Insights
Latest thinking for technology leaders.
Practical perspectives on delivery reality, data foundations, security posture, and how to scale AI responsibly —
written by Scout AI Group founder Stuart Arthur.
🎄 12 Days of CTO/CIO Challenges
Behind every successful digital transformation is a stressed tech leader juggling multiple challenges —
budget constraints, legacy debt, data trust, supplier performance, cyber risk, compliance burden and AI readiness.
The solution is foundations, ruthless prioritisation and measurable outcomes.
What makes a world-class CTO/CIO?
The real role is a rare blend: deep technical mastery, systems thinking, emotional maturity under pressure,
people leadership, commercial instinct, transformation capability, and security/risk fluency — especially in the AI era.
2025 DORA report: AI hype versus reality
AI is a force multiplier — it speeds up strong teams and amplifies fragility in brittle systems.
The fundamentals still matter: architecture, testing discipline, modular design, governance and ownership.
🎄12 Days of CTO/CIO Challenges🎄
Behind every successful digital transformation is a stressed tech leader juggling multiple challenges.
🎁 Day 1 — Lack of Budget & Resource
Challenge: Being asked to deliver Ferrari outcomes with KA money.
Solution: Ruthless prioritisation, clearer value narratives, and shifting from project to product thinking to unlock sustainable investment.
🎁 Day 2 — Legacy Tech Debt / Burning Platforms
Challenge: Years of “just patch it for now”.
Solution: A rolling data-enabled, modernisation roadmap tied to risk and business value — not big-bang rewrites that never land.
🎁 Day 3 — Data Quality & Trust Issues
Challenge: Fancy dashboards built on sand.
Solution: Invest in data governance, build a strategic data platform strategy, automate lineage, and make data quality visible to the business.
🎁 Day 4 — AI-Era Talent & Skills Shortages
Challenge: Competing with tech giants for niche data and AI skills.
Solution: Grow internal capability via academy, redefine roles to be outcome-based, and use specialist partners strategically — not as a crutch.
🎁 Day 5 — Losing the Boardroom Battle
Challenge: When tech is seen as a cost centre, not a growth engine.
Solution: Speak business language e.g., revenue, risk, and resilience — not K8s, APIs, and platforms.
🎁 Day 6 — Lack of Delivery Predictability
Challenge: “Why are we always struggling to ship software?”
Solution: Product centred operating models, open-working / transparency over velocity, and small, quickly-validated increments.
🎁 Day 7 — Underperforming Suppliers & Partners
Challenge: Great salespeople, shiny decks, but disappointing delivery.
Solution: Outcome-based contracts, cultural alignment, stronger vendor governance, and in-house strategic leadership, architecture, and product ownership.
🎁 Day 8 — Underperforming Teams
Challenge: Teams working hard… but not working on the right things or delivering activity not outcomes.
Solution: Clear missions, psychological safety, modern engineering practices, and removing organisational blockers.
🎁 Day 9 — Cyber Security Vulnerabilities
Challenge: Attackers innovate faster than cyber budgets.
Solution: Shift-left security, continuous monitoring, and moving from periodic audits to real-time risk intelligence.
🎁 Day 10 — Procurement Inertia
Challenge: “We’ll get that contract sorted… early Q3 2026.”
Solution: Take the lead, pre-approved patterns, outcome-aligned frameworks, and collaborative early engagement with commercial teams.
🎁 Day 11 — Compliance & Regulatory Burden
Challenge: More rules, more audits, more cost.
Solution: Automate the audit trail, embed compliance by design, and make regulation a competitive advantage.
🎁 Day 12 — AI Readiness & Shadow AI
Challenge: Teams adopting AI tools faster than you can govern them.
Solution: Build AI foundations (data, ethics, security), provide sanctioned tools, and turn shadow AI into staff innovation.
What makes a world-class CTO/CIO?
It’s far rarer — and far harder — than most people realise.
The journey is never linear. It’s forged through cycles of building, breaking, learning, and rebuilding. You collect scar tissue. You make poor decisions or ones that don’t land. Life gets in the way. You trust the wrong people. You fall for false promises. You fight the wrong battles. And those experiences — not the job titles — are what forge real judgement and leadership.
Startups often assume a “CTO” is simply an engineer who can code the best, and communicate well enough with non-engineers. But that’s a founding engineer, not a world-class CTO/CIO. The real thing is a unicorn blend of traits that almost never coexist in one person:
Deep + broad technical mastery across architecture, platforms, data, systems, security and AI,
Architecture + systems thinking, not just shipping features,
Emotional intelligence and maturity under pressure,
Strong people leadership — inspiring, aligning, resolving conflict, building culture,
Commercial + entrepreneurial instinct to focus on value, not vanity,
Change + transformation capability, because organisations don’t move just because tech improves,
And security, compliance + risk fluency in an environment where the stakes rise every year.
This combination is incredibly rare. Far rarer than most boards or founders understand — which is why the role is often misunderstood, overloaded, or lonely.
And in the age of AI, the role matters more than ever. Anyone can prototype. Anyone can make a demo look impressive. But scaling AI safely, sustainably and beyond the hype requires foundations:
data quality,
governance,
operating models,
security,
real adoption,
and leadership that knows when to slow down to go faster.
Another fact: the CTO/CIO role is not the same everywhere - there are lots of nuances. For example:
Startups might need a product-driven technical generalist,
Scaleups might need an architect and organisational stabiliser,
SMEs might need a commercial moderniser,
And enterprises need a strategist who can navigate legacy, politics, and complexity.
Very few people can operate across all four contexts.
The best CTOs and CIOs evolve — continually reinventing themselves as technology, teams and expectations shift. And they leave the organisation stronger, safer and more capable than they found it.
2025 DORA Report Raises Valid Points On AI Hype Versus Reality
I’ve just been reviewing the 2025 DORA Report, and it’s a firm reminder that we’re living through a real shift in software engineering - but one that’s often sadly being buried under a sea of noise and hype.
AI is everywhere in the headlines, LinkedIn feeds are full of self-proclaimed “AI experts” showing off one-off experiments and quick hacks (never mind shouting about awards for work they often haven’t been directly involved in delivering!).
Sadly, a lot of these experts have never written a line of code pre Chat-GPT, let alone have the ability to validate its output, but we won’t even go there in this article!
The real problem with this level of AI hype? Most of it is built on shaky ground. No solid architecture, no guardrails, no long-term thinking, no ability to scale it beyond an experiment or prototype.
Reality check: this is not innovation - it’s just adding organisational fragility with a shiny new badge.
What the DORA Report really showcases is this:
AI is a force multiplier (for both good and bad).
Yes, it drives speed, throughput, and quality. But it also magnifies weaknesses. If your systems are brittle, AI makes them more brittle.
Engineering is evolving: The role of the engineer is shifting - prompt design, validation, architecture - but it’s still engineering. Not smoke and mirrors.
Systems thinking isn’t optional: Architecture, process, tooling, people, and change. They have to work together. Bolt-ons and shortcuts won’t cut it.
The fundamentals still matter: Testing discipline, modular design, clear ownership, and governance. These aren’t outdated ideas - they’re the only way to stop AI adoption becoming chaos.
Where my AI bets would be placed:
Developer experience as a priority, not an afterthought.
Guardrails and shared patterns, instead of endless “experiments“.
Raising AI fluency across the whole team/organisation, not just a few enthusiasts.
Measuring long-term resilience, not just short-term speed.
The DORA Report effectively cuts through the AI hype, and is aligned with my perspective.
AI is here, but the real wins won’t come from flashy demos or prototypes. They’ll come from teams who invest in foundations, build responsibly, and evolve how they work. People are still involved in these processes, and we still need to design great services and experiences.
Otherwise, it’s just another round of “move fast and break things” - with bigger consequences this time around. The Covid response should have taught us the importance of technical debt.
Executives must understand that there’s no getting away from the hard yards of transformation - don’t be fooled by the AI experts, trust people who are delivery proven because AI is ultimately just another creation tool; otherwise we’ve learned nothing from the technical debt accrued during the previous decade of short term thinking.
Don't be fooled by public sector organisations that are suddenly leading the AI arms race when the reality is they still can’t deliver relatively simple digital experiences for citizens, such as self-service, let alone personalised services. In these cases, using AI is often just putting lipstick on the pig.
