There are not many chances to see a big, successful company try to rewire itself around AI in public.
One recent example is Snowflake. On a recent episode of the No Priors podcast, “Meet Snowflake Intelligence: A Personalized Enterprise Intelligence Agent,” Sarah Guo interviews Snowflake CEO Sridhar Ramaswamy about his first 18 months in the role and the company’s push to become “AI-first.”
They cover a lot of ground: org design, partnerships, and the launch of Snowflake Intelligence, their “opinionated agentic platform” for getting more value out of data already in Snowflake.
This post is not a product review. It is about what an operator can steal from stories like this and actually put to work.
Our headline takeaway for any enterprise trying to get real value from AI, not just pilots:
Make coding agents your first AI win.
Snowflake’s story is one useful case study in why that works.
What We Actually Learn From Snowflake, Not Just About It
From the episode and coverage of Snowflake’s pivot, a few patterns are worth paying attention to.
1. They anchored AI on existing data gravity.
Instead of trying to compete with foundation model labs, Snowflake focused on turning the data already sitting in its platform into decisions and actions faster, through Snowflake Intelligence and adjacent AI features.
2. They treated AI as an organizational change, not a feature.
The early moves Sridhar describes are about accountability, faster iteration, and clearer lines of ownership, not model architectures. AI success gets framed as “shorter feedback loops between builders and customers,” not “cool demo.”
3. They picked a lane for their agentic platform.
Snowflake Intelligence is described as an “opinionated agentic platform” that democratizes access to enterprise data. It is not positioned as the one AI to rule every workflow in the company, it is aimed squarely at making Snowflake-resident data more accessible and useful to every employee.
For most companies, the punchline is not “go build your own Snowflake Intelligence.” It is:
- Start where you already have leverage,
- Be clear about the first job AI is doing for you,
- Treat adoption like a product, not a side project.
Which brings us to coding agents.
Why Coding Agents Should Be Your First AI Win
When boards and CEOs ask “where do we start with AI, in a way that actually pays off,” the answers cluster in a few places: developer productivity, customer support, and data access. You hear the same themes in this No Priors episode once they get to AI ROI: narrow, high-leverage use cases, not moonshots.
Our view at Chiri: start with coding agents. Here is why.
1. You already have the users
Even if you are not “a software company,” you have:
- Engineers and SREs,
- Data engineers and analytics engineers,
- Architects, admins, and ops folks writing scripts and glue code.
Coding agents plug into work they already do, in tools they already use. No new persona, no exotic workflow.
2. The ROI is visible and unemotional
You can measure, quickly:
- Time to ship a feature or change request,
- Time to build an internal tool or migration,
- Defect rates, rework, incidents traced back to code issues.
It is much harder to tell whether an “AI brainstorming” tool is worth it than whether your teams are shipping more, better code with the same people.
3. They demystify the stack
Good coding agents:
- Raise the floor for juniors and “non-traditional” engineers,
- Make it safer for solution engineers and sales engineers to build demos and prototypes,
- Make migrations, refactors, and cleanup work less painful, which in turn unlocks future roadmap.
In other words, they increase the number of people who can safely move your systems forward.
4. They lay the groundwork for everything else
Once you have coding agents in place, it becomes easier to:
- Build and maintain support agents and internal tools faster,
- Wire AI into CI/CD, QA, and security checks,
- Keep your new data and AI projects from turning into a pile of half-finished scripts.
Coding agents are not “sexier” than generative marketing or chatbots. They are just more foundational.
A Practical Rollout Plan (Borrowed, Then Adjusted)
If you treat Snowflake’s pivot as an existence proof that AI adoption is mostly about speed, feedback loops, and opinionated scope, a simple rollout for coding agents looks like this:
Step 1: Start with the builders
- Roll out coding agents to a limited set of engineering teams.
- Instrument usage and basic metrics, for example pull requests per engineer, cycle time, bug regressions.
- Encourage working out loud: short Looms and internal posts on what works and what does not.
The goal is not “100 percent adoption in week one,” it is real stories and baselines.
Step 2: Elevate internal champions
In the Snowflake story, change is pushed not just by the CEO, but by internal leaders and early adopters who can speak credibly to their peers.
Do the same:
- Identify 3–5 engineers who get outsized value from the agent.
- Give them a platform internally to show concrete before/after examples.
- Let the social proof work in your favor instead of forcing top-down compliance.
Step 3: Extend to technical go-to-market teams
Once engineering has a handle on the agent:
- Bring in solution engineers, sales engineers, and technical account managers.
- Focus them on high-leverage work: custom demos, proof-of-concepts, migration plans for prospects.
- Track how long those activities take before and after.
This is where AI-driven developer productivity turns into visible revenue impact.
Step 4: Put guardrails and reviews in the path, not on the side
High-leverage coding agents do not mean bypassing controls. They mean baking the controls into the workflow:
- Require human review for changes above a risk threshold.
- Keep code review norms, you are accelerating them, not deleting them.
- Use linters, tests, and security scanners as an always-on second pass.
You want to make it more likely that good code ships fast, not easier for risky code to sneak through.
How Coding Agents Fit With Support And Data Agents
If coding agents are the first wedge, what comes next? The same clusters that show up in the No Priors conversation:
- Support and knowledge agents: AI that drafts responses, surfaces relevant docs, and handles well-bounded Tier 1 questions, with clear escalation to humans.
- Data access agents: Interfaces that let non-technical users query and explore data safely, similar in spirit to what Snowflake Intelligence is aiming for inside their ecosystem.
The ordering matters. Coding agents strengthen your ability to build and maintain the other two. Trying to stand up complex support and data agents without solid developer tooling is like launching a new product line while your factory is still on paper.
The Chiri Take: Use AI To Multiply Output, Not Hype
What we like about stories like Snowflake’s is not the branding or the feature set. It is the underlying posture:
- Treat AI as a way to change how work gets done,
- Anchor it in existing data and workflows,
- Make adoption and iteration the main event.
At Chiri, that is our lane.
We help teams:
- Find the workflows, like coding, where agents and copilots actually collapse cycle time.
- Engineer guardrails so speed improves your risk posture instead of quietly eroding it.
- Make adoption stick so you do not just get a “10x engineer,” you get more output from everyone around them.
Starting with coding agents is not the flashiest AI story you can tell, but it is often the one that pays for everything else.
If you want a partner to map your first wave of agents and get them into the hands of real users, we are happy to help. Reach out to us.