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How Logical Clocks Keep Distributed Systems in Sync

Sales Pitch Shake-Up: Part Two—The Pitch Hack So Obvious, You’ll Kick Yourself

The “Why GenAI” problem It is now without question that GenAI is the transformative technology of our times. While the business world is grappling with policy, change management, and “general technophobia,” as this HBR article calls it, I see it as being similar to the early days of the internet. At first, it seemed like a novel technology. Then, people were scratching their heads at its application beyond academia, defense, and other niches, followed by an outpour of startups as infrastructure developed and costs reduced, a dot-com crash, and up to where we are today as a society that runs mostly digitally. All that is to say, GenAI has the footprint of a disruptive trend that will be integrated into all aspects of society. What remains to be seen is the pace at which this integration happens in various parts of our lives. A lack of this understanding is probably why we’re seeing businesses say that they’re unclear about investment priorities when it comes to GenAI: In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI So, businesses are unsure why they should be using GenAI even though they are interested in the technology.  I think that startups and teams working in this space have the opportunity to educate and evangelize the technology in a way that businesses understand the value directly. Topline and bottom line approach While considering this, I wanted to explore how businesses should be thinking about the technology: As a way to enhance customer value and add new revenue streams (top-line growth)


As a way to enhance operational efficiency and reduce costs (bottom-line growth) I think it's important to think of top-line and bottom-line growth when adopting GenAI. For businesses, it helps to be very clear on what capabilities of the technology they need to hit the specific growth area. Topline GenAI Businesses are often looking to add new lines of revenue by innovating with their products and services. If a business has customers that have these needs, then GenAI is a good fit to solve those needs and add a new source of revenue to them: Customers want to synthesize the information they create on the business’s platform
Customers want to search and find information without having to do mental gymnastics
Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message.
Customers want to be less involved in doing mundane and routine tasks on the business’s platform. Some examples of topline GenAI integrations are beautifully captured by the folks at Obvious Ventures: Almost all of the products and services above have integrated GenAI to enhance the value they deliver to their customers. In doing so, these businesses seek to increase user engagement and, therefore, have a solid “why GenAI” answer. Bottomline GenAI While growing user engagement and revenue streams are always great, businesses need to sustain topline growth by thinking about their bottom line, i.e., costs. In fact, I think GenAI is most obvious for internal deployment within a business to make operations vastly more efficient and cost-effective. McKinsey agrees: We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office.\ Plenty of research has been done on what sectors have the potential to benefit, as shown in the PWC report. However, the use cases aren’t always obvious from reading reports such as these. If you are a developer or a founder, you would want to know more about the value proposition that you can build, which you will not always find in reports like these. So, let’s talk about the use cases briefly…. Any business has these needs where GenAI will be a great fit: Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads)
Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management
Assisting employees in their training, learning, and development programs
Synthesizing customer feedback efficiently
Assisting in the creation of impactful marketing assets easily If you focus on these needs and the GenAI capabilities, you should be on the right path to delivering a compelling value-prop to your potential customers in the sectors mentioned above, and possibly even more. GenAI Feature - Business growth fitness summary I’m not going too in-depth with how I rate the problems and how capable GenAI is at solving them, but I think this table is useful for thinking about what problems you’d want to solve for businesses. This will help your team craft a value-proposition that businesses find immediately valuable: GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples



Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management



Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication



Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing



Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation



Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response Takeaway So, if you are thinking about a B2B GenAI idea or are someone looking to adopt GenAI for your own business, I hope this is useful to build a mental model of where GenAI can be leveraged in your case and which specific capabilities deliver the highest ROI for you and your customers. The “Why GenAI” problem It is now without question that GenAI is the transformative technology of our times. While the business world is grappling with policy, change management, and “general technophobia,” as this HBR article calls it, I see it as being similar to the early days of the internet. At first, it seemed like a novel technology. Then, people were scratching their heads at its application beyond academia, defense, and other niches, followed by an outpour of startups as infrastructure developed and costs reduced, a dot-com crash, and up to where we are today as a society that runs mostly digitally. the this HBR article this HBR article All that is to say, GenAI has the footprint of a disruptive trend that will be integrated into all aspects of society. What remains to be seen is the pace at which this integration happens in various parts of our lives. A lack of this understanding is probably why we’re seeing businesses say that they’re unclear about investment priorities when it comes to GenAI: In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG | From Potential to Profit with GenAI In short, despite realizing the need to increase their investments in AI, too many organizations are slow to embrace the revolution. - BCG BCG | So, businesses are unsure why they should be using GenAI even though they are interested in the technology.  I think that startups and teams working in this space have the opportunity to educate and evangelize the technology in a way that businesses understand the value directly. Topline and bottom line approach While considering this, I wanted to explore how businesses should be thinking about the technology: As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance customer value and add new revenue streams (top-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) As a way to enhance operational efficiency and reduce costs (bottom-line growth) I think it's important to think of top-line and bottom-line growth when adopting GenAI. For businesses, it helps to be very clear on what capabilities of the technology they need to hit the specific growth area. Topline GenAI Businesses are often looking to add new lines of revenue by innovating with their products and services. If a business has customers that have these needs, then GenAI is a good fit to solve those needs and add a new source of revenue to them: Customers want to synthesize the information they create on the business’s platform Customers want to search and find information without having to do mental gymnastics Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message. Customers want to be less involved in doing mundane and routine tasks on the business’s platform. Customers want to synthesize the information they create on the business’s platform synthesize synthesize Customers want to search and find information without having to do mental gymnastics search search Customers want to share their knowledge with others on the business’s platform, and they want to be creative and effective in their message. creative creative Customers want to be less involved in doing mundane and routine tasks on the business’s platform. routine routine tasks Some examples of topline GenAI integrations are beautifully captured by the folks at Obvious Ventures: Almost all of the products and services above have integrated GenAI to enhance the value they deliver to their customers. In doing so, these businesses seek to increase user engagement and, therefore, have a solid “why GenAI” answer. Bottomline GenAI While growing user engagement and revenue streams are always great, businesses need to sustain topline growth by thinking about their bottom line, i.e., costs. In fact, I think GenAI is most obvious for internal deployment within a business to make operations vastly more efficient and cost-effective. McKinsey agrees: We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office.\ We estimate that gen AI could offer savings opportunities of $1.4 trillion to $2.6 trillion across operations functions, including customer service, R&D, manufacturing, supply chain, and procurement, alongside its impact on the back office .\ savings opportunities of $1.4 trillion to $2.6 trillion across operations impact on the back office Plenty of research has been done on what sectors have the potential to benefit, as shown in the PWC report. However, the use cases aren’t always obvious from reading reports such as these. If you are a developer or a founder, you would want to know more about the value proposition that you can build, which you will not always find in reports like these. So, let’s talk about the use cases briefly…. Any business has these needs where GenAI will be a great fit: Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads) Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management Assisting employees in their training, learning, and development programs Synthesizing customer feedback efficiently Assisting in the creation of impactful marketing assets easily Saving time drafting, summarizing, and following up on internal communications (meetings, emails, posts, and chat threads) summarizing, Saving time creating documentation, Customer troubleshooting guides, etc., for knowledge management creating Assisting employees in their training, learning, and development programs Assisting Synthesizing customer feedback efficiently Synthesizing Assisting in the creation of impactful marketing assets easily creation If you focus on these needs and the GenAI capabilities, you should be on the right path to delivering a compelling value-prop to your potential customers in the sectors mentioned above, and possibly even more. GenAI Feature - Business growth fitness summary I’m not going too in-depth with how I rate the problems and how capable GenAI is at solving them, but I think this table is useful for thinking about what problems you’d want to solve for businesses. This will help your team craft a value-proposition that businesses find immediately valuable: GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples



Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management



Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication



Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing



Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation



Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response GenAI features

How big of a problem is it today?

How well does GenAI natively do it?

Top-line examples

Bottom-line examples Automated summarization

Low

Very well

Publishers, Content platforms

Business reporting, Meetings, and internal communication, Knowledge Management Creation

High

Good enough

Marketing businesses, content creation platforms

Documentation, internal communication Synthesis of information

High

Very well

Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps

Learning and Development, Business Analytics, Research, Feedback processing Search in natural language

Medium

Very well

Broad applicability

Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Automated Actions

High

Additional skills and fine-tuning to be added for each new use-case

Productivity use-cases, payments, home automation, community moderation

Customer support, Meetings, Internal communication, Incident response GenAI features How big of a problem is it today? How well does GenAI natively do it? Top-line examples Bottom-line examples GenAI features GenAI features GenAI features How big of a problem is it today? How big of a problem is it today? How big of a problem is it today? How well does GenAI natively do it? How well does GenAI natively do it? How well does GenAI natively do it? Top-line examples Top-line examples Top-line examples Bottom-line examples Bottom-line examples Bottom-line examples Automated summarization Low Very well Publishers, Content platforms Business reporting, Meetings, and internal communication, Knowledge Management Automated summarization Automated summarization Automated summarization Low Low Very well Very well Publishers, Content platforms Publishers, Content platforms Business reporting, Meetings, and internal communication, Knowledge Management Business reporting, Meetings, and internal communication, Knowledge Management Creation High Good enough Marketing businesses, content creation platforms Documentation, internal communication Creation Creation Creation High High Good enough Good enough Marketing businesses, content creation platforms Marketing businesses, content creation platforms Documentation, internal communication Documentation, internal communication Synthesis of information High Very well Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning and Development, Business Analytics, Research, Feedback processing Synthesis of information Synthesis of information Synthesis of information High High Very well Very well Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning, wellness&healthcare, habit trackers, personal finance apps, productivity apps Learning and Development, Business Analytics, Research, Feedback processing Learning and Development, Business Analytics, Research, Feedback processing Search in natural language Medium Very well Broad applicability Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Search in natural language Search in natural language Search in natural language Medium Medium Very well Very well Broad applicability Broad applicability Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Learning and Development, Knowledge Management, Feedback processing, internal communication, Documentation Automated Actions High Additional skills and fine-tuning to be added for each new use-case Productivity use-cases, payments, home automation, community moderation Customer support, Meetings, Internal communication, Incident response Automated Actions Automated Actions Automated Actions High High Additional skills and fine-tuning to be added for each new use-case Additional skills and fine-tuning to be added for each new use-case Productivity use-cases, payments, home automation, community moderation Productivity use-cases, payments, home automation, community moderation Customer support, Meetings, Internal communication, Incident response Customer support, Meetings, Internal communication, Incident response Takeaway So, if you are thinking about a B2B GenAI idea or are someone looking to adopt GenAI for your own business, I hope this is useful to build a mental model of where GenAI can be leveraged in your case and which specific capabilities deliver the highest ROI for you and your customers.

Make Your GenAI Idea Obvious to Businesses

আপনার GenAI আইডিয়াকে ব্যবসার কাছে সুস্পষ্ট করুন

Machen Sie Ihre GenAI-Idee für Unternehmen offensichtlich

Haga que su idea GenAI sea obvia para las empresas

Rendez votre idée GenAI évidente pour les entreprises

अपने GenAI आइडिया को व्यवसायों के लिए स्पष्ट बनाएं

GenAIのアイデアを企業にわかりやすく伝える

귀하의 GenAI 아이디어를 기업에 분명하게 알리십시오

Torne sua ideia GenAI óbvia para as empresas

Сделайте свою идею GenAI очевидной для бизнеса

GenAI Fikrinizi İşletmelere Açık Hale Getirin

Làm cho ý tưởng GenAI của bạn trở nên rõ ràng với doanh nghiệp

让你的 GenAI 理念对企业显而易见

They’re often (mis)used by malicious hackers and other cyber-criminals, so the question about their legality could come to mind easily. Privacy coins are cryptocurrencies designed to enhance user anonymity by hiding transaction details, such as the sender, receiver, and amount. Popular examples include Monero and Zcash, but there are more of them. The concerns about them are obvious: potential use in illegal activities, such as money laundering or tax evasion. To be fair, that could also be perfectly done (and it is done) with traditional cash. Even with electronic fiat money, and with cryptocurrencies that aren’t privacy-focused. If criminals have a can-do attitude, they surely can accomplish their dark goals, no matter the tool involved. Privacy coins have legitimate use cases, of course, including protecting personal financial information, safeguarding against identity theft, enabling secure donations to sensitive causes, and facilitating private transactions in oppressive regimes. That’s maybe why privacy coins aren’t illegal in most countries, per se. However, there are some exceptions and limitations. Regulations on Privacy Coins Privacy coins are subject to varying regulations globally, reflecting the balance between financial privacy and the need for security against illicit activities. While these cryptocurrencies remain legal in most of the world, countries like Japan, South Korea, and Australia have implemented restrictions, leading to the delisting of prominent privacy coins such as Monero and Zcash from some exchanges. The European Union has also considered a comprehensive ban, highlighted by a draft anti-money laundering bill proposing that financial institutions and crypto service providers be prohibited from dealing with anonymity-enhancing coins. Mainstream exchanges like Kraken, Huobi and BitBay have acted on these regulatory pressures by ceasing support for privacy coins. In the U.S., the government has taken a hard stance against privacy services, exemplified by sanctions imposed on Tornado Cash, a mixing service associated with money laundering activities. The Internal Revenue Service (IRS) has even invested in potential technology to de-anonymize privacy coins, reflecting a broader governmental commitment to transparency in cryptocurrency transactions. Now, we have to say this: as long as transactions are made directly peer-to-peer (P2P), users are completely safe using these coins. Current bans apply to businesses, and not to individual users. Things get complicated when they touch centralized crypto exchanges, subjected to strict regulations worldwide. Especially about identifying their customers (KYC) —which, by the way, kills all the purpose of using privacy coins in the first place. Blackbytes in Obyte Blackbytes (GBB) is a privacy-focused currency on Obyte, designed to enhance transaction confidentiality. This token conceals recipient information and payment amounts on the public ledger and only transaction hashes are published, while sensitive data is shared via private messages. This approach allows users to verify the legitimacy of transactions without exposing the full history, thereby providing a higher level of privacy compared to public assets like GBYTE and other cryptocurrencies. Blackbytes are legal and safe to use by individual users, since they were designed to be exchanged solely through peer-to-peer (P2P) transactions. This means that these privacy tokens don’t engage with centralized exchanges, which have faced increasing scrutiny and restrictions regarding privacy coins worldwide. By focusing on a P2P trading directly from the Obyte wallet, Blackbytes can avoid the complicated landscape faced by other privacy coins worldwide. In many jurisdictions, while these tokens may face significant regulatory challenges, the operation of tokens like Blackbytes can continue without the same level of scrutiny, as they don’t require engagement with regulated financial institutions. This positioning may provide a path for privacy-focused cryptocurrencies to navigate the current legal landscape while maintaining user anonymity. Featured Vector Image by Freepik They’re often (mis)used by malicious hackers and other cyber-criminals, so the question about their legality could come to mind easily. Privacy coins are cryptocurrencies designed to enhance user anonymity by hiding transaction details, such as the sender, receiver, and amount. Popular examples include Monero and Zcash, but there are more of them. The concerns about them are obvious: potential use in illegal activities, such as money laundering or tax evasion. To be fair, that could also be perfectly done ( and it is done ) with traditional cash. Even with electronic fiat money , and with cryptocurrencies that aren’t privacy-focused. If criminals have a can-do attitude, they surely can accomplish their dark goals, no matter the tool involved. Privacy coins have legitimate use cases, of course, including protecting personal financial information, safeguarding against identity theft, enabling secure donations to sensitive causes, and facilitating private transactions in oppressive regimes. and it is done and it is done electronic fiat money electronic fiat money Privacy coins have legitimate use cases, of course, including protecting personal financial information, safeguarding against identity theft, enabling secure donations to sensitive causes, and facilitating private transactions in oppressive regimes. That’s maybe why privacy coins aren’t illegal in most countries, per se. However, there are some exceptions and limitations. Regulations on Privacy Coins Privacy coins are subject to varying regulations globally, reflecting the balance between financial privacy and the need for security against illicit activities. While these cryptocurrencies remain legal in most of the world, countries like Japan, South Korea, and Australia have implemented restrictions , leading to the delisting of prominent privacy coins such as Monero and Zcash from some exchanges. varying regulations varying regulations While these cryptocurrencies remain legal in most of the world, countries like Japan, South Korea, and Australia have implemented restrictions The European Union has also considered a comprehensive ban, highlighted by a draft anti-money laundering bill proposing that financial institutions and crypto service providers be prohibited from dealing with anonymity-enhancing coins. Mainstream exchanges like Kraken, Huobi and BitBay have acted on these regulatory pressures by ceasing support for privacy coins. consider ed consider In the U.S., the government has taken a hard stance against privacy services, exemplified by sanctions imposed on Tornado Cash , a mixing service associated with money laundering activities. The Internal Revenue Service (IRS) has even invested in potential technology to de-anonymize privacy coins, reflecting a broader governmental commitment to transparency in cryptocurrency transactions. Tornado Cash Tornado Cash invested invested Now, we have to say this: as long as transactions are made directly peer-to-peer (P2P), users are completely safe using these coins. Current bans apply to businesses, and not to individual users. Things get complicated when they touch centralized crypto exchanges, subjected to strict regulations worldwide. Especially about identifying their customers (KYC) —which, by the way, kills all the purpose of using privacy coins in the first place. Now, we have to say this: as long as transactions are made directly peer-to-peer (P2P), users are completely safe using these coins. Blackbytes in Obyte Blackbytes (GBB) is a privacy-focused currency on Obyte, designed to enhance transaction confidentiality. This token conceals recipient information and payment amounts on the public ledger and only transaction hashes are published, while sensitive data is shared via private messages. This approach allows users to verify the legitimacy of transactions without exposing the full history, thereby providing a higher level of privacy compared to public assets like GBYTE and other cryptocurrencies. Blackbytes (GBB) Blackbytes (GBB) Blackbytes are legal and safe to use by individual users, since they were designed to be exchanged solely through peer-to-peer (P2P) transactions. This means that these privacy tokens don’t engage with centralized exchanges, which have faced increasing scrutiny and restrictions regarding privacy coins worldwide. Blackbytes are legal and safe to use by individual users, since they were designed to be exchanged solely through peer-to-peer (P2P) transactions. By focusing on a P2P trading directly from the Obyte wallet , Blackbytes can avoid the complicated landscape faced by other privacy coins worldwide. In many jurisdictions, while these tokens may face significant regulatory challenges, the operation of tokens like Blackbytes can continue without the same level of scrutiny, as they don’t require engagement with regulated financial institutions. This positioning may provide a path for privacy-focused cryptocurrencies to navigate the current legal landscape while maintaining user anonymity. Obyte wallet Obyte wallet Featured Vector Image by Freepik Featured Vector Image by Freepik Freepik Freepik

Educational Byte: Are Privacy Coins Like Monero and Zcash Legal?

Byte Educativo: ¿Monero ukat Zcash ukham Privacidad Monedas ukanakax Legal ukhamäpachati?

Vzdělávací Byte: Jsou soukromé mince jako Monero a Zcash legální?

Byte educativo: ¿Son legales las monedas de privacidad como Monero y Zcash?

Koulutustavu: Ovatko Monero- ja Zcashin kaltaiset tietosuojakolikot laillisia?

Byte éducatif : les pièces de confidentialité comme Monero et Zcash sont-elles légales ?

शैक्षिक बाइट: क्या मोनरो और ज़ेकैश जैसे गोपनीयता सिक्के कानूनी हैं?

Educational Byte: Apakah Koin Privasi Seperti Monero dan Zcash Legal?

教育バイト: Monero や Zcash のようなプライバシー コインは合法ですか?

Byte Éducatif: Est-ce que ba mbongo ya vie privée lokola Monero na Zcash ezali légal?

Educational Byte: Ara-dalàna ve ny vola madinika momba ny fiainana manokana toa an'i Monero sy Zcash?

Educational Byte: Czy prywatne monety, takie jak Monero i Zcash, są legalne?

Байти таълимӣ: Оё тангаҳои махфият ба монанди Monero ва Zcash қонунӣ ҳастанд?

The Cyclicality of the Crypto Markets Shows Its Hand Once Again

Building Scalable E-commerce Infrastructure on Magento

Is Bias in AI Quantifiable?

Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes. Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes.

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