Engineering Speed: The Use of AI Technology Buys Back Your Roadmap
Your runway ties directly to how many product cycles you complete before the next raise. Every sprint you spend on boilerplate, test scaffolding, or migration plumbing burns months you cannot recover. The use of AI technology in engineering attacks that waste first, and the best evidence comes from controlled studies rather than vendor slides.(randomized experiment with 95 developers)
In 2023, researchers at GitHub and Microsoft ran a randomized experiment with 95 professional developers. Each developer wrote an HTTP server in JavaScript. The group with GitHub Copilot finished 55.8% faster than the control group. That result applies to a well-scoped, greenfield task, and you should read it that way. It tells you the tools work on the kind of work your team does constantly: standing up services, writing endpoints, and wiring integrations.
Now read the counterexample, because it matters just as much. In 2025, the research group METR studied 16 experienced open-source developers across 246 real tasks in mature codebases they knew well. The developers took 19% longer when they used AI tools. They also predicted the tools would speed them up by about 20%, so their own perception missed the result by a wide margin. That finding should change how you roll out the use of AI technology: expert engineers working in dense, familiar code gain less than new hires or engineers building something fresh.
So match the tool to the task. Point AI at the work where context is thin and repetition is high: unit tests, API clients, data migrations, internal admin tools, documentation, and first drafts of throwaway prototypes. Keep your senior engineers on architecture and on the gnarly corners of the core product, and let them decide when to reach for a model.
Then measure. Pick two numbers before you buy a single license: median pull-request cycle time and the share of sprint commitments your team completes. Record both for the month before rollout. Run the pilot for six weeks with a subset of engineers, then compare. If cycle time drops and defect rates hold, expand. If cycle time stays flat, you saved yourself a year of subscription fees.
Here is illustrative math, not a benchmark. Say your team of eight ships four meaningful features per quarter. If disciplined use of AI technology gives you even one extra feature per quarter, you gain an additional product experiment every three months. At Series A, that extra experiment often decides whether you find the pricing model or the onboarding flow that unlocks growth before the money runs out. The license cost for eight seats barely registers against that.
Technical founders also carry a built-in advantage here. You can read the generated code, judge its quality in seconds, and reject what looks wrong. Use that advantage by setting the review standard yourself: every AI-written change gets the same code review as a human-written one, with no exceptions for speed.
Customer Operations: The Use of AI Technology Moves Your Cost Curve
Support and operations scale linearly with customers if you hire your way through growth. Series A companies feel this first, because headcount in support competes directly with headcount in engineering and sales. The use of AI technology in customer operations bends that curve, and two public examples show both the upside and the limits.
Start with the research. Erik Brynjolfsson, Danielle Li, and Lindsey Raymond studied 5,179 customer support agents at a software company that deployed a generative AI assistant. Average productivity, measured as issues resolved per hour, rose 14%. Novice and low-skilled agents gained 34%. The assistant effectively spread the habits of top performers to everyone else. For a startup that constantly onboards new support hires, that result means faster ramp time, which converts directly into lower cost per ticket.
Next, look at Klarna. In early 2024, the company reported that its AI assistant handled 2.3 million conversations in its first month, covering two-thirds of its customer service chats. Klarna said the work matched what 700 full-time agents would do, and that average resolution time fell from 11 minutes to under two. Klarna also projected a $40 million profit improvement for 2024. Those are the company’s own figures, so treat them as a claim from an interested party, not an audit. Still, the scale is real, and the direction matches the academic research.
The follow-up matters just as much. In 2025, Klarna’s CEO acknowledged that an aggressive focus on cost had hurt quality in some areas, and the company began bringing human agents back for work that needed judgment and empathy. The lesson for you: use AI technology to handle the volume of simple, repetitive requests, and route anything sensitive, ambiguous, or high-value to a human. Founders who skip that routing step save money in month one and lose customers in month six.
Build your support rollout in three layers. First, deploy an assistant that drafts replies for your agents, which delivers the Brynjolfsson-style gains with almost no customer-facing risk. Second, automate full resolution for a narrow set of ticket types with known answers, such as password resets, invoice copies, and status checks. Third, review the escalation data weekly and widen the automated set only when satisfaction scores hold steady.
Track three metrics: cost per resolved ticket, first-response time, and customer satisfaction on automated versus human-handled tickets. If satisfaction on automated tickets trails by more than a few points, tighten the scope. A support queue that costs half as much but drives churn up has not improved your business.
The same pattern extends beyond support. Sales operations, finance reconciliation, and recruiting coordination all contain high-volume, rules-heavy work. Start with the function where your team complains most loudly about repetitive tasks, because that complaint points to the best first target for the use of AI technology.
ROI Discipline: Treat the Use of AI Technology Like Any Other Capital Decision
Founders get burned by AI when they buy tools on enthusiasm and never check the return. You would not sign an annual contract for a new data warehouse without a usage estimate. Apply the same standard here, because the use of AI technology adds costs that do not appear on a pricing page.
Use this formula for every workflow you automate:
Net gain = (hours saved × loaded hourly cost) − (license and API fees + human review time + cost of errors)
Each term needs a real number. Hours saved comes from your baseline measurement, not from the vendor’s demo. Loaded hourly cost includes salary, benefits, and overhead for the person whose time you free up. License and API fees include usage-based charges, which grow as adoption spreads across your team. Human review time is the cost most founders forget: if an engineer spends 20 minutes checking output that saved 30 minutes of writing, you gained 10 minutes, not 30. The cost of errors covers incidents, refunds, and rework when the model gets something wrong.
The METR result shows why that review term matters. Developers in that study spent time prompting, waiting, and correcting output, and those costs outweighed the generation speed. Your own numbers may differ, but you will not know until you measure.
Set kill criteria in advance. Write down the threshold at which you will cancel a tool, such as “less than 10% reduction in cycle time after six weeks.” Teams that skip this step keep paying for tools that nobody uses, simply because canceling feels like admitting a mistake.
Account for data risk as well. Before any engineer pastes proprietary code or any agent pastes customer records into a third-party model, check the vendor’s data retention and training policies, and confirm the terms in writing. Enterprise customers will ask about this during security reviews, and a vague answer can stall a deal that your sales team spent three months building. Document which tools touch which data, and keep that list current.
Finally, separate build from buy. Buy off-the-shelf tools for commodity tasks: code completion, meeting transcription, and support drafting. Build custom systems only where your proprietary data creates an advantage a competitor cannot copy, such as a model fine-tuned on your own transaction history or a retrieval system over your unique documentation. Founders who build commodity features in-house burn engineering months on work that vendors improve every quarter for free.
The 90-Day Plan: Put the Use of AI Technology to Work
Strategy only matters if it ships. Here is a schedule that a Series A team can run without hiring a dedicated AI lead.
Weeks 1 and 2: Pick targets and set baselines. List your five most repetitive workflows across engineering, support, sales, and finance. Rank them by hours consumed per week and by error tolerance. Choose three: one high-volume and low-risk, one medium, and one that touches customers directly. Record baseline metrics for each. Write a one-page data policy that states which tools can see which information. Assign one owner per workflow, and give each owner authority to stop the pilot.
Weeks 3 through 6: Run narrow pilots. Give each workflow a small group, not the whole company. Keep the pilots short, and review the numbers weekly. Hold a fifteen-minute meeting every Friday where each owner reports the metric, the surprises, and the cost. Do not tune the tools mid-pilot unless something breaks, because changing variables destroys your ability to attribute results.
Weeks 7 and 8: Decide. Compare each pilot against its kill criteria. Expand the winners, cancel the losers, and write down why. Losing pilots still produce value, since they tell you which tasks need human judgment. Share the results with the whole company so that skeptics see data and enthusiasts see limits.
Weeks 9 through 12: Scale and standardize. Roll out the winning workflows to every relevant team member. Train people directly, with live examples drawn from your own codebase and ticket history, not generic tutorials. Update your onboarding documents so that new hires learn the approved tools on day one. Review costs again, because usage-based fees climb quickly once adoption broadens.
Two habits separate the founders who profit from the use of AI technology from those who merely spend on it. First, they keep humans accountable for outcomes: a model drafts, a person decides. Second, they revisit their choices every quarter, because tool capabilities and pricing shift fast, and last quarter’s best option may no longer win.
Hold yourself to the same standard you hold your team. Run your own pilot, read your own numbers, and share the results at your next all-hands. Founders who do this build a company that learns faster than its competitors, and at Series A, learning speed is the only moat you can build before the next round.