The first 100 days
10 moves that drive real business turnaround
Nowadays the market is so volatile, speed and precision matter more than ever. If you need to turn your business around, bringing in interim leadership can deliver clarity, pace and impact without delay.
Here are ten practical actions that an experienced interim can take to get results in the first 100 days:
- Get under the skin of the numbers – Not just headline financials, but product-level margins, customer acquisition costs, and operational inefficiencies.
- Meet the frontline early – Spend time with sales, operations, and customer service teams. They usually know what’s really holding performance back.
- Review the top 20 customers and deals – Understand what drives revenue and margin. Prioritise retention, upsell, and renegotiate any bad deals.
- Rebuild delivery confidence – Audit the core processes behind quality, delivery, and service levels. Quick fixes here restore customer and team confidence.
- Stop non-strategic activity – Identify and pause initiatives that drain time and budget without moving the dial.
- Refocus the leadership team – Clarify roles, strip out overlap, and create clear accountability for outcomes - not just tasks.
- Speed up decision-making – Identify bottlenecks, delegate authorities and streamline approvals. Fast, well-informed decisions are a competitive advantage.
- Tighten cash and cost control – Move quickly to find non-headcount savings. Reinvest in sales and delivery where it drives short-term gain.
- Improve internal comms – Make communication regular, transparent and two-way. Teams move faster when they understand the direction and feel like they are being listened to.
- Build the plan beyond 100 days – Show the board and teams there’s a roadmap, not just a fire drill. Stability follows action with foresight.
Are you putting a Value Creation Plan together?
Here's a checklist of where to start digging for the gold
1. Revenue Growth
✅ Strategic M&A – 10–25% EBITDA boost via synergies and cost efficiencies.
✅ Market Expansion – 5–15% uplift through new customer acquisition.
✅ Pricing Optimization – 3–10% improvement by refining pricing strategies.
2. Operational Efficiency
✅ Process Automation – 5–20% EBITDA gain by reducing labor costs and errors.
✅ Shared Services Model – 5–15% uplift by centralizing administrative functions.
✅ Resource Optimisation – 3–12% improvement through supplier rationalisation and renegotiation.
3. Customer Experience & Retention
✅ Loyalty Programs – 5–12% uplift through repeat business and referrals.
✅ Personalization & AI – 4–10% EBITDA increase via higher retention rates.
✅ Omnichannel Engagement – 3–8% boost by improving customer satisfaction.
4. Innovation
✅ New Revenue Models – 5–15% EBITDA expansion via subscriptions or bundling.
✅ Technology-Driven Solutions – 3–10% improvement through efficiency gains.
✅ Sustainable Initiatives – 2–8% boost by reducing waste and compliance costs.
5. Financial & Risk Management
✅ Capital Optimization – 5–12% EBITDA gain via debt restructuring.
✅ Predictive Analytics – 4–10% uplift through better forecasting and risk mitigation.
✅ Regulatory Compliance – 3–7% improvement by avoiding fines and inefficiencies
What's going wrong with Business Transformations?
10 lessons I have learned to ensure success
I’ve been thinking through my previous transformations and comparing them with some public domain case studies and the patterns are clear. It’s not usually the tech that’s fails, it’s the approach.
Here are 10 things the most successful transformations had in common:
1. Senior leaders stayed visibly engaged (not just as sponsors on a slide)
2. Strategy came first, and the tech followed
3. They treated change management as essential—not optional
4. They celebrated early wins, and built momentum from there
5. They used technology like AI, cloud, and automation to improve business outcomes, not just modernise the stack
6. Data was core to everything—decisions, feedback loops, course correction
7. Teams were agile, empowered, and trusted to move fast
8. People were trained, involved, and encouraged to experiment
9. Progress was measured obsessively, and tracked over time
10. Above all, accept that transformation is never ‘done’
All of these sound obvious, but are rarely applied in full. Most of the pain I've seen in failing programmes is avoidable, caused by skipping the fundamentals, not by picking the wrong tools.
The lesson? Stop treating transformation as a procurement exercise. Start treating it as business strategy, led with intent.
Top Tech trends CEOs & Investors should watch
(That aren't AI)
In 2025, it’s easy to assume “technology” equals AI. But beyond the LLM headlines, a quiet revolution is underway across frontier technologies that offer just as much potential to reshape industries and unlock value.
So, in the spirit of looking beyond the obvious, here are five tech trends that deserve serious attention, especially for CEOs and hashtag#PE professionals focused on value creation, deal pipelines, and exit readiness.
- Digital Infrastructure & Data Centres
Impact: Scale + resilience
Bain forecasts the global data centre market, spanning hardware, software and renewables, will grow 40–55% annually, reaching $1.4 trillion by 2027.
EY notes over $100bn of PE investment already flows into digital infrastructure.
Why it matters: Owning the right infrastructure enables scalability, reduces risk, and lifts exit multiples—especially for tech-led portfolios. - Cybersecurity & Software Resilience
Impact: Risk defence + valuation uplift
PE investment in cyber reached $8.5bn by mid‑2024 amid rising breaches and regulation.
Firms with zero-trust architecture are commanding premium multiples.
Cybersecurity businesses often trade at 8–10× revenue—outpacing many SaaS peers.
Why it matters: Security is now a strategic asset. Strong frameworks reduce risk and increase investor confidence. - Cloud-First & SaaS Migration
Impact: Scalability + operational efficiency
Singulier highlights cloud transition as key to PE value creation.
CTO mandates increasingly focus on cloud-native architecture to reduce technical debt and boost EBITDA.
Why it matters: Cloud platforms enable leaner ops and cleaner exits. Buyers expect it; sellers must deliver it. - Data Monetisation & Advanced Analytics
Impact: Data as a revenue engine
Data is becoming a monetisable asset.
Firms using analytics for pricing, segmentation and efficiency often outperform peers.
Integrated data platforms enhance agility and reporting.
Why it matters: Applied well, analytics boost performance and unlock stronger growth stories at exit. - ESG Infrastructure & Sustainable Tech
Impact: Value + compliance
73% of PE firms now embed ESG in due diligence (Grant Thornton).
Carbon tracking tools and renewables are increasingly operationalised across portfolios.
Why it matters: ESG-readiness cuts reputational risk, aligns with LP expectations, and opens capital flows.
Final Thoughts:
The most strategic moves in 2025 lie beyond AI. Focus on the infrastructure of value - security, cloud, data, ESG - and you’ll be building businesses with true scale, resilience and exit appeal.
Mastering the art of managing Systems Integrators for success
So, you’ve mobilized a major business transformation which includes a large chunk of technology, and you’ve appointed a Systems Integrator. These are great for scaling up fast, providing specialist resource and for transferring risk, but it’s never all plain sailing.
Your SI has its own constraints and priorities, so you’ll need to manage them carefully.
Here’s a few things to look out for:
- Choosing the Right Partner - Not all SI’s are created equal. Yes, look for reputation and technical expertise, but also look at the delivery culture of that SI. What is the mix of onshore/offshore delivery? Do they understand and embed operating model change and OCM?
- How important are you as a client to them?
- Defining Clear Objectives – It’s a truism that you need to agree on clear scope, goals and expectations from the outset. Be transparent on what good looks like for both parties, and recognise the inherent conflicts between your positions – you’re trying to reduce costs, risks, disruption and time to value, meanwhile the SI is also trying to reduce its own delivery costs and risks by keeping things vanilla. They will also be looking to upsell and recover margins through change requests
- Monitoring Progress and Performance - Implement metrics to assess your SI’s performance, regularly review milestones, service delivery, and quality. Never skimp on setting up your own PMO, as this will be your primary source of information and will provide early warning of risks and issues
- Communication – this is the backbone of any successful partnership. Establish regular check-ins at all levels from workstream to executive, and encourage honesty and transparency throughout. Invest in building relationships and informal channels which will carry you through the inevitable bumps in the road
- Innovation – SI’s often have insights into emerging technologies and trends, and have ideas of how you can accelerate delivery and save costs. Find a way to encourage and incent your SI to bring this innovation to the table, especially where they lead to more effective systems and improved business outcomes.
- Training and Knowledge Sharing - Ensure that internal teams are equipped to work alongside systems integrators. Train your employees to understand the systems being implemented, and streamline onboarding for all new team members.
Delivering a successful outcome will require a detailed understanding of transformation methodologies, and also an appreciation of what is driving the SI’s behaviour. Hiring an Interim transformation director as a client-side advisor, will help you to bridge the gap between your internal team and the SI, and will provide strategic oversight and delivery assurance which will pay dividends in avoided project costs and delays.
In 2025 volatility isn't just a possibility - it's business as usual
How to make one plan for many futures
From a historic two-day stock market crash triggered by new tariffs, to unpredictable central bank signals, energy shocks, and geopolitical flashpoints, business leaders are navigating uncharted turbulence, and sadly this doesn't look like its going to change any time soon.
In this environment, the point of the strategic plan isn’t to predict the future, it’s to be ready for multiple versions of it. That often feels at odds with annual planning and budget cycles, where assumptions are baked in and investor expectations are set.
We all know the goal posts will probably change before the next Financial years even begins, so how can we be ready for that volatility and still organise our business? Here’s how:
1️⃣ Define 3–4 plausible futures, not just best/worst case, but specific shifts (tech disruption, demand shocks, regulatory change).
2️⃣ Stress-test your model. Look at people, processes, supply chains, and capital allocation under each scenario.
3️⃣ Create trigger points. Define the external signals that tell you when to pivot.
4️⃣ Build playbooks. Outline the actual actions you’d take, not just risks you’d track.
💡 But what about fixed budgets and investor expectations?
- Use scenarios to inform the budget narrative - show investors you’ve explored the alternatives.
- Allocate small pools of flexibility (discretionary capex, phasing projects) that can be redirected as conditions change.
- Tie budgets to trigger points: “If X happens, we’ll reallocate to Y.”
- Use scenarios to reframe board and investor updates. Less about making excuses for variances, more about adaptability.
📌 Real-world illustrations:
- A consulting firm models the impact if AI automates 30% of its work. Their response? Shift toward strategy & change services where human expertise is irreplaceable.
- A telecom operator plans for accelerated infrastructure sharing. In one scenario, they’ve already pre-built JV agreements to execute quickly.
- An electricity provider tests carbon pricing scenarios. In the high-cost case, they fast-track renewables; in the low-cost case, they double down on storage.
- An automotive supplier stress-tests a future where EV adoption outpaces forecasts. Their hedge: diversify into battery systems to offset declining ICE demand.
Annual planning sets the baseline. Scenario planning makes it resilient. Together, they help organisations deliver both discipline and agility - and build investor confidence that you’re ready for whatever comes next.
👉 What scenarios is your organisation preparing for — and which ones are you quietly ignoring?
Your tech programme isn't going to deliver the business case
Most complex Tech programmes fail to deliver their committed ROI. According to Gartner 70% of ERP initiatives will fail to meet their original business‑case goals, and 25% fail outright. That’s a big problem. These are often multi-year, multi-million pound investments on which the success of the business and the careers of its execs depend.
I’ve seen this across all sorts of programmes, not just ERP: CRM rollouts, Telecom BSS/OSS overhauls and Enterprise cloud & AI migrations. Often the problem starts when the business case is first quantified, particularly when it has been shaped by technology vendors or SIs with a vested interest. Productivity gains are overstated, simplifying assumptions made, and the real costs of data migration, integration and change are downplayed. This can set up an overly optimistic NPV that sets unrealistic expectations.
Once the programme starts to mobilise I regularly see a few common themes that can conspire to destroy value:
❌ Procurement functions , though well intentioned, can make things worse: fixed-price, fixed-outcome contracts drive competition but kill flexibility, creating an adversarial relationship where scope and change requests become a battleground.
❌ Executive sponsorship fades as leaders move on, leaving no one truly accountable for outcomes, and allowing the programme to drift
❌ Too much energy goes into delivering the technology, not into changing the business processes and ways of working that actually create value.
❌ Benefits tracking stops at go-live, so promised outcomes are never followed through
How to avoid the trap? Here’s five points to get you started
- Redefine the programme – treat it as an operating model change, not an IT install. Business-led programmes are 1.5x more likely to hit ROI (McKinsey).
- Make benefits tangible – anchor to hard KPIs like cycle times, working capital and cost-to-serve, with named owners accountable beyond go-live. Without this discipline, 70% of programmes lose track of benefits in the first year (Gartner).
- Contract smarter – avoid rigid fixed-price deals that drive conflict. Use rolling-wave scope, joint governance, or even gainshare models to keep delivery partners aligned to outcomes. Deloitte research shows that flexible contracting improves delivery confidence by 30%.
- Phase with intent – deliver in modular steps (finance core before procurement in ERP, sales pipeline before service in CRM, analytics before full cloud migration). Incremental approaches are 35% more likely to realise expected benefits (Deloitte).
- Stay focused on adoption – technology only delivers value if people use it. Prosci benchmarking shows that projects with excellent change management are 6x more likely to meet objectives, and McKinsey finds that 70% of large-scale change efforts fail largely due to poor adoption.
No, Agentic won't save you money - at least not on its own
There’s no shortage of hype around Agentic right now. Everyone’s talking about the benefits, and the case studies are eye-catching: 9,500 hours saved in Salesforce legal ops, 52% faster case handling at ServiceNow, an 80% cycle time reduction in UiPath expense processing, and a bank seeing >50% productivity uplift modernising legacy systems with agent squads (McKinsey). On the surface, it all looks really compelling. But there’s a catch: efficiency gains are not the same as cash savings. Hours saved and faster cycle times are nice, but unless they translate into fewer people, lower run-rate costs, or new revenue streams, they don’t move the dial on the P&L.
McKinsey calls this the “Gen AI paradox”: 80% of companies have adopted hashtag#generativeAI, yet just as many report no material impact on earnings. The reasons are simple:
🔷 Horizontal use cases like copilots and chatbots spread thin productivity gains across thousands of employees. They’re easy to deploy, but the benefits are diffuse — hard to measure and impossible to book as cash.
🔷 Vertical, process-specific use cases in Finance, hSupplyChain, IT, orHR have far more potential, but most remain stuck in pilot mode or are limited to optimising individual tasks rather than rewiring the whole process. In these areas, throughput time can be cut by 20–80%. But unless businesses stop backfilling roles, reduce contractor spend, or resize teams, those efficiency gains remain just opportunity cost savings that disappear into the noise.
If businesses want real EBITDA improvements, three things must sit alongside the technology:
1. Hardwiring the cash case
🔹 Tie automation directly to opex reduction or headcount savings.
🔹 Use finance-approved benefit tracking, not vendor dashboards.
2. Redesigning operating models
🔹 Don’t bolt AI onto legacy processes — simplify and standardise first.
🔹 Reimagine workflows so AI handles scale, and humans handle exceptions, oversight, and complex judgement.
3. Managing exceptions intelligently
🔹 Ensure escalations flow seamlessly to people with full context.
🔹 Design governance to prevent agent sprawl, autonomy drift, and shadow AI.
The Bottom Line is that you need to think about Agentic holistically. Start by proving the concept with a few use cases, but treat this as a transformation programme that touches technology, the operating model and people. Once you have proof that benefits are real, build a benefitrealisation plan to make sure you capture the EBITDA savings, think through the impact on the Operating Model and put together a Organisational Change Management plan to make sure you take your workforce on the journey with you.
Did you know that every month a programme slips can wipe 7-10% of its NPV?
That’s what happens when you look at transformation through an investors lens. In a typical mid-market case, a one-year delay almost wipes the programme value out entirely. Two years late and the NPV turns negative: destroying value destroying rather than creating it.
The problem isn’t just cost or time overruns. It’s profit leakage. Each month of drift pushes benefits further to the right, reduces adoption, and shortens the window for returns inside the hold period. The impact lands straight in EBITDA and ultimately in the exit multiple.
Independent research shows that even when transformations are deemed “successful,” much of the value targeted never reaches the P&L. Benefits leak away through execution gaps, unclear ownership and loss of momentum. For PE or FTSE250 boards, that gap is a big deal.
What to do now:
🔹 Re-base in-flight programmes against today’s timeline and benefit assumptions. Fix or kill anything that no longer clears the hurdle.
🔹 Run a rapid healthcheck to surface scope, governance and adoption risks.
🔹 Quantify cost of delay in time and money. Know exactly how much value each month of drift is costing.
🔹 Reset ownership of benefits, so that they land in the P&L, not just on milestone slides.
Great returns are built, not bought
I’ve just been through the August 2025 edition of a mid-market PE market report, and thought I'd share my take aways:
🔹 Scaling well, not just fast – Sustainable growth now depends on getting the operating model, data, and culture ready for the next stage before the numbers take off.
🔹 Leadership and capability first – The best investors are building leadership benches early, often bringing in interim CFOs, CTrOs, and digital specialists within weeks of close.
🔹 M&A with purpose – Buy and build remains the dominant accelerator, but integration, data, and cultural alignment determine whether it adds value or destroys it.
🔹 Digital and data as core enablers – ERP, CRM, and AI upgrades are no longer “nice to have.” They’re foundational to scaling decisions, pricing insight, and customer engagement.
🔹 International expansion re-energised – More mid-market firms are making transatlantic or APAC plays, helped by embedded M&A teams and stronger data infrastructure.
The bottom line is that the market is rewarding operational excellence, digital maturity, and leadership readiness just as much as topline growth.
So what?
➡️ For PEs: Double down on post-deal enablement - build functional playbooks, digital and AI centres of excellence, and fast-track operating partners who can move beyond governance into delivery.
➡️ For portcos: Treat systems, data, and talent as your scaling infrastructure. Build the platform you’ll need when you're twice the size now, not when you're already there.
Sales Transformation is the Killer App for AI
There are so many ways to get real, bottom line value from AI, but I’m hearing that the function delivering the fastest and most measurable ROI is Sales.
When done right, Sales Transformation routinely delivers:
- 10–30% uplift in win-rates
- Forecast accuracy above 90%
- Cycle times cut by 20–30%
- Payback less than 6 months
Higher win rates and shorter cycles drop straight through to contribution, while predictable forecasting allows tighter working capital and opex control. That can translate to +2–4 percentage points of EBIT margin, or a 5–10% uplift in net margin, within the same revenue base.
The winning formula is not just about dusting off your sales playbook, running another Miller Heiman refresher and sharpening your Revenue Ops discipline - its about rewiring the sales operating model around intelligence, governance, and incentives:
1️⃣ Process Discipline – Move from “update the CRM when you remember” to a rhythm of structured pipeline and forecast reviews driven by live data.
2️⃣ Technology Enablement – Embed AI scoring, conversation analytics, and deal health indicators into daily workflow, not as extra admin.
3️⃣ Revenue Operations (RevOps) – Unify Marketing, Sales, and Customer Success under one data model and one definition of pipeline health.
4️⃣ Playbook Evolution – Build dynamic, AI-fed playbooks that guide salespeople by persona, sector, and probability of success.
5️⃣ Manager Enablement – Equip managers to coach from data, not gut feel, using call analytics and pattern insights.
Anyone who’s led a sales team knows the hard truth: building the STOM is only half of the battle. The other half is motivation. Salespeople follow the money - and they hate admin.
To be successful, you’ll need the team’s hearts and minds, or rather their hearts and their wallets. Appeal to their wallets by reviewing your incentive structures to blend outcome and behaviour: 80% quota, 20% data & enablement quality (reward use of insights, recognise leaders for improving team conversations and data integrity). You could even give this an edge by making payment of bonuses contingent on data and process compliance
But appealing to wallets alone will not work. Capture hearts by reframing the transformation as a performance advantage. Prove the benefit to each salesperson: improved relationships with buyers, higher win rate, a more enjoyable job, faster proposals and better quality business. Show that the AI isn’t replacing them - it’s amplifying them.
Stop thinking about Agentic Tools, instead focus on the Agentic Operating Model
We’ve moved past the “copilot” phase of GenAI. What’s emerging now is the agentic organization, where humans and AI agents work side-by-side, driving outcomes at low marginal cost.
McKinsey calls it the next paradigm after the industrial and digital revolutions. In practical terms, it’s the evolution of the operating model itself from functional silos to flat, outcome-aligned agentic networks. Humans sit above the loop steering direction and governance, while agents execute, learn, and optimise at scale.
For CEOs and PE-backed leadership teams, this isn’t about tinkering with chatbots, it’s a transformation of workflows, governance, and value delivery. One year in, the results are mixed: early adopters who redesigned work from first principles are seeing exponential productivity and faster decision cycles; others are stuck with “AI slop” - flashy demos, little P&L impact, and rising rework costs.
The lessons are:
1️⃣ Rewire the workflows, not just the tech. Real value comes when you redesign processes end-to-end around AI-first logic.
2️⃣ Treat agents like employees. They need job descriptions, onboarding, evaluation, and feedback loops just like humans.
3️⃣ Modernise governance. Move from quarterly reviews to real-time guardrails and agent-to-agent controls, keeping human oversight where it matters.
4️⃣ Reskill for orchestration. Your next “manager” might lead 100 agents, not 10 people - M-shaped generalists will define the new leadership tier.
At WBC Ltd, we see this as the next major ValueCreation frontier: helping portfolio companies re-architect their operating models for an agentic era where cost, speed, and learning curves all compress, and the winners will be those who move boldly, fast, and deep.
A cautionary tale about Deloitte's $440k AI-generated Audit
The one riddled with fake citations, a made-up court quote, and a “human review” that somehow missed all of it. Days later, Deloitte announced a global rollout of Anthropic’s Claude to 500,000 staff. Was this coincidence or course correction? Its hard to say, but to be clear this wasn't a technology failure.
The models generated an output based on the training data they had, but it was the humans that didn't verify. What happened to the Engagement Partner's QualityAssurance? Why weren't the audit team properly trained to understand the limitations of the tool and to verify the facts? Why wasn't the client aware upfront that their deliverable had been "augmented" by AI?
This whole debacle opens up some uncomfortable questions on both sides of the table:
💼 For clients:
If AI is now endemic and integral to research, drafting, and analysis, why keep paying a premium to a big brand when smaller, expert boutiques can deliver faster, leaner, and with clearer accountability? Scale doesn’t equal safety anymore. Look for transparency - firms that can prove how AI was used, and how it was verified.
🏢 For professional service firms:
The danger isn’t the model, it’s the lack of governance, oversight, and education. AI literacy can’t be an afterthought or a training module. It has to be built into every workflow, sign-off, and assurance layer. Without that, you’re taking a massive risk with your brand.
Reportedly, just $60k was finally refunded, but I'm sure the cost of the reputational damage to the firm was eye watering 😬
Do you really need a Chief AI Officer?
My recent podcast interviews confirmed that AI is no longer a “what if”, it’s a “how fast.” The businesses that operationalise it now will reshape costs, margins, and customer experience. Those that wait will fall behind.
But how do you kick-start it? Do you appoint a CAIO?
This role isn’t for every company, and it’s definitely not forever. You need one when:
🔹 Your business has ambition but no ownership
🔹 Your board sees the potential but lacks the confidence to lead it
🔹 You have fragmented experimentation but no enterprise plan
🔹 You need to signal to investors, employees, and partners that AI is central to your next phase of growth
How They Work with Your Business
A good CAIO doesn’t “run AI” - they wire it into the operatingmodel, turning potential into performance:
🔹 Embed AI into business strategy, not write a separate one
🔹 Translate between functions: help the CFO link AI to cash and margin; the COO redesign synthetic (human + agent) teams; the CPO reshape roles and skills; and the CIO secure data and ethics
🔹 Build AI fluency across the board through live examples and use-case sprints
🔹 Set governance and guardrails for responsible, auditable deployment
The ultimate test of success is when AI stops being a standalone initiative and becomes integral to how your business works.
What Makes the Ideal CAIO?
This isn’t a job for a technologists in isolation - it’s for a transformation leader, with depth in AI, who understands business mechanics, people, culture, andvaluecreation.
🔹 Strategic clarity – sees where AI impacts the P&L
🔹 Commercial fluency – talks EBITDA, working capital, and customer value
🔹 Change leadership – engages sceptics and shifts mindsets
🔹 Cross-functional depth – spanning commercial, operations, finance, HR, and IT
🔹 Influence and storytelling – makes complexity board-ready
🔹 Ethics and risk literacy – keeps governance aligned with innovation
Like the ChiefDigitalOfficer a decade ago, the CAIO is a bridge, not a destination. Most organisations need one for two to three years - long enough to make AI integral. After that, the title should disappear, replaced by leaders who are AI-literate and automation-intelligent.
A great CAIO makes themselves obsolete by design - leaving behind the structures, governance, and capability for others to carry forward. That’s why the interim or fractional model makes sense: you get senior impact without permanent headcount, you bypass politics and inertia, and most importantly you move fast.