Park Chan-wook’s 12th feature-length movie, No Other Choice, begins with Man-su (Lee Byung-hun) as a proud patriarch at the barbecue, a vision of the platonic ideal domestic life he will spend most of the movie defending. In the long middle where life is lived, the movie offers its audience mirth and pathos and deep social critique. Also: murders. After being laid off from a paper company, Man-su realizes that his best chance at getting hired for his next job is to knock off the three other qualified candidates.
Artificial Intelligence
In No Other Choice, the real job killer is this guy
Adapted from Donald Westlake’s novel The Ax, No Other Choice captures — most delightfully and cathartically — the perpetual and unsolvable anxiety of living under an economic system built around extracting surplus value from its workers. Or the dark irony that if a corporation makes a person redundant, it is strategy; if a human does the same, it’s a crime.
With this film, not to mention his earlier works like Oldboy and The Handmaiden, Park establishes himself as a director who understands intimately that tragedy and comedy cannot be separated. Here, it’s the tragedy that life must be lived, that we ought to work at all, that so much in this life in fact depends on this work, set against the comedy of how somebody like Man-su sets about solving this impossible riddle for himself.
The Verge spoke with Park about his relationship to his source material, artificial intelligence, and how he recovers after wrapping a picture.
This interview has been edited and condensed.
The Verge: Have you ever been fired from a job?
Park Chan-wook: That’s never happened to me, mercifully. Those kinds of things actually happen quite often in our industry. I’ve been fortunate enough to avoid that fate, but there have been many times when I’ve been afraid of being let go. While working on any project, invariably comes a time when differences in opinion form between the studio or the producers. In that instance, whenever I stubbornly stick to my original position, I do so knowing I am exposing myself to that kind of danger.
And when a movie comes out and it doesn’t do well, then comes the fear that I won’t be able to find a job again, or that I won’t be able to raise funds for my next project.
But also that fear isn’t something that accompanies you after you get your report card from the box office exclusively. All throughout the filmmaking process, it stays with you, that fear. It stays with you from the initial planning stages of a movie. And then if the movie doesn’t do well, that fear sharpens, and it never goes away. It is near to you always.
At the screening I attended, you said you first encountered the source material, the Donald Westlake novel The Ax, via your love of the movie Point Blank, which you cite as your favorite noir. Do you remember how you discovered the movie, and are there other Westlake novels you are curious about?
Point Blank is a film directed by John Boorman, a British director, and I watched it for two reasons. The first is that I’ve always liked John Boorman. The first Boorman film I ever saw was Excalibur.
Second, I’m a fan of the actor Lee Marvin. Because Point Blank was a collaboration between a director I like and an actor I also like, I had always wanted to see it. But accessing the movie was difficult in Korea for a long time, so it was only later that I got to watch it.
As for Westlake, surprisingly not too many of his books are in translation. That The Ax was translated into Korean was itself an anomaly. And so I’ve only read a few of his books.
You’ve been trying to make No Other Choice for 16 years. You also said you tried going through Hollywood first. How come?
Since the novel was written with an American setting, I naturally thought making it into an American film would be the best option. At that time, I had already made Oldboy, Thirst, Lady Vengeance, and Stoker, and so making a movie in America was not intimidating.
What was the most common feedback you received in these early years?
In 2010, we secured the rights and began actively pursuing the project. Initially, we met with French investors. Although it was to be an American movie filmed in America, we met with French investors thanks to Michèle Ray-Gavras, wife of [director] Costa-Gavras, who was among our producers, and through her we contacted various studios, from France to the United States.
Starting then, I continued receiving offers that were slightly less than what I wanted, which is why I could not possibly accept them.
As for notes from the studios, beyond anything, they doubted whether the audience would believe that Man-su would resort to murder because he lost his job. They wanted to know how I was going to bring the audience along.
Other than that, people’s senses of humor varied slightly. Some said this part isn’t funny. Others said that part isn’t funny. We faced some challenges.
You mentioned there are Easter eggs strewn about the movie and I am curious about them. You mentioned that the oven mitt Man-su uses during his attempted murder can be seen later back in his kitchen. A Christmas stocking from the same scene can be seen in a family photo in the background. What other such details are there to look out for?
I can’t guarantee that the framed photo with the Santa Claus costume can be seen properly. We did place it on set during filming. In fact, we gathered the entire family, dressed them up and took pictures specifically for that framed photo. But I don’t know if it is actually visible in the final movie. It will definitely, however, be in the extended cut that I’m preparing for the Blu-ray release.
And rather than considering it an Easter egg, it might be more accurate to consider it part of creating a believable world for the actors. So that once the actors enter that world, they feel like they can more easily become their characters. And for there to be that trust and sense of a stable reality, the better it is to attend to props or anything else spatially. The more consideration, the better.
AI shows up at the end of the movie, which I imagine was not part of the original idea you had when you began the project. When did you know to add AI to the film?
Had this been made into an American film, such a plot point would not have been available. It was only because the process took so long that the issue could be incorporated.
Any director making a movie about employment, or unemployment rather, would be remiss to not mention AI. Moreover — and this was important for me — by the end, Man-su’s family catches on to what he has done in the name of the family. Of course, Man-su isn’t entirely sure if they know, but the audience knows. The very thing he does for his family will be the thing that leads to its collapse. All of his efforts are for naught, which echoes the situation with AI.
He painstakingly eliminated his human competitors to secure a job. But what he confronts at his new workplace is a competitor more formidable than any mortal. Meaning Man-su likely won’t last long before AI takes over. He will lose his job, yet again, at which point, what was it all for? What were the murders for? This too can be seen as a colossal wasted effort.
Therefore, the introduction of AI technology from a creative perspective was a great addition to the movie.
How do you feel about the use of AI in film? Would you use it in your own work? I am sensing the answer is “no.”
I hope that never happens.
It’s not easy for young film students out there. And if there were a technology that allows them to make their own movies at a reduced cost, in a way that could not have been possible before, who could stop them? It would not be possible to tell them not to.
What is the question No Other Choice is asking?
Those who have arrived at the middle class, those who have become accustomed to a certain way of life, and it wasn’t inherited, they obtained it of their own accord — for that class of people, giving all that up would be very difficult. Slipping from that station would be challenging to accept. I would certainly find it difficult to accept.
Of course, that doesn’t mean I am going to commit murder — three, no less — but it’s an impossible situation.
“My child desperately needs private cello lessons. Not only that, it’s a vital part of them becoming an independent adult.” Giving that up would be staggeringly hard. I am imagining what I might be capable of in such a scenario.
I wanted to create a space in which people might ask themselves that question. Not to simply criticize Man-su, but to ask themselves, what if, what might happen, if there was such a person in such a situation? It’s an exercise in imagination.
What was the most difficult time in your career and how did you recover from it?
When my first two films failed at the box office. Before I made JSA, the period between the first film and the second film, and between the second film and the third film, was most difficult. I had no choice but to make the rounds with my screenplay — not unlike how Man-su does with his resume — looking for producers and studio executives. Often I was rejected. That was a tough time.
By then I had married and had dependents and so I resorted to film criticism to make a living. Being a film critic is a great profession, but it was not what I wanted, so I suffered. What’s more, I wanted to be making my own movie, but instead I was reduced to analyzing other people’s movies. If I watched an excellent movie, I would be filled with envy. The reality that demanded I live like that seemed to also be mocking my pain, a kind of taunting. But I had no other means of surviving.
What will you work on next?
Actually, I have two projects that are already prepared. I have a script for a Western that has been written and revised several times. There is also a sci-fi action film for which I haven’t written the script yet, but I put together a fairly involved treatment for.
How do you recover after filming a movie?
Luckily, I am traveling with Lee Byung-hun at the moment. I might drink a glass of wine with him. He is rather serious about wine, and so if I drink with him, I am bound to drink something good.
Have you any deep and profound advice for young filmmakers?
In film school, you might learn certain lessons from your instructors. You might also learn from directors who are already successful. If you are a fan of genre, you might study the convention of your chosen genre.
That is all very well, but before anything, the first order is to really have your own voice. And to examine yourself honestly. And to tell the story that comes spontaneously from within. In my opinion, spontaneity is the most important thing. Not to say “this is popular,” or “people like this,” but what is the true thing that comes from your own and inner self? Follow that thread with sincerity.
Of course it’s easy for me to say this — anybody can say it — but putting it into practice is another thing entirely.
No Other Choice is in select theaters December 25, 2025, with a wider release planned in January.
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Artificial Intelligence
Ronnie Sheth, CEO, SENEN Group: Why now is the time for enterprise AI to ‘get practical'
Before you set sail on your AI journey, always check the state of your data – because if there is one thing likely to sink your ship, it is data quality.
Gartner estimates that poor data quality costs organisations an average of $12.9 million each year in wasted resources and lost opportunities. That’s the bad news. The good news is that organisations are increasingly understanding the importance of their data quality – and less likely to fall into this trap.
That’s the view of Ronnie Sheth, CEO of AI strategy, execution and governance firm SENEN Group. The company focuses on data and AI advisory, operationalisation and literacy, and Sheth notes she has been in the data and AI space ‘ever since [she] was a corporate baby’, so there is plenty of real-world experience behind the viewpoint. There is also plenty of success; Sheth notes that her company has a 99.99% client repeat rate.
“If I were to be very practical, the one thing I’ve noticed is companies jump into adopting AI before they’re ready,” says Sheth. Companies, she notes, will have an executive direction insisting they adopt AI, but without a blueprint or roadmap to accompany it. The result may be impressive user numbers, but with no measurable outcome to back anything up.
Even as recently as 2024, Sheth saw many organisations struggling because their data was ‘nowhere where it needed to be.’ “Not even close,” she adds. Now, the conversation has turned more practical and strategic. Companies are realising this, and coming to SENEN Group initially to get help with their data, rather than wanting to adopt AI immediately.
“When companies like that come to us, the first course of order is really fixing their data,” says Sheth. “The next course of order is getting to their AI model. They are building a strong foundation for any AI initiative that comes after that.
“Once they fix their data, they can build as many AI models as they want, and they can have as many AI solutions as they want, and they will get accurate outputs because now they have a strong foundation,” Sheth adds.
With breadth and depth in expertise, SENEN Group allows organisations to right their course. Sheth notes the example of one customer who came to them wanting a data governance initiative. Ultimately, it was the data strategy which was needed – the why and how, the outcomes of what they were trying to do with their data – before adding in governance and providing a roadmap for an operating model. “They’ve moved from raw data to descriptive analytics, moving into predictive analytics, and now we’re actually setting up an AI strategy for them,” says Sheth.
It is this attitude and requirement for practical initiatives which will be the cornerstone of Sheth’s discussion at AI & Big Data Expo Global in London this week. “Now would be the time to get practical with AI, especially enterprise AI adoption, and not think about ‘look, we’re going to innovate, we’re going to do pilots, we’re going to experiment,’” says Sheth. “Now is not the time to do that. Now is the time to get practical, to get AI to value. This is the year to do that in the enterprise.”
Watch the full video conversation with Ronnie Sheth below:
Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour.
The “build it and they will come” model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide deployments because initial financial modelling ignored the realities of production scaling.
“When we integrate FinOps capabilities with automation, we’re looking at a change from being very reactive on cost management to being very proactive around value engineering,” says Holmes.
This shifts the assessment criteria for technical leaders. Rather than waiting “months or years to assess whether things are getting value,” engineering teams can track resource consumption – such as cost per transaction or API call – “straight from the beginning.”
The unit economics of scaling intelligent automation
Innovation projects face a high mortality rate. Holmes notes that around 80 percent of new innovation projects fail, often because financial opacity during the pilot phase masks future liabilities.
“If a pilot demonstrates that automating a process saves, say, 100 hours a month, leadership thinks that’s really successful,” says Holmes. “But what it fails to track is that the pilot sometimes is running on over-provisioned infrastructure, so it looks like it performs really well. But you wouldn’t over-provision to that degree during a real production rollout.”
Moving that workload to production changes the calculus. The requirements for compute, storage, and data transfer increase. “API calls can multiply, exceptions and edge cases appear at volume that might have been out of scope for the pilot phase, and then support overheads just grow as well,” he adds.
To prevent this, organisations must track the marginal cost at scale. This involves monitoring unit economics, such as the cost per customer served or cost per transaction. If the cost per customer increases as the customer base grows, the business model is flawed.
Conversely, effective scaling should see these unit costs decrease. Holmes cites a case study from Liberty Mutual where the insurer was able to find around $2.5 million of savings by bringing in consumption metrics and “not just looking at labour hours that they were saving.”
However, financial accountability cannot sit solely with the finance department. Holmes advocates for putting governance “back in the hands of the developers into their development tools and workloads.”
Integration with infrastructure-as-code tools like HashiCorp Terraform and GitHub allows organisations to enforce policies during deployment. Teams can spin up resources programmatically with immediate cost estimates.
“Rather than deploying things and then fixing them up, which gets into the whole whack-a-mole kind of problem,” Holmes explains, companies can verify they are “deploying the right things at the right time.”
When scaling intelligent automation, tension often simmers between the CFO, who focuses on return on investment, and the Head of Automation, who tracks operational metrics like hours saved.
“This translation challenge is precisely what TBM (Technology Business Management) and Apptio are designed to solve,” says Holmes. “It’s having a common language between technology and finance and with the business.”
The TBM taxonomy provides a standardised framework to reconcile these views. It maps technical resources (such as compute, storage, and labour) into IT towers and further up to business capabilities. This structure translates technical inputs into business outputs.
“I don’t necessarily know what goes into all the IT layers underneath it,” Holmes says, describing the business user’s perspective. “But because we’ve got this taxonomy, I can get a detailed bill that tells me about my service consumption and precisely which costs are driving it to be more expensive as I consume more.”
Addressing legacy debt and budgeting for the long-term
Organisations burdened by legacy ERP systems face a binary choice: automation as a patch, or as a bridge to modernisation. Holmes warns that if a company is “just trying to mask inefficient processes and not redesign them,” they are merely “building up more technical debt.”
A total cost of ownership (TCO) approach helps determine the correct strategy. The Commonwealth Bank of Australia utilised a TCO model across 2,000 different applications – of various maturity stages – to assess their full lifecycle costs. This analysis included hidden costs such as infrastructure, labour, and the engineering time required to keep automation running.
“Just because of something’s legacy doesn’t mean you have to retire it,” says Holmes. “Some of those legacy systems are worth maintaining just because the value is so good.”
In other cases, calculating the cost of the automation wrappers required to keep an old system functional reveals a different reality. “Sometimes when you add up the TCO approach, and you’re including all these automation layers around it, you suddenly realise, the real cost of keeping that old system alive is not just the old system, it’s those extra layers,” Holmes argues.
Avoiding sticker shock requires a budgeting strategy that balances variable costs with long-term commitments. While variable costs (OPEX) offer flexibility, they can fluctuate wildly based on demand and engineering efficiency.
Holmes advises that longer-term visibility enables better investment decisions. Committing to specific technologies or platforms over a multi-year horizon allows organisations to negotiate economies of scale and standardise architecture.
“Because you’ve made those longer term commitments and you’ve standardised on different platforms and things like that, it makes it easier to build the right thing out for the long term,” Holmes says.
Combining tight management of variable costs with strategic commitments supports enterprises in scaling intelligent automation without the volatility that often derails transformation.
IBM is a key sponsor of this year’s Intelligent Automation Conference Global in London on 4-5 February 2026. Greg Holmes and other experts will be sharing their insights during the event. Be sure to check out the day one panel session, Scaling Intelligent Automation Successfully: Frameworks, Risks, and Real-World Lessons, to hear more from Holmes and swing by IBM’s booth at stand #362.
See also: Klarna backs Google UCP to power AI agent payments
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
FedEx is using AI to change how package tracking and returns work for large enterprise shippers. For companies moving high volumes of goods, tracking no longer ends when a package leaves the warehouse. Customers expect real-time updates, flexible delivery options, and returns that do not turn into support tickets or delays.
That pressure is pushing logistics firms to rethink how tracking and returns operate at scale, especially across complex supply chains.
This is where artificial intelligence is starting to move from pilot projects into daily operations.
FedEx plans to roll out AI-powered tracking and returns tools designed for enterprise shippers, according to a report by PYMNTS. The tools are aimed at automating routine customer service tasks, improving visibility into shipments, and reducing friction when packages need to be rerouted or sent back.
Rather than focusing on consumer-facing chatbots, the effort centres on operational workflows that sit behind the scenes. These are the systems enterprise customers rely on to manage exceptions, returns, and delivery changes without manual intervention.
How FedEx is applying AI to package tracking
Traditional tracking systems tell customers where a package is and when it might arrive. AI-powered tracking takes a step further by utilising historical delivery data, traffic patterns, weather conditions, and network constraints to flag potential delays before they happen.
According to the PYMNTS report, FedEx’s AI tools are designed to help enterprise shippers anticipate issues earlier in the delivery process. Instead of reacting to missed delivery windows, shippers may be able to reroute packages or notify customers ahead of time.
For businesses that ship thousands of parcels per day, that shift matters. Small improvements in prediction accuracy can reduce support calls, lower refund rates, and improve customer trust, particularly in retail, healthcare, and manufacturing supply chains.
This approach also reflects a broader trend in enterprise software, in which AI is being embedded into existing systems rather than introduced as standalone tools. The goal is not to replace logistics teams, but to minimise the number of manual decisions they need to make.
Returns as an operational problem, not a customer issue
Returns are one of the most expensive parts of logistics. For enterprise shippers, particularly those in e-commerce, returns affect warehouse capacity, inventory planning, and transportation costs.
According to PYMNTS, FedEx’s AI-enabled returns tools aim to automate parts of the returns process, including label generation, routing decisions, and status updates. Companies that use AI to determine the most efficient return path may be able to reduce delays and avoid returning things to the wrong facility.
This is less about convenience and more about operational discipline. Returns that sit idle or move through the wrong channel create cost and uncertainty across the supply chain. AI systems trained on past return patterns can help standardise decisions that were previously handled case by case.
For enterprise customers, this type of automation supports scale. As return volumes fluctuate, especially during peak seasons, systems that adjust automatically reduce the need for temporary staffing or manual overrides.
What FedEx’s AI tracking approach says about enterprise adoption
What stands out in FedEx’s approach is how narrowly focused the AI use case is. There are no broad claims about transformation or reinvention. The emphasis is on reducing friction in processes that already exist.
This mirrors how other large organisations are adopting AI internally. In a separate context, Microsoft described a similar pattern in its article. The company outlined how AI tools were rolled out gradually, with clear limits, governance rules, and feedback loops.
While Microsoft’s case focused on knowledge work and FedEx’s on logistics operations, the underlying lesson is the same. AI adoption tends to work best when applied to specific activities with measurable results rather than broad promises of efficiency.
For logistics firms, those advantages include fewer delivery exceptions, lower return handling costs, and better coordination between shipping partners and enterprise clients.
What this signals for enterprise customers
For end-user companies, FedEx’s move signals that logistics providers are investing in AI as a way to support more complex shipping demands. As supply chains become more distributed, visibility and predictability become harder to maintain without automation.
AI-driven tracking and returns could also change how businesses measure logistics performance. Companies may focus less on delivery speed and more on how quickly issues are recognised and resolved.
That shift could influence procurement decisions, contract structures, and service-level agreements. Enterprise customers may start asking not just where a shipment is, but how well a provider anticipates problems.
FedEx’s plans reflect a quieter phase of enterprise AI adoption. The focus is less on experimentation and more on integration. These systems are not designed to draw attention but to reduce noise in operations that customers only notice when something goes wrong.
(Photo by Liam Kevan)
See also: PepsiCo is using AI to rethink how factories are designed and updated
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
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