Strategic Technology Services The Future of Technology in Travel & Leisure

Strategic Technology Services: The Future of Technology in Travel & Leisure

Key Discussion Points & Takeaways

1. AI Is Already Delivering Material Productivity Gains

What was discussed

• Participants reported 20–50% efficiency improvements across the end‑to‑end software lifecycle.
• Proofs of concept that previously took 6–12 weeks are now being delivered in 1–3 weeks — in some cases days.
• AI is being used not just for greenfield builds, but increasingly for legacy refactoring, enhancement, and maintenance.

Why it matters

• AI has crossed the threshold from experimentation to tangible operational leverage.
• The barrier to “trying” ideas has fallen dramatically, shifting teams from risk‑avoidance to iteration.

Key takeaway

AI is no longer a future capability in travel tech — it is already changing delivery speed, feasibility, and cost curves.

2. Shift from Off‑the‑Shelf to Bespoke Is Accelerating

What was discussed

• A strong theme emerged around challenging traditional off‑the‑shelf (OTS) software thinking, particularly in:
  • Booking engines
  • ERP‑adjacent platforms
  • Front‑of‑house and guest experience systems
• Frustrations with OTS included:
  • Paying for 100% of a platform but benefiting from ~20%
  • Expensive, inflexible license agreements
  • Slow or misaligned roadmaps
• AI is making bespoke-first approaches viable, where they were previously cost‑prohibitive

Limits acknowledged

• Core finance and HR platforms are still likely to remain OTS.
• Differentiation opportunities lie primarily in customer‑facing, operational, and experience‑driven platforms.

Key takeaway

AI is resetting the build‑vs‑buy equation, particularly for systems that define competitive differentiation.

3. AI Adoption Works Best When Treated Like a “Junior Team Member”

What was discussed

• Participants likened AI to a junior developer:
  • Very capable
  • Extremely fast
  • Requires oversight, review, and governance
• “Do not trust — verify” was a recurring phrase.
• Mistakes occur most dangerously when confidence is high (e.g., 99% accuracy leading to complacency).

Common governance practices

• Human sign‑off remains mandatory
• Pull requests, code reviews, acceptance criteria validation still apply
• Some teams compare outputs across multiple models (Claude vs GPT) as a quality filter

Key takeaway

AI accelerates teams — but governance, quality gates, and accountability are non‑negotiable.

4. Cultural Adoption Is as Important as Technical Adoption

What was discussed

• Successful rollout depended less on tooling and more on behavioural change:
  • Universal or near‑universal engineer adoption drove momentum
  • Internal “listen & learn” sessions helped spread practical use cases
  • Informal sharing (Teams “AI Corner” chats) built confidence and capability
• Resistance tends to stem from job‑loss fears, not technical concerns.

Observed reality

• AI does not reduce demand — it unlocks more demand.
• Backlogs expand as teams realise what is now feasible.

Key takeaway

The biggest AI wins come from normalising everyday usage, not isolated centres of excellence.

5. Product, Design & Business Functions Are Starting to Catch Up

What was discussed

• AI has moved beyond engineering into:
  • Product discovery
  • User story creation
  • Acceptance criteria definition
  • Design system exploration
  • Documentation and analysis
• Strong interest in AI‑augmented product management, not AI‑replaced PMs.

Important nuance

• Guests and customers often do not want to talk to bots
• AI is most effective when:
  • Invisible to the customer
  • Empowering humans behind the scenes

Key takeaway

AI should enhance human interaction, not replace it — especially in experience‑led travel brands.

6. Security, Data, and IP Remain Active Concerns

What was discussed

• Questions around:
  • Code ownership
  • Model training on proprietary IP
  • Risks from agentic or overly‑permissive AI tools
• Some organisations are exploring:
  • Offline or edge models for sensitive environments
  • Tight model restrictions (e.g. AI drafting but not sending communications)

Consensus

• Risk tolerance varies by organisation
• Clear policies and education matter more than blocking tools outright

Key takeaway

AI risk is manageable — but unmanaged AI is a genuine threat.

7. Commercial Models Are Starting to Feel Pressure

What was discussed

• From a delivery perspective:
  • Customers increasingly understand AI reduces delivery effort
  • This raises questions around pricing and value perception
• Counter‑argument:
  • AI doesn’t reduce ambition — it raises expectations
  • Conversations are shifting from “how cheap” to “how much more can we do”

Key takeaway

Value conversations are evolving from cost savings to accelerated growth and richer platforms.

8. Industry‑Wide Opportunity, Uneven Maturity

What was observed

• Attendees ranged from:
  • Early adopters with mature AI usage
  • To organisations at the very start of their journey
• The travel & leisure sector remains fragmented and peer‑isolated
• Strong appetite for safe knowledge sharing across competitors

Key takeaway

The sector is moving — but maturity is uneven, creating both opportunity and risk.

Overall Summary

What this session revealed

• AI is already reshaping how quickly, how cheaply, and how confidently travel businesses can innovate.
• The winners will be those who:
  • Treat AI as a team capability, not a tool
  • Invest in governance as much as experimentation
  • Use AI to amplify human experience, not replace it
  • Rethink legacy assumptions about platforms and scale

One line takeaway

AI isn’t changing what travel companies want to do — it’s changing what’s finally possible.