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How To Un-Botch Predictive AI: Business Metrics

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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.



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UK economy saw zero growth in July

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The UK economy failed to grow in July, according to the latest official figures.

The Office for National Statistics (ONS) said the economy saw zero growth in the month, following a 0.4% expansion in June.

However, monthly figures are volatile, and over the three months to the end of July, the economy grew by 0.2% compared with the previous three months, the ONS said.

The government is under mounting pressure to deliver on its key priority of boosting economic growth ahead of the Budget on 26 November.

The UK’s statistics body said the service sector performed well, helped by the health sector, computer programming and office support services.

However, this was offset by a weak performance in the manufacturing sector.

In the Budget, Chancellor Rachel Reeves will outline the government’s tax and spending plans with increasing speculation she will have to raise taxes to meet her self-imposed fiscal rules.

Yael Selfin, chief economist at KPMG UK, said the “weak start to the third quarter [is] a sign of things to come”.

“Economic activity is expected to slow in the second half of the year as the temporary factors which pushed up growth in the first half of 2025 begin to fade,” she said.

“Additionally, the later date of the Autumn Budget could prolong some uncertainties for businesses, delaying investment decisions and acting as a drag on growth until more clarity emerges.”

Responding to the latest growth figures, a Treasury spokesperson said: “We know there’s more to do to boost growth because whilst our economy isn’t broken, it does feel stuck.

“That’s the result of years of underinvestment, which we’re determined to reverse through our plan for change.

Shadow chancellor Sir Mel Stride said: “Any economic growth is welcome – but this government is distracted from the problems the country is facing.

“While the government lurch from one scandal to another, borrowing costs recently hit a 27-year high – a damning vote of no confidence in Labour that makes painful tax rises all but certain.”



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South Korea workers detained in US raid head home

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How the massive immigration raid on a Georgia car plant unfolded

More than 300 South Koreans who were detained in a massive immigration raid at a Hyundai plant in the US state of Georgia last week are due to arrive home on Friday.

Their return comes as the country’s president and Hyundai’s chief executive have warned about the impact of the raid.

A chartered Korean Air jet carrying the workers and 14 non-Koreans who were also detained in the raid took off from Hartsfield-Jackson Atlanta International Airport at midday local time on Thursday (17:00 BST). One South Korean national has reportedly chosen to stay in the US to seek permanent residency.

The plane is expected to arrive at Incheon International Airport at about 15:30 Seoul time (07:30 GMT) on Friday.

The departure was delayed by more than a day because of an instruction from the White House, South Korean President Lee Jae Myung said on Thursday.

President Donald Trump ordered the pause to check whether the workers were willing to remain in the US to continue working and training Americans, according to a South Korean foreign ministry official.

The BBC has contacted the White House for comment.

Lee also said companies would be “very hesitant” about investing in the US following the raid.

“The situation is extremely bewildering,” Lee added, while noting it is common practice for Korean firms to send workers to help set up overseas factories.

“If that’s no longer allowed, establishing manufacturing facilities in the US will only become more difficult… making companies question whether it’s worth doing at all,” he added.

Seoul is negotiating with Washington on visa options for South Korean workers “whether that means securing [higher] quotas or creating new visa categories”, Lee said.

On Friday, the South Korean foreign ministry said it had called for the US Congress to support a new visa for Korean firms.

During meetings with US senators in Washington this week, Foreign Minister Cho Hyun reiterated concerns among South Koreans over the arrests, the ministry said in a statement.

Meanwhile, Hyundai’s chief executive José Muñoz has said the raid will delay the factory’s opening.

Mr Muñoz told US media that the raid will create “minimum two to three months delay [in opening the factory] because now all these people want to get back”.

AFP A Korean Air Boeing 747-8I from Seoul, to repatriate hundreds of South Korean workers who were detained in an immigration raid at a Hyundai-LG battery plant under construction in the US state of Georgia last week, is seen in the cargo area of Hartsfield-Jackson Atlanta International Airport in Atlanta, Georgia, on 10 September, 2025.AFP

A Korean Air plane has been chartered to bring more than 300 South Korean workers home from the US

Last week, US officials detained 475 people – more than 300 of them South Korean nationals – who they said were working illegally at the battery facility, one of the largest foreign investment projects in Georgia.

LG Energy Solution, which operates the plant with Hyundai, said that many of its employees who were arrested had various types of visas or were under a visa waiver programme.

A worker at the plant spoke to the BBC about the panic and confusion during the raid. The employee said the vast majority of the workers detained were mechanics installing production lines at the site, and were employed by a contractor.

South Korea, a close US ally in Asia, has pledged to invest tens of billions of dollars in America, partly to offset tariffs.

Media in the country have described the raid as a “shock,” with the Dong-A Ilbo newspaper warning that it could have “a chilling effect on the activities of our businesses in the United States”.

The Yonhap News Agency published an editorial on Thursday urging the two countries to “cooperate to repair cracks in their alliance”.

The timing of the raid, as the two governments engage in sensitive trade talks, has raised concern in Seoul.

The White House has defended the operation at the Hyundai plant, dismissing concerns that the raid could deter foreign investment.

On Sunday, US President Donald Trump referenced the raid in a social media post and called for foreign companies to hire Americans.

The US government would make it “quickly and legally possible” for foreign firms to bring workers into the country if they respected its immigration laws, Trump said.

Additional reporting by Hosu Lee in Seoul



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Could AI nursing robots help healthcare staffing shortages?

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Around the world, health care workers are in short supply, with a shortage of 4.5 million nurses expected by 2030, according to the World Health Organization (WHO).

Nurses are already feeling the pressure: around one-third of nurses globally are experiencing burnout symptoms, like emotional exhaustion, and the profession has a high turnover rate.

That’s where Nurabot comes in. The autonomous, AI-powered nursing robot is designed to help nurses with repetitive or physically demanding tasks, such as delivering medication or guiding patients around the ward.

According to Foxconn, the Taiwanese multinational behind Nurabot, the humanoid can reduce nurses’ workload by up to 30%.

“This is not a replacement of nurses, but more like accomplishing a mission together,” says Alice Lin, director of user design at Foxconn, also known as Hon Hai Technology Group in Taiwan.

By taking on repetitive tasks, Nurabot frees up nurses for “tasks that really need them, such as taking care of the patients and making judgment calls on the patient’s conditions, based on their professional experience,” Lin told CNN in a video call.

Nurabot, which took just 10 months to develop, has been undergoing testing at a hospital in Taiwan since April 2025 — and now, the company is readying the robot for commercial launch early next year. Foxconn does not currently have an estimate for its retail price.

Foxconn partnered with Japanese robotics company Kawasaki Heavy Industries to build Nurabot’s hardware.

The firm adapted Kawasaki’s “Nyokkey” service robot model, which moves around autonomously on wheels, uses its two robotic arms to lift and hold items, and has multiple cameras and sensors to help it recognize its surroundings.

Based on its initial research on nurses’ daily routines and pain points — such as walking long distances across the ward to deliver samples — Foxconn added features, like a space to safely store bottles and vials.

The robot uses Foxconn’s Chinese large language model for its communication, while US tech giant NVIDIA provided Nurabot’s core AI and robotics infrastructure. NVIDIA says it combined multiple proprietary AI platforms to create Nurabot’s programming, which enables the bot to navigate the hospital independently, schedule tasks, and react to verbal and physical cues.

AI was also used to train and test the robot in a virtual version of the hospital, which Foxconn says helped its speedy development.

AI allows Nurabot to “perceive, reason, and act in a more human-like way” and adapt its behavior “based on the specific patient, context, and situation,” David Niewolny, director of business development for health care and medical at NVIDIA, told CNN in an email.

Staffing shortages aren’t the only issue facing the health care sector.

The world’s elderly population is growing rapidly: the number of people aged 60 and over is expected to increase by 40% by 2030, compared to 2019, according to the WHO. By the mid-2030s, the UN predicts that the number of individuals aged 80 and older will outnumber infants.

Over the past decade, the number of health care workers has steadily increased, but not fast enough to beat population growth and aging. Southeast Asia is expected to be one of the worst-impacted regions for health care workforce shortages.

With these impending stressors on the health care system, AI-enhanced systems can provide huge time and cost savings, says nursing and public health professor Rick Kwan, associate dean at Tung Wah College in Hong Kong.

“AI-assisted robots can really replace some repetitive work, and save lots of manpower,” says Kwan.

Foxconn plans to commercially launch Nurabot in 2026.

There will be challenges, though: Kwan highlights patient preference for human interaction and the need for infrastructure changes in hospitals.

“You can look at the hospitals in Hong Kong: very crowded and everywhere is very narrow, so it doesn’t really allow robots to travel around,” says Kwan. Hospitals are designed around human needs and systems, and if robots are to become central to the workflow, this will need to be reimagined in hospital design going forward, he adds.

Safety is also paramount, says Kwan — not just in terms of mitigating physical risks, but the development of ethical and data protection protocols, too — and he encourages a slow and cautious approach that allows for rigorous testing and assessment.

Robots are not entirely new to health care: surgical robots, like da Vinci, have been around for decades and help improve accuracy during operations.

But increasingly, free-moving humanoids are assisting hospital staff and patients.

In Singapore, Changi General Hospital currently has more than 80 robots helping doctors and nurses with everything from administrative work to medicine delivery.

Robots are revolutionizing the healthcare industry with increased precision and diagnostics power. Changi General Hospital, pictured, employs more than 50 robots to help care for patients. <strong>Scroll through to see more innovative robots reinventing healthcare.</strong>

And in the US, nearly 100 “Moxi” autonomous health care bots, built by Texas-based Diligent Robots with NVIDIA’s AI platforms, carry medications, samples, and supplies across hospital wards, according to NVIDIA.

But the jury is still out on how helpful nursing robots are to staff. A recent review of robots in nursing found that, while there was a perception among nurses of increased efficiency and reduced workload, there is a lack of experiential evidence to confirm this — and technical malfunctions, communication difficulties and the need for ongoing training all presented challenges.

Tech companies are investing heavily health care: in addition to NVIDIA, the likes of Amazon and Google are both exploring new opportunities in the $9.8 trillion health care market.

The smart hospital sector is a small, but rapidly expanding, component of this. It was estimated at $72.24 billion in 2025, according to market research company Mordor Intelligence, with the Asia Pacific region the fastest-growing market.

Nurabot is currently being piloted in Taichung Veterans General Hospital in Taiwan, on a ward that treats diseases associated with the lungs, face and neck, including lung cancer and asthma.

During this experimental phase, the robot has limited access to the hospital’s data system, and Foxconn is “stress testing” its functionality on the ward. This includes tracking metrics like the reduction in walking distance for nurses and the delivery accuracy, as well as qualitative feedback from patients and nurses. Early results indicate that Nurabot is reducing the daily nursing workload by around 20–30%, according to Foxconn.

Taichung Veterans General Hospital declined to comment on Nurabot for this story.

According to Lin, Nurabot will be formally integrated into daily nursing operations later this year, including connecting to the hospital information system and running tasks autonomously, ahead of its commercial debut in early 2026.

While Nurabot won’t solve the lack of nurses, Lin says it can help “alleviate the problems caused by an aging society, and hospitals losing talent.”





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