From AI capability to value
The risk was never that AI would not work. The real risk is moving quickly without making the decisions required to translate capability into value.
Industry
Travel and Logistics
Data and AI in Travel Industry for a Memorable Passenger Experience
Highlights
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- Security leaders are shifting their focus from merely attempting to stop attacks to ensuring robust business continuity when prevention eventually fails.
- Enterprises are successfully leveraging artificial intelligence to parse massive volumes of alerts instantly, reduce investigation times, and govern deployments with rigour.
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Why does AI value stall?
Over the past few years, there has been a recurring pattern in conversations about artificial intelligence (AI).
The industry and the organisation may differ, but the discussion often follows a similar path. It begins with models, platforms, vendors and use cases. It usually ends somewhere quite different, with questions about ownership, priorities, evidence and how the organisation needs to work differently. By then, the technology is rarely the central issue.
An organisation may have selected a capable platform, launched a promising pilot and secured senior sponsorship. The technology may even be performing exactly as expected. Yet, several months later, leaders are still struggling to explain what has materially changed. The initiative has generated activity and, perhaps, useful learning, but the business value remains difficult to see.
This is often described as an AI adoption or scaling problem. However, it is more fundamental. Organisations do not generally struggle to access AI capability. They struggle to translate that capability into measurable business value because the decisions that make that translation possible have not been made clearly enough.
Across travel, transportation, and hospitality, that challenge increasingly has less to do with whether AI can generate insight and more to do with how organisations choose to act on it. The conversations most often are no longer about models or platforms. They are about ownership, prioritisation, governance, operating models and who has the authority to make different decisions when new insight becomes available.
Why AI value is harder to realise in operational businesses
Travel, transportation, and hospitality organisations face many of the same AI adoption challenges seen in other industries, but they often experience them more acutely because of the environments in which they operate.
Many of the decisions AI is expected to support sit within complex operational systems that are constantly changing. Airlines manage disruption, demand fluctuations, customer commitments and operational recovery in real time. Logistics operators coordinate assets, inventory, workforce capacity, and transport networks across multiple functions. Hospitality organisations balance guest experience, workforce productivity, and commercial performance while adapting to changing demand patterns.
In these environments, the challenge is rarely whether AI can generate insight. The challenge is determining how that insight fits into existing decision-making processes and who has the authority and confidence to act on it.
Value can also be difficult to isolate because it often sits across organisational boundaries. The benefits of a better decision may be realised in another department, another process, or even another part of the customer journey. As a result, ownership, governance, and accountability become just as important as the technology itself.
The value created is often indirect. It may appear through improved resilience, faster responses to disruption, more effective planning, better customer outcomes, or stronger employee productivity, rather than a simple automation metric. These outcomes can be more difficult to measure, but they are often where the greatest impact is realised.
Perhaps, most importantly, the people expected to use AI are rarely waiting for technology to tell them what to do. They are already balancing competing priorities, responding to disruption, and making dozens of operational decisions every day. The challenge is not replacing decision-makers. It is helping them make better decisions more consistently, and with greater confidence.
This is why conversations about AI in travel, transportation, and hospitality often end up focusing on ownership, governance, and operating models.
What are we changing?
Technology rarely fails because of technology.
It fails because organisations underestimate the work required to change how decisions are made, how people operate, and how value is created.
One of the first questions that clients should be asked is what they are actually trying to change. It sounds obvious, but it is often surprisingly difficult to answer. Most organisations can produce a long list of AI opportunities. Few lack ideas. The challenge is deciding which opportunity deserves attention first and why it matters more than the alternatives.
A use case tells us what an organisation could do. It does not necessarily explain the outcome the organisation wants to create. Until leaders are clear about that change, it becomes difficult to prioritise investment, align stakeholders or make sensible trade-offs between competing opportunities. Decisions can quickly be shaped by visibility, enthusiasm, or the strength of a technology demonstration rather than by the value an initiative is expected to create.
What counts as proof?
The same ambiguity often appears when organisations talk about proof of value.
Almost every leadership team agrees that AI investment should be evidence-led and that value should be demonstrated before an initiative is scaled. Far fewer define, before the work begins, what evidence would actually convince them.
This is one of the most useful conversations a leadership team can have. What would we need to see to justify further investment? Equally importantly, what would persuade us to stop?
Those questions establish what the organisation is testing, which assumptions matter, and how the results will inform a real decision. Without clear criteria, successful pilots can continue indefinitely because nobody is prepared to end them, while promising initiatives can be abandoned because stakeholders never agreed what success would look like. Proof of value should not be assembled retrospectively to defend an initiative. It should be designed into the work from the beginning.
How does scope drift?
Even when organisations agree on the evidence they want to see, another challenge often emerges.
Initiatives that begin by solving a specific problem gradually acquire broader ambitions. New stakeholders become involved, additional objectives are introduced, and measures of success begin to shift. What started as an effort to address a defined operational issue becomes a wider transformation programme, but without the ownership, investment, or governance needed to support that change.
This is where apparently successful technology can become disconnected from operational value. Technical teams may measure model accuracy, processing time, or system usage. Programme teams may measure milestones and delivery progress. These are useful indicators, but they do not necessarily tell us whether the business has improved. In travel, transportation, and hospitality, value rarely appears as a technical metric. It appears when operational teams can make decisions with greater confidence, when people spend less time gathering information and more time acting on it, or when organisations are able to respond more effectively to disruption, demand changes, or emerging commercial opportunities. Ultimately, value is reflected in faster, better-informed actions that improve operational performance, customer experience, and business outcome.
The specific outcome will vary between airlines, logistics operators, and hospitality businesses, but the pattern is remarkably consistent. Value is realised when insight changes behaviour and improves decisions.
Organisations need to define value in terms of what changes in the business, not simply how the technology performs.
Who owns the change?
Defining value also means deciding who owns the change required to create it.
Technology implementation usually has a clear owner, as does programme delivery. Ownership becomes less certain when the conversation moves to adoption, governance, capability building, and changes to the operating model.
This is a challenge that crops up across sectors. AI programmes often have technology owners, delivery owners, and executive sponsors. What is less clear is who owns adoption, who owns business readiness, and who owns the decisions required to embed new ways of working. Those questions can seem secondary at the outset, but they often become the difference between promising capability and measurable value. These responsibilities are often divided across several teams or deferred until implementation is almost complete.
Yet, this is where capability either becomes part of the organisation or remains something separate from it. People need to understand when to use the new insight, how it changes their decisions, and what they remain accountable for. Processes, measures, and governance may need to evolve. Leaders need to reinforce new expectations and resolve the tensions that emerge when established ways of working are challenged. None of this happens automatically because the technology works.
What is the real risk?
The organisations creating meaningful value from AI are not necessarily those with the most advanced platforms or the largest investment portfolios.
Across airlines, logistics providers, hospitality organisations, and other travel businesses, organisations creating the most value from AI recognise that AI is not simply a technology initiative. It is a change initiative that affects how priorities are set, how decisions are made, and how accountability is distributed across the organisation. As a result, they are clear about what they want to change, define the evidence that will guide investment, maintain discipline as scope develops, and take responsibility for embedding change into the organisation.
The risk was never that AI would not work. The real risk is that organisations invest heavily, move quickly, and never make the decisions required to translate capability into value.