Business Strategy & Growth
Strategists, economists and entrepreneurs who help organisations identify opportunity and execute with conviction
Building a marketplace from zero is a different discipline from running marketing inside a mature business. Leaders who have only operated inside the enterprise tend to under-invest in supply-side acquisition and over-invest in demand-side spend. The question is how to apply enterprise marketing rigour to early-stage growth without losing the founder economics that make scale-up possible.
Most growth capital still flows through the same networks it always has, leaving credible founders outside those networks structurally underfunded. Senior teams know the talent exists. The harder question is how to source it, back it, and build the surrounding infrastructure that turns a fundable founder into a scaled company.
Most capital flows to founders who pattern-match to the people allocating it. The result is a structural blind spot: viable businesses, large markets, and disciplined operators get passed over because they do not fit a familiar template. Closing that gap is a commercial problem before it is a values one.
Brand trust has collapsed faster than most marketing functions can rebuild it. Customers, employees and investors now treat corporate claims as suspect by default, and the playbooks that worked when trust was assumed produce diminishing returns. The harder question is what an authentic commercial proposition looks like when audiences arrive sceptical, and how to plan brand and innovation strategy when the operating environment keeps shifting underneath the plan.
Most founders can build a small business. Few can turn it into a structured firm that survives their own attention. The gap between a sole operator with a strong personal brand and a multi-division business with paying clients, regulated divisions and a real team is where most growth stalls, and where most accountants, advisors and consultants quietly give up on scaling.
Most organisations have run AI pilots. Almost none have rebuilt how work actually gets done. The gap between board ambition and operational reality is where competitive position is now being lost, and senior teams are running out of room to keep treating AI as an experiment rather than an operating model.
Most companies cannot explain what they sell in a sentence a customer will repeat. Internal language creeps into external messaging, websites get cluttered, sales teams improvise, and the cost shows up in conversion rates and wasted media spend. The tension is not creative, it is operational: every day without a clear message is a day competitors look easier to buy from.
Downtowns are competing for residents, employers and investment against suburbs, other cities and the option of remote work. The decisions that determine whether they win, where streets go, how wide they are, what is built at ground level, are made one project at a time by people who rarely see them as a single strategy. The cost of getting that wrong shows up later in vacancy rates, carbon footprints, public health budgets and the talent that quietly leaves.
Boards know they need to convert AI and automation pilots into operating advantage, but the path between policy ambition, capital allocation and a working factory or service line keeps stalling. Megatrends are easy to name. Translating them into a sequenced bet that survives a budget cycle is not. Leaders need a frame of reference built from inside the policy and standards machinery, not above it.
Running an institution through a structural reinvention rarely fails because the strategy is wrong. It fails because the operating model, the people, and the brand cannot move in step. Senior leaders need a credible account of what it actually takes to hold a large business together while changing what it does.
Most customer experience programmes stall in the gap between brand promise and frontline behaviour. Leaders fund the technology, redraw the journey maps, and find that nothing material changes in what the customer actually receives. The harder problem is moving an organisation from compliance with policy to ownership of outcome, at the scale where it shows up in retention and growth numbers.
Most enterprise AI programmes stall in the gap between vendor demos and operational reality. Leaders are asked to commit capital and reorganise teams before the evidence base for what actually works at scale exists. The pressure is to move fast on technology that rewrites how work gets done, without a credible read on which adoption patterns produce measurable outcomes.