AI can turn floor plans into 3D housing models. What does it take?

10

October

2025

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I read “Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans” by Cambeiro Barreiro et al. (2023). The goal they had was to turn scanned plans into vector 3D models that you can export as IFC, a building data format. It lets different BIM (Building Information Modeling) tools share the same data, like walls, rooms, and doors, etc. This matters especially for big real estate portfolio holders, like housing associations. These parties would be able to gather insights from the vast amounts of data, which are normally only available on paper.

The core problem

Symbols and techniques differ, and old drawings are clumsy. Windows and doors break through walls. The scans are biased. Redrawing by hand is labour-intensive, expensive, and error-prone. The stakes are higher for large portfolio holders, such as housing associations. They oversee thousands of residences. They are unable to compare layouts, energy features, or risks across the stock in the absence of structured data.

How it actually works

You begin with floor plan scans or PDFs. Like a human, the system reads the plan. It recognises windows, doors, and walls. It corrects gaps and crooked lines. After that, it creates clean vectors from these shapes. Lastly, it exports IFC and creates a basic 3D model. Common BIM tools can use that file. You can then measure areas, tag units, and count rooms. Additionally, you can link maintenance notes or energy data. Structure is crucial. You can perform the same checks on buildings in the portfolio at large scale once the data is in a standardised format.

source: Cambeiro Barreiro et al., 2023

Why portfolio owners should care

Data is locked in drawers by paper plans. They become structured BIM thanks to this pipeline. This opens up the portfolio’s unit mix, wall types, window counts, and layout features. It facilitates large-scale renovation planning, accessibility assessments, and energy audits. You receive a cleaner handoff to designers and contractors and expedited due diligence.

Takeaway

Digitising plans is the first step if you own a sizeable housing portfolio and would like to digitize. Segment walls, identify symbols, and carefully translate everything using floor-plan AI. To prevent lock-in and maintain the data’s utility across tools, export to a common data type like IFC. The improvements are easy to understand in terms of speed, consistency, and insight at the portfolio level.

Reference: Cambeiro Barreiro, A., Trzeciakiewicz, M., Hilsmann, A., & Eisert, P. (2023). Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans.

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Agentic AI in Customer Relationship Management (CRM)

10

October

2025

5/5 (1)

One of the most transformative developments in enterprise technology today is the emergence of Agentic AI in the field of CRM. The way campaigns are being designed, executed and how data is being processed fundamentally shifts towards the integration of Agentic Systems. 

But how does that actually work in practice?

Unlike conversational AI tools that only assist users through predictive analytics or content generation, agentic AI actively makes decisions and executes tasks, without constant human intervention or the need for prompts. In todays practice, this means CRM systems are evolving from static databases into intelligent ecosystems where AI agents autonomously manage lead follow-ups, orchestrate personalized customer journeys and interestingly, also initiate retention campaigns when churn risk is detected. When companies decide to implement those agentic capabilities into their CRM, the implications for efficiency and scalability are profound. Those companies can engage customer continuously and react to behavioural changes in real time. The most important aspect is that a level of personalization for the individual consumer can be achieved, which was previously impossible at scale. 

How to implement that into existing workflows?

Many firms overestimate their technology readiness, meaning that the often launch isolated pilots, rather than focussing on clean data, orchestration frameworks, or proper human oversight. To be able to implement this technology successfully companies need to follow a balanced approach between bottom-up and top-down. Only when the employees are being enabled and empowered to identify areas where the agentic AI can help, the implementation will work out. Especially in CRM it is of high importance, that the system development begins with clear process mapping, well-defined guardrails, and incremental deployment. This way the firms can expand the given autonomy as trust in the system grows. If Agentic AI in CRM is implemented right the CRM moves from a reporting tool about campaign success, customer churn, or CLV into a living, learning collaborator that augments every stage of the customer lifecycle. 

How does the agentic workflow look like in practice?

First, the Agent sets a Budget-Goal for a campaign (Increase of Abonnement-Conversion by 15%). Then it accumulates data from CRM and other sources. Third, the agent analyses and prioritizes the given data (Decision who, when, on which channel and with which offer we can contact the client). The fourth step is about Asset generation such as creating personalized text or visuals. Here the highlight is, that the agentic AI personalizes every Client contact based on the accumulated data in the previous steps. The next step is about Optimizing the output and flushes the campaign to the client base. The next step is crucial for the agentic system since feedback loops, as well as deep learning capabilities come into play. Here the agent will focus on the interpretation of the performance of the previous campaigns and adjusts where it is necessary. 

Conclusion

Agentic AI in CRM is without any doubt the biggest transformation in today’s business-world. Companies are constantly searching for better ways to run client campaigns, to reduce churn and to increase interaction with clients to consequently generate more revenue. With the integration of an Agentic CRM system, topics like scalability and marginal costs are important. Companies need to focus on the implementation now, instead of falling behind competitors.

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AI in Housing Valuations: Make it explainable

19

September

2025

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AI can now help to set property values. What are the risks involved?

I came across this paper called Enhancing Explainable AI Land Valuations Reporting for Consistency, Objectivity, and Transparency. By Y. Yim and C. Shing (2025). The paper goes into detail on how explainable AI can ethically support the valuation of properties. This is a sensitive topic, since the valuation of properties affects many parties, like banks, buyers, sellers and the cities. There are major efficiency gains to be made by implementing artificial intelligence and machine learning in this sector. However, their integration also raises legal and ethical questions.

The Core problem

Many models act like a black box; this undermines the duty of the valuer to deliver transparent and consistent valuations. The legal system requires the properties to be thoroughly inspected. and the process to be well-documented.

A possible solution: Making AI explain itself

There are 3 pillars to ethically implement these innovations while integrating these technologies. Consistency: The model should provide repeatable results, following the same process. Objectivity: There should be a clear separation between the developers and validators of a model. Transparency: the model, data and limits should be well documented and easy to understand.

XAI tools like SHAP can be used to make the AI explain itself. These tools show how each feature/variable pushes the price up or down. The chart shown below ranks drivers such as zoning, building age, and floor area. This turns a score into a story that a client can follow.

“The visualisation sets a foundational baseline value (E[f(X)]) of 13.98 on the x-axis, representing the model’s average prediction when no specific feature information is provided. This is the expected value if we were to make a prediction without any additional information. The Output Value (f(x)), which in this instance is 14.07, reflects the actual prediction after accounting for the cumulative effect of the individual features. The colour-coded bars represent the push and pull of each feature on the prediction: the red bars show the features that contribute to an increase in the predicted land value, while the blue bars indicate a decrease.“

Takeaway

AI can increase efficiency and scale valuations. But it must earn trust. Build on three pillars: consistency, objectivity, and transparency. Use SHAP for explanations. Ship reports with a clear checklist and keep human judgement in charge.


References:
Yiu CY, Cheung KS. Enhancing explainable AI land valuations reporting for consistency, objectivity, and transparency. Land. 2025;14(5):927. https://www.proquest.com/scholarly-journals/enhancing-explainable-ai-land-valuations/docview/3212060417/se-2. doi: https://doi.org/10.3390/land14050927.

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AI and the automation of accounting 

9

October

2024

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AI has changed the landscape in many work fields, such as in data, law, finance and even accounting. Recently I have taken an assistant-accounting job at a firm paired with my master studies. I have discovered a lot about AI and what it could do for accounting that I thought were not possible.  Having been there for 2 months, I have learned a lot about accounting itself, but also what AI did for me while I was at the job. According to an article by the open journal of business and management, accountants become more efficient in handling the bookkeeping and become more productive in their work due to the use of AI (peng et al, 2023). I found this to be true as well. 

The first week I began working the invoices in the bookkeeping system, I was very reliant on my own knowledge. I tried categorizing each invoice in a specific item for the ledger, which was confusing sometimes. Because there were a lot of different items and different kind of invoices. With the help of ai however, it showed me what type of invoice belongs to what items in the ledger.  For example, one particular invoice showed me a bill of the restaurant where a particular client of the accounting company was having dinner. At first, I was not sure where this type of cost could be booked in the ledger. I asked AI: “at which item does a restaurant bill belong to in a ledger?” The prompt I gave ChatGPT provided me with this answer:

I asked my employer, who is an RA (registered accountant) for verification is this was true, he confirmed that it was indeed true. From then on, I started using AI a lot more for bookkeeping. Especially for items I was unsure of. It helped me become more knowledgeable, but also helped me to become more efficient and productive. 

Even though there are a lot of benefits of using ai in accounting, there are also downsides. Each bookkeeping for a specific client is different. Tailoring to these clients cannot be done always done through AI. My employer explained to me that the accuracy of knowledge AI has on accounting and bookkeeping is broad, but that I should not always rely on it to keep the books. Because clients sometimes require specific wishes in what purpose their ledger serves and on what books costs are categorized. For example, some clients want to receive a larger tax return. Therefore, they would categorize some cost to a specific item in the ledger that are relevant for that outcome, and others use It to justify cost that they made for the past few months that could be lost in other items, due to inaccuracy of the AI. This type of variety is sometimes very confusing for the AI making some prompts not always accurate to what the client needs. As accepting is also a type of consulting and advisory to companies.

In my opinion, AI is beneficial for the future of financial bookkeeping, and it will probably change a lot of aspects in the financial field. I do however think that when it comes to personalized tailoring to clients with jobs such as consulting and advice, especially in the tax field. It is still of relevancy that accountants or financial advisors take responsibility in helping clients themselves to keep them satisfied and use AI to their benefit. Becoming more efficient and accurate themselves due to the clever use of it.

Peng, Y., Ahmad, S. F., Ahmad, A. Y. B., Al Shaikh, M. S., Daoud, M. K., & Alhamdi, F. M. H. (2023). Riding the waves of artificial intelligence in advancing accounting and its implications for sustainable development goals. Sustainability, 15(19), 14165

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Tech-Driven Triumphs: The New Era of Sports Performance

20

September

2024

5/5 (1)

In 2016, the National Basketball Association (NBA) tournament witnessed the best record done by a team in a regular season. The Golden States Warriors (GSW) finished the regular season with a record of 73-9 (win-loss). They broke the previous 72-10 record established in 1996 by the Michael-Jordan-led Chicago Bulls. In that 2016 season, GSW’s point guard Stephen Curry set the record for most 3-point attempts made, with 402 made 3’s. Before that, no player has made 300 three-pointers in a season. The Warriors did not win the trophy that year, but they went on to become champions in the following 2 seasons.

It was not only the talent of the players and coaching staff (which, of course, played a big role in the historic run) that propelled the success of the franchise. Behind the curtain, it was the technology and analytics that the GSW leveraged that helped them transform into such a dominant sports team. The Warriors was one of the first teams to adopt tracking technologies and analytics (Tina, 2017). From 2010, they have installed SportsVU, a camera tracking technology that utilizes computer vision, to track what’s happening on the court. The tool provides a rich dataset of the movement and coordinates of players, and the analytics team and coaching staff can use this data to drive decision-making processes. GSW was one of the best teams to utilize the technology, leading them to achieve the 2016 MIT Sloan Sports Analytics Conference award of “Best Analytics Organization” (Fahey, 2016).

The technology-immersed world of sports

Embracing the use of technology is not only just a competitive advantage for sports teams anymore; it has become a do-or-die thing, and it has touched every branch of sports. Baseball, for example, was probably the first sport to popularize the concept of sports analytics, with the publishing of Michael Lewis’s book “Moneyball: The Art of Winning an Unfair Game” (2003) and the later movie adoption “Moneyball” (2011). Nowadays, the Major League Baseball (MLB) teams use Statcast, a tracking technology to gather real-time data, including pitch velocity, exit velocity, launch angle and more (Becoming a Baseball Analytics Expert, 2023). 

Wearable technologies have also been used heavily by sports teams. In the National Football League (NFL), teams are using Catapult, a wearable tracking device that could track all performance and physical condition indicators of players (Becker, 2019). The same wearable technology is applied in football to capture player movements, actions on the field and physical exertions, along with other metrics such as player workload , movement efficiency, and game-specific physical profiles (Ambler, 2024).

Not just team-performance enhancement, technologies have been invasive in every aspect of sports. Teams have been using analytics in scouting to pinpoint how much an athlete is worth by looking at player’s longevity, heart rate, natural health condition and prediction on how these impact the player’s career (Hanchett, 2012). Artificial intelligence has been incorporated in football’s Goal-Line Technology to help referees determine goals. Virtual Reality (VR) have been used to simulate training environment for athletes without physical strain, and Augmented Reality (AR) technology has been enforcing fans’ engagement with live stadium tours or interactive gaming during live events. The list goes on and on.

The future: Super athletes, super teams, and super sporting?

The past 50 years have witnessed incredible sports feats by athletes and teams. A marathon finished under 2 hours was deemed impossible, until Eliud Kipchoge did it in 2019. Arnand Duplantis, a Swedish pole vaulter, broke his own world record for the 9th time this Olympics. Great athletes like Lebron James, Ronaldo and Novak Djokovic keep pushing the age limit for a competitive professional sports career. The GSW’s 73-9 record, Real Madrid’s three-peat of the Champions League, Man City’s 100-point season are among impossible feats achieved recently. All of these amazing sports achievements have been greatly assisted with the help of technology advancement. And now, when we step into the age of another technology breakthroughs with AI, will the world witness the making of super athletes, surpassing Lebron James and Cristiano Ronaldo?

References

Tina. (2017, November 9). How analytics drives the Golden State Warriors. Chartio. https://chartio.com/blog/how-analytics-drives-the-golden-state-warriors/

Fahey, A. (2016, March 13). Warriors earn “Best Analytics Organization” award at 2016 MIT Sloan Sports Analytics Conference. NBA.com. https://www.nba.com/warriors/news/warriors-earn-best-analytics-organization-award-2016-mit-sloan-sports-analytics-conference

Becoming a baseball analytics expert. (2023, August 15). Kore Baseball Products. https://www.korebaseball.com/blogs/blog/mastering-baseball-analytics-a-comprehensive-guide-to-analyzing-and-interpreting-game-data

Becker, J. (2019b, October 21). How analytics is changing sports. American University Online. https://programs.online.american.edu/mssam/sports-management-masters/resources/how-analytics-is-changing-sports

Ambler, W. (2024, July 3). Sports Analytics: What is it & How it Improves Performance? – Catapult. Catapult. https://www.catapult.com/blog/what-is-sports-analytics#Applications-of-Sports-Analytics

Hanchett, D. (2012). Playing Hardball With Big Data: How Analytics Is Changing The World of Sports. EMC, pp. 2. Retrieved from https://www.emc.com/collateral/article/137534-sports-analysis.pdf.

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The Transformation of Supermarkets: From Products to Digital Platforms

18

September

2024

5/5 (2)

Purchasing goods in bulk, stocking shelves, and selling to customers; this is how supermarkets used to operate as they were using a traditional product-based business model. Over time, supermarkets evolved by integrating technology, such as self-service, price scanners, and loyalty programs (Sundarabharathi & Muthulakshmi, 2023). The landscape is changing rapidly as supermarkets shift toward a digital platform business model. This shift focuses on personalized experiences, with data analytics predicting consumer behavior and adapting to innovations (Sundarabharathi & Muthulakshmi, 2023). One key player embracing this transformation is Albert Heijn, a Dutch supermarket chain leading the way in digital innovation.

Albert Heijn was the first Dutch supermarket who took the omnichannel approach where in-store meets online (Albert Heijn Launches Subscription, ‘My Albert Heijn Premium’, 2021). The introduction of digital tools, like Albert Heijn’s bonus card and mobile app “AH app”, have transformed the way customers interact with the brand. Their bonus card initially started as a loyalty card for discounts, but has since evolved into a sophisticated data-gathering tool. The combination of the bonus card and the multifunctional AH app, lets Albert Heijn track customers’ shopping habits. With the use of artificial intelligence, the supermarket can recommend personalized offers, recipes based on what you buy, and even predict future purchases. This personalized shopping experience keeps customers engaged while generating valuable data.

As these apps and digital tools grow in functionality, they are part of a broader trend where supermarkets collect an increasing amount of data. Every interaction, from the products scanned to online searches, feeds into algorithms that help supermarkets optimize inventory, suggest new products, and refine marketing strategies. This approach transitions supermarkets from merely being places to purchase goods to digital platforms that provide value-added services based on consumer behavior.

The future allows supermarkets to become even more platform oriented due to new technological innovations and upcoming trends. There are different opportunities around the corner for supermarkets to make this transformation. Examples are the replacement of barcodes with QR codes in 2027 in the Netherlands and Artificial Intelligence becoming more sophisticated (DigitalTrends, 2023)(NOS, 2024).

As supermarkets adopt these technologies, their business models will need to continue evolving towards more digitally-driven, data-powered operations. It is interesting to see how it affects the business models of supermarkets like Albert Heijn right now, and how it will develop in the future years. How far can we transform to this digital platform model and will the traditional physical supermarket as we know disappear completely in the future?

References:

Albert Heijn launches subscription, ‘My Albert Heijn Premium’. (2021, 27 oktober). https://www.aholddelhaize.com/en/news/albert-heijn-launches-subscription-my-albert-heijn-p

remium/

DigitalTrends. (2023, 19 juli). How Artificial intelligence (AI) is becoming increasingly sophisticated. Medium. https://medium.com/@digitaltrends1/how-artificial-intelligence-ai-is-becoming-increasingly-sophisticated-4b837f7e31ba

NOS. (2024, 26 juni). De streepjescode bestaat 50 jaar, maar het einde nadert. https://nos.nl/artikel/2526164-de-streepjescode-bestaat-50-jaar-maar-het-einde-nadert

SUNDARABHARATHI, M., & MUTHULAKSHMI, C. (2023). American Supermarkets – PAST, Present Vs. Future Trends and Technologies. In G. Venkataswamy Naidu College & Manonmaniam Sundaranar University, Res Militaris (Vol. 13, Nummer 2, pp. 6270–6271).

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Physical goods, information goods, & neurological goods(?)

17

September

2024

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I am fascinated by the idea of information goods – firstly introduced during the start of the 1950s, they prevail to be an incredibly profitable and state of the art product type.1 My fascination arises from the impressive difference between information and physical goods. If the gap between information goods and the next product type is equally big or even larger, which products could we think of?

Imagine, all a person knows is physical goods – the world hasn’t developed non-object (information) goods yet. This fictitious individual is used to buy products, which need to be newly produced, whenever an item is ordered. Additionally, personalizing a product takes a lot of extra work, as the product needs to be adjusted physically, by steps in a manufacturing process not being standardized. Ultimately, the product will break down sooner or later. Telling this person, that there will be another kind of product, being transformable and customizable easily, without any massive extra costs, would leave this person stunning. Further, explaining that the product could be reproduced an unlimited amount of time, without any significant additional cost as well, and that this product wouldn’t be subject to wear and tear over time, would sound unimaginable for this person. This whole process left me thinking: How could the next tremendous product development look like?

Upcoming trends show that this could be cognitive or even neurological goods. In my understanding, both enable physical or knowledge enhancements to be directly bought. On the one hand, cognitive goods would include obtaining knowledge, being entailed in a book, directly (without reading the actual book), by buying and transferring it. Neuralink, for instance, tries to develop enhanced communication between the brain and computers.2 Accelerating this communication to real-time speed, would enable a market for cognitive goods. On the other hand, neurological goods could include all types of physical capabilities. This could not only be skills, which otherwise would need to be learnt over time (e.g., playing the piano), but also those, which can not be learnt anymore, for example because of paralyzation (e.g., walking even though being paraplegic). The precision, with which neuronal activity can be surveilled, replicated, and even stimulated, becomes comprehensible, when considering experiments such as the one of Takagi and Nishimoto (2022), in which they tried to measure neuronal activity of people seeing things, in order to replicate the objects they have seen.3 The results, visualized in the graphic, should leave us stunning and maybe realizing that a market for neurological as well as cognitive goods is not too far away anymore.

  1. Timothy Williamson (2023). History of computers: A brief timeline. Retrieved from: https://www.livescience.com/20718-computer-history.html ↩︎
  2. Ben Kendal (2024). Was wollen Neuralink und Musk mit ihrem Gehirnchip erreichen – und ist er überhaupt sicher? Retrieved from: https://www.rnd.de/wissen/neuralink-was-ist-das-und-was-will-elon-musk-mit-dem-chip-im-gehirn-erreichen-ZFEHGH2WDVDZ3AGVYXP6OOWYU4.html#; https://neuralink.com ↩︎
  3. Sarah Kuta (2023). This A.I. Used Brain Scans to Recreate Images People Saw. In: Smithsonian Magazine, retrieved from: https://www.smithsonianmag.com/smart-news/this-ai-used-brain-scans-to-recreate-images-people-saw-180981768/ ↩︎

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Data Privacy and GenAI

16

September

2024

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When ChatGPT launched at the end of 2022, most data protection professionals had never heard of generative AI and were then certainly not aware of the potential dangers it could bring to data privacy (CEDPO AI Working Group, 2023). Now that AI platforms grow more sophisticated, so do the risks to our privacy, and therefore, it is important to discuss these risks and how to disarm them as effectively as possible.

GenAI systems are built on vast datasets, often including sensitive personal and organizational data. When users interact with these platforms, they unknowingly share information that could be stored, analyzed, and even potentially exposed to malicious actors (Torm, 2023). The AI itself could potentially reveal confidential information learned from previous interactions, leading to privacy breaches. This could have some major implications for the affected individuals or organizations if sensitive information is being shared without proper anonymization or consent.

Continuing on the topic of consent: Giving consent for generative AI platforms to use your data can be tricky, as most platforms provide vague and complex terms and conditions that are difficult for most users to fully understand. These agreements often include legal jargon and technological terminology, making it hard to know exactly what data is being collected, how it’s being used, or who it’s being shared with. This lack of transparency puts users at a disadvantage, as they may unknowingly grant permission for their personal information to be stored, analyzed, or even shared without fully understanding the risks involved.

To reduce the potential dangers of GenAI platforms, several key measures must be implemented. First, transparency should be prioritized by simplifying terms and conditions, making it easier for users to understand what data is being collected and how it is being be used. Clear consent mechanisms should be enforced, requiring explicit user approval for the collection and use of personal information. Additionally, data anonymization must be a standard practice to prevent sensitive information from being traced back to individuals. Furthermore, companies should limit the amount of data they collect and retain only what is necessary for the platform’s operation. Regular audits and compliance with privacy regulations like GDPR or HIPAA are also crucial to ensure that data handling practices align with legal standards (Torm, 2023). Lastly, users should be educated on best practices for protecting their data when using GenAI, starting with being cautious about what they share on AI platforms.

In conclusion, while generative AI offers transformative potential, it also presents significant risks to data privacy. By implementing transparent consent practices, anonymizing sensitive data, and adhering to strict privacy regulations, we can minimize these dangers and ensure a safer, more responsible use of AI technologies. Both organizations and users must work together to strike a balance between innovation and security, creating a future where the benefits of GenAI are harnessed without compromising personal or organizational privacy.

References:

CEDPO AI Working Group. (2023). Generative AI: the Data protection Implications. https://cedpo.eu/wp-content/uploads/generative-ai-the-data-protection-implications-16-10-2023.pdf

Torm, N. (2023, December 11). Steps to safeguarding privacy in the Gen AI era. www.cognizant.com. https://www.cognizant.com/se/en/insights/blog/articles/steps-to-safeguarding-privacy-in-the-gen-ai-era

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Netflix’s (seemingly too?) Perfect Recommendation System.

7

September

2024

5/5 (1)

Netflix is widely seen as one of the world’s most successful streaming platforms to date. Many might accredit this success to its broad library of fantastic titles and simple, yet effective, UI. However, behind the scenes a lot more is going on, which keeps users on the platform longer, and most importantly, reduces subscriber churn.

While Netflix has 277 million paid subscribers across 190 countries, no user experience is the same for any of these users. Over time, Netflix has developed its incredibly intelligent Netflix Recommendation Algorithm (NRE) to leverage data science, and create the ultimate personalized experience for every user. I think most of us are aware of some personalization algorithms, but not the extent to which they go!

The NRE is composed of multiple algorithms that filter Netflix’s content based on a user’s profile. These algorithms filter through more than 5000 different titles, divided in clusters, all based on an individual subscriber’s preferences. The NRE works by analyzing a wealth of data, including a user’s viewing history, how long they watch specific titles, and even how often they pause or fast-forward. This, in turn, results in videos with the highest likelihood of being watched by the user, being pushed to the front. Which is, according to Netflix, essential, since the company estimates that they only have around 90 seconds to grab a consumer’s attention. I think, as consumer attention drops even further (with apps like TikTok destroying our attention span), this might become even more of a problem in the future. I mean, who has the time to sit down and watch a whole movie these days??

This also ties into the concept of the Long Tail which we discussed, which refers to offering a wide variety of niche products that can appeal to smaller audience segments. Netflix can now surface lesser-known titles to the right audiences using its recommendations algorithms. While these niche titles might have never been discovered by users in the past, Netflix can now monetize the Long Tail of its Library. You must have definitely noticed that your family or friends have titles on their Homepage that you would never see on your own, and this is the NRE at work.

While this model is largely successful, it might raise concerns around content bias. For example, Netflix’s use of different promotional images for the same content based on a user’s perceived race or preferences has sparked debate. Although the intent is to tailor recommendations more effectively, it risks reinforcing stereotypes and narrowing the scope of content that users are exposed to.

Ultimately, user data is exchanged for a super personalized experience, though this experience can sometimes be flawed. What do you think about Netflix’s NRE and its effects on users? Do you think this data exchange is fine, or would you rather just see the same Homepage as everyone else?

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“Gas-as-a-Service”? Fuelling servitization through digital

4

September

2024

5/5 (2)

In the very first class, professor Ting Li mentioned an app – WeFuel – which aims to radically change the way we think about fuelling our vehicles: on-demand gas delivery.
I was striked by this innovation, enabled by digital technologies: thanks to real time geolocalization, WeFuel will reach to the customer to fill up their gas tanks, instead of having the customer going to a gas station. This could be an important solution in case of emergency needs or to overcome accessibility issues in gas stations.

source: Wefuelinc.com

Given that the value proposition, as well as the business model, changed, I started wondering how extreme this change could be. At first, I thought of the possible downsides under the customers’ perspective: this service may be more expensive than traditional gas stations, because of its on-demand nature and the variable costs connected to the way it works – even though the usual (huge) fixed costs of running a gas station aren’t faced by the company. And so I thought: since fuelling vehicles is a repetitive action and what WeFuel‘s customers look for – apparently, at least – are comfort and convenience, why not charging a fixed price, a subscription, instead of having prices per gallon/liter? After all, gas is a commodity, just like water or electricity for households are: that’s how I thought of a “Gas-as-a-Service” approach. Customers can pay a monthly subscription tailored on their needs – whether in terms of quantity, frequency or assistance they require for each “fueling session”. Users can then upgrade their subscription if needed, or pay for extra “sessions” or extra liters every once in a while. Facing a premium price to be sure not to run out of gas ever again, or to never have to spend precious time waiting in a queue at a gas station seems completely reasonable. At the same time, WeFuel could gather much more data than traditional gas stations, enabling them to put the customer at the center and to use data analytics to optimize their offers.

I must note that, while writing this article, I’ve come across a company which has a very similar business model: 4Refuel. However, it is a B2B company, while I decided to focus on B2C business models.

source: Sensorfy.ai

Nevertheless, many questions arise: who would be willing to pay for this service? How pervasive does WeFuel‘s network of tank cars need to be? But most importantly: is there a future for businesses in the gas fuel market, given the transition to EVs?
Actually, the electric revolution could boost a service like WeFuel‘s: given the wind down of traditional gas stations (or, more probably, their reinvention as electric charging stations), an on-demand, Gas-as-a-Service approach could fulfill the needs of a to-be niche market. In fact, petrol-powered car enthusiasts could still enjoy their traditional vehicles while (happily) paying a premium price, which could be also seen as a way to deal with consumption externalities. But how long could this business last?

source: The New York Times’ website

Overall, customer centricity and the ability to collect data, as well as the blend of digital and physical, position this idea as a possible digital disruption, but how feasible is it? And what’s its futurity?

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