Alexander Suleimanov
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Software delivery transformation · Product and engineering management

I shorten software delivery times when local team improvements no longer move the whole system

−64%time for 85% of major initiatives · 295 → 106 days
16 → 54average Epics delivered per month
20+product teams in the current transformation

I identify delays between strategy and release, then change the rules for portfolio, dependency, and decision management. My current scope covers 20+ product teams.

Check whether a diagnostic fitsSee the 20+ team case

Signals for CTOs, CPOs and founders

When growth stops producing outcomes

Delivery times grow, additional teams do not increase output, and AI remains a pilot. Choose the situation closest to yours.

01

Plans keep missing delivery dates

The quarterly plan is approved and every team is busy, yet Lead Time grows, forecasts move, and Time to Market depends on manual escalation.

  • dates move after commitments have already been made
  • the portfolio grows while throughput stays flat
  • team metrics look healthy while the customer outcome waits
What changes after the reviewIdentify where work waits, which decision affects elapsed time, and what to test over the next 30–90 days
20+ team transformation caseFind the source of delay
02

You hired more people, but output did not increase

The founder or CPO still has to reconcile priorities, dependencies, and decisions across product, engineering, and the business.

  • headcount and team count grow faster than throughput
  • product, engineering, and the business report different versions of progress
  • priorities and cross-team decisions stall without the founder
What changes after the reviewDefine the unit of outcome, decision rights, and a management cadence that does not depend on manually assembled status
Case on a shared unit of customer outcomeDiagnose the slowdown
03

You bought AI, but the business process did not change

Tools save individual time without shortening the decision cycle or improving the quality of the outcome.

  • licences and pilots exist, but the business effect is not measurable
  • AI sits outside the team's regular workflow
  • people recheck outputs without a clear quality criterion
What changes after the reviewSelect one management process, the human decision point, and a measure that can demonstrate the effect
AI in delivery caseDiscuss an AI experiment

Primary engagement

Delivery diagnostic for a technology organisation

For leaders who see long lead times, overloaded portfolios, or late dependencies but do not yet agree on the cause or the next management decision.

See what the diagnostic includes

What I examine

  1. 01the unit of delivery and the boundaries of total elapsed time
  2. 02WIP, aging, queues, blocked work, and dependencies
  3. 03decision rights for priorities, capacity, and escalation
  4. 04the starting SLT, Lead Time, throughput, and predictability measures

What you receive

  1. 01a full flow map showing where work waits
  2. 02the main systemic constraint or a short list of testable hypotheses
  3. 03management actions with owners and success measures
  4. 04a sequence of experiments for the next 30–90 days

Contact

Is the problem affecting several teams or the portfolio?

Tell me how many teams are involved, which delivery time or outcome is not working, and where the work most often gets stuck.

  1. 01
    Share the context

    Two or three sentences about the scale, the problem, and what has already been tried are enough.

  2. 02
    Find where time is lost

    We will identify which part of the system affects elapsed time and which decision is actually needed.

  3. 03
    Choose the format

    A diagnostic, ongoing support, a focused experiment, or a referral when the problem falls outside my practice.

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