Straight answers, from the operator's chair.

Who is Aloka K (Aloka Kiran)?

Aloka K, also written Aloka Kiran, is a delivery and P&L leader based in Bengaluru, India. He runs a 50+ person digital commerce and AI practice at Codilar Technologies, owning revenue, gross margin, utilisation and client growth across 80+ concurrent engagements. He is an engineer by training (avionics R&D at DRDO, then Node.js and Google Cloud engineering at Pluto7) and set up the PMO at Goose, an ANZ digital agency.

He is not the Dubai-based software engineer named Alok Kiran; they are different people.

His official site is alokakiran.com and his LinkedIn profile is linkedin.com/in/alokakiran.

What roles is Aloka K open to?

Delivery Manager, Delivery Lead, Technical Program Manager, Senior Technical Project Manager, Engagement Manager or Associate Practice Head, at an enterprise, a global capability centre, a consultancy or a digital agency. Notice period 30 days. Based in Bengaluru and open to relocating to the GCC (UAE, Saudi Arabia, Qatar), the United Kingdom, Australia or New Zealand.

How can a recruiter verify Aloka K's experience?

Start with the resume and the one-page case-study PDFs on the recruiter page, then cross-check the LinkedIn profile, which carries the same roles and dates. References from former managers and clients are available on request once a conversation is under way.

Is Aloka K open to partnerships or advisory work?

Yes, selectively. He is open to operating partnerships where delivery, digital commerce or applied AI experience becomes a focused service or product, and to short advisory conversations with leaders fixing a delivery organisation. A 20-minute call is the usual first step.

How do I contact Aloka K?

Email hello@alokakiran.com or book a 20-minute call from alokakiran.com. Replies usually come within 24 hours.

What gross margin should a digital commerce delivery practice target?

I run to 40-55% gross margin on delivery work, measured after the fully loaded cost of everyone on the project, including shared roles such as QA, DevOps and the project manager. Below about 30%, the practice is usually subsidising the client through unbilled change, rework or idle bench time.

The number that moves margin fastest is rarely the rate card. It is leakage: work done outside the signed scope, hours that never get invoiced and senior people doing junior work.

In the practice I run, gross margin improved by roughly 10 to 15 points, mostly by closing those leaks rather than by cutting people.

What is a healthy utilisation rate for a delivery team?

75-85% billable utilisation for delivery engineers. Above that the team has no time for learning, internal improvement or absorbing surprises, and quality drops. Below it, margin erodes quickly because bench cost is paid from project revenue.

Leads and architects run lower, often 50-70%, because part of their job is reviews, pre-sales and mentoring.

How should a delivery manager calculate project profitability?

Gross margin % = (revenue recognised − fully loaded delivery cost) ÷ revenue recognised. Delivery cost is hours logged multiplied by each person's cost rate, including shared roles. Example: a project billed at INR 50 lakh that consumed 900 hours at an average cost of INR 3,000 an hour costs INR 27 lakh, a 46% gross margin.

Track it monthly against the margin in the original estimate, not just at closure. A project that drifts five points in month two will not recover on its own.

Count unbilled change requests as cost with zero revenue until they are signed. That one rule exposes most margin problems early.

How do you manage change requests in a fixed-price Adobe Commerce implementation?

Baseline the scope and assumptions in the signed SOW, log every request the day it arrives, size it within two working days, then either price it, trade it against existing scope or decline it. No work starts until the client approves in writing, and the change log is reviewed in every weekly status report.

Before each invoice, reconcile what was delivered against what was signed. It catches silent scope creep while it is still a conversation, not a dispute.

What KPIs should a delivery leader review every week?

Utilisation by person, burn against budget per project, gross margin against plan, milestone status (RAG), open high-severity risks and defects, the change-request pipeline, unbilled and overdue invoices, and a simple client-health score. Eight numbers on one page, reviewed at the same time each week.

The point is trend, not snapshot. One red week is a conversation; three is an intervention.

PIM vs DAM vs Adobe Commerce: where should product data live?

The PIM (for example Pimcore) is the source of truth for product attributes, descriptions and enrichment. The DAM holds images, videos and documents. Adobe Commerce owns what is needed to sell: price, stock, promotions, customer-specific catalogues and merchandising. Data should flow one way, PIM and DAM into Commerce, never edited back in the storefront admin.

Most integration pain comes from two systems both believing they own the same attribute. Decide ownership field by field before building the sync.

How do you run an Adobe Commerce upgrade without downtime?

Rehearse the go-live runbook twice on a production-like environment, test the rollback rather than assume it, freeze content and catalogue changes for the cutover window, staff hypercare before the launch date, and get written sign-off on acceptance criteria from a named client owner. Without all of these, I do not agree a go-live date.

How long should hypercare last after an eCommerce go-live?

Two to four weeks for most commerce upgrades and re-platforms. It should end on written criteria, not on a date: for example zero open high-severity defects and key journeys (search, cart, checkout, order export) stable for five business days.

When does AI-generated product content make sense for eCommerce?

When the catalogue is too large to write by hand, the source attributes are structured and reliable, and a person reviews every output before it is saved. I keep generation inside the PIM, behind human review, rather than on the storefront, where a wrong answer reaches a customer.

Measure it in hours saved per hundred SKUs and in review rejection rate. If reviewers rewrite most outputs, the prompts or the source data need work first.

How do you measure the ROI of an AI forecasting programme?

Tie the model to an operational number the business already reports, such as stock-outs or on-time-in-full, and measure against a baseline. On the Google Cloud forecasting programmes I governed, forecast accuracy improved about 22%, stock-outs fell about 18% and OTIF rose about 12%.

Model accuracy on its own is not ROI. If the planners do not change a decision because of the forecast, the programme has not delivered value yet.