I have spent much of my career inside and alongside retailers, helping leadership teams find growth in places their current operating model struggled to reach. The businesses have ranged from listed groups to privately owned companies. I have also built and sold my own data intelligence, digital innovation and email marketing technologies.
Across retail, travel-related services, consumer products and other sectors, the pattern is familiar. The strategy is researched. The numbers have been challenged. Leaders understand the customer problem. Stakeholders have agreed a direction. Then the work enters the delivery queue.
That is where a convincing strategy can lose momentum.
The gap between approval and action
My starting point as a consultant has usually been to examine two kinds of evidence. The first is what the business can measure: customers, margin, channel performance, repeat purchase, technology, capacity and the cost of change. The second is what its people know: why work happens as it does, where customers get stuck and which decisions are harder than the process chart suggests.
Put those together and you can identify an opportunity, build a credible return case and win agreement. That is valuable work. It does not create an extra engineering team or clear a crowded technology roadmap.
Consider a retailer with rich customer data but generic campaigns. The commercial case for more relevant journeys may be straightforward. Delivering them means connecting data, stock, pricing, permissions, content and customer service. Each team has existing commitments. Each dependency has an owner. A small change in a slide deck becomes a substantial change in daily operations.
The same issue appears in a service business that wants faster onboarding, or a travel operator trying to join up fragmented customer interactions. The bottleneck is often a combination of scarce skills, competing priorities and limited technical capacity. Adding AI to the ambition can increase the number of possible initiatives faster than the organisation can deliver them.
McKinsey's 2026 State of AI survey captures the tension: 80% of respondents said AI had improved their individual productivity, while 37% attributed at least some company-wide EBIT impact to AI. These are self-reported survey findings, not a prediction for any one business. They do show why personal productivity and enterprise results need different measures.
What I now ask before signing off a strategy
Who owns the first release? What work can the existing team stop or defer? Which systems and approvals stand between the idea and a live customer experience? What is the smallest test that would change our confidence in the business case?
Those questions helped lead me to create Kinsugi. I wanted to connect the commercial work of understanding and prioritising an opportunity with the practical work of building and checking a solution. It starts with a business problem, brings engineers into the design, and uses reusable software modules where they fit. The people responsible for the outcome still make the important decisions.
If you have an approved growth idea stuck between the boardroom and the backlog, create a free Kinsugi account and use Gain to outline the problem, the desired result and the constraints. That gives your team a clearer starting point for deciding whether a pilot is warranted.
Where does your organisation most often lose momentum: agreement, capacity, integration or ownership after launch?