Last week, I had the opportunity to attend DFCE 2025 organized by AITI. At the conference, I wore two hats from work: that of a data governance enabler and a data architect for our effort in migrating (and re-architecting) a data platform to the cloud. There are a lot of interesting topics covered, generally around data governance (specifically, the Personal Data Protection Order, PDPO) and, of course, AI, with optimism mixed with cautionary reminders.

At the heart of the conference, though, is an important message: That to facilitate economic growth through digital innovation, trust is the foundation that business organizations and the governing bodies need to build to attract more consumers and extend their reach to broader regional markets. And it needs to be built at the outset—while we’re still early in our digital transformation phase—using policies as the tools, though admittedly at the cost of friction.

In this post, I explore the concept of regulatory friction—which I agree is a crucial foundation for a digital economy—and its impact on innovation, and how we can utilize metadata management tools to streamline the implementation of policy enforcement.

Regulatory Friction vs. the Speed of Innovation

On the drive back home after the event, I listened to a recent a16z podcast where Marc Andreessen lamented the EU’s heavy-handed regulatory approach—among other things:

Marc Andreessen: And so the startup process is a way to especially get smart young people to do ambitious things. So that’s great. It’s all fantastic. Against that is just this incredible drag by bad governments and bad policies. As you mentioned, in Europe, you’re persistent shooting itself, not just in the foot but in the other foot and in the ankle and in the knee and in the gut. And they’re just on this absolute frenzy to regulate and kill tech in Europe [and] the UK.

Eric Torenberg: And they’re proud of it.

Marc Andreessen: And they’re proud of it. Yes. The actual European line now is we quote—this is in the [Financial Times]—this is an actual quote from a European senior politician: “We know we cannot be the global leader in tech innovation, so therefore we will be the global leader in tech regulation.” And you can imagine being like a German or French tech founder and reading that just being like, “Oh God, … get to the U.S. embassy and apply for a visa as fast as possible.”

And going back just a year ago in July 2024, Stratechery wrote an article aptly titled “The E.U. Goes Too Far”, where the author lamented about “Europe’s data obsession”, which leads to the annoying consent pop-ups, personal data collections, and unnecessary frictions:

… for the first time in a while, I was traveling as a tourist with my family, and thus visiting things like museums, making restaurant reservations, etc.; what stood out to me was just how much information all of these entities wanted: seemingly every entity required me to make an account, share my mailing address, often my passport information, etc., just to buy a ticket or secure a table. It felt bizarrely old-fashioned, as if services like OpenTable or Resy didn’t exist, or even niceties like “Sign In With Google”; what exactly is a museum or individual restaurant going to do with so much of my personal information - I just want to see a famous painting or grab a meal!

The friction faced by consumers, thanks to heavy-handed regulation, is the least of the problems. The more critical secondary effect of regulations is how they either cultivate or stifle innovation and influence economic growth. Spending more time to ensure regulatory compliance means less time spent getting products to market faster; unless, of course, you spend more resources to account for that time. This also means additional risks that otherwise would not have existed without stringent regulations, scaring off startups and investors. A recent report published by the European Commission highlights the lack of appetite by venture capital firms to invest in AI in the EU compared to the US and China:

The EU’s efforts in advanced technologies, such as artificial intelligence and cloud computing, are far from matching those of the US. The main instrument available to the EU, the European Innovation Council, had a budget of 256 million euros in 2024, while the US allocated more than 6 billion dollars for this purpose, including 4.1 billion from the Defense Advanced Research Projects Agency and 2 billion dollars from other related agencies.

The situation is repeated when looking at venture capital investment. In 2023, they invested about \$8 billion in venture capital in artificial intelligence in the EU, compared to \$68 billion in the U.S. and \$15 billion in China. The few companies that are creating generative AI models in Europe, such as Aleph Alpha and Mistral, need large investments to avoid losing the race to U.S. firms. However, European markets do not meet this need, pushing European firms to look outside for funding.

To get some idea about the cost of this friction from GDPR enforcement alone, you can head over to the GDPR Enforcement Tracker website¹. It’s no surprise, then, as the senior EU politician is well aware, that the EU lags behind the US and China in the AI race, in many respects, due to the continent being the “global leader in tech regulation”, instead of lack of talent—which, if this trend continues, may eventually be true due to brain drain. From the same report:

Excessive regulation and administrative barriers in the EU are obstacles to technology companies deciding to settle or simply stay in Europe. In fact, if between 2008 and 2021 147 unicorns were founded in Europe, i.e. companies whose valuation exceeds 1 billion dollars, 40 moved their headquarters abroad, the bulk of them to the United States.

Now, I don’t mean to undermine AITI’s—and the Brunei Government’s—good intentions behind PDPO. To the conference’s credit, this was discussed at some length in a panel discussion titled Striking the Balance: Innovation, Regulation, and Building Public Trust. Here’s the description of the panel discussion in the programming document:

As technology reshapes economies and societies, policy must ensure innovation delivers public value while safeguarding trust.

How do we ensure that regulatory frameworks remain agile enough to respond to fast-evolving technologies like AI and IoT? Can Brunei position itself as a model for digital trust in the region by embedding governance into innovation from the outset?

Clearly, AITI understands the need to strike a balance, hopefully to be careful so as to avoid the missteps of the EU, where the economy is stagnating. And given Brunei’s current economic climate, the last thing we want is to repeat the same mistake.

So if there’s one key observation I can take away from the conference, it is that AITI—and by extension, the Minister of Transport and Info-communications and the Government of Brunei Darussalam—is aware of the general perplexity from the private sectors about what PDPO asks from them, and the potentially enormous complexity and efforts it might entail. Hence, the many presentations around AI and data governance throughout the conferences, which I thought were well delivered and pertinent to the discussion around balancing innovations and regulations, are welcome.

Among the many presentations that stood out to me (and still too many to discuss in one article) are the presentations by DST on how it transformed itself into a more digital company using AWS, embracing economies of speed, and by Drew & Napier—a Singaporean law firm that contributed to the PDPO—on principles of data ethics, IoT securities, and a little overview on concepts (or “Accountability Tools”, as the DPO guide calls it) such as Data Protection by Design (DPbD), Data Inventory Mapping, and Data Protection Impact Assessment (DPIA). The former boasts about innovations that leverage public cloud services, while the latter emphasizes data ethics, security, and the importance of regulations as key enablers. Again, the key question is, can we strike a good balance between innovation and regulation?

I think DST’s presentation is pertinent to the question of balance between innovation and regulation, at least to the local Bruneian audience: First and foremost, DST is familiar to everyone in Brunei—everyone who owns a mobile phone pre-UNN most likely was a customer to DST—and those who stick with DST may observe how much they’ve embraced digital transformation throughout the last five years, perhaps much more than their competitors do, at least amongst the three salescos. After all, they have an “army of developers”, as one panel speaker puts it. Secondly and more critically, they’re able to make this transformation to the cloud in spite of the regulatory scrutiny by the regulators from AITI, while PDPO was still being drafted as early as 2021. In fact, DST’s response to AITI’s public consultation on PDPO highlighted that the PDPO draft “should not hinder any innovation development.” I don’t see this sense of concern about PDPO potentially slowing down innovation in either Progressif’s response or Imagine’s response. DST may own the bragging rights at the conference (and deservedly so), but the main beneficiary of the presentation here is AITI, who want to see increasing confidence in the private sector to comply with PDPO. I’d go further to suggest that it is no coincidence that DST is making such a timely presentation about its transformation into a digital organization using public cloud technology—especially for a company that holds a huge portion of Bruneians’ personal data—at the same conference where AITI announced that it is publishing its guide for appointing Data Protection Officers (DPOs). (PDPO requires an organization to assign at least one DPO to be responsible for ensuring that the organization complies with the law.)

From this perspective, at least, the conference fosters awareness, trust, and confidence in the practical feasibility of complying with PDPO for local companies—especially for those that want to innovate more by leveraging the cloud. Of course, all this is in the hope that this buildup of trust and confidence doesn’t stop there; when done right, the consumers and investors should feel it too.

Reducing Friction with More Software

To be sure, friction isn’t inherently bad—we need friction as a society to be stable. During COVID, we had to take an ART test every few days before we could enter the office; it’s pretty tedious and uncomfortable, but at least everyone feels a bit safer. But what if the ART test takes seconds instead of minutes, and is less intrusive to carry out, and yet still offers the same accuracy (or better) for its test results? Everyone would still feel safer, but with less friction!

To that point, what’s not explicitly discussed in the conference, though, is how the costs of friction introduced by regulations like PDPO can be reduced significantly with scalable technological implementations to meet PDPO requirements. Within the last decade, the rising costs and complexities incurred by regulations like GDPR in the EU and CCPA and HIPAA in the US have increased the demand for technology that streamlines tasks and processes for compliance with these regulations .

Take, for example, the Data Inventory Mapping exercise, which is an essential exercise as highlighted in the presentations by AITI and Drew & Napier. “Producing a Data Inventory Map” is listed as an “Accountability Tool” under the DPO guideline. From the guideline, in section 4.1.2, the exercise is described below:

(a) A Data Inventory Map is a type of data mapping which identifies the data assets and the flows of data relating to a system or process.

(b) It maps out the types of personal data, the purposes of its collection, the access controls, the methods of transfer, the types of storage and the disposal method.

(c) It captures the processes by mapping the flow of data including the assigned staff involved in the management of personal data ranging from internal departments or external third-party organisations.

(d) Organisations should review and update its inventory map periodically along with any developments relating to the system or processes.

(e) Generally, the data inventory map can be implemented by project managers involved in the development of the system or process, with the assistance of the DPO

If this exercise sounds too resource-intensive as organizations and their data grow exponentially—it is because it is indeed a taxing exercise. Data catalogs or metadata management tools, such as DataHub, Amazon DataZone on AWS, Microsoft Purview on Azure, and Unity Catalog on Databricks—among many others—exist to make this exercise less taxing through automation and streamlining metadata curation workflows. All share similar features to make data observable, thereby fostering the accountability and trustworthiness of the data.

For example, DataHub supports the ingestion of metadata from Oracle and SQL Server databases (among many others), so that it can (1) parse the SQL scripts in the databases to infer the schema of the tables and views in the databases, (2) infer the lineage between tables and views, even across data systems², (3) use AI to classify whether a table or view contains PII automatically (and have it vetted manually), and, if so, (4) automatically test for policy compliance such as “Does this dataset have an owner?” or “Has this dataset been stored beyond its retention period?” Since parts (1) and (2) of this flow are done automatically, the DPO’s responsibility for carrying out task (d) in Data Inventory Mapping becomes operationally trivial.

This development in data governance software isn’t surprising. What DPOs are responsible for doing is inherently dealing with software problems; that of ensuring data in their organization’s software systems is secure (with DPbD), observable (with Data Mapping), and measurable (with DPIA). That data in discussion just happens to be personally identifiable.

If we observe the general trend of how software is eating the world, the pattern is that most of the time, the answer to software problems is to add more software on top! This is true for many aspects of IT: from upfront IT infrastructure procurement to using X as a service on demand; from manual testing and deployment to CI/CD; and from centralized and intermediary-dependent payment rails to decentralized value-settlement networks without intermediaries. So, it is not surprising that the data governance aspect follows a similar trajectory too.

Organizations with more sophisticated data engineering teams will find the effort to do the exercise above a much less tedious and time-consuming activity, as they are capable of leveraging the emerging capabilities of data systems and their integration with data catalogs or metadata management tools. Those without such a team would be doomed to resort to Excel (or worse, Microsoft Word!) as their data inventory map repository. Of course, this is a moot point when discussing organizations with data that grows very slowly or are not large enough to necessitate such a scalable approach.

More Than Just Good Intentions

Good intentions don’t work. Mechanisms do.

– Jeff Bezos

The conference did well to underscore the importance of building trust as a strategy to pave the way for economic growth through digital transformation. Pertinent to that strategy is the challenge of balancing regulation and innovation, and being cautious of the potential second-order complexities, such as those faced in the EU. As usual, the market finds its ways to even things out, and data catalogs and metadata management tools emerge from the necessity for reducing regulatory friction. Looking ahead, the next conference in 2026 should aim to close this gap with more examples showcasing how conformance to regulatory compliance is done in the real world at scale, not just with good intentions in mind, but also supplemented with effective mechanisms that make it generally feasible.

Magnus Carlsen announced in a podcast that he would no longer defend the title he’d won in 2022 after consecutively remaining undefeated in the World Chess Championship since 2013. Many critics speculated he had lost his passion or was too afraid to lose to the next generation of players. About two years later, in March 2024, he announced the inaugural tournament of the Freestyle Chess Grand Slam Tour—a series of five major Chess960 tournaments held across different continents throughout 2025.

Chess960, also known as Freestyle Chess or Fischer Random, radically increases the possible combinations of the starting positions in a game by randomizing the positions of the back-rank pieces, leaving the front-rank pawns untouched. As the name suggests, the number of possible starting positions is increased to 960 possible combinations. The Freestyle Chess Grand Slam Tour, though, bans the traditional Chess starting position, including swapping the King and Queen.

Clearly, the motivation behind the Freestyle Chess Grand Slam Tour is to make chess more interesting and entertaining in the hope of drawing in a larger audience. This is reflected by the fact the tournament offers a bigger prize pool than traditional chess tournaments and features an innovative player-focused gameplay approach, such as the “confession booth.” Players are also equipped with heart rate monitors to provide “a novel layer of drama,” as Carlsen puts it.

I’m no chess fan, but this seems like a good revitalization of the ancient game, sparking enthusiasm and debate amongst the sport’s fans and grandmasters alike (though the first tournament of Chess960 dates back to 1996). It is definitely pertinent now with how AI has crept into the sport over the last three decades, starting with a brute-force approach of Deep Blue by IBM in 1997, to the use of artificial neural network and reinforcement learning pioneered by Google DeepMind with AlphaZero in 2017. It is no surprise then of AI’s increasing influence on how the game is played. Magnus Carlsen wrote in The Economists for the By Invitation section:

Why freestyle? Following my fifth consecutive victory in the World Chess Championship in 2022, I announced that I would no longer defend the title. Many speculated that I was exhausted, or that I was scared of the next generation of players. On the contrary, my passion for chess remains as strong as ever, and I am as ambitious as I’ve always been. What changed was my perspective on the format of the classical world championship itself.

⁠⁠The challenge wasn’t the games, which often stretch for hours. I enjoy the length of time allowed under the rules. My title defence in 2018 against Fabiano Caruana, for instance, pushed us, over 12 drawn games, to our mental and physical limits, before I emerged victorious in the tiebreak. The issue lay elsewhere, in the months of grinding preparation leading up to the event. Modern World Chess Championships demand endless memorisation of computer-generated opening lines, reducing the sport’s artistry to rote learning. As someone who treasures the creativity of chess, I wanted to focus more on this aspect of the game. Also, life beyond chess deserved my attention too.

Saving Creativity

In a way, I share Carlsen’s sentiment. Chess960 was originally introduced to reduce the emphasis on opening preparation and encourage creativity. By moving to freestyle, player decision-making in chess may be free from the need to internalize patterns over years of grinding to gain a competitive edge. Going freestyle is how the grandmasters strike back against AI with a renewed hope for creativity in the sport.

Should the same thought be entertained about programming? Programming, after all, is an art. It is a skill that’s improved with creativity rather than mere rote memorization of patterns of codes (though having some repertoire of knowledge on software design patterns does help). Many prominent programmers have expressed similar sentiments on the importance of taste, elegance, and beauty with regard to programming. Here’s how Linus Torvalds, the original hacker behind the Linux kernel and Git, explained good taste in 2007:

To me… the sign of people I really want to work with is that they have good taste… Good taste is about really seeing the big patterns and kind of instinctively knowing what’s the right way to do things.

And what are instincts but the use of implicit patterns so deeply familiar that we no longer remember learning them after years of repetition and experience—embedded deep in the back of our mind? If this is true, then AI is just a monstrous instinct machine that has learned to recognize the big patterns of our collective knowledge that we’ve put out on the Internet. It doesn’t truly understand the right way to do things, but it can infer them from the big patterns—and it will only get better.

To that point, AI has already internalized a wealth of creative output of perhaps some of the best programmers in the world and makes them readily available as generative code assistants. They help us scaffold code from a blank page, suggest code improvements for refactoring opportunities, and even fix our code plagued with cryptic error messages. If this effectively takes away the creative aspect of programming from the programmers, then will the programmers too, strike back?

The problem with this line of thinking is that chess is an adversarial zero-sum activity (one must lose for another to win), while programming—like many productive economic activities—is a positive-sum activity (everyone wins something). So while AI takes away the creative value of chess from the players (by eating away their creative decision-making opportunities), AI adds creative value to the programmers (by adding more options for creative decision-making opportunities). It makes no sense to strike back. We’re living the dream!

Exploiting the Kind and Capitalizing on the Wicked

There’s also another interesting angle of AI’s influence on chess, and it is related to learning and decision-making. Carlsen’s issue with the overreliance on rote learning computer-generated patterns and his desire to revive the creative aspect of chess reminds me of two thought-provoking ideas introduced by Robin Hogarth about how we learn and make decisions: the kind learning environment and its antithesis, the wicked learning environment.

To frame the two antithetical learning environments into questions: Does our decision-making improve with more experience? Or does having more experience make us myopic to the broader worldview, making us more susceptible to being biased in our judgment?

If your belief leans more to the former, then you’re in the camp of expert intuition who believes that good decision-making comes with years of repeated practice and experience. Otherwise, if you’re skeptical that having more experience is a good predictor for excellent decision-making, then you’re in the camp of heuristics and bias, who believe that experience leads to overconfidence and results in more decision errors.

A seminal paper titled Conditions for Intuitive Expertise: A Failure to Disagree reconciles the opposing views of these two camps. The two authors, Daniel Kahneman (camp heuristic and bias) and Gary Klein (camp expert intuition) touched on their differing perspectives and their reconciliation:

In this article we report on an effort to compare our views on the issues of intuition and expertise and to discuss the evidence for our respective positions. When we launched this project, we expected to disagree on many issues, and with good reason: One of us (GK) has spent much of his career thinking about ways to promote reliance on expert intuition in executive decision making and identifies himself as a member of the intellectual community of scholars and practitioners who study naturalistic decision making (NDM). The other (DK) has spent much of his career running experiments in which intuitive judgment was commonly found to be flawed; he is identified with the “heuristics and biases” (HB) approach to the field.

A surprise awaited us when we got together to consider our joint field of interest. We found ourselves agreeing most of the time. Where we initially disagreed, we were usually able to converge upon a common position. Our shared beliefs are much more specific than the commonplace that expert intuition is sometimes remarkably accurate and sometimes off the mark. We accept the common-place, of course, but we also have similar opinions about more specific questions: What are the activities in which skilled intuitive judgment develops with experience? What are the activities in which experience is more likely to produce overconfidence than genuine skill?

And so the answer to the above questions is that it depends. Ask a chess grandmaster, and you may get a nod of agreement. Ask a senior programmer, and he may disagree vehemently (simply because programmers are an angry bunch). What sets them apart is the domain in which their decision-making is applied. One lives in a world where the rules seldom change, and the other has to adjust and adapt frequently to meet the demands of the rapidly shifting world (and gets angry every time a new feature is requested “at the last minute,” or a change introduced an unexpected behavior).

Chess is considered the gold standard of a kind learning environment. It has simple and predictable rules and provides immediate feedback, and yet it is quite complex thanks to the astronomical size of the possible positional configuration of the pieces on a 8x8 board. (For traditional chess, a recent estimate is in the order of $~10^{44}$ reachable legal positions.) On the contrary, a wicked learning environment is the opposite: the rules are unclear and complex and may change overnight. Feedback in the environment is often delayed. And what you learn today may not be applicable tomorrow. These are the defining features of problems faced by any white-collar workers—and sure is true for programmers!

The table below summarily compares the two:

In a kind, zero-sum environment like chess, the incentive is to use AI’s super computational capability to exploit the solution space—commonly through “prepping”—to devise optimal opening strategies and gain competitive leverage over competitors. This effectively shrinks the space for human creativity, turning expertise into a matter of rote memorization rather than tactical ingenuity. In contrast, in a wicked, positive-sum environment like programming, the incentive is to capitalize on existing knowledge and tools using AI to accelerate discovery and expanding our horizons. Here, AI doesn’t limit our creativity; rather, it enables us to explore and traverse more complex solution spaces that were previously beyond our reach. In other words, AI’s interaction with the kind environment reduces the creativity space for humans; but it expands the creativity space for humans in a wicked environment.

Though in this post I limit myself to only use chess vs. programming to drive my musing, I’m sure this observation rings true to many other domains as well. As I’m writing this post, another example that came to mind is that of F1 (a kind, zero-sum environment) vs. forecasting long-term stock prices (a wicked, positive-sum environment).

In the case of F1, teams uses AI to exploit the useful patterns from real-time data to help in making fast pit strategy decisions, a job which traditionally relies on a team of engineers to make the call. This narrows down human choices to those already exploited by AI. As for traders interested in the long-term future price of stocks, the logical thing to do is capitalize on the wealth of financial and economical knowledge—a job better suited for AI than human—to help them make better decisions.

Well, does this mean AI’s involvement in chess—or any other kind environment—is bad? Not really. While it is generally true that in a kind environment AI reduces the number of available choices to go for optimizations, it also shift the creative frontier downstream into regions of small pockets of wicked environment within the domain. This is apparently Magnus Carlsen’s play style: by going for offbeat opening choices, he embraces ambiguity and face it with ingenuity. By steering away from exploited openings, he’d purposely land himself and his opponents into small pockets of wicked environments in chess—in the middlegames and the endgames—where creativity triumphs over rote memorization.

Creativity is About Knowing Where to Aim

With the advent of AI assistant tools like GitHub CoPilot and Cursor, I’ve come across many claims about the impending irrelevancy of programmers in the future where AI reigns supreme. In the mean time, the programming culture spawn yet another subculture, called vibe coding—where the main language for software development is English, and codes generated by AI are not meant to be fully understood (if you do review, understand, and test the code, you’re not vibing).

Perhaps one of most vocal leader who shared this thought is Jensen Huang, the CEO of NVIDIA. In 2024 last year at World Government Summit, he mentioned something about the future of programming to the chagrin of many programmers (myself included):

I’m going to say something, and it’s going to sound completely opposite of what people feel.

You probably recall, over the course of the last 10 to 15 years, almost everybody who sits on a stage like this would tell you it is vital that your children learn computer science. Everybody should learn how to program. In fact, it’s almost exactly the opposite: it is our job to create computing technology such that nobody has to program, and that the programming language is human.

Everybody in the world is now a programmer.

This is the miracle; this is the miracle of artificial intelligence. For the very first time, we have closed the gap. The technology divide has been completely closed, and this is the reason why so many people can engage with artificial intelligence. It is the reason why every single government, every single industry conference, and every single company is talking about artificial intelligence today.

His speech would ring true, if only programming is only constrained to the problem of writing code to make the interpreter or compiler happy. But programming is more than just about generating code. Software needs to be useful in order for it to continue to exist, and much of the input to the activities that generate real values in the software product requires humans. While writing code is an important part of the business, it is not complete in itself to deliver real software value. To give some examples, think software architecture and domain-driven design—both of which require very hard-to-master skills (especially as programmers), like developing business acumen, making architectural decisions, and making complex topics stick.

We should also be aware of the fallacy of thinking that the programming domain is a zero-sum game: That for one party to win, another must lose; that if AI dominates programming, then the programmers are pushed out of job. This worldview disregards the fact that programmers and software developers operate in a collaborative positive-sum world. In fact, as AI joins us in this positive-sum world, it is just so that AI needs to capitalize on human programmers’ inputs for its outputs to be useful, and in turn, human programmers need to capitalize on AI to remain relevant.

This doesn’t mean I completely disregard the notion of AI replacing human programmers in the future. The key difference between the AI we’re using right now and the one that Jensen Huang portends is in their capacity (or lack thereof) to be a manager instead of the individual contributor; to be an architect instead of the builder; or, to borrow Ben Thompson’s analogy, to be an Artificial Super Intelligence (ASI)—the rifle barrel—instead of Artificial General Intelligence (AGI)—the ammunitions:

What o3 and inference-time scaling point to is something different: AI’s that can actually be given tasks and trusted to complete them. This, by extension, looks a lot more like an independent worker than an assistant — ammunition, rather than a rifle sight. That may seem an odd analogy, but it comes from a talk Keith Rabois gave at Stanford…My definition of AGI is that it can be ammunition, i.e. it can be given a task and trusted to complete it at a good-enough rate (my definition of Artificial Super Intelligence (ASI) is the ability to come up with the tasks in the first place).

In this perspective the future of programming is certainly bright: We may not be too far from a future where we can “hire” cheap individual contributors—the ammunitions—to help generate 100% of the code. But ammunitions are only useful when we—the rifle barrel—point them to the desired targets. It gets trickier in a wicked world, where the targets would often dance unpredictability. Often times, it is even unclear which targets we should point to. In other words, creativity is about knowing what and where to aim; simply being the projectile isn’t!

In an era where AI reigns supreme, the game we must learn to play will be that of unpredictability, chaos, and ambiguity. It is no longer about perfecting the established art, but mastering the art of exploration and experimentation. And if history is any guide, humanity has always been remarkably good at just that.

Digitalization, or digital transformation, has long since become a key factor responsible for the progress and development of the world’s economies. “Every company is a technology company,” or variations of the phrase, is an oft-spoken mantra for highlighting the importance of digitalization to every organization, no matter the industry. Recently, the push for digital transformation is usually induced by the furor surrounding emerging technologies, such as artificial intelligence (AI), cloud computing, big data, and blockchain.

With the hype around the emerging tech, many organizations, perhaps due to fear of missing out, are racing to transform their business to be “digital”. But what does that mean? If it’s about adopting digital technology, we’ve had a track record of that going back to a few decades ago. (The first digital alarm clock was first patented in 1956.)

From experience, when people talk about going digital, they usually mean adopting emerging technologies to realize its lofty promises: that big data and data analytics enable data-driven business decisions, and in some way or another, AI is a silver bullet that can solve an organization’s myriad problems.

Of course, the promises of big data and AI are real. However, they have led some organizations to develop a lackadaisical strategy for digital transformation. Decisions for digital transformation are mostly pivoted around the procurement of emerging technologies, rather than by reimagining the operating system of the business and changing the ways of working; that is, transforming the business operating model.

This is unfortunate. Digitalization should be an enabler and offer opportunities for lifestyle change instead of a mere leap in the technology being used. To do otherwise is to only assume the appearance of a smart organization. A masquerade.

Masquerading Digitalization with Digitization

Simply carrying out a mere transition from paper to its digital forms (e.g., PDF documents, image files, etc.) does not imply digitalization. Here’s an example.

There are a few local restaurants that I’ve personally experienced where their effort to digitalize backfires miserably, to the point where it would have been much easier to order the menu without the digital technology.

There’s no longer any physical menu placed on the counter or the dining table. QR code stickers, however, are conspicuously glued onto each table. When scanned, it would lead to a shared Google Photos link containing the images of the restaurant menu. With a bad cellular connection, it would take a while for the thumbnails of the images to load at their full quality. There was quite a number of images; one image for each page. After a few swipes left and right or scrolling up and down the list of images, it can take a while to read most of the menus to find what I’m looking for before I can finalize my order.

In the end, replacing physical menus with digital copies does not make discovering (and re-discovering) menus easier. The overall experience turned out to be frustrating. To make things worse, everyone can see who has visited the shared Google Photos link.

Clearly, whoever decided to replace the physical menu with digital pictures muddled digitization for digitalization. Lifting and shifting your physical copies of data into a digital landscape does not make your organization digital. In fact, the meaning of digital transformation is evolving, with more than 72% of industry leaders viewing digitalization as much more than lift-and-shift.

To give them the benefit of the doubt, for all I know, the restaurants were just trying to save some paper, albeit at the cost of a somewhat disappointing customer experience. But some of the worst offenders yet are those who lauded and coveted digitalization the most: Enterprise IT.

Masquerading Digitalization with Emerging Technology

Migrating to the cloud is a no-brainer when organizations decide to “go digital”. The cloud had risen in favor over on-premise infrastructure for more than a decade since Amazon first revealed a unique business model at the time, stemming from an idea—a multi-billion dollar idea¹—that their existing compute and storage infrastructure can be offered as a service, with great agility and scalability.

According to a 2023 cloud adoption statistics by Zippia, 61% of businesses migrated their workloads to the cloud in 2020, although the share of workflows on the cloud is quite low, at only 25%, according to The Economist.

In Brunei, a 2019 report by the Authority for Info-communications Technology (AITI), a statutory body that focuses on the development of IT in Brunei, showed that most IT services infrastructure (e.g., data analytic tools, business process automation, HR management system, etc.) are on-premise², with less than 5% cloud adoption for each sector. That was five years ago. (Unfortunately, I couldn’t find recent reports on cloud adoption in Brunei.)

Nevertheless, over the past few years, signs for cloud adoptions by local businesses are increasingly popping up, such as the adoption of AWS Managed Services by Datastream Digital Sdn Bhd (DST) and Progresif’s partnership with Alibaba Cloud. Brunei’s Digital Economy Masterplan 2025 by the Digital Economy Council (DEC) even mentioned “Public Cloud Adoption” as part of its key projects in 2021, among other digital transformation initiatives.

It is all the more important, then, to understand what it really takes for an organization, especially those already operating for decades with the support of Enterprise IT to undergo digital transformation.

Many Enterprise IT are prone to hold on to their long-held traditions. Most are unable to forgo IT processes and operational culture that are already well-established and deeply entrenched in their organization. The processes are followed almost down to a dogmatic level, in the name of minimizing risks and ensuring stability and reliability.

The typical tradition includes loathsome bureaucratic IT processes. They are the manual and tedious checks and balances organically injected at almost every step of the value streams throughout the organization’s lifetime, thanks to the learnings from past incidents and failures. In other words, they’re the organization’s counter-responses to past disappointments, which, unfortunately, end up introducing waste in the organization’s value streams. Fittingly, in the DevOps community, they are referred to as “scar tissues”.

Traditional change management processes are one example of scar tissue, where each “normal” change requires a lengthy process of approvals, careful planning and coordination, and manual testing and deployment methods. To make things worse, these are usually exacerbated by the lack of cooperation between teams in organizations that suffers the symptoms of organizational silos.

Organizational silos are the defining feature of traditional IT, where teams are locally optimized, but end-to-end cross-team value delivery are not. Thus, works that require cross-team collaborations are impeded due to friction. The existence of the friction is easy to explain from economic standpoint: the builders (e.g., software engineers, data engineers, data scientists) are incentivized by making more changes or adding more features in response to the changing business needs, whereas the infrastructure team and the support team are disincentivized because they want to keep the software reliable and easy to support (by the virtue of less frequent changes and fewer features to support). As economists are wont to say, our actions are driven by incentives. When the incentives are not aligned, you get friction.

Without fixing the organization’s processes and organizational structure, whatever fancy technology the organization is trying to adopt will go to waste. Suppose you have set up Continuous Integration and Continuous Delivery pipelines (CI/CD) which automatically run unit tests and integration tests against your application each time a change is made to your code in your version control repository. Suppose also that you’ve painstakingly written some custom scripts to allow an application custodian to pick a snapshot version of the application to be automatically deployed to the production environment. If your organization still fanatically follows a traditional change management process—the likes that require approvals across multiple stages and require you to fill in numerous forms—the value proposition of the continuous property made available by the CI/CD pipelines will be diminished. In short, the inherent culture of dogmatism and fanaticism for traditional ways of working inhibits digital transformation from squeezing out its maximal value outcome.

Understanding the implication of this masquerade, as described above, is paramount for successful digital transformation. In the effort to transform an organization to be digital, it would be neglectful to be obsessed with the procurement and implementation of emerging technologies without expending more energy and resources on transforming their people and the operating model they’re clinging to.

Seeing Through the Masquerade

Mixing emerging technology with traditional IT culture is a recipe for disaster. As Gregor Hohpe, an Enterprise Strategist at Amazon Web Services wrote in his book Cloud Strategy:

If you don’t know how to drive, buying a faster car is the worst thing you can do.

Viral videos online showing amateur drivers driving recklessly and crashing their cars are all too common. Its dour consequences are obvious; and, grim as they may be, they serve as effective reminders and warnings to other drivers.

Organizations driving the digitalization vehicle do not normally have this luxury. Instead, digitalization mishaps are difficult to notice: We’re using the cloud now, we’re digital!, or We’re using Docker to do the deployment! are just fanciful masquerades if symptoms of organizational silos are still rampant across the organization, or deliverables take months before they can be deployed and realized.

For digitalization strategy and execution to be successful, it is paramount that everyone across all levels in the organization can see through the masquerades and understand the desired outcome of digitalization for the organization that maximizes its business value.