Business
How To Un-Botch Predictive AI: Business Metrics

Data scientists consider business metrics more important than technical metrics – yet in practice they focus more on technical ones. This derails most projects. So, why?
Eric Siegel
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.
Business
Rising cost of school uniform is scary, says mum from Luton

Julita WaleskiewiczEast of England

A mother-of-three said she has found it “scary” trying to keep up with the cost of sending her children to school.
Lauren Barford-Dowling, 27, from Luton, described the price of uniforms, shoes, meals and trips as “daunting”.
Level Trust, a Luton-based charity that provides free school supplies to families, said demand for its services had risen by up to 20% compared with last year.
“You want them to look their best, but it’s hard to keep up,” Ms Barford-Dowling added.

Ms Barford-Dowling has three children aged 10, six and five – and a fourth on the way.
She said branded jumpers and tops have risen in price, adding: “I worry about having enough money for all the essentials like shoes, trainers, trousers, dresses, tops.
“Three pairs of trainers cost over £100 – and they’ll be ruined in a couple of months. It’s scary.”
School meals also add to the pressure, she said, and her eldest child’s lunches cost £44 a month.
“When all three move up to Key Stage 2, I’ll be paying nearly £100 a month just so they can eat,” she added.

Ms Barford-Dowling said the Level Trust provided her children with free school shoes and trainers for PE.
Kerri Porthouse, the deputy chief executive of the charity, explained demand for the organisation’s services have risen.
“We’ve already seen an increase of between 15% and 20% compared with last year.
“That’s 200 more families in July and August alone. It’s a huge increase for a charity to cope with.
“Parents with children moving into reception or secondary often don’t realise how much uniform is needed until school begins. Then they come to us in a panic,” she said.
Research by the Child Poverty Action Group found it cost £1,000 a year to send a child to primary school and £2,300 for secondary.
Kate Anstey, the group’s head of education policy, said children from low-income families were dropping subjects because of the price of trips and equipment.
“Too many children are growing up in poverty, and it’s having a stark impact on their school day,” she said.
A Department for Education spokesperson said: “No child should face barriers to their education because of their family’s finances.
“We are capping the number of branded uniform items schools can require, and from 2026 all children in households on Universal Credit will be entitled to free school meals.”
Business
Millions missing out on benefits and government support, analysis suggests

Dan WhitworthReporter, Radio 4 Money Box

New analysis suggests seven million households are missing out on £24bn of financial help and support because of unclaimed benefits and social tariffs.
The research from Policy in Practice, a social policy and data analytics company, says awareness, complexity and stigma are the main barriers stopping people claiming.
This analysis covers benefits across England, Scotland and Wales such as universal credit and pension credit, local authority help including free school meals and council tax support, as well as social tariffs from water, energy and broadband providers.
The government said it ran public campaigns to promote benefits and pointed to the free Help to Claim service.
Andrea Paterson in London persuaded her mum, Sally, to apply for attendance allowance on behalf of her dad, Ian, last December after hearing about the benefit on Radio 4’s Money Box.
Ian, who died in May, was in poor health at the time and he and Sally qualified for the higher rate of attendance allowance of £110 per week, which made a huge difference to their finances, according to Andrea.
“£110 per week is a lot of money and they weren’t getting the winter fuel payment anymore,” she said.
“So the first words that came out of Mum’s mouth were ‘well, that will make up for losing the winter fuel payment’, which [was] great.
“All pensioners worry about money, everyone in that generation worries about money. I think it eased that worry a little bit and it did allow them to keep the house [warmer].”
Unclaimed benefits increasing
In its latest report, Policy in Practice estimates that £24.1bn in benefits and social tariffs will go unclaimed in 2025-26.
It previously estimated that £23bn would go unclaimed in 2024-25, and £19bn the year before that, although this year’s calculations are more detailed than ever before.
“There are three main barriers to claiming – awareness, complexity and stigma,” said Deven Ghelani, founder and chief executive of Policy in Practice.
“With awareness people just don’t know these benefits exist or, if they do know about them, they just immediately assume they won’t qualify.
“Then you’ve got complexity, so being able to complete the form, being able to provide the evidence to be able to claim. Maybe you can do that once but actually you have to do it three, four, five , six, seven times depending on the support you’re potentially eligible for and people just run out of steam.
“Then you’ve got stigma. People are made to feel it’s not for them or they don’t trust the organisation administering that support.”
Although a lot of financial support is going unclaimed, the report does point to progress being made.
More older people are now claiming pension credit, with that number expected to continue to rise.
Some local authorities are reaching 95% of students eligible for free school meals because of better use of data.
Gateway benefits
Government figures show it is forecast to spend £316.1bn in 2025-26 on the social security system in England, Scotland and Wales, accounting for 10.6% of GDP and 23.5% of the total amount the government spends.
Responding to criticism that the benefits bill is already too large, Mr Ghelani said: “The key thing is you can’t rely on the system being too complicated to save money.
“On the one hand you’ve designed these systems to get support to people and then you’re making it hard to claim. That doesn’t make any sense.”
A government spokesperson said: “We’re making sure everyone gets the support they are entitled to by promoting benefits through public campaigns and funding the free Help to Claim service.
“We are also developing skills and opening up opportunities so more people can move into good, secure jobs, while ensuring the welfare system is there for those who need it.”
The advice if you think you might be eligible is to claim, especially for support like pension credit, known as a gateway benefit, which can lead to other financial help for those who are struggling.
Robin, from Greater Manchester, told the BBC that being able to claim pension credit was vital to his finances.
“Pension credit is essential to me to enable me to survive financially,” he said.
[But] because I’m on pension credit I get council tax exemption, I also get free dental treatment, a contribution to my spectacles and I get the warm home discount scheme as well.”
Business
Alphabet’s AI Edge Survives Court Ruling, but Is There a Long-Term Risk?

The tech conglomerate is now required to share its valuable Google search data with the competition.
Google parent Alphabet (GOOG 0.27%) (GOOGL 0.22%) faced a frightening challenge after its search engine business was declared an illegal monopoly last August. Since then, investor concern over the potential consequences dampened Alphabet stock’s performance.
That changed on Sept. 2, when a federal judge finally delivered the legal penalties, and they largely favored Alphabet. The news sent the company’s stock to a record high.
Even so, Alphabet didn’t escape unscathed. While the penalties pose no immediate threat, over the long run, the possibility exists for damage to its critical artificial intelligence (AI) business. Digging into the court ruling’s implications can reveal if the tech titan’s AI aspirations face long-term risk.
Image source: Getty Images.
How the court’s decision affects Alphabet’s AI ambitions
The Sept. 2 legal ruling bars Alphabet from signing exclusive contracts with partners such as Apple. Deals are still allowed, as long as exclusivity isn’t a component, so no immediate revenue impact is involved here.
But another legal stipulation mandates sharing some of Google’s search data with competitors. This is where AI comes in.
Artificial intelligence relies on massive troves of data to perform tasks accurately. The court’s decision arms Alphabet’s rivals with ammunition to improve their AI models.
That competition includes Microsoft, which battles Alphabet on several fronts, including search, digital advertising, cloud computing, and of course, AI. The court’s requirement would deliver Google’s data insights to Microsoft’s Bing search engine, and feed across all the areas where the two corporations compete. But where it can really provide value is in AI.
Microsoft incorporates AI models developed by ChatGPT creator OpenAI into its offerings, since it has a stake in the company. ChatGPT’s introduction of generative AI to the world is one of the key drivers that kicked off the current artificial intelligence frenzy. Adding Google data to the mix could strengthen both Microsoft and OpenAI’s tech.
In fact, the judge who delivered the Sept. 2 ruling, Amit Mehta, noted, “The emergence of GenAI changed the course of this case.”
Is Alphabet’s AI position at risk?
Alphabet has the option to appeal the court’s penalties, but even if it doesn’t, the tech conglomerate’s impressive use of AI to date could be enough to prevent erosion of its businesses.
For instance, new AI features introduced to its Google search engine boosted usage. This enabled Google search revenue to hit $54.2 billion in the second quarter, up 12% from 2024’s $48.5 billion.
Alphabet’s AI advancements helped Google maintain a search market share of 90% in August, compared to next-closest competitor Bing’s 4%. Even if Google’s data helps Bing gain share, the gap between the rivals is so huge, Bing is unlikely to make a meaningful dent in Google’s lead anytime soon.
AI contributed to growth in Alphabet’s cloud computing segment, Google Cloud, as well. The division is bringing AI-powered shopping capabilities to PayPal. Such customer adoption of AI drove Google Cloud’s Q2 sales to $13.6 billion, a whopping 32% year-over-year increase.
Should cloud competitors improve their AI with Google data, the difference would have to be significant to get Alphabet’s customers to switch. Google Cloud integrations aren’t easily unfurled, leading to high switching costs.
Beyond search and cloud computing, Alphabet has injected AI into YouTube, its Waymo robotaxi service, Gmail, and more.
Alphabet isn’t out of the woods yet
Overall, Alphabet dodged a bullet in the Google search antitrust case. The legal penalties could have been as far-ranging as a forced divestiture of its popular Chrome browser and Android mobile operating system.
Considering these worst-case scenarios, Alphabet got off pretty light, and the ruling’s impact to its business over the long term looks minimal. The conglomerate’s widespread use of AI across its operations gives it a solid lead against competitors who may benefit from access to Google data.
But the legal dangers aren’t over yet. Earlier this year, Alphabet lost a separate antitrust case directed against its advertising empire. The penalties in that case are yet to be determined. However, Google was slapped with a $3.5 billion antitrust fine by the European Union on Sept. 5 for violating rules designed to protect a competitive advertising marketplace.
Compared to the Google search case, this separate antitrust lawsuit poses a lower risk. That’s because it involves advertising tech related to the company’s Google network, which produced $7.35 billion in Q2 sales, a drop from the $7.44 billion generated in the previous year. By comparison, Google search accounted for $54.2 billion of Alphabet’s $96.4 billion in Q2 revenue.
So while Alphabet isn’t out of legal trouble yet, the biggest long-term risk to its business is behind it, as long as the conglomerate can continue pushing AI innovation across its operations.
Robert Izquierdo has positions in Alphabet, Apple, Microsoft, and PayPal. The Motley Fool has positions in and recommends Alphabet, Apple, Microsoft, and PayPal. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft, long January 2027 $42.50 calls on PayPal, short January 2026 $405 calls on Microsoft, and short September 2025 $77.50 calls on PayPal. The Motley Fool has a disclosure policy.
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