When a product goes wrong, it rarely fails in code first. It fails in conversation.
PMI’s research on requirements management reports that nearly half of unsuccessful projects miss goals because requirements were inaccurate. That number points to an alignment problem. Business teams describe outcomes. Engineering teams see constraints. When those views do not meet early, teams pay later.
This is where co-creation matters. I have seen teams get better results when business, design, data, and engineering plan together, not in handoffs. Done well; this approach shortens feedback loops, reduces late surprises, and produces clearer choices.
Below is a playbook for co-creating digital products with business and engineering together.
Why do siloed product and engineering planning fail?
Silos create a false sense of certainty. A business plan can read “clear” on paper while still being technically vague. An engineering plan can look “solid” while still being misaligned with the real business risk.
Three failure patterns show up:
- Outcome without operating detail- “Increase conversions” is not enough. What actions change? What data is required?
- Solution drafted before the problem is bounded- Teams jump to features because they are easy to describe, then discover later that the real constraint is policy, data quality, or workflow.
- Hidden coupling- A new user flow can quietly depend on identity, permissions, audit logs, and integration of SLAs. When those are missed, timelines slip.
The economics of late discovery are harsh. IBM notes that the cost of fixing defects rises sharply as software moves through the lifecycle, so earlier detection matters. The same idea applies to product decisions. The later you question a decision, the more it costs to reverse.
Workshop formats for joint discovery and alignment
Many teams run workshops, but the format decides the outcome. The goal is shared decisions and clear next steps.
I use three formats, often in this order:
If you provide digital engineering services, treat these sessions as part of delivery, not as “pre-work.”
Format A: “Problem framing” session (90 minutes)
Purpose: align on the problem, boundaries, and success signals.
Inputs:
- One page of context: market, customer pain, and what is already tried
- Known constraints: policy, legal, budget, dependencies
Outputs:
- Problem statement in plain language
- What “good” looks like in measurable terms
- Risks list with owners
Format B: “Workflow and data walkthrough” (2 hours)
Purpose: map the real work, not the ideal work.
Method:
- Start from an actual user task.
- Walk through current steps, exceptions, and approvals.
- Identify systems touched and data created.
Outputs:
- Current-state map
- Candidate “thin slice” for first delivery
- Data needs and data gaps
Format C: “Decision sprint” (half day)
Purpose: make hard calls early.
This is where joint discovery workshops work best. You ask teams to decide, not just discuss. The sprint produces a short list of decisions that will shape the build.
Typical decisions:
- Must-have versus can-wait features
- Integration approach and sequencing
- Security and audit requirements
- “No-go” constraints that kill an option early
Cap attendance at 8 to 10 people. Add others through short interviews before the session.
Using technical insights early in product shaping
Engineering’s job in discovery is to surface tradeoffs and prevent blind spots.
I like to bring three kinds of technical inputs into early discussions:
1. System reality checks
a. What are the hard dependencies?
b. Which systems are stable, and which are fragile?
c. Where do we lack observability?
2. Data reality checks
a. What data exists today?
b. What is the source of truth?
c. What data quality issues will break the experience?
3. Risk-reduction experiments
a. A small prototype to validate latency
b. A spike to validate integration auth
c. A sample dataset to validate business rules
This is a tech-informed product shaping in practice. It is not “architecture first.” It is “risk first.” Teams that do this early protect the roadmap from sudden stops later.
Simple rule: every major feature needs a “technical unknowns” list.
Balancing feasibility, viability and desirability
You do not need a complex model. You need a visible way to compare options.
I use a one-page scorecard that sits in the room during discussions:
| Lens | What you are testing | Example question | Evidence to collect |
| Desirability | Does the user want it? | Would users change behavior for this? | Interviews, usability test notes |
| Feasibility | Can we build and run it? | What breaks under peak load? | spikes, dependency review |
| Viability | Does it pay off? | How will it reduce cost or drive revenue? | unit economics, finance assumptions |
Define evidence standards up front:
- Desirability: at least 5 user conversations
- Feasibility: a short spike or dependency review
- Viability: a metric plus a baseline
This is also where digital engineering services can add real value. You are not only building features. You are helping teams pick options that have a clear path to delivery and operations.
Documentation and decision logs that keep teams aligned
Teams do not drift because they are careless. They drift because memory is fragile and people change.
A lightweight documentation set prevents drift:
- Decision log. One page updated weekly. Each entry has: decision, date, owner, options considered, why chosen, what would change it.
- Assumptions list. Every assumption gets a validation plan. Example: “We assume partner API supports 200 RPS” plus how you will test it.
- Glossary. Shared definitions remove hidden confusion. “Active user,” “conversion,” and “case” must mean one thing.
- Interface notes. A simple diagram of major integrations, with what data moves and what errors look like.
Keep these docs short and close to daily work.
Strong documentation is often the difference between digital engineering services that feel tactical and those that feel strategic.
Real-world stories of co-created digital products
Story 1: Claims intake that stopped bouncing between teams
A insurer wanted faster claims intake. The business team started with a long list of screens. Engineering flagged an issue early: the bottleneck was not UI. It was inconsistent document classification and a manual approval step.
We ran joint discovery workshops with claims ops, compliance, product, and engineering. In the workflow walkthrough, we found the “exception path” was 40% of real cases, yet it was missing from the original plan. That insight changed the scope.
We used tech-informed product shaping to test two risks in week one:
- OCR and classification accuracy on a sample set
- Latency for pulling policy details from a legacy system
The first delivery was not the full portal. It was a thin slice that handled the most common claim type end-to-end, including the exception path. Ops time dropped, and the backlog was shaped by evidence.
Story 2: A B2B ordering tool that finally matched how sales worked
A manufacturing distributor wanted a new ordering experience for key accounts. Past attempts failed because product teams designed for “ideal” ordering, while sales teams lived in negotiated pricing, substitutions, and partial shipments.
In discovery, engineering mapped the real coupling: pricing rules lived in three places, and the “quote to order” step depended on a nightly batch. Business leaders did not know that dependency existed.
Together, we redesigned the first version around the actual sales motion:
- pricing visibility with clear “as of” timestamps
- substitution suggestions when inventory was low
- a simple offline mode for spotty warehouse networks
The decision log was guardrail. Each compromise was recorded with a revisit trigger, so teams knew what they were trading and when to reassess.
In both stories, the difference was not a new tool. It was shared ownership. When product, business, and engineering co-create, digital engineering services become more than delivery capacity. They become a way to make better choices earlier.
A checklist you can use next week
If you offer digital engineering services, this is a concrete way to show how you reduce risk, not just write code.
- Bring engineering into problem framing, not just estimation.
- Run a workflow walkthrough in a real case, including exceptions.
- Write the top 10 unknowns and assign validation work.
- Use a one-page desirability, feasibility, and viability scorecard.
- Keep a decision log and update it weekly.
Co-creation is a discipline. Fewer surprises follow.