Pattern 01 — Design for the System, Not the Surface

Parable Patterns is a series of conversations with operators who've run transformations about what AI is catalyzing and amplifying inside organizations right now. The name is the point: transformation as a discipline isn't new, and the themes and frameworks that show up here tend to recur, whether the impetus is AI or something else.

I met Cindy Chastain through an executive roundtable breakfast we hosted during AI Agent Week in May. That conversation covered a lot of ground, but it was evident in follow-up chats that Cindy really understood what we're building at Parable, so asking her to feature in this series was a no-brainer.

Meet Cindy Chastain

Cindy Chastain spent the last decade inside Mastercard as SVP, Customer Experience & Design, leading transformation work across customer experience, brand, product, and innovation culture. In her final role there, she worked directly for the Chief Innovation Officer in the Mastercard Foundry and built their first single-agent productivity tool for front-line customer implementation specialists. Prior to Mastercard, she stewarded multi-year digital transformation efforts for household-name brands: rethinking B2B go-to-market with Nike, and reorienting the Volvo B2C customer journey toward a more luxury experience, among other engagements. Most recently, Cindy founded an executive advisory practice focused on helping teams redesign how their organization creates value in the age of AI.

The Service Design Lens

Given her background, it makes sense that we immediately launched into the topic of "service design." There's a good primer on what service design actually means, but broadly speaking:

Service design is the activity of planning and organizing people, infrastructure, communication, and material components of a service in order to improve its quality and the interaction between service provider and customer.

Cindy summed up the core idea below.

The front-stage experience a person has is always a reflection of backstage realities. That's why you get so many disjointed, terrible customer experiences: different teams own separate pieces of something that's meant to be seamless. When you apply a service design lens, you can see what's actually happening.

AI and agentic deployments have only amplified pre-existing fragmentation, and the delivery of positive, unified experiences, whether the end user is an external customer or an internal team, suffers for it. We revisited this idea throughout the conversation, because the service design lens exposes a truth many businesses have failed to realize, let alone act on. AI is not a corrector, it's an accelerant, and it's indifferent to what it amplifies. Bad inputs, bad outputs: a familiar adage when it comes to prompting, but one very few organizations apply to their own processes, enterprise data, and status quo.

AI alone won't solve bottlenecks that impede productivity and velocity, and it can just as easily exacerbate hidden dysfunction stemming from poor managerial practice. It also won't reveal those blind spots on its own — a gap we built Parable to close — so many enterprise AI deployments end up optimizing processes that should have been thrown out and redesigned entirely.

Cindy sees the fix starting further upstream, with how the org itself gets treated and managed. She recommends that leaders start to think about organizations and operating models as products, and apply similar rigor.

Rather than thinking of an org structure as fixed, the old way of designing boxes and roles and governance and then just letting it run, thinking of an organization as a living, ongoing product requires intentional design, consistent iteration, real measurement, and feedback loops.

Traditional process mining and mapping can help, but they generally capture ideal conditions — the way an output should come to life, the way an end-to-end process should run, in a specific tech system. But Cindy underlined a reality we've seen play out with numerous customers. Much of the work itself, and therefore the opportunity to automate or augment it, is largely invisible to on-paper renditions, if those renditions exist at all.

Organizations often don't see their own coordination bottlenecks: how work actually happens. In every transformation journey, I was continuously amazed by things that needed to be sunset but weren't. Areas of misalignment, decision latency, all the informal work: the structure is visible, but the reality of how work actually gets done is not.

The problem she keeps seeing is familiar. Organizations leap into agentic deployments as a bolt-on to existing processes, then are mystified when they don't see the level of gains AI promised.

Service design teaches us to understand the system before we redesign it. AI raises the stakes because it dramatically increases our ability to optimize that system, but optimization only creates value if we've first chosen the right systems to optimize.

Adoption vs. Adaptation

That lens led us naturally to a second tension, which is that optimization is actually coming at the expense of value creation. Most organizations measure adoption (are people using the tools?), when the more revealing question is adaptation — how the organization itself needs to change to make AI transformation stick, and how to harvest the impact of that change.

Adoption metrics capture usage and individual productivity gains. They say almost nothing about the second — what leadership does with the capacity AI returns.

Whatever gains AI hands back do not compound on their own, and are actually more likely to leak back into the ether. Cindy put it well in a follow-up note: Freed capacity is not value in itself; it is a leadership decision waiting to be made.

That decision — what to do with freed capacity, and how fast the organization adapts around it — is what separates programs that stick from programs that stall.

The companies that win won't be those that adopt AI fastest, but those that adapt most effectively — redesigning how decisions are made, capabilities are developed, and work gets done, without losing customer relevance, strategic clarity, or execution quality.

In a product-led transformation, teams need outcome metrics tied to business goals — and a separate set of indicators that track whether the change itself is taking hold. Those tend to be leading, behavioral signals you can eventually correlate with results, not lagging activity counts. Adoption rates and token usage are the most visible proxies for value that get optimized because they're convenient to report, not because they tell you anything about whether the transformation is working.

The org-as-product framing also has a payoff for how change spreads. Start at the team or function level, create room to test and learn, then carry what works into the next pocket of the organization. Adaptation becomes durable when neighboring teams can see something working and want in — thus combining executive mandates with cultural and peer momentum.

Conversations like this remind me that many of the hardest parts of this moment aren't net-new. The tooling and pace of change are unprecedented, but the transformation — seeing the organization clearly, redesigning it honestly, and making deliberate decisions about capacity and organizational health — is the work good leaders have always done.

That's the whole point of Parable Patterns — to remind the leader who feels overwhelmed and behind that there's real benefit in revisiting a playbook older than the panic.

We barely scratched the surface with Cindy, so she'll almost certainly be back. For now — thank you, Cindy!