The long take, the unbroken tracking shot, “the oner” — whatever you want to call it, filmmakers agree that it’s one of the most difficult technical achievements in cinema. It’s a feat of creativity, but also great coordination and choreography when a single, tiny mistake can ruin a shot.
Artificial Intelligence
How Bi Gan pulled off the most technically impressive movie scene of the year
Some famous examples: the casino scene of Martin Scorsese’s GoodFellas; more recently, the action sequences in Alfonso Cuarón’s Children of Men and the entirety of Alejandro González Iñárritu’s Birdman. Even a recent episode of The Studio titled “The Oner” — which captures the difficulty of filming a long, unbroken shot — was itself presented as a oner. Seth Rogen’s character calls it “the ultimate cinematic achievement; it’s the perfect marriage of artistry and technicality.” (He then name-checks the three movies I just did, maybe a sign that the continuous single take is something only a certain kind of film bro cares about.)
Yet none of these examples compare to the work of Chinese filmmaker Bi Gan, who is responsible for the most ambitious and impressive long take of the year. His new sci-fi film Resurrection — a labyrinthine expedition through 20th-century China — is capped by an extraordinary 30-minute tracking shot, one that was actually accomplished as a single take. (For comparison, the longest cut in Sam Mendes’ war film 1917 is nine minutes long, each segment stitched together to make the film appear contiguous.) Taking the viewer from nighttime to daybreak, Bi’s oner trails its characters from a violent gang fight on the docks through rainy alleyways to a raucous karaoke bar before returning to the port, where the romance between two leads takes an unexpected, monstrous turn at sea. The effect is dazzling, destabilizing, and unlike anything else you’ll see on screen this year.
What Bi has achieved with Resurrection is an extraordinary technical accomplishment, but also familiar territory for him. His debut feature Kaili Blues culminated in a 41-minute trek up, down, and across the rural mountain town of Dangmai; his last film, Long Day’s Journey into Night, ended with a dreamlike, unbroken 59-minute long take shot in 3D.
Though he spoke through a Mandarin interpreter, Bi, only 36 years old, was extremely talkative and maybe even a bit sly. The long shot might be a tremendous effort of collaboration and coordination across cinematographers, set designers, and a horde of extras, but for Bi, it’s kind of his thing. He’s gotten pretty good at it. He told The Verge how he pulls it off.
This interview has been edited and condensed.
The Verge: You’re strongly associated with the long take. What effect does the long shot have on the viewer?
Bi Gan: For me, I really think about long takes and its impact on the audience. By using long takes, the audience will understand time much better. Because of the fact that when you are watching this in the long duration and because of the mise en scène, that might actually force you to stop thinking about using the kind of jump cuts or fragmented way of telling a story. Now, you are experiencing time in real time, along with the camera.
And when something is a single take, do you want viewers to notice that it’s one long shot?
It doesn’t really matter whether or not they notice that it is a long take, but I do think that most of the audience will. They know that this point of view somehow is moving along with you. The most important is for them to understand time, somehow being uninterrupted because of the long take. but also that time is being compressed in such a way that you experience it as it evolves.
You even play with time in the long take in this film when you use time lapse. Were you trying to warp the experience of time?
In my first film, Kaili Blues, you have three different tenses within that particular long take: something happening in the past, in the present, in the future, but all included in that one long take. Whereas going into my second film, Long Day’s Journey into Night, I really wanted to somehow use this particular long take in such a compressed way to talk about memories. It’s also done in 3D. That brings out something very unique about memories, how some things are very fragmented. But because of the use of the long take, it becomes more holistic and concrete than our actual fragmented memories.
Moving on to Resurrection, I just think that this is the best way to depict that one particular day, which is the last day of the 20th century, going into the 21st century, where two characters elope and then become vampires. One long take for that is because that is the best vehicle to tell that particular story.
Did you know you wanted one of the film’s vignettes to be a long take when you were writing the script, or did you figure that out later?
This particular chapter came first because it’s actually an adaptation of one of my novels titled UFO, which is about two lovers eloping. I do think that when you translate from a written novel to a film, then you need to find the best film language for it.
At first, I was still trying to figure out what would be the best way to sort of make those words come alive in a visual way. It is not until during the shooting process that I had a discussion with my [director of photography] Dong Jingsong and with [production designer] Liu Qiang that we asked, How are we going to do this?
Dong showed me this particular painting by Mark Rothko, an abstract painting with the red colors, with a little bit of other colors. And somehow, the kind of combination of particular colors inspired me to start thinking about using one long, uninterrupted take as a way to tell a story.
We really take this type of film language in a very careful way. We didn’t want to just do it carelessly.
Do you worry that whenever anyone sees one of your films in the future, they’ll expect a long take?
That’s okay, because, you know, I’m not really bound by any rules. You can tell from the films I make.
This movie is in conversation with so much film history. Staying on the long take, are there other filmmakers who do it that you are influenced by?
In terms of this idea of this is very much about a film about films, it’s just on the surface. That is the way that I enter the subject matter of what happened in the 20th century in China.
In terms of the influence from other filmmakers or other films about long takes, I don’t really think that I was informed by them that much. For me, starting with Kaili Blues, the reason why I choose to use a long take was very much on a philosophical level. That was the best way to somehow present, philosophically, what I want to express in that particular film. As a rule, I tend to try to subvert a lot of preconceived notions or concepts about films. So I don’t really see myself as either paying homage or being informed or influenced by other filmmakers’ long takes.
When you start planning out a long take and you’re blocking it, like, what does that even look like? How do you do it?
In terms of the script itself, it’s very much action-based because that’s the only way that you can actually pull this off. One of the biggest challenges for us is to find the right location and space for this story to happen. That’s the start. I was discussing with the art directors how to scout a space that would be the best setting to tell this type of story. We were lucky enough with the team of directors and assistant directors that they looked around and finally found this particular ideal location with railroads, docks, and ports, and then the karaoke bar and the hospital.
Then it’s a constant interaction and collaboration between the teams focusing on the scripts, and then the teams focusing on the sets, and then the teams focusing on technical rehearsals. They have to constantly be working together and evolve in such a way to actually finally make this happen and make it a reality.
The DP and the cinematographer involve the art directors and how we somehow have to work closely and make a lot of adjustments. If during the technical rehearsals, I notice a certain type of technical difficulty that needs to be overcome, then we discuss the possibility of changing or adjusting the script. And then we do another technical rehearsal, and then, finally, finding out that that is the best way to bring everything together.
And then we’ll bring the actors down and do actual rehearsals.
How long is the rehearsal period?
From the moment that art directors are finding space to the actual rehearsals, it took a month.
We could only rehearse at night, and then during the day, the actors and actresses would do some other practices, such as learning to operate a boat and how to sing karaoke.
Are they actually driving the boat?
We also have a lot of extras involved for this particular long take. So as a director, I will give them some context — that this is the last night of 1999, and then they will be operating in certain spaces, such as barber shops or karaoke bars. And then based on that, I will ask them to really do character development themselves to think about, “Who you are at this moment of time, in the space, and how would you serve the role as an extra for this particular take?”
In Resurrection, is it all one take or is it stitched together?
In terms of stitching it together, as long as you feel that it is one take, it’s a good thing. And there’s nothing wrong with it. But for my case, for this particular take, it is actually one uninterrupted take.
I was looking for the seams but I couldn’t find anything.
How many times did you have to film it?
Just three? That’s impressive.
Resurrection is in select theaters now.
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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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