[{"content":"","date":"10 April 2026","externalUrl":null,"permalink":"/posts/","section":"","summary":"","title":"","type":"posts"},{"content":"","date":"10 April 2026","externalUrl":null,"permalink":"/tags/ai/","section":"Tags","summary":"","title":"Ai","type":"tags"},{"content":"","date":"10 April 2026","externalUrl":null,"permalink":"/tags/innovation/","section":"Tags","summary":"","title":"Innovation","type":"tags"},{"content":"","date":"10 April 2026","externalUrl":null,"permalink":"/tags/management/","section":"Tags","summary":"","title":"Management","type":"tags"},{"content":"","date":"10 April 2026","externalUrl":null,"permalink":"/","section":"MKCG.PL","summary":"","title":"MKCG.PL","type":"page"},{"content":"The software industry is entering a phase where the boundary between a tool and a worker is dissolving. For two decades, the dominant economic model for digital innovation was Software-as-a-Service (SaaS). We built platforms that acted as force multipliers for human employees. You bought a license for Salesforce or Figma so that your sales team or designers could be more efficient. The work itself, however, remained a human-led endeavor. The software was the infrastructure; the human was the labor.\nThis is changing. We are moving toward a model where the software is the labor. This paradigm, often referred to as Service-as-Software, represents a fundamental shift in how organizations procure value. Instead of buying access to a tool to do the work, companies are beginning to \u0026ldquo;hire\u0026rdquo; software to deliver the outcome directly. This transition is not merely a technological iteration but a restructuring of the global services economy.\nThe predecessors: SaaS and Tech-Enabled Services # Understanding the distinction between Service-as-Software and its predecessors requires examining the models it seeks to replace. Traditional SaaS was revolutionary because it decoupled software from physical infrastructure. It offered high margins and low distribution costs, but its scalability was still tied to the customer\u0026rsquo;s ability to provide human labor to operate the tools. A company with more customers needed more licenses, and consequently, more employees.\nThen there are tech-enabled services. This model, often seen in business process outsourcing (BPO) or consulting, uses proprietary software to make human employees more efficient at delivering a service. While this provides a better experience for the customer, who is buying a finished product rather than a tool, it is economically constrained. The gross margins are lower than SaaS, and the operation is still limited by the complexities of managing a human workforce. Scaling requires hiring, which introduces overhead and linear costs.\nRobotic Process Automation (RPA) attempted to bridge this gap by automating repetitive tasks. It offered a glimpse into a world where software replaces human labor, but it was fragile. RPA requires rigid rules and structured environments. It functions as a sequence of hard-coded scripts. If an invoice format changes or an email arrives with ambiguous phrasing, the RPA bot fails, requiring a human to intervene. RPA could not handle the \u0026ldquo;messy\u0026rdquo; reality of unstructured business data.\nThe Service-as-Software model # Service-as-Software, enabled by AI agents, circumvents these limitations by introducing reasoning into the workflow. Unlike RPA, which executes instructions, AI agents operate by pursuing goals. They can process unstructured text, images, and non-deterministic requests. They adapt to changes in their environment without manual reprogramming.\nIn this model, you do not buy an AI tool for your legal team; you hire an AI paralegal. You do not buy an IDE extension for your developers; you hire an AI software engineer. The shift is from selling a productivity multiplier to selling the work product itself 1. This allows vendors to target payroll and outsourced services budgets—which are orders of magnitude larger than IT budgets. It is a migration from the IT budget to the services budget.\nThe economic implications are significant. Because the marginal cost of compute (inference) is collapsing, the cost of \u0026ldquo;digital labor\u0026rdquo; is falling much faster than human labor ever could. While traditional SaaS has near-zero marginal costs, Service-as-Software does have higher COGS due to GPU requirements. However, unlike human services, these costs do not scale linearly with headcount. Once an agentic workflow is established, it can handle a near-infinite volume of tasks with software-like scalability 2.\nIdentifying the jobs to be done # For leadership, the challenge is identifying where this model provides the most value. We can look at this through the lens of How AI changes our jobs to be done. Much of the corporate structure is composed of intermediate steps—reports, documentation, coordination—that exist only because we lacked the technology to achieve the outcome directly.\nService-as-Software works best in areas where intellectual work is the majority of the task, but the \u0026ldquo;judgement\u0026rdquo; requirements are manageable. Software engineering, legal review, and financial reporting are prime candidates because they are predominantly about intellectual synthesis and rules-based logic. As I noted in Is AI more tech? Or less?, we are developing the ability to mix algorithmic-rigid systems with human-flexible decision points. Service-as-Software allows us to automate the \u0026ldquo;intelligence\u0026rdquo; while reserving human judgment for the most ambiguous or high-stakes scenarios 3.\nFrom operator to orchestrator # This transition changes the role of the senior manager and the CTO. In the SaaS era, the goal was to select the right tools and ensure the staff was trained to use them. In the Service-as-Software era, the goal is to orchestrate a fleet of autonomous services. The human role shifts from being an operator of software to being an architect of reliable outcomes.\nOrganizations that cling to the \u0026ldquo;tool-based\u0026rdquo; mindset will find themselves burdened with high labor costs and slow execution. The competitive advantage will shift to those who can effectively integrate AI agents into their core business processes, treating them not as additions to the tech stack, but as a scalable, digital workforce. This is the great budget migration: moving from paying for seat licenses and human hours to paying for delivered outcomes 4.\nThe software is no longer just helping us do the work. The software is the work.\nThoughtworks: Service-as-software: A new economic model for the age of AI agents\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nMichael Burnett: From SaaS to \u0026lsquo;Service-as-Software\u0026rsquo;: How AI Is Repricing the Global Services Economy\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nSequoia Capital: Services: The New Software\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nHFS Research: Ditch same-old SaaS and differentiate with Services-as-Software\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","date":"10 April 2026","externalUrl":null,"permalink":"/posts/what-is-service-as-software/","section":"","summary":"","title":"Service as software: The software is the work","type":"posts"},{"content":"","date":"10 April 2026","externalUrl":null,"permalink":"/tags/software/","section":"Tags","summary":"","title":"Software","type":"tags"},{"content":"","date":"10 April 2026","externalUrl":null,"permalink":"/tags/strategy/","section":"Tags","summary":"","title":"Strategy","type":"tags"},{"content":"","date":"10 April 2026","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"The integration of generative AI into the corporate environment is frequently framed through the lens of efficiency. Organizations look at their existing workflows and ask how large language models might accelerate them. This perspective, while pragmatic, risks missing the transformative potential of the technology. It treats AI as a faster horse rather than a combustion engine. When we view AI solely as an automation tool for existing tasks, we fail to recognize that it fundamentally alters the nature of the \u0026ldquo;jobs to be done.\u0026rdquo;\nClayton Christensen’s theory of Jobs to be Done1 argues that customers do not buy products or services; they hire them to make progress in their lives. In a corporate context, we hire processes, software, and employees to achieve specific strategic outcomes. The danger lies in confusing the means with the end. Much of our organizational machinery—the reports, the meetings, the documentation—are intermediate steps we have codified over decades to ensure quality or alignment. They are not the job itself.\nGenerative AI forces a re-evaluation of these intermediate steps. Consider the maintenance of a corporate knowledge base. For years, companies have invested heavily in wikis and repositories, hiring technical writers and enforcing strict update cycles. The job to be done was never \u0026ldquo;maintain a wiki\u0026rdquo;; it was \u0026ldquo;ensure employees have access to accurate information.\u0026rdquo; If an AI can synthesize answers from a raw document repository on demand, the intermediate step of curating a static knowledge base becomes obsolete. Automating the curation process would be a mistake; the goal is to eliminate the need for curation entirely.\nWe see a similar pattern in software engineering. We have long emphasized comprehensive code documentation. The job to be done is enabling a developer to understand and maintain a system. If an AI agent can explain a complex function or generate a readme on the fly, the necessity of writing and maintaining static comments diminishes. The value shifts from the artifact (the documentation) to the outcome (understanding).\nThis shift extends to human capital development. Corporate training often focuses on skill acquisition for tasks that may no longer require human intervention. If the job to be done is \u0026ldquo;analyze quarterly sales data,\u0026rdquo; and an AI can perform this analysis autonomously, training a junior analyst to use spreadsheet macros is an inefficient allocation of resources. The training should instead focus on interpreting the AI\u0026rsquo;s output and making strategic decisions based on it.\nFurthermore, AI democratizes technical capability, allowing non-technical staff to perform jobs that previously required specialized skills. A marketing manager can now prototype an application or generate SQL queries without waiting for engineering resources. This flattens the execution curve and reduces the friction between having an idea and realizing it. It changes the job of the technical team from gatekeepers of execution to architects of reliability and scale.\nFor leaders responsible for strategy, the challenge is to look beyond the immediate gains of faster processing. The strategic imperative becomes identifying which corporate workflows are merely legacy scaffolding—structures built to support limitations that no longer exist. If a goal can be achieved directly through AI, automating the intermediate steps is a waste of effort.\nThe arrival of this technology requires a deliberate rethinking of our organizational habits. The task is to return to first principles and ask what progress we are trying to make. The answers will likely reveal that many of the jobs we have been so busy optimizing are no longer jobs that need to be done at all. The organizations that thrive will be those that use AI not just to do things better, but to do better things.\nCompeting Against Luck, Clayton M. Christensen\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","date":"22 February 2026","externalUrl":null,"permalink":"/posts/ai-changes-jtbd/","section":"","summary":"","title":"How AI changes our \"jobs to be done\"","type":"posts"},{"content":"","date":"22 February 2026","externalUrl":null,"permalink":"/tags/jobs-to-be-done/","section":"Tags","summary":"","title":"Jobs to Be Done","type":"tags"},{"content":"","date":"17 January 2026","externalUrl":null,"permalink":"/pages/","section":"","summary":"","title":"","type":"pages"},{"content":"","date":"17 January 2026","externalUrl":null,"permalink":"/tags/projects/","section":"Tags","summary":"","title":"Projects","type":"tags"},{"content":" Overview # VocalFibre is a customer research and interview platform.\nIts outstanding feature is ability to collect customer CRM data using open-ended questions. Dialogues are designed in a way that enable attribute extraction without explicitly asking for it. Users do not need to know all answers and do not need to know what they precisely need. Dialogue engine extracts information in real time and shifts conversation accordingly as it goes.\nKey features # Structured, ready-to-use conversation templates for industry verticals. Can be deployed in days. Designed to identify customer needs and preferences with natural, open conversation. Extracts meaningful data from vague and chaotic responses. Voice and text channels, including telephony support. Polish language support with correct inflection and cultural context EU data residency. Independent platform with full DevOps support. Key technologies # VocaFibre is built on industry standards:\nLiveKit for low latency voice communication, WebRTC and telephony integration. LLamaIndex for complex document processing and agentic workflows. ","date":"17 January 2026","externalUrl":null,"permalink":"/pages/vocal-fibre/","section":"","summary":"","title":"VocalFibre","type":"pages"},{"content":"","date":"17 January 2026","externalUrl":null,"permalink":"/tags/voicebots/","section":"Tags","summary":"","title":"Voicebots","type":"tags"},{"content":"Knowledge is becoming commodity. Most of it is just a prompt away. So, when hiring or training employees, which skills are defensible and stand the test of AI?\nThe three key differentiating factors of your employees vs AI are:\nInsider info. Soft skills. Mental models they know and apply. Let\u0026rsquo;s break them down.\nInsider info # This is all your people know about the business, clients and processes. Many of these things are likely not documented anywhere and LLMs have no way to know. Having said that, some industry insights are (increasingly) accessible. Books, websites, blog posts and social networks, like Reddit contain valuable information. Not everything your employees know, but still. This may also be your employees discuss proprietary topics with AI chats at home and LLMs learn.\nSoft skills # It is ability to understand others, make sense of their feeligs, their body languages, moods, preferences and best mates. Some people are great at navigating this space and have ability to deliberately and positively influence own and others\u0026rsquo; performance via relationship building. This is not accessible to LLMs at all, because they are not present in the environment to be able to see. One may argue that emails and chats give access, but it is out of context, stripped of non-verbal communication, moods, health and surroundings.\nMental models # \u0026ldquo;An internal representation of external reality\u0026rdquo;.1 This is what people use to guide their cause-and-effect understanding and decision making. To name few:\nBeing aware of cognitive biases and their mechanics. Example: anchoring. Normal distribution and its applications. Ability to understand human behavior and motivations. Game theory This is the toughest one to crack for AI, because AI has no means to apply it. The required features are just not there; AI can\u0026rsquo;t distinguish imagination from reality and has very rudimentary ability to reason. Regardless of what marketing teams of model vendors tell you, AI reasoning is currently algorithmic (\u0026ldquo;hardcoded\u0026rdquo;), lacking human flexibility and independence. Existing methods are brittle and time consuming (i.e. \u0026ldquo;chain of thought\u0026rdquo;). We are able to simulate executive functions with reinforcement learning but we fail to make AI reason autonomously. And on top of these unreliable methods, AI has now way to experience reality; it mostly only read about it.\nSo what? # Lack of autonomy mixed with unpredictability makes your AI reckless or idle. It does not know when to reason and why. Without this skill, mental models are indistinguishable and vague. Why would AI use them? How would it choose between them?\nFor us, mental models are transferable knowledge that enables us to reason and organize knowledge. They allow us to make sound decisions based on insider info, relationships, experience and common knowledge. Interestingly, mental models do not make much sense as a theory. It takes time and practice to learn to apply them.\nBecause of all the above, if there is one skill to bet on in the age of AI, I bet on mental models. Hiring them and teaching them is my secret team productivity booster.\nBreakdown for reference # Skill Transferable? Accessible to AI? Is AI able to learn it? Insider info No Partly Yes Soft skills Yes No Yes Mental models Yes Yes No Mental Model, Wikipedia\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","date":"17 January 2026","externalUrl":null,"permalink":"/posts/in-the-post-ai-world-look-for-mental-models/","section":"","summary":"","title":"In the post AI-world, your employees need mental models","type":"posts"},{"content":"","date":"17 January 2026","externalUrl":null,"permalink":"/tags/recruitment/","section":"Tags","summary":"","title":"Recruitment","type":"tags"},{"content":" Active projects # VocalFibre # Open questions customer interview and research with voice and text. Read more.\nMówisz / Mieszkasz # Rich-data leads acquisition platform via customer interviews for the real estate sector.\n","date":"17 January 2026","externalUrl":null,"permalink":"/pages/projects/","section":"","summary":"","title":"Projects","type":"pages"},{"content":" The structural advantage # AI has prompted widespread consideration of new capabilities across all sectors. The prevailing narrative often highlights startups as the primary engines of change, framing corporate environments as overly cautious or resistant to rapid shifts. However, large organizations are structurally well-prepared to innovate.\nStartups operate on high-risk capital models with unpredictable outcomes. Their agility comes at the cost of high failure rates and reliance on continuous external funding. Small businesses, conversely, typically lack ambitious growth targets. They function with stable client volumes and operate without the mandate to disrupt their own operations or the broader market.\nLarge companies have explicit growth targets, substantial capital, and access to sophisticated tools. The management layer in corporate environments usually consists of capable professionals trained to navigate complex growth strategies, allocate significant budgets, and scale operations globally.\nThe reputation of large enterprises having limited capacity for change applies mostly to their core, legacy businesses. These core processes are optimized for predictability and risk mitigation. Yet, with significant resources, a corporation can structure innovative activities separately. By creating distinct entities, an organization sheds the red tape that typically governs its primary operations, allowing new ideas to develop without immediate friction.\nManaging hurdles # Executing this separation requires deliberate strategy. When an enterprise attempts to innovate within its standard operational framework, it encounters predictable friction points.\nCorporate cultures often penalize mistakes because stability is the default objective. Innovation requires experimentation and a tolerance for early failures. When organizations apply traditional performance evaluations to experimental projects, they inadvertently discourage the risk-taking necessary for breakthroughs. Employees will naturally optimize for the metrics that define their compensation and career progression.\nFinancial expectations create a similar barrier. The pressure to meet quarterly earnings often overshadows the patience required for long-term investments. Traditional budgeting cycles, which allocate funds annually based on predictable returns, strangle agile iterations. Furthermore, evaluating early-stage innovations using mature-market metrics, such as strict return on investment or market share, demotes promising ideas before they find their footing1.\nInternal alignment is another critical factor. Innovation requires careful positioning and executive management support. Without this protection, promising initiatives fall victim to office politics. Stakeholders invested in the status quo may resist changes that threaten established workflows or departmental influence. Protecting these initiatives is a core responsibility of executive leadership, ensuring that new ventures have the runway to prove their value.\nThe true nature of customer progress # To direct corporate resources effectively, many organizations adopt the Jobs-to-be-Done framework. This approach shifts the focus from product features to the underlying progress a customer is trying to achieve in a specific circumstance.\nWhen we buy a product, we essentially \u0026ldquo;hire\u0026rdquo; it to help us do a job. If it does the job well, the next time we\u0026rsquo;re confronted with the same job, we tend to hire that product again.2\nDespite its adoption, teams frequently misinterpret the concept. A common error is confusing a \u0026ldquo;job\u0026rdquo; with a product attribute, a routine activity, or a customer demographic34. For instance, a demographic focus might define the target as a specific age group, which offers no actionable insight into why they make a purchase. This misunderstanding leads to building solutions that do not address the actual reason a customer hires a product, resulting in wasted development cycles.\nSiloed organizational structures exacerbate this misinterpretation. Executing a strategy based on customer progress requires cross-functional collaboration. Corporate silos often prevent departments from sharing vital customer insights. Marketing, sales, and product teams may each hold a piece of the puzzle, but rigid boundaries keep them from assembling a coherent understanding of the customer\u0026rsquo;s goal. Breaking down these information silos is a structural prerequisite for any customer-centric innovation.\nArchitecture and management # From a technology leadership perspective, driving change requires architectural decoupling. A chief technology officer can enable innovation teams to build on top of enterprise data without being constrained by legacy release cycles. The integration of new technologies into an enterprise requires both the agility to experiment and the infrastructure to scale.\nProgress through enterpreneurship # Coprorate leaders can unlock innovation potential by isolating teams from legacy metrics. Providing teams with autonomy to fail early and learn rapidly, aligns operations with realities of discovery. It requires patience and a willingness to evaluate progress based on learning milestones, prototype validations, and customer feedback loops, rather than immediate financial returns.\nWhen an organization successfully combines its inherent resources with a protected, well-positioned innovation strategy, it creates an environment highly conducive to sustainable growth. Corporate ecosystem possesses all necessary components to lead technological and market advancements when managed with clarity and intent.\nInnovation Killers: How Financial Tools Destroy Your Capacity to Do New Things\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nKnow Your Customers’ “Jobs to Be Done”\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nAvoid These Common Mistakes When Getting Started With Jobs-To-Be-Done\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nWhy most people get Jobs to be Done wrong\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","date":"14 January 2026","externalUrl":null,"permalink":"/posts/enterprise-innovation-ecosystem/","section":"","summary":"","title":"Why enterprise is a great innovation ecosystem","type":"posts"},{"content":"With over 20 years in the software industry — including nearly five years as a Chief Technology Officer - I have built systems that drive growth, and led engineering teams laser-focused on business value. I have learned firsthand that successful innovation is engineered, not accidental.\nToday, I apply this experience to help clients capture the full power of Agentic and Voice AI. I bridge the gap between technical possibilities and business strategy, ensuring that innovation is balanced with compliance (EU AI Act, GDPR).\nI am based in Poland, EU.\nMentoring # I am an experienced mentor, with strong emphasis on strategy, accountability and measurable outcomes.\nMy mentoring profiles for organizations I work with:\nTechnical University of Łódź Think! Foundation Contact # Reach out to me at: \u0026#32;\u0026#32; ","date":"14 January 2026","externalUrl":null,"permalink":"/pages/about/","section":"","summary":"","title":"About","type":"pages"},{"content":" We were all DevOps, once # When I started my journey through the IT industry, which was in middle 2000\u0026rsquo;s, average software engineer was expected to be a jack-of-all-trades type of person. I worked in teams in which software engineers were supposed to do all of backend, frontend, db reporting, testing, devops and documentation. I did not consider it being a stretch. The amount of knowledge was manageable and benefits were obvious: relatively cheap, fast, small teams. None of these tasks were of lesser importance and all team members knew it.\nWhen I moved to software services few years later, the situation started to change. We were building teams that consisted of highly specialized yet less demanding, junior roles. For instance:\nSQL report writer / data analyst PSD-to-HTML / junior frontend Unit test writer (\u0026ldquo;90% of code must be tested\u0026rdquo;) Technical writer (API documentations) Teams grew, and senior software engineers became more specialized with many of them becoming software architects. When cloud turned infrastructure into software, software architects turned into cloud architects. There was no shortage of candidates for junior roles and both candidates and budget owners were happy with compensation levels. Values of software projects covered all costs.\nSo it all made sense. Especially from the service delivery standpoint; bigger headcount meant more money and it is generally easier to hire specialized, junior roles.\nUntil it didn\u0026rsquo;t.\nIt was never the right setup # I never believed in this. IT is high tech. It is demanding, complex and rapidly changing. Few people are genuinely capable to perform in it and even fewer enjoy the idea of spending most of their waking weeks in front of an IDE. During my CTO-ship, I promoted the T-shaped-skills-profile approach, in which people specialize on the solid foundation of breadth of their industry knowledge. It allows for smaller, nimbler teams; fewer people means fewer meetings. It makes doubling up natural. Crucially, in service setting, where people switch assignments and tech stacks, it does not leave you with non-billed \u0026ldquo;SQL report writer\u0026rdquo; on your payroll.\nComing of age # AI is blamed for endangering numerous, mainly junior positions in the IT services industry. Some reckon it will make the seniority funnel dry out - seniors have to be juniors first, which is not possible, when companies abstain from hiring them.\nI do not think it is a valid concern. These juniors in question were never poised to grow. They made it to the industry because it was easy money, as opposed to having passion and talent. They develop their skills slower and often are forever stuck in middle-seniority positions. They do not become your client-facing stars nor your fix-it men.\nAI is the culprit. It writes SQL queries, unit tests and many other types of content with at-least-human quality. And it is great! It is an opportunity to go back to nimble, smart teams of experts that may have no time to deal with all the IT red tape themselves, but know what to ask for and are able to judge whatever AI throws at them. With way fewer meetings.\nThank you, AI.\n","date":"7 January 2026","externalUrl":null,"permalink":"/posts/how-ai-helps-to-balance-the-it-industry/","section":"","summary":"","title":"How AI helps to (finally) balance the IT industry","type":"posts"},{"content":"","date":"7 January 2026","externalUrl":null,"permalink":"/tags/outsourcing/","section":"Tags","summary":"","title":"Outsourcing","type":"tags"},{"content":"","date":"7 January 2026","externalUrl":null,"permalink":"/tags/services/","section":"Tags","summary":"","title":"Services","type":"tags"},{"content":"","date":"1 January 2026","externalUrl":null,"permalink":"/tags/agents/","section":"Tags","summary":"","title":"Agents","type":"tags"},{"content":" AI does not equal \u0026ldquo;more digital\u0026rdquo; # Customer interaction has never been more digital. AI hype of recent years predominantly frames it as a revolution in technology and the connection between customer interaction and AI is very obvious. But by looking at vendors\u0026rsquo; marketing, what they mean by \u0026ldquo;high quality AI\u0026rdquo; is usually not technology itself (remember megapixels?), but how indistinguishable it is from us. Some examples of recent copy-writing:\nThe world\u0026rsquo;s most realistic \u0026amp; expressive voice AI\n— hume.ai\nChat freely, interrupt, and ask follow-up questions, just like you would with a friend.\n— store.google.com\nA supportive and empathetic conversational AI.\n— pi.ai\nIn the world of apps, forms and online services, it is a welcome change. AI is the most human technology of all. In certain contexts, we want it exactly this way, conversational AI being the prime example. The less visible the technology is, the better. There is no need to learn anything new to use it and we forget about it being an intermediary between our intentions and goals.\nWe know we wanted to get there since before the LLM revolution. Serendipity is a studied attribute of recommender systems1. Variable rewards are an important component of social engagement2\nHuman mistakes # But it is not in all contexts that we praise AI for being \u0026ldquo;human-like\u0026rdquo;. There is no room for spontaneity in regulatory reporting and limited tolerance for magnanimity when filing a claim. Take a look at the comparison of how different the perception can be, depending on who the actor is:\nHuman AI wisdom bias spontaneity instability magnanimity data loss thoughtfulness latency imagination hallucination seniority obsolescence Research in human-computer interaction and organizational psychology reveals different expectations towards technology and humans3:\nTechnology is expected to reduce uncertainty. We position technology as a way to eliminate variance. We want a calculator or a GPS to be predictable. When AI mimics human unpredictability, we experience \u0026ldquo;Algorithm Aversion\u0026rdquo;4; we judge machines much more harshly for a single mistake than we do a human for the same error. Humans are expected to manage ambiguity. We position humans as superior in contexts requiring discovery of meaning and handling chaos. In creative or emotional scenarios, variability in judgment is framed as a feature, not a bug, because it allows for empathy and \u0026ldquo;reading the room\u0026rdquo; - skills that require deviating from a script. So\u0026hellip; what? # Software engineering took us to a point of free choice between how human-flexible or algorithmic-rigid our systems are. Agentic frameworks enable to mix and match binary if/else, and LLM, human-like decision points, and to defer to AI \u0026ldquo;brain\u0026rdquo; the decision on which of these two to choose and when. Agentic systems are yet to mature and LLMs are not 100% human-like but the choice is there.\nAnd I think it is a very exciting one, because it asks for a new measure to apply to virtually everything software is being applied to: A \u0026ldquo;human factor\u0026rdquo;. The \u0026ldquo;uncanny valley index\u0026rdquo; and \u0026ldquo;sensibleness\u0026rdquo; are some attempts to it, but I think we are going to develop something more applicable and aligned.\nWhatever the measure is going to be, agentic AI gives all it takes to consciously design human attributes into your systems. On both client-facing and internal, even backed processes. When organizations shake off the AI-shock, the next best step will be to introduce this conscious design and measures to track it. It is going to have a tangible impact4.\nWhat Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender Systems\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nThe Hooked Model: How to Manufacture Desire in 4 Steps\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nNoise: A Flaw in Human Judgment\u0026#160;\u0026#x21a9;\u0026#xfe0e;\nPeople Erroneously Avoid Algorithms After Seeing Them Err\u0026#160;\u0026#x21a9;\u0026#xfe0e;\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","date":"1 January 2026","externalUrl":null,"permalink":"/posts/ai-removes-tech-from-our-lives/","section":"","summary":"","title":"Is AI more tech? Or less?","type":"posts"},{"content":"","date":"1 January 2026","externalUrl":null,"permalink":"/tags/ux/","section":"Tags","summary":"","title":"Ux","type":"tags"},{"content":"Last updated: 2025-09-26\n1. Who We Are # This website is operated by MK Consulting, registered in Poland, with registered office at Tylna 4c/56 90-365 Łódź. We provide B2B AI voice-automation SaaS.\nContact # Email: privacy@mkcg.pl\nPostal address: Tylna 4c/56 90-364 Łódź, Poland\n2. Scope # This Privacy Policy covers personal data processed when you browse our website, use our forms (e.g., trial registration, contact), and interact with our marketing and support channels. If you are a customer of our SaaS, our Data Processing Agreement (DPA) governs how we process end-user data on your behalf.\n3. Categories of Personal Data # Identification \u0026amp; contact data: name, business email, phone, company, role. Content you submit: messages, trial goals, notes in forms. Usage \u0026amp; technical data: IP address, device and browser info, pages viewed, timestamps, referral source (via analytics and server logs). Account/contract data (customers): billing/contact persons, subscription status, invoicing details. 4. Sources of Data # Directly from you (forms, emails, chat, calls). Automatically via cookies/analytics and server logs. From your employer/colleagues if they designate you as a contact. 5. Purposes \u0026amp; Legal Bases # Purpose Examples Legal basis (GDPR Art. 6) Provide and administer trials Create trial account, onboarding, support Contract (b) Respond to inquiries Contact form replies, demos Legitimate interests (f) to operate and grow the business; or Contract (b) when pre-contractual Improve website \u0026amp; services Audience measurement, UX diagnostics Consent (a) for analytics cookies Security \u0026amp; fraud prevention Detect abuse, ensure availability Legitimate interests (f) Customer management \u0026amp; billing Account contacts, invoicing Contract (b) and Legal obligation (c) Marketing to business contacts News, product updates (B2B) Consent (a) where required; otherwise Legitimate interests (f) with opt-out You can withdraw consent at any time (this does not affect processing prior to withdrawal).\n6. Cookies \u0026amp; Analytics # We use cookies and similar technologies. Non-essential cookies (e.g., Google Analytics) are used only with your consent via our banner. See our separate Cookie Policy for details and controls.\n7. Sharing of Personal Data # We share personal data with trusted providers only as necessary:\nAnalytics: Google Analytics (Google Ireland Ltd.) Forms/CRM/Email: e.g., Formspree Hosting/CDN/Infrastructure: e.g., Cloudflare and cloud hosting providers Professional services: legal, accounting, consulting (under confidentiality) Where required by law or to protect our rights, users, or systems We do not sell personal data.\n8. International Transfers # If data is transferred outside the EEA/UK, we use appropriate safeguards such as an adequacy decision or the European Commission’s Standard Contractual Clauses (SCCs), plus supplementary measures where appropriate.\n9. Data Retention # We keep personal data only as long as necessary for the purposes described above. Specifically:\nTrial registrations \u0026amp; contact form data: retained for the duration of the trial and up to 24 months after the last interaction, unless you request earlier deletion. Customer contract \u0026amp; billing data: retained for the contract term and thereafter for as long as required by law (typically up to 5 years under Polish accounting/tax rules). Analytics data (Google Analytics): retained according to our analytics settings (currently up to 14 months). Server \u0026amp; security logs: typically up to 180 days for security and fraud-prevention. After these periods, we delete or irreversibly anonymize the data.\n10. Your Rights (EEA/UK) # You have the right to:\nAccess your personal data and obtain a copy Rectify inaccurate or incomplete data Erase data (right to be forgotten) in applicable cases Restrict processing in applicable cases Object to processing based on legitimate interests (including direct marketing) Data portability (where technically feasible) Withdraw consent at any time (where processing is based on consent) Lodge a complaint with a supervisory authority (in Poland: Prezes UODO) To exercise your rights, contact us at privacy@mkcg.pl. We may need to verify your identity.\n11. Security # We apply technical and organizational measures appropriate to the risk, including access controls, encryption in transit, network protections, and supplier due diligence.\n12. Children # Our website and services are intended for business users and are not directed to children under 16.\n13. Automated Decision-Making # We do not make decisions producing legal or similarly significant effects solely by automated processing about website visitors or trial users.\n14. Acting as Processor for Customers # For SaaS functionality where we process end-user data on behalf of a customer, we act as a processor and the customer acts as the controller. Such processing is governed by our Data Processing Agreement (DPA).\n15. Changes to This Policy # We may update this Policy from time to time. The latest version will always be posted on this page. Material changes will be highlighted for a reasonable period.\n16. Contact # For questions or requests regarding this Policy or your personal data:\nMK Consulting\nTylna 4c/56 90-364 Łódź\nEmail: privacy@mkcg.pl\n","date":"26 September 2025","externalUrl":null,"permalink":"/pages/privacy-policy/","section":"","summary":"","title":"Privacy Policy","type":"pages"},{"content":"","externalUrl":null,"permalink":"/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":"","externalUrl":null,"permalink":"/series/","section":"Series","summary":"","title":"Series","type":"series"}]