Written by Luke Smith, Principal Consultant
Many organisations start their decisioning journey the same way: they buy (or build) a platform, then invest in data engineering to integrate multiple systems, and build the back-end infrastructure to support real-time decisioning.
Let’s be clear: this alone is a huge undertaking. Connecting disparate data sources, ensuring data quality, building ETL pipelines, and setting up consent and privacy controls is months, sometimes years, of work.
And yet, after all this effort, the next challenge awaits: demonstrating that your decisioning engine actually creates value for the customer and in the business.
This is where many organisations hit a problem. They spin up a proof of concept (PoC) in one channel (often an app) with a limited set of Next Best Actions (NBAs), and assess whether decisioning is working. But technical success doesn’t automatically translate to business impact.
A decisioning platform can only choose from the options it is given.
If your PoC has a handful of offers and a narrow set of contexts, you’re not testing decisioning’s true capability – you are testing whether it can rank and deliver a very limited menu.
The engine works and the offers get sent, but you have not seen how it really behaves. You have proven technical feasibility, not business value.
Forgive my racing analogy, but it’s like rolling into an aid station on a triathlon and finding a bruised banana, some warm cola and a brand of gels you’ve never used before. Technically, there are multiple options to decide from, but what you really needed was a bottle of electrolytes 30 minutes ago.
In this instance, you’re not deciding on the next best action from the aid station, but rather the least worst.
In my last article, I talked about content intelligence as the foundation for scalable, decisioning-led engagement.
It’s about making content modular, metadata-rich, and reusable so the engine can dynamically assemble the right message for the right customer in the right moment.
Without content intelligence, your decisioning engine is the equivalent of riding a bike stuck in first gear. It can technically go forward, but nowhere near its potential.
True content intelligence means:
- Assets broken into building blocks
- Content tagged by audience, lifecycle stage, emotional tone
- Shared libraries for reuse across squads, reducing duplication
- Agile governance that balances speed with brand, compliance, and risk
To really prove decisioning’s value, you need to move towards broader content sets, multiple channels, complex arbitration, measured by real business metrics, such as customer lifetime value, retention, incremental revenue, and cost-to-serve reduction.
So what is the right scope for a PoC? How do you get the right balance between not doing too much, too soon, and doing enough that you can demonstrate value?
It can help to move away from tying transformation to arbitrary timelines (e.g. “we’ll be in Phase 2 in 2026”) and instead link progress to readiness. Define the maturity signals that must be true before moving forward. For example, modular content availability, decisioning logic embedded in multiple channels, or customer value metrics in place. Then build plans and investments around these markers.
That way, you avoid over-engineering the early stages, but also prevent the trap of least worst action PoCs that prove only technical functionality, not business impact.
In transformation as in racing, the best results come from knowing when to push, when to hold back, and when you’re genuinely ready to go faster.
Contact Optima Partners to learn more.
