Sber Life Insurance: Why AI Without Processes Does Not Speed Up Product Launches

Companies increasingly expect AI to instantly accelerate product launches, but in practice the effect is often the opposite. Sergey Abramovich explains: without established processes and clear accountability, AI tools don't reduce Time-to-Market—they add a new layer of chaos.

Why Speed Isn't Growing The main mistake is trying to solve organizational problems with technology.

If a team can't quickly align on requirements, takes a long time to pass tasks between departments, and doesn't assign clear ownership of decisions, AI won't eliminate these bottlenecks. It only speeds up individual operations within a broken system, and the overall cycle can actually become longer due to new checks, manual rework, and disputes about result quality. This is exactly where the promised business acceleration is usually lost.

Abramovich writes that in most cases AI is implemented too early—before the company has figured out basic operational discipline. Hence the disappointment: the business buys a tool, expects shorter timelines, but gets more artifacts, intermediate versions, and dependencies between people. Speed in such a system falls not because AI is weak, but because there's no clear process into which it can be integrated without unnecessary friction.

Where

Losses Occur Losses typically don't start in the model—they start at the junctions between people and functions. When product, development, legal, and marketing teams operate on different rhythms, any AI service begins to replicate the misalignment. It quickly writes texts, summaries, and solution options, but these materials get stuck in queues, go through endless revisions, or duplicate work already done. As a result, the team looks busy while the release still moves slowly.

• Requirements are formulated too late or change without being recorded AI results have no one to quickly review and accept into work Teams duplicate tasks because they don't see the common context * Speed metrics reduce to activity rather than actual release output > "In 8 out of 10 cases, companies implement AI where basic processes haven't even been established." Another trap is confusing local automation with accelerating time-to-market. If AI helped write a brief or draft specification in an hour instead of a day, that doesn't mean the product will launch sooner. The gain disappears if the document sits without decision for weeks, and the neighboring team isn't ready to take it on. That's why you need to measure not just model productivity in isolation, but the maturity of the entire decision-making and execution chain.

Where to

Start Instead of betting on another tool, the author offers a roadmap that begins not with the model, but with how work is organized. First, the company maps the path from idea to release and finds real delays: alignments, review queues, long handoffs between analytics, development, legal, and marketing. Only then can you decide at which step AI actually saves time, and where it will just create another stream of content to review.

The next step is to assign owners and agree on usage rules. For AI tasks, it's especially important to define upfront the format of input data, acceptance criteria, acceptable error rate, and the moment when human intervention is mandatory. Then technology becomes a service layer on top of the process: it helps prepare options, gather materials, speed up analysis, and reduce routine work without replacing accountability.

For managers, the key takeaway is simple: a combination of culture, process, and technology works—and in exactly that order.

What

This Means For the market, this is an important signal: betting only on the model no longer works. Companies that want to genuinely reduce Time-to-Market must first put their decision-making route, daily team interaction, and quality criteria in order. Only then does AI become an accelerator rather than an expensive overlay on a chaotic process. Otherwise, even the best tool remains a costly experiment with no noticeable impact on timeline and release quality.