Latest news with #businessimprovement
Telegraph
03-08-2025
- Business
- Telegraph
Morrisons' Mr Fix-it under pressure as turnaround loses steam
For the past year and a half, senior staff have been expected to dial into a Google Teams meeting daily at 6.30pm to discuss where Morrisons can improve. A spokesman for Morrisons said: 'The evening online chat isn't mandatory and there's no inquisition if you don't attend. It's been part of our daily routine for some time now and it's well-supported by senior colleagues.' When issues are raised with managers at those meetings, they are expected to reply, 'Leave it with me, Rami.' Another phrase insiders associate with Baitiéh is 'it starts with 'I'' – a form of words intended to make managers take personal responsibility. 'He believes all these things are fostering a high-performance culture,' says one senior retail source. 'But what you've got is a business full of people who won't upset the status quo because they're worried about getting fired.' Within grocery circles, those who have stuck around at Morrisons are branded 'survivors'. Several senior figures have left including chief customer officer Darren Blackhurst and, last week, its head of property David Scott. 'The problem is he's now surrounded by people who are worried about challenging Rami,' says one former Morrisons worker. 'But Rami has strong views on everything and, if you ask me, his ideas are not working.' Already, there have been growing signs that not everything is going smoothly. Last month, The Grocer magazine revealed Morrisons was facing a row with suppliers over the supermarket's demands that they brought forward spending on promotions and in-store marketing to help it hit revised financial targets. Morrisons rebuffed the characterisation, saying this was a 'normal part of how the industry does business' and that plans often changed as the year progressed. However, there have been other hiccups. Baitiéh has overseen a push to start selling more 'when it's gone it's gone' general merchandise within stores in an attempt to see off competition from Aldi and Lidl. Some of that stock has not been up to standards. A delivery of pillows had to be sent back after it failed to meet UK regulations. Staff frustration There have also been rumblings of staff frustration. Earlier this year, Morrisons is understood to have told staff in its commercial team that they must work in its head office five days a week. It later had to row back on the mandate after a staff survey showed resistance. Workers in its commercial teams – who often log on between 7am and 7.30am – can now work from home one day a week. 'Everybody there is feeling it,' says one insider. 'Rami is putting everyone under enormous pressure to deliver results.' Perhaps with good reason. Within weeks, experts say Morrisons could drop another place in the rankings of Britain's largest supermarkets, having already dipped below Aldi in 2023. According to the latest Kantar figures published last month, Morrisons held an 8.4pc of the grocery market in the three months to mid-July – versus Lidl's 8.3pc. It is a gap that has narrowed significantly over the past year, with Morrisons having held a 8.7pc share last summer compared with Lidl's 7.8pc.
Japan Times
23-07-2025
- Business
- Japan Times
JFTC gives OK to Visa Worldwide's business improvement plan
The Japan Fair Trade Commission (JFTC) has approved a business improvement plan submitted by Visa Worldwide, which undertakes Visa card operations in Japan and other parts of the Asia-Pacific region. Through its investigation into a suspected violation of the antimonopoly law by Visa Worldwide, the JFTC concluded that swiftly executing proposed measures by the Singapore-based firm would restore a competitive environment under the so-called commitment procedures, the Japanese antimonopoly watchdog said in a press release Tuesday. In credit card transactions, sales data are transmitted from acquirers, or companies that enables retailers to accept payments by card, to card issuers through settlement service networks. An acquirer should pay a certain rate of fees to an issuer unless they are the same. The JFTC conducted an on-site inspection of Visa Worldwide's Japanese operation in July last year and has since found that the credit card operator notified acquirers in February 2018 of a plan to apply preferential rates to such fees only when they exclusively use the Visa Worldwide network and implemented the plan in November 2021. Some acquirers have actually come to take the advantage, according to people familiar with the matter. The commission, which took an administrative step against an international credit card brand for the first time ever, believes that Visa Worldwide's practice amounts to a transaction with a restraint condition prohibited by the law. However, it decided to refrain from imposing a severer sanction on the firm. "Better, cheaper networks should be provided through friendly competition between card companies," Masahiko Sogawa, a JFTC investigation division chief, said. In a comment, the Visa Worldwide Japan unit vowed to carry out the improvement measures to keep complying with the law.
CTV News
26-06-2025
- Business
- CTV News
Centre Wellington Council removes Elora BIA board of management, citing 1992 bylaw
Despite pleas for collaboration, council for the Township of Centre Wellington Council rescinded the appointment of directors to the Elora Business Improvement Area (BIA) Board of Management. At a township meeting on June 16, councillors were told there had been an error when the latest BIA board was installed. In a report, township staff cited a bylaw from 1992 that states the BIA board can only have four members appointed by council, one of which must be a member of council. The most recent iteration of the board included six members and Councillor Kim Jefferson. In the past, other boards have also surpassed the membership permitted in the bylaw, but nothing was done to correct the issue. 'The record is clear, council has appointed more than three business members to the Elora BIA for well over a decade,' BIA member Catherine Daultrey said while delegating to council. 'This isn't an anomaly, it's custom and convention. These were formal appointments by council, signed by the mayor and clerk. This board was not self-installed, it was appointed, approved and operating in line with past practice.' The chair of the BIA, Erika Montero, said the oversized board was just one issue BIA members had uncovered, along with concerns about a previous lack of public meetings and general transparency. 'We did not create this situation, we inherited it,' Montero said. 'Now, having worked to correct it, we are being punished for it.' 'This is not just an administrative reset, it's a political move to silence a squeaky wheel and a board that believes in transparency, sustainability, a local voice and protecting the soul of this community,' Montero claimed. The BIA members also requested council update the current bylaw to allow the BIA board to consist of more members. Instead, council passed a bylaw to dissolve the board and appointed a new interim board consisting of Centre Wellington's CAO Dan Wilson, Managing Director of Corporate Services and Treasurer Adam McNabb, Municipal Clerk Kerri O'Kane and Mayor Shawn Watters. The newly passed bylaw stated the township will help the new interim board call an Annual General Meeting, potentially within the next few weeks, so a new board can be elected. Wilson said members of the former board can run for re-election. The township also said this is not an issue that is unique to the Elora BIA and staff will work with the Fergus BIA to bring them into compliance as well.
Forbes
27-05-2025
- Business
- Forbes
Predictive AI Must Be Valuated – But Rarely Is. Here's How To Do It
Most predictive AI projects neglect to estimate the potential profit – a practice known as ML ... More valuation – and that spells project failure. Here's the how-to. To be a business is to constantly work toward improved operations. As a business grows, this usually leads to the possibility of using predictive AI, which is the kind of analytics that improves existing, large-scale operations. But the mystique of predictive AI routinely kills its value. Rather than focusing on the concrete win that its deployment could deliver, leaders get distracted by the core tech's glamor. After all, learning from data to predict is sexy. This in turn leads to skipping a critical step: forecasting the operational improvement that predictive AI operationalization would deliver. As with any kind of change to large-scale operations, you can't move forward without a credible estimation of the business improvement you stand to gain – in straightforward terms like profit or other business KPIs. Not doing so makes deployment a shot in the dark. Indeed, most predictive AI launches are scrubbed. So why do most predictive AI projects fail to estimate the business value, much to their own demise? Ultimately, this is not a technology fail – it's an organizational one, a glaring symptom of the biz/tech divide. Business stakeholders delegate almost every aspect of the project to data scientists. Meanwhile, data scientists as a species are mostly stuck on arcane technical metrics, with little attention to business metrics. The typical data scientist's training, practice, shop-talk and toolset omits business metrics. Technical metrics define their comfort zone. Estimating the profit or other business upside of deploying predictive AI – aka ML valuation – is only a matter of arithmetic. It isn't the "rocket science" part, the ML algorithm that learns from data. Rather, it's the much-needed prelaunch stress-testing of the rocket. Say you work at a bank processing 10 million credit card and ATM card transactions each quarter. With 3.5% of the transactions fraudulent, the pressure is on to predictively block those transactions most likely to fall into that category. With ML, your data scientists have developed a fraud-detection model that calculates a risk level for each transaction. Within the most risky 150,000 transactions – that is, the 1.5% of transactions that are considered by the model most likely to be fraud – 143,000 are fraudulent. The other 7,000 are legitimate. So, should the bank block that group of high-risk transactions? Sounds reasonable off the cuff, but let's actually calculate the potential winnings. Suppose that those 143,000 fraudulent transactions represent $18,225,000 in charges – that is, they're about $127 each on average. That's a lot of fraud loss to be saved by blocking them. But what about the downside of blocking them? If it costs your bank an average of $75 each time you wrongly block due to cardholder inconvenience – which would be the case for each of the 7,000 legit transactions – that will come to $525,000. That barely dents the upside, with the net win coming to $17,700,000. So yeah, if you'd like to gain almost $18 million, then block those 1.5% most risky transactions. This is the monetary savings of fraud detection, and a penny saved is a penny earned. But that doesn't necessarily mean that 1.5% is the best place to draw the line. How much more might we save by blocking even more? The more we block, the more lower-risk transactions we block – and yet the net value might continue to increase if we go a ways further. Where to stop? The 2% most risky? The 2.5% most risky? To navigate the range of predictive AI deployment options, you've just got to look at it: A savings curve comparing the potential money saved by blocking the most risky payment card ... More transactions with fraud-detection models. The performance of three competing models is shown. This shows the monetary win for a range of deployment options. The vertical axis represents the money saved with fraud detection – based on the same kind of calculations as those in the previous example – and the horizontal axis represents the portion of transactions blocked, from most risky (far left) to least risky (far right). This view has zoomed into the range from 0% to 15%, since a bank would normally block at most only the top, say, two or three percent. The three colors represent three competing ML models: two variations of XGBoost and one random forest (these are popular ML methods). The first XGBoost model is the best one overall. The savings are calculated over a real collection of e-commerce transactions. So was the previous example's calculations. Let's jump to the curve's peak. We would maximize the expected win to more than $26 million by blocking the top 2.94% most risky transactions according to the first XGBoost model. But this deployment plan isn't a done deal yet – there are other, competing considerations. First, consider how often transactions would be wrongly blocked. It turns out that blocking that 2.94% would inconvenience legit cardholders an estimated 72,000 times per quarter. That adverse effect is already baked into the expected $26 million estimate, but it could incur other intangible or longer-term costs; the business doesn't like it. But the relatively flatness that you can see near the curve's peak signals an opportunity: If we block fewer transactions, we could greatly reduce the expected number wrongly blocked with only a small decrease in savings. For example, it turns out that blocking 2.33% rather than 2.94% cuts the number of estimated bad blocks in half to 35,000, while still capturing an expected $25 million in savings. The bank might be more comfortable with this plan. As compelling as these estimated financial wins are, we must take steps to shore up their credibility, since they hinge on certain business assumptions. After all, the actual win of any operational improvement – whether driven by analytics or otherwise – is only certain after it's been achieved, in a "post mortem" analysis. Before deployment, we're challenged to estimate the expected value and to demonstrate its credibility. One business assumption within the analysis described so far is that unblocked fraudulent transactions cost the bank the full magnitude of the transaction. A $100 fraudulent transaction costs $100 (while blocking it saves $100). And a $1,000 fraudulent transaction indeed costs ten times as much. But circumstances may not be that simple, and they may be subject to change. For example, certain enforcement efforts might serve to recoup some fraud losses by investigating fraudulent transactions even after they were permitted. Or the bank might hold insurance that covers some losses due to fraud. If there's uncertainty about exactly where this factor lands, we can address it by viewing how the overall savings would change if such a factor changed. Here's the curve when fraud costs the bank only 80% rather than 100% of each transaction amount: The same chart, except with each unblocked fraudulent transaction costing only 80% of the amount of ... More the transaction, rather than 100%. It turns out, the peak decreases from $26 million down to $20 million. This is because there's less money to be saved by fraud detection when fraud itself is less costly. But the position of the peak has moved only a little: from 2.94% to 2.62%. In other words, not much doubt is cast upon where to draw the decision boundary. Another business assumption we have in place is the cost of wrongly blocking, currently set at $75 – since an inconvenienced cardholder will be more likely to use their card less often (or cancel it entirely). The bank would like to decrease this cost, so it might consider taking measures accordingly. For example, it could consider providing a $10 "apology" gift card each time it realizes its mistake – an expensive endeavor, but one that might turn out to decrease the net cost of wrongly blocking from $75 down to $50. Here's how that would affect the savings curve: The same chart, except with each wrongly-blocked transaction costing only $50, rather than $75. This has increased the peak estimated savings to $28.6 million, and moves that peak from 2.94% up to 3.47%. Again, we've gained valuable insight: This scenario would warrant a meaningful increase in how many transactions are blocked (drawing the decision boundary further to the right), but would only increase profit by $2.6 million. Considering that this guesstimated cost reduction is a pretty optimistic one, is it worth the expense, complexity and uncertainty of even testing this kind of "apology" campaign in the first place? Perhaps not. For a predictive AI project to defy the odds and stand a chance at successful deployment, business-side stakeholders must be empowered to make an informed decision as to whether, which and how: whether the project is ready for deployment, which ML model to deploy and with what decision boundary (percent of cases to be treated versus not treated). They need to see the potential win in terms of business metrics like profit, savings or other KPIs, across a range of deployment options. And they must see how certain business factors that could be subject to change or uncertainty affect this range of options and their estimated value. We have a name for this kind of interactive visualization: ML valuation. This practice is the main missing ingredient in how predictive AI projects are typically run. ML valuation stands to rectify today's dismal track record for predictive AI deployment, boosting the value captured by this technology up closer to its true potential. Given how frequently predictive AI fails to demonstrate a deployed ROI, the adoption of ML valuation is inevitable. In the meantime, it will be a true win for professionals and stakeholders to act early, get out ahead of it and differentiate themselves as a value-focused practitioner of the art.
Zawya
12-05-2025
- Business
- Zawya
Bain & Company report finds generative AI adoption has skyrocketed, but Scaling remains a hurdle
Dubai, UAE – Bain & Company has released new insights from its ongoing Generative AI Readiness Survey, revealing a dramatic rise in generative AI adoption across US companies now reaching 95%, up 12 percentage points from the prior year. The report, Survey: Generative AI's Uptake Is Unprecedented Despite Roadblocks, explores how companies are moving from experimentation to scaled implementation, even as they contend with concerns around talent, data security, and output quality. According to Bain's latest findings, the average number of generative AI use cases in production has doubled over the past year. IT departments are leading in growth, while software development remains the most widely adopted use case. Over 80% of reported use cases are meeting or exceeding expectations, and nearly 60% of satisfied companies report measurable business improvements. Yet scaling comes with friction. Companies cite difficulty sourcing in-house expertise, concerns over data security, and challenges with vendor reliability. As generative AI moves from pilot to production, frustrations diverge and early-stage users struggle with internal alignment, while more mature adopters face issues related to vendor quality and solution robustness. Despite these growing pains, investment in generative AI is accelerating. The average annual budget has doubled to nearly $10 million, and 60% of companies plan to fund AI initiatives through standard operating budgets. The workforce is also evolving, and firms now report that 160 employees on average are dedicating time to generative AI, up 30% from previous levels. The report notes that while adoption is nearly universal, only half of companies have developed a clear roadmap for generative AI implementation, leaving a significant portion without a defined path forward. Bain & Company outlines key recommendations for businesses aiming to scale responsibly: secure leadership support, close internal talent gaps, evaluate vendor ecosystems carefully, and invest in systems that prioritize secure and accurate outputs. The firms that take deliberate, strategic steps now will not only lead in generative AI—they will reshape their industries. About Bain & Company Bain & Company is a global consultancy that helps the world's most ambitious change makers define the future. Across 65 cities in 40 countries, Bain works alongside clients to achieve extraordinary results and redefine industries. The firm complements its expertise with a vibrant ecosystem of digital innovators to deliver faster and more enduring outcomes. Bain has also committed more than $1 billion in pro bono services to organizations tackling today's urgent challenges and earned a platinum rating from EcoVadis, placing it in the top 1% of companies globally. Since 1973, Bain has measured its success by the success of its clients.
