with new tools, while seniors are far better at spotting slop and refining output because they are used to coaching juniors. So they run an AI Builders Week every couple of months, four synchronized days where juniors teach new tools and seniors teach quality control. The hardest thing for a 10, 20, or 30-year veteran is unlearning how they work. The juniors never learned the old way, so they treat vibe coding as simply how you code.

Running underneath all three contradictions: skills. Every feature Atlassian built for humans, they exposed as a tool for agents, on and off their platform, through MCP. “Group my sticky notes into themes” became a human feature and an agent tool. Build it once for people, expose it everywhere.

Top 3 learnings from Atlassian:

• Ship chat to find your roadmap. The universal interface tells you which workflows to pull out into dedicated UI, through three patterns: automate the prompt, prompt to workflow, and conversation-to-UI-and-back handoffs.
• Make “anything a human can do, an agent can do” a design principle. Every problem you solve for humans (tools, context, goals, accountability) you have to solve for agents, and the primitives are nearly identical.
• Build the 10x team, not the 10x builder. Flip the hiring pyramid toward more juniors and more seniors, and expose every human feature as an agent skill so you build it once.

Top 3 mistakes Atlassian made:

• Almost did not ship chat at all. Two years of internal debate over whether chat even belonged in the products nearly cost them the discovery engine that ended up driving their roadmap.
• Assumed chat usage would fade. They expected to learn from chat and then take it away. Instead millions use it daily, and treating it as temporary would have been the wrong bet.
• Staffed “everyone as an AI builder.” About 10 projects took the immediate speed gain and then stalled because everyone was rowing and no one was steering.