Strategic Scenario Analysis

The partnership model isn't evolving. It's being systematically dismantled. PwC UK majorly restructured its business: → 123 partners exited (2x historical average) → 74 partners left in December alone → Tech apprenticeship program suspended → Career ceiling permanently institutionalized with MD title. This isn't just another cost-cutting cycle. It's the collapse of a centuries-old business model. For 150+ years, partnerships operated on a simple premise: senior experts scale through junior teams. But the economics have inverted: → Partner profits dropping across the Big 4 → The response? Cut owners, not just costs → Protect profit pools by shrinking the top → Automate and eliminate the bottom Value creation being completely rewired: → Expertise shifts from humans to systems → Leverage with technology, not junior staff → IP and platforms replacing billable hours → Scale without headcount growth The same pattern is emerging across firms: → EY's failed Projectsct Everest → KPMG's merger of 100+ units into 32 → Deloitte's reorganization to cut costs → All racing to transform as the consulting market slows The professional services pyramid isn't just shrinking—it's being replaced by a model where technology and orchestration create more value than armies of junior staff delivering services. The existential question: How does a partnership-based knowledge business survive when expertise can be digitized, automated, and deployed at near-zero marginal cost? What we're witnessing isn't the evolution. It's the beginning of its reinvention. - Numbers referenced from FT article.

Modeling something like time series goes past just throwing features in a model. In the world of time series data, each observation is associated with a specific time point, and part of our goal is to harness the power of temporal dependencies. Enter autoregression and lagging -  concepts that taps into the correlation between current and past observations to make forecasts.  At its core, autoregression involves modeling a time series as a function of its previous values. The current value relies on its historical counterparts. To dive a bit deeper, we use lagged values as features to predict the next data point. For instance, in a simple autoregressive model of order 1 (AR(1)), we predict the current value based on the previous value multiplied by a coefficient. The coefficient determines the impact of the past value on the present one only one time period previous. One popular approach that can be used in conjunction with autoregression is the ARIMA (AutoRegressive Integrated Moving Average) model. ARIMA is a powerful time series forecasting method that incorporates autoregression, differencing, and moving average components. It's particularly effective for data with trends and seasonality. ARIMA can be fine-tuned with parameters like the order of autoregression, differencing, and moving average to achieve accurate predictions. When I was building ARIMAs for econometric time series forecasting, in addition to autoregression where you're lagging the whole model, I was also taught to lag the individual economic variables. If I was building a model for energy consumption of residential homes, the number of housing permits each month would be a relevant variable. Although, if there’s a ton of housing permits given in January, you won’t see the actual effect of that until later when the houses are built and people are actually consuming energy! That variable needed to be lagged by several months. Another innovative strategy to enhance time series forecasting is the use of neural networks, particularly Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks. RNNs and LSTMs are designed to handle sequential data like time series. They can learn complex patterns and long-term dependencies within the data, making them powerful tools for autoregressive forecasting. Neural networks are fed with past time steps as inputs to predict future values effectively. In addition to autoregression in neural networks, I also used lagging there too! When I built an hourly model to forecast electric energy consumption, I actually built 24 individual models, one for each hour, and each hour lagged on the previous one. The energy consumption and weather of the previous hour was very important in predicting what would happen in the next forecasting period. (this model was actually used for determining where they should shift electricity during peak load times). Happy forecasting!

We can’t predict the future. But we can approach it more systematically. That’s where futures thinking (or strategic foresight) comes in. And it’s a critical part of good strategic design. You’ll often hear futurists say: “Foresight precedes strategy.” That’s only true if we treat strategy as a fixed plan, built in a linear way. When we instead see strategy as a testable hypothesis, futures thinking becomes more powerful. The two start to shape each other. One of the hardest parts of futures work is that it asks us to question our own values and beliefs. At its best, it creates a scaffold that helps people think the unthinkable. Here’s how futures thinking shows up in my strategic design practice. FRAMING AND SCOPING Getting alignment early matters. Futures tools can be used for different challenges, so framing the right question is essential. Clear scope and shared intent give the work its best chance of success. SCANNING Often called horizon scanning. This is where we lift our gaze and look for weak signals of change. These early signs can point to larger shifts ahead. They form the raw material for scenarios, alongside drivers of change and, to a lesser extent, trends. UNDERSTANDING IMPACT Not all signals matter equally. We explore which ones could have the biggest impact, or where uncertainty is highest. Tools like impact wheels and probability–impact matrices help build shared perspectives and increase situational awareness. SCENARIOS Scenarios turn signals into stories about alternate futures. They help us test assumptions, surface risks, and spot opportunities. Importantly, they let us rehearse decisions before we have to make them. STRATEGY FORMULATION In a linear process, strategy is the end point. In a complex world, that rarely works. Rather than a single plan, I’m interested in strategy as a system. New information about the future feeds into decisions in regular cycles, not as a one-off exercise. This is only a personal snapshot. Each stage has more depth and nuance, and many practitioners would break this into more steps. Because I also work with a complexity lens, I’m less interested in futures as a way to design an ideal future and “close the gap”. For me, the real value of futures thinking is its ability to: - Broaden what we notice - Challenge hidden assumptions - Build resilience in strategic decision-making Futures thinking isn’t a silver bullet. But its value grows when it’s used alongside other complementary practices. It expands what we can imagine, while understanding complex adaptive systems helps us respond to what’s emerging in the present. #StrategicDesign #FuturesThinking #Strategy #DesignThinking #StrategicForesight

The latest study from the Council of Economic Advisers, The White House states that ~10% of jobs are vulnerable to AI disruption. That may seem alarming, but let’s take a step back. In 2018, 60% of the jobs Americans held didn't even exist in 1940—created by technologies that emerged over the years (David Autor). Here’s the real concern: Many AI-vulnerable jobs haven’t evolved to match their increasing complexity. Workers in these roles are more exposed to disruption because they haven’t been given the chance to upskill. But this isn't new. Economic evolution is the hallmark of a dynamic economy. Just like we’ve adapted to past technologies, workers and industries will adapt to AI. The key lies in how we approach it. Why businesses should care: Organizations that proactively identify and support employees vulnerable to AI disruption aren’t just doing good—they’re making smart financial decisions. 💡 Investing in upskilling and mobility for these workers could unlock millions in retention and productivity. Mass layoffs due to AI aren’t likely. The real shift? Slower hiring and reduced demand for certain roles. We’re already seeing fewer job postings for writers, coders, and even artists. So, what activities are at risk? Roles involved in processing information, analyzing data, scheduling, and administrative tasks are prime targets. Industries to watch? Architecture, engineering, legal, computer science, and mathematics. Surprising jobs at risk of AI disruption: Airline Pilots, Copilots, and Flight Engineers Nuclear Power Reactor Operators Private Detectives and Investigators Commercial and Industrial Designers These highly specialized roles, which traditionally require significant human judgment, are surprisingly vulnerable to AI-driven changes. Business leaders, what barriers are preventing you from launching upskilling initiatives to future-proof your workforce? The future of work is evolving, but we can shape how it unfolds. #FutureOfWork #AIandJobs #Upskilling #WorkforceTransformation #AI

Investing in a Changing Climate: Climate change presents two major financial risks for #investors, transition and physical risks; together, these risks accelerate the devaluation of #assets, potentially rendering them stranded long before the end of their expected lifecycles. 🔹 Transition risks—driven by rapid policy shifts, evolving market behaviors, and technological innovations—impact industries beyond fossil fuels, including real estate, automotive, agriculture, and heavy industry. 🔹 Physical risks—such as extreme weather, rising sea levels, and prolonged heat stress—can disrupt supply chains, reduce worker productivity, and devalue assets. A delayed transition brings hidden risks—while some sectors (utilities, basic resources) may see short-term relief, they face sharper, more destabilizing corrections when policy action eventually accelerates. Using NGFS climate transition scenarios (Baseline, Net Zero 2050, and Delayed Transition) alongside Discounted Cash Flow (DCF) and Interest Coverage Ratio (ICR) valuation methods, we identify sector-specific vulnerabilities across the US and Europe. 📉 Sectors at risk under a Net Zero 2050 scenario: 🔹 Real estate (-40% in Europe) due to energy efficiency mandates and rising costs. 🔹 Telecommunications (-26.3%) and consumer staples (-24.8%) facing stricter carbon regulations. 🔹 Energy (declines of -6% to -7%) as fossil fuel operations become costlier. 🔹 Basic resources (-11.9%) and technology (-11.7%) showing relative resilience but still facing policy-driven adjustments. 📈 Sectors showing resilience across scenarios: 🔺Technology & Healthcare remain stable due to innovation and lower emissions intensity. 🔺Consumer discretionary in the US (-16%) sees moderate declines but adapts through renewables and supply chain shifts. A well-orchestrated transition is critical to minimizing financial shocks. Scenario-based risk assessments allow investors to safeguard portfolios, mitigate stranded asset risks, and capitalize on opportunities in the green economy. #ClimateRisk #NetZero #SustainableFinance #ESG #Investing #ClimateTransition #RiskManagement #AllianzTrade #Allianz

Uncertainty isn’t the enemy of leadership. Silence in uncertainty is. Markets shift. Geopolitics flare. Technology disrupts. No leader can predict exactly what comes next. The mistake isn’t saying “I don’t know.” The mistake is leaving it there. Silence creates space for fear. Scenarios create space for confidence. The leaders I know say this: “We don’t know the future…But here are three ways it could play out, and here’s how we’ll respond to each.” That shift replaces anxiety with structure. Here’s how scenarios guide decisions: 1. Best Case → Maximise Opportunity • If growth rebounds, be ready to scale • Line up resources and move first • Optimism matters only if you’re prepared 2. Base Case → Navigate Steady State • In uneven recovery discipline wins • Tier your investments • Forecast cash tightly • Normalise quarterly adjustments 3. Worst Case → Build Resilience • Protect non-negotiables • Pre-approve cost levers • Over-communicate with empathy, reinforce purpose • Trust is forged in downturns, not booms. The real power is in cascading this skill to teams: → Model vulnerability (“I don’t know yet”) → Teach them to sketch 3 scenarios in 15 minutes → Anchor every path to concrete actions → Repeat until it becomes part of culture At 6 months, fear gives way to clarity. At 2 years, resilience becomes second nature. Remember, great leaders don’t eliminate uncertainty. They equip their people to move confidently within it. That’s how you scale trust, resilience, and momentum, inside your company and across your partnerships. --------------------------- Avoid missing insights like this. Get cheatsheets like this each Wednesday. Subscribe to my free newsletter: https://philhsc.com ➕ Follow me, Phil Hayes-St Clair for more like this.

We need to debate the labor market in a AI-mediated reality. David Deming, Christopher Ong & Lawrence H. Summers have done exactly that with their report, which explores past episodes of technological disruption in the US labor market to draw lessons for the likely future impact of artificial intelligence (AI). Their findings challenge conventional wisdom: despite the rapid technological advancements of recent decades, the pace of labor market change has actually slowed compared to earlier periods. The disruptions from 1990 to 2017 were less dramatic than those triggered by past general-purpose technologies (GPTs) like steam power and electricity—largely because those historical shifts were so profound. However, signs suggest that there is a dramatic change undergoing now due to AI. Among the key indicators: 🔹 The labor market is no longer polarizing—low- and middle-paid jobs are shrinking while high-paid employment grows. 🔹 Employment growth in low-wage service jobs has stalled. 🔹 STEM employment has surged by over 50% since 2010, driven by software and computing occupations. 🔹 Retail employment has declined by 25% in the past decade due to e-commerce advancements. In other words, AI is driving an unprecedented transformation of the labor market. This makes it more urgent than ever to explore and debate the scenarios that will shape the future of work in an AI-mediated reality. #FutureOfWork #AI #Retail #LaborMarket #TechnologicalDisruption #Innovation

The 29th United Nations Climate Change Conference (COP29), held in Baku, Azerbaijan, concluded. Here are the 10 key outcomes which will impact businesses: 1.Global Carbon Credit Market Established Businesses can trade carbon credits globally, incentivising emission reductions. Economic impact: Potential cost savings for compliant businesses and revenue opportunities for those investing in renewable projects. 2. $300 Billion Annual Climate Finance Commitment Funding will assist developing nations’ transitions to greener economies. Economic impact: Opportunities for businesses in infrastructure, renewable energy, and technology transfer in emerging markets. 3. $120 Billion Annual Pledge by Multilateral Banks Increased lending for climate-related projects in low- and middle-income countries. Economic impact: New markets for sustainable technologies and climate adaptation solutions. 4. Loss and Damage Fund Operationalised Financial assistance for nations affected by climate impacts. Economic impact: Businesses must account for increased disaster recovery costs and integrate resilience measures into operations. 5. Strengthened Nationally Determined Contributions (NDCs) Governments will require stricter compliance from businesses to meet climate targets. Economic impact: Increased compliance costs but also market opportunities for businesses offering low-carbon solutions. 6. Global Carbon Market Valuation at $250 Billion by 2030 Expansion of the carbon market creates a high-value trading ecosystem. Economic impact: New revenue streams for businesses innovating in emissions reduction technologies. 7. Increased Private Sector Investment Expected Policy alignment with 1.5°C goals will drive private sector financing of sustainable projects. Economic impact: Greater competition for investment in renewable and energy-efficient technologies. 8. Focus on Adaptation and Resilience Emphasis on addressing climate risks encourages businesses to prioritise resilient infrastructure. Economic impact: Increased costs for climate-proofing operations but reduced long-term risks. 9. Opportunities in Emerging Markets Developing nations receiving climate finance create demand for green technology and services. Economic impact: Growth prospects for businesses specialising in clean energy, water management, and waste reduction. 10. Economic Penalties of Inadequate Action The $2.5 trillion annual cost of climate impacts underscores the need for rapid action. Economic impact: Delayed adaptation exposes businesses to higher costs from supply chain disruptions, infrastructure damage, and reduced productivity. These outcomes highlight a dual impact: businesses face rising costs from compliance and climate risks, but proactive strategies aligned with COP29 goals offer significant opportunities for growth in the green economy. #cop29 #decarbonisation #co2 #emissions #carbon #carbonmarket #cop #un #unitednations #co2market

Trump has announced his intention to impose 25% tariffs on all EU goods. We immediately ran the numbers through the Kiel Institute for the World Economy's KITE model, and the results show a significant economic impact—not just for the EU, but also for the US. Our model estimates: 🇪🇺 EU real GDP would decline by -0.4% in the short run—a substantial hit. 🇺🇸 The US would see a -0.17% contraction, but if the EU responds with its own tariffs, the damage to the US economy would double. This is largely driven by a significant price impact in the US with up +1.5%. Importantly, this inflationary pressure is not only driven by pricier final products, but US production becomes more expensive through tariffs on imported intermediate inputs. The trade impact is also notable: EU exports to the US would drop by 15-17%, with Germany taking the hardest hit (-20%). However, this translates to only -1.5% of Germany’s total exports. At the sectoral level, manufacturing in Germany would bear the brunt. The German automotive industry could see a 4% decline in nominal production, with ripple effects across machinery, equipment, and supply chains. Caveat as always: The exact implementation of these tariffs remains unclear, as does the EU’s response. Past tariff threats have not always materialized as announced. However, uncertainty itself is already an economic factor, slowing investment, disrupting supply chains, and dampening growth. #TradePolicy #Tariffs #Economy #US #EU