Predictive AI offers tremendous potential – but it has a notoriously poor track record. Outside Big Tech and a handful of other leading companies, most initiatives fail to deploy, never realizing value. Why? Data professionals aren’t equipped to sell deployment to the business. The technical performance metrics they typically report on do not align with business goals – and mean nothing to decision makers.

For stakeholders and data scientists alike to plan, sell and greenlight predictive AI deployment, they must establish and maximize the value of each machine learning model in terms of business outcomes like profit, savings – or any KPI. Only by measuring value can the project actually pursue value. And only by getting business and data professionals onto the same value-oriented page can the initiative move forward and deploy.

Why Business Metrics Are So Rare for AI Projects

Given their importance, why are business metrics so rare? Research has shown that data scientists know better, but generally don’t abide: They rank business metrics as most important, but in practice focus more on technical metrics. Why do they usually skip past such a critical step – calculating the potential business value – much to the demise of their own projects?

That’s a damn good question.

The industry isn’t stuck in this rut for only psychological and cultural reasons – although those are contributing factors. After all, it’s gauche and so “on the nose” to talk money. Data professions feel compelled to stick with the traditional technical metrics that exercise and demonstrate their expertise. It’s not only that this makes them sound smarter – with jargon being a common way for any field to defend its own existence and salaries. There’s also a common but misguided belief that non-quants are incapable of truly understanding quantitative reports of predictive performance and would only be misled by reports meant to speak in their straightforward business language.

But if those were the only reasons, the “cultural inertia” would have succumbed years ago, given the enormous business win when ML models do successfully deploy.

The Credibility Challenge: Business Assumptions

Instead, the biggest reason is this: Any forecast of business value faces a credibility question because it must be based on certain assumptions. Estimating the value that a model would capture in deployment isn’t enough. The calculation has still got to prove its trustworthiness, because it depends on business factors that are subject to change or uncertainty, such as:

• The monetary loss for each false positive, such as when a model flags a legitimate transaction as fraudulent. With credit card transactions, for example, this can cost around $100.
• The monetary loss for each false negative, such as when a model fails to flag a fraudulent transaction. With credit card transactions, for example, this can cost the amount of the transaction.
• Factors that influence the above two costs. For example, with credit card fraud detection, the cost for each undetected fraudulent transaction might be lessened if the bank has fraud insurance or if the bank’s enforcement activities recoup some fraud losses downstream. In that case, the cost of each FN might be only 80% or 90% of the transaction size. That percentage has wiggle room when estimating a model’s deployed value.
• The decision boundary, that is, the percentage of cases to be targeted. For example, should the top 1.5% transactions that the model considers most likely to be fraudulent be blocked, or the top 2.5%? That percentage is the decision boundary (which in turn determines the decision threshold). Although this setting tends to receive little attention, it often makes a greater impact on project value than improvements to the model or data. Its setting is a business decision driven by business stakeholders, representing a fundamental that defines precisely how a model will be used in deployment. By turning this knob, the business can strike a balance in the tradeoff between a model’s primary bottom-line/monetary value and the number of false positives and false negatives, as well as other KPIs.

Establishing The Credibility of Forecasts Despite Uncertainty

The next step is to make an existential decision: Do you avoid forecasting the business value of ML value altogether? This would prevent the opening of a can of worms. Or do you recognize ML valuation as a challenge that must be addressed, given the dire need to calculate the potential upside of ML deployment in order to achieve it? If it isn’t already obvious, my vote is for the latter.

To address this credibility question and establish trust, the impact of uncertainty must be accounted for. Try out different values at the extreme ends of the uncertainty range. Interact in that way with the data and the reports. Find out how much the uncertainty matters and whether it must somehow be narrowed in order to establish a clear case for deployment. Only with insight and intuition into how much of a difference these factors make can your project establish a credible forecast of its potential business value – and thereby reliably achieve deployment.

Sabre Corporation has partnered with Travelin.Ai, a next generation corporate booking platform.

The deal gives Travelin.Ai customers access to the SabreMosaic Travel Marketplace, including traditional airfares, NDC offers, low-cost carrier content and lodging options, as well as Sabre’s Lodging AI capabilities.

They will also benefit from AI-powered capabilities that drive hotel attachment, as well as the ability to book leisure and corporate travel in one booking flow.

Sabre’s Lodging AI analyses property attributes, trip context and traveller preferences to give personalised accommodation options, recommend alternatives when a chosen hotel is sold out, and suggest accommodation when flights are booked without a hotel.

The combination of Sabre and Travelin.Ai technologies will help travel management companies (TMCs) increase hotel attachment rates and capture additional leisure volume.

In an internal Sabre study, when travellers engaged with AI-suggested hotels, the likelihood of completing a booking increased by up to 14%, helping TMCs capture incremental revenue, reducing leakage and giving companies stronger duty of care and more complete reporting.

“The ability for TMCs and their corporate customers to book business trips with leisure components opens access to a $1 trillion market,” said Richard Viner, Head of Sabre UK and Ireland.

“In EMEA we see strong potential to raise hotel attach rates, and this agreement helps TMCs boost revenue and bookings while strengthening duty of care. All of this sits within a unified workflow that delivers a consumer-grade experience for travellers and agents.”

The platform automatically separates business and leisure costs through its proprietary split-payment technology, ensuring compliance while allowing employees to extend trips or use travel as an incentive.

Travelin.Ai will launch these capabilities with TMCs in the UK, the Nordics, US, Australia and Germany and says onboarding can be completed in minutes rather than weeks.

Founded in Norway, the company has expanded its presence across Europe and North America and is building a customer base among TMCs and corporates.

“Business travel should never force a choice between compliance and convenience,” said Roy Golden, CEO of Travelin.Ai.

“Compliance is the baseline, and technology must make it seamless. By combining Sabre content with its Lodging AI solution we embed policy into the booking flow while keeping the traveller experience intuitive.”

sabre.com

travelin.ai

Study of 300+ executives identifies misalignment between marketing and IT as key barrier to AI transformation

CHICAGO, Sept. 10, 2025 /PRNewswire/ — Bounteous, a leading global digital transformation consultancy, released a new white paper titled “The AI Whitespace: Addressing Challenges to Unlock Potential.” The report helps enterprises accelerate artificial intelligence (AI) adoption by identifying and closing organizational gaps between marketing and technology functions.

Based on a survey of more than 300 senior executives across North America and Europe, the report highlights key misalignments slowing AI adoption and provides a framework to help enterprises align, invest, and lead with AI. The full report is available here for enterprises looking to lead with AI.

With generative AI rapidly moving from experimentation to business-critical operations, Bounteous emphasizes that successful AI integration requires more than technical implementation; it demands a coordinated, company-wide transformation.

“Integrating AI across a business isn’t just a technology play; it’s an organizational shift,” said Martin Young, EVP, Data & AI at Bounteous. “To bridge the gap from early experimental wins to more impactful value across core business functions, organizations transform skillsets across their workforce.”

Young was recently appointed to lead the company’s global AI practice, reinforcing its commitment to helping clients scale AI initiatives responsibly and effectively. With more than 20 years of experience driving digital transformation, Young brings deep expertise in AI strategy, data governance, and enterprise change management.

“The AI Whitespace” provides C-level executives with practical strategies to assess AI maturity, identify organizational bottlenecks, and chart a path toward scalable, business-driven AI adoption.

Additionally, for the third time in a row, Bounteous was recognized as a Representative Vendor in the 2025 Gartner® Market Guide for Global Digital Marketing Agencies. The report noted, “Agencies are making significant investments in AI training and technology,” citing the Bounteous merger with Accolite Digital as an example.

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About Bounteous
Bounteous is a premier end-to-end digital transformation consultancy dedicated to partnering with ambitious brands to create digital solutions for today’s complex challenges and tomorrow’s opportunities. With uncompromising standards for technical and domain expertise, we deliver innovative and strategic solutions in Strategy, Analytics, Digital Engineering, Cloud, Data & AI, Experience Design, Digital Experience Platforms, and Marketing. Our Co-Innovation methodology is a unique engagement model designed to align interests and accelerate value creation. Our clients worldwide benefit from the skills and expertise of over 4,500+ expert team members across the Americas, APAC, and EMEA. By partnering with leading technology providers, we craft transformative digital experiences that enhance customer engagement and drive business success. Discover more about our impactful work and expertise by visiting www.bounteous.com and following us on X, LinkedIn, Facebook, and Instagram.

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