TTM Is a Trap: Why Predictability Matters More Than Speed in Platform Work

This reflection is for platform builders, engineering leaders, and systems thinkers who want to measure real value , not just motion.

1. Introduction: The Illusion of Acceleration

Platform teams often celebrate Time to Market Acceleration (TTMA) as a clear indicator of success. "We cut delivery times in half" sounds compelling. And I admit, I have fallen into this trap myself. But this measurement hides a dangerous assumption: that internal teams use your platform effectively, predictably, and as intended. In reality, TTMA is highly volatile and fragile. When stakeholders misuse a capability, deliver late, or misunderstand the interface, TTMA crumbles and the platform team gets blamed.

2. Reintroducing Core Metrics: TQS, TTME, TTMA, EVP

Before we go further, it is worth reintroducing some key metrics from the Fluid Organisation platform model for readers unfamiliar with them:

• TQS (Transduction Quality Score): Captures how well repeated platform inflow (like pain points or manual workarounds) is converted into a reusable, mature artefact. It is a measure of structural quality.
• TTME (Time to Market Enablement): Assesses whether product teams can activate and use platform capabilities without needing the platform team's help, as a proxy for autonomy.
• EVP (Enablement Value Proxy): Measures how much of the product value surface is structurally and meaningfully covered by the platform. It reflects alignment with product priorities.
• TTMA (Time to Market Acceleration): Quantifies the observed speed gain at the product level due to the platform. The ultimate sign of realised value, if all else works well.

These four originally formed a chain:

Inflow → TQS → TTME → EVP → TTMA

This model assumed that acceleration (TTMA) was the final goal. But as this article shows, it is worth questioning that assumption. If we accept that platform teams cannot fully control outcomes, then perhaps our goal is not speed itself, but predictable conditions that enable speed.

In that spirit, we propose a refined chain:

Inflow → TQS → TTME → EVP → TTMΔP

Here, the end state is not acceleration but predictable, reinforced delivery across known inflows. It reflects a more honest contract between platform and product: we do not promise speed. We promise consistency, trust, and enablement.

Structural quality enables self-service, which enables coverage of valuable flows, which unlocks speed , and only if the consumption layer behaves predictably.

3. The Fragility of Speed Metrics

TTMA measures real-world acceleration:

TTMA = (Initial TTM – New TTM) / Initial TTM

But platform teams do not control the full delivery pipeline. Conversion, adoption, and enablement may be high, yet poor usage by consuming teams sabotages the outcome. This creates a dangerous gap:

Let us break this down with the three metrics introduced earlier:

• TQS (Transduction Quality Score) is high → the platform team successfully converted recurring pain into a reusable artefact.
• TTME (Time to Market Enablement) is decent → teams can self-serve with little support.
• But TTMA (Time to Market Acceleration) remains unreliable → because outcomes depend on how effectively teams adopt and apply the platform work. It can also become a vanity metric, easy to celebrate, but vulnerable to Goodhart's Law: once it becomes a target, it ceases to be a truthful measure.

This illustrates a structural gap: quality and enablement exist, but results still vary.

This disconnect fuels unfair criticism and misaligned governance.

4.Predictability as a New North Star

Speed is seductive, but fragile. Predictability is robust. What if platform value was measured not just by median speed, but by reduction in variance? In other words: fewer surprises, fewer exceptions, and higher confidence.

Let us define a new proxy:

TTMΔP (Delta Predictability) = (Previous StdDev – Current StdDev) / Previous StdDev

It tells us: did we reduce the spread in delivery time across comparable use cases?

This does not require full visibility into every product team’s delivery. We are not pretending to know or manage Time to Market end-to-end. Instead, we focus on where platform enablement brings measurable certainty. For instance, on repeatable, high-friction inflows.

These are the areas where we can demonstrate clear reinforcement of delivery conditions. For example: with the new API gateway, deploying a governed interface now takes minutes, and it includes built-in observability with SLO enforcement. Previously, it meant scattered user stories (sometimes even filed on the platform board) and took weeks. This is not about measuring speed, but about strengthening the delivery path for critical surfaces.

TTMΔP in this context reflects how much more consistently we deliver these specific flows . Not how fast entire teams ship.

But this metric has its own risks. First, it may highlight weak spots across the organisation, such as teams that struggle to adopt new capabilities, lack the skills to benefit from enablement, or fail to give actionable feedback. These are not platform failures, but visibility into them can create political tension.

Second, there is a risk of mistaking local predictability for global reliability. Just because a platform capability is consistent does not mean the full delivery system is. Overconfidence in predictable inflows may hide deeper system fragility.

Lastly, without care, TTMΔP could itself fall into the same trap as TTMA: becoming a vanity metric if decoupled from meaningful inflows, or used to justify volume-driven governance.

Still, TTMΔP offers a compelling alternative. After examining these dynamics, it becomes clear that we could trade TTMA, a fragile and outcome-dependent metric, for a more grounded, enablement-aligned indicator like TTMΔP. In doing so, we shift from chasing speed to reinforcing confidence. And that is a trade worth making.

5. From Chaos to Confidence: A Concrete Example

TTMΔP measures reinforced delivery confidence, not universal speed.
Consider a new self-service deployment pipeline. Before:

• Teams opened a Slack ticket
• Waited 2–3 weeks
• Outcomes varied by team, request type, or human error

After:

• Fully automated
• 80% of flows complete in under 2 hours
• Outliers are traceable, not systemic

TTMA might show 80% improvement. But more importantly, TTMΔP drops from 15 days to 3 hours. Teams trust the system because it behaves consistently.

6 Better Governance Starts with Better Questions

Rather than ask "Did this feature make us faster?", we should ask:

• Is time to market more predictable across teams?
• Do we know which layers introduce delay?
• Can we forecast delivery with confidence?

These are systemic questions. They reflect engineering maturity.

7. Conclusion: Predictability Builds Trust

TTMA tells a story of speed. TTMΔP tells a story of reliability. In platform work, especially in the early stages of Platform 2.0 initiatives, predictability appears as the most valuable outcome. It lays the foundation for trust, scale, and future acceleration.

In other words:

A predictable system scales. A fast one breaks.

A good metric does not claim total insight. It invites better questions.

8. Where This Leaves Us

This reflection is not just theoretical. As we evolve Platform 2.0, we will favour TTMΔP over TTMA, not to ignore speed, but to reinforce the conditions that make speed sustainable. Measurement is not control. It is navigation.