Decision-making Velocity using AI

Article 1 from the Series:  Data, AI, Velocity and Value

Topics covered:

1. The ‘rear view mirror’ test
2. Closing the intelligence gap
3. How do you know if it’s your software that is a problem
4. Conclusion

Introduction

The era of patient growth in the telecommunications sector is well and truly over. As we navigate the 2026 subscription economy, the competitive opportunity has shifted from those that have the best data to those who can actually use that data before it goes stale.

For many executives, the most significant threat to their bottom line is not a rival’s pricing plan, but their own internal velocity. It’s often called the ‘inertia tax’ and it describes the hidden cost of moving slower than the market opportunity.

Whilst speed has always been the home-advantage for disruptive MVNOs that’s no longer enough unless the execution cycle includes data driven intelligence.

If your organisation can’t turn a raw signal into execution in days, you are not just lagging behind; you are actively losing market share.

The ‘rear-view mirror’ test

We think of velocity in three parts: How long a decision takes, how you glean insights and how fast you can execute. How would you answer these questions?

1. When a risk or opportunity is identified, how long does it take to decide on a response?

1. 1. 1. Hours or days
      2. Weeks
      3. Months
      4. Too late – it’s usually after the financial damage is done

2. How do you use data to make decisions?

1. 1. 1. We have predictive certainty about what will happen next and understand causality and correlation
      2. We report close to real time so that we can optimise in flight
      3. We have dashboards, but they only tell us what has already happened.
      4. We’re flying totally blind; if we want to build a new report it needs a data analyst, data engineer or IT

 3. Can you move as fast as the market?

1. 1. 1. We’re pretty fast – we can pivot strategy in hours.
      2. We can move fast but only if we have a plan and are really focused
      3. Depends – we can move fast if we work within known constraints.
      4. Usually, the market has moved and we’re still debating what to do

How did you go? Mostly 1’s and 2’s then you’re doing ok….and certainly better than most!

For most brands, it takes months to move from data to execution unless the work is part of a considered plan that has organisational attention.

That means that they are identifying risks and opportunities only after the financial damage is already done. In 2026 if insights are consumed weeks after the data was generated, you are performing an autopsy rather than an intervention.

Closing the intelligence gap

This delay creates an intelligence gap. It is the widening chasm between the massive volume of data your systems can collect and your brand’s ability to act on it. The sheer volume of data bombarding decision makers, combined with an institutional desire to have a ‘full picture before acting, means that insights are often stale by the time they are leveraged.

In a high stake’s environment like telcos, where every basis point of churn matters, stale insights are worse than no insights at all. They lead to misaligned offers, wasted marketing spend, and frustrated customers that feel your brand is out of touch. In 2026 it is time to move from reactive reporting to predictive certainty, ensuring that the right insight reaches the right execution point in days not months.

Why your software might be the problem

If your marketing team wants to act but IT backlogs or complex data schema mapping come first, then your software is the problem. Many brands are trapped in a cycle where their decisioning cycles are dictated by the limitations of rigid platforms.

These systems create a paradox where you have no shortage of business intelligence tools and no shortage of execution capability, yet the friction between them prevents swift action. This friction between data science and commercial value is where brilliant models go to die instead of being productionised.

To increase your velocity, you need to decouple your decisioning speed from your infrastructure. You need an intelligence layer that acts as a central nervous system, sitting above your existing tools to ensure that every interaction is backed by high fidelity science.

In conclusion

The ability to execute on a strategy pivot in days rather than months is the ultimate competitive advantage in the current market. This is what decision-making velocity means. It means empowering commercial teams to lead, rather than leaving them at the mercy of technical bottlenecks.

When you increase your decision-making velocity, you stop managing the plumbing and start driving the intelligence of your growth. You move away from ‘if then’ decision trees and manual exports toward a unified reasoning engine that can handle thousands or millions of decisions regarding churn, pricing, and yield with precision.

The question for subscription leaders today is no longer whether you have a platform to reach your customers. The real question is: does that platform have the intelligence to know which customers are worth reaching, and the velocity to reach them before the opportunity disappears? It is time to stop paying the inertia tax and start moving at the speed of the market.

I hope you’ve got some useful ideas from this article – I help telcos build their decisioning and execution systems. Please be in touch if you’d like some advice.

What’s next in this Series: Data, AI, Velocity and Value

I’ll be writing a follow up article later this month building on this blog. In the meantime, feel free to be in touch if there’s anything specific that you’d like to know about data, data science or AI.