{"id":4373,"date":"2026-10-10T12:30:00","date_gmt":"2026-10-10T10:30:00","guid":{"rendered":"https:\/\/thenech.com\/ai-in-consulting\/"},"modified":"2026-10-10T15:31:38","modified_gmt":"2026-10-10T13:31:38","slug":"ai-in-consulting","status":"publish","type":"post","link":"https:\/\/thenech.com\/en\/ai-in-consulting\/","title":{"rendered":"AI in consulting: collecting data is easier than interpreting it correctly"},"content":{"rendered":"\n<!-- the nech | EN draft v2 | slug: \/en\/ai-in-consulting\/ | focus keyword: AI in consulting -->\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@graph\": [\n    {\n      \"@type\": \"WebSite\",\n      \"@id\": \"https:\/\/thenech.com\/#website\",\n      \"url\": \"https:\/\/thenech.com\/\",\n      \"name\": \"the nech\",\n      \"publisher\": { \"@id\": \"https:\/\/thenech.com\/#organization\" },\n      \"inLanguage\": [\"pl\", \"uk\", \"en\", \"es\"]\n    },\n    {\n      \"@type\": \"Organization\",\n      \"@id\": \"https:\/\/thenech.com\/#organization\",\n      \"name\": \"the nech\",\n      \"alternateName\": [\"thenech\", \"thenech.com\", \"the nech consulting\", \"the nech Warszawa\"],\n      \"disambiguatingDescription\": \"the nech is a Warsaw-based B2B consulting firm. 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Nechyporenko, founder of the nech, in Warsaw at dusk with a view of the Palace of Culture and Science\" fetchpriority=\"high\" decoding=\"async\">\n  <div class=\"tn-aik-photo-shade\" aria-hidden=\"true\"><\/div>\n  <div class=\"tn-aik-wrap tn-aik-photo-wrap\">\n    <div class=\"tn-aik-photo-text\">\n      <p class=\"tn-aik-kicker\">AI and consulting \u00b7 Energy and engineering<\/p>\n      <h1 class=\"tn-aik-h1\">AI in consulting: collecting data is easier than <em>interpreting it correctly<\/em><\/h1>\n    <\/div>\n  <\/div>\n<\/header>\n\n<!-- INTRO -->\n<section class=\"tn-aik-full tn-aik-intro\">\n  <div class=\"tn-aik-wrap\">\n    <div class=\"tn-aik-intro-inner\">\n      <p class=\"tn-aik-lead\">More and more often, clients tell us directly or hint that a lot can now be done in-house with AI. That is true. But the question is not whether to use AI, it is how to use the tool so that you get a high-quality result, a sound structure of answers and data you can trust.<\/p>\n      <dl class=\"tn-aik-meta\">\n        <div><dt>Industries<\/dt><dd>Energy and engineering<\/dd><\/div>\n        <div><dt>Topic<\/dt><dd>Strategy, data and its interpretation<\/dd><\/div>\n        <div><dt>Updated<\/dt><dd>10 October 2026 \u00b7 7 min read<\/dd><\/div>\n      <\/dl>\n    <\/div>\n  <\/div>\n<\/section>\n\n<div class=\"tn-aik-wrap\">\n<div class=\"tn-aik-prose\">\n\n  <!-- SUMMARY -->\n  <section class=\"tn-aik-summary tn-aik-reveal\" aria-labelledby=\"tn-aik-short\">\n    <h2 id=\"tn-aik-short\">In short<\/h2>\n    <p><strong>AI in consulting<\/strong> delivers fast and useful preliminary research. For an initial understanding of a market, standard models are enough. But building a strategy only on data collected by a model is risky: AI does not know your business, does not carry your risks and often gets things wrong not so much in the facts as in how it interprets them.<\/p>\n    <p>That is why the key skills today are these: understanding what the data actually means, interpreting it correctly, checking it and validating it. And in B2B, people and the relationships between them remain decisive.<\/p>\n  <\/section>\n\n  <!-- TOC -->\n  <nav class=\"tn-aik-toc tn-aik-reveal\" aria-labelledby=\"tn-aik-toc-title\">\n    <h2 id=\"tn-aik-toc-title\">Contents<\/h2>\n    <ol>\n      <li><a href=\"#stratehiia\">Can a strategy be built solely on AI data?<\/a><\/li>\n      <li><a href=\"#shcho-zminylos\">What has AI really changed in consulting?<\/a><\/li>\n      <li><a href=\"#interpretatsiia\">Why is AI&#8217;s main problem data interpretation?<\/a><\/li>\n      <li><a href=\"#veryfikatsiia\">Why does data verification matter more than speed of analysis?<\/a><\/li>\n      <li><a href=\"#liudyna\">Why do people remain decisive in B2B?<\/a><\/li>\n      <li><a href=\"#industriia\">How is AI changing the consulting industry?<\/a><\/li>\n      <li><a href=\"#faq\">Questions and answers<\/a><\/li>\n    <\/ol>\n  <\/nav>\n\n  <!-- 01 -->\n  <section id=\"stratehiia\" class=\"tn-aik-section tn-aik-reveal\">\n    <div class=\"tn-aik-num\"><b>01<\/b><span>Strategy<\/span><\/div>\n    <h2 class=\"tn-aik-h2\">Can a company build a strategy solely on data collected with AI?<\/h2>\n    <p class=\"tn-aik-answer\">Starting with AI is possible and worthwhile. But data collected by a model, without verification and interpretation, rarely becomes a reliable foundation for a strategy.<\/p>\n    <p>Preliminary research with AI is useful: within an hour you get an overall picture of the market, a list of players and the main regulatory frameworks. For an initial understanding, this is enough even when you use standard, publicly available models.<\/p>\n    <p>Problems start when this picture is treated as the basis for decisions. A model&#8217;s answer is assembled from what has already been published and averaged out to a level where it is right for everyone and for no one in particular. The model does not know your business, does not see your economics and, most importantly, does not carry your risks. If a conclusion turns out to be wrong, it is the company that loses money, time and reputation, not the tool.<\/p>\n    <p>For AI to deliver a high-quality result, it has to be used properly: understanding the structure of the industry, framing the task in the right way, requiring a structured answer from the model and then checking that answer. Without understanding what the information is made of and how a practical result is achieved, relying only on data collected by a model is risky.<\/p>\n  <\/section>\n\n  <!-- 02 -->\n  <section id=\"shcho-zminylos\" class=\"tn-aik-section tn-aik-reveal\">\n    <div class=\"tn-aik-num\"><b>02<\/b><span>Practice<\/span><\/div>\n    <h2 class=\"tn-aik-h2\">What has AI really changed in consultants&#8217; work?<\/h2>\n    <p class=\"tn-aik-answer\">The biggest change is in working with data: it can now be structured and analyzed much faster and in greater depth.<\/p>\n    <p>Like many consultants, we have started working with more structured data. Our own data, or data a client provides for analysis, can now be processed in days rather than weeks: combining different sources, spotting patterns, testing hypotheses on a larger volume of information. This has significantly raised the level of what can be done within a single project.<\/p>\n    <p>This matches what researchers are recording. In a 2026 study by the University of M\u00fcnster, consultants describe AI primarily as a way to automate the processing and analysis of information, which frees up time for client work and complex tasks. Most of them do not see AI as a threat to their core value, which rests on human interaction.<\/p>\n  <\/section>\n\n  <!-- 03 -->\n  <section id=\"interpretatsiia\" class=\"tn-aik-section tn-aik-reveal\">\n    <div class=\"tn-aik-num\"><b>03<\/b><span>Interpretation<\/span><\/div>\n    <h2 class=\"tn-aik-h2\">Why is AI&#8217;s main problem not the data, but its interpretation?<\/h2>\n    <p class=\"tn-aik-answer\">Because a model sees a number but does not see how it came about. Without that, the same number can mean opposite things.<\/p>\n    <p>Energy and engineering are the sectors we work with most often, and this is where the gap between data and understanding it is particularly visible. Working with data in these industries requires two things:<\/p>\n    <dl class=\"tn-aik-def\">\n      <dt>First: understanding what the data is<\/dt>\n      <dd>Who counts it, what it includes, by what rules it is produced and at which stage of the chain it arises: with the developer, the EPC contractor, the grid operator or the regulator.<\/dd>\n      <dt>Second: interpreting it correctly<\/dt>\n      <dd>Understanding what the number means for a specific business in a specific market. This is the stage where models go wrong more often than in collecting data.<\/dd>\n    <\/dl>\n    <p>Here is an example from the Polish renewable energy market, which we know well:<\/p>\n    <div class=\"tn-aik-stats\">\n      <div class=\"tn-aik-stat\"><strong>38.9 GW<\/strong><span>installed renewables (RES) capacity in Poland at the end of February 2026 (ARE)<\/span><\/div>\n      <div class=\"tn-aik-stat\"><strong>~240 GW<\/strong><span>reserved under issued grid connection conditions: about 150 GW of renewables and 90 GW of storage (Ministry of Energy)<\/span><\/div>\n      <div class=\"tn-aik-stat\"><strong>107 GW<\/strong><span>total capacity covered by 4,897 refusals to issue connection conditions in 2025 (URE)<\/span><\/div>\n    <\/div>\n    <p class=\"tn-aik-note\">Source: report of the President of URE (Energy Regulatory Office) for 2025, data from Agencja Rynku Energii (ARE) and the Ministry of Energy, as published by Gramwzielone.pl on 11 May 2026.<\/p>\n    <p>Each of these numbers is easy to misread, and that is how models often read them:<\/p>\n    <div class=\"tn-aik-table-wrap\">\n      <table>\n        <thead><tr><th>Data<\/th><th>Surface interpretation<\/th><th>What it actually means<\/th><\/tr><\/thead>\n        <tbody>\n          <tr><td>~240 GW of reserved capacity<\/td><td>A huge project pipeline, so strong demand for equipment and EPC<\/td><td>A significant share of these conditions belongs to projects that may never be built. Real demand is much smaller and concentrated among those who have financing and permits<\/td><\/tr>\n          <tr><td>The number of refusals fell from 7,817 in 2024 to 4,897 in 2025<\/td><td>The grid connection situation is improving<\/td><td>At the same time, refused capacity hit a record: 107 GW versus 73.6 GW. There are fewer refusals, but they concern larger projects. The grid remains the main bottleneck<\/td><\/tr>\n          <tr><td>107 GW in refusals<\/td><td>The market is closed, there is no point entering<\/td><td>The constraint concerns grid access, not demand. For some companies this is precisely an opportunity: storage, hybrid solutions, commercial connection<\/td><\/tr>\n          <tr><td>Connection rules<\/td><td>A description of the procedure from articles of previous years<\/td><td>Since 30 April 2026, the amended Energy Law (Prawo energetyczne) has been in force: a shorter validity period for connection conditions, higher procedural costs, checkpoints for projects. Part of the &#8220;pipeline&#8221; will disappear for this reason<\/td><\/tr>\n        <\/tbody>\n      <\/table>\n    <\/div>\n    <p>None of the surface interpretations is made up. Each follows logically from the number. But each leads to the wrong decision: where to go, whom to sell to, how much to invest. The correct reading is much easier to find when you understand the structure of the industry and know which questions to ask next: about regulatory acts, the competitive environment and project economics in that specific country.<\/p>\n  <\/section>\n\n  <!-- 04 -->\n  <section id=\"veryfikatsiia\" class=\"tn-aik-section tn-aik-reveal\">\n    <div class=\"tn-aik-num\"><b>04<\/b><span>Verification<\/span><\/div>\n    <h2 class=\"tn-aik-h2\">Why does data verification matter more than speed of analysis?<\/h2>\n    <p class=\"tn-aik-answer\">Because fast analysis of unverified data only leads to a mistake faster.<\/p>\n    <p>Verifying, checking and validating data today is not an optional step after working with AI. It is a condition without which the output of that work is hard to rely on in decisions. Every number a conclusion rests on should be traced back to its primary source, checked for currency and, ideally, confirmed by people who work in that market.<\/p>\n    <p>Even specialized tools do not remove this need. A preregistered Stanford study published in the Journal of Empirical Legal Studies showed that leading legal AI services gave incorrect or unsupported answers in 17-33% of cases, albeit less often than general-purpose chatbots. Energy regulation is no simpler than legal databases, and it changes more often. This is one of the main limits of AI in consulting today.<\/p>\n    <blockquote class=\"tn-aik-quote\">\n      <p>AI does not know your business and does not carry your risks. That is why responsibility for which data goes into a decision stays with people.<\/p>\n      <cite>Dima V. Nechyporenko, founder of the nech<\/cite>\n    <\/blockquote>\n  <\/section>\n\n  <!-- 05 -->\n  <section id=\"liudyna\" class=\"tn-aik-section tn-aik-reveal\">\n    <div class=\"tn-aik-num\"><b>05<\/b><span>People<\/span><\/div>\n    <h2 class=\"tn-aik-h2\">Why do people remain decisive in B2B?<\/h2>\n    <p class=\"tn-aik-answer\">Because B2B deals are made between people, and trust between them does not come from an email exchange.<\/p>\n    <p>Market research ends with a report, but that is where business only begins. Companies need to find each other, get acquainted and, most importantly, understand each other: how decisions are made, what matters to the partner, where the limits of compromise lie. That is very hard to achieve through emails or messages on LinkedIn or messaging apps.<\/p>\n    <p>In energy and engineering this is felt particularly strongly. Deal cycles are long, the sums are large and the risks for both sides are measured in years. In one country the key decision is made by the technical director, in another by a procurement committee, in a third the position of the project&#8217;s bank or investor will be decisive. A model can describe these differences in general terms. Someone who has worked there is much better placed to sense them in a specific conversation.<\/p>\n    <p>A great deal in business remains human: relationships, presence, an understanding of the mentality and cultural specifics of markets, real-life experience. These elements are absent from a model&#8217;s answer, yet they are often what decides whether a deal happens.<\/p>\n  <\/section>\n\n  <!-- 06 -->\n  <section id=\"industriia\" class=\"tn-aik-section tn-aik-reveal\">\n    <div class=\"tn-aik-num\"><b>06<\/b><span>Industry<\/span><\/div>\n    <h2 class=\"tn-aik-h2\">How is AI changing the consulting industry?<\/h2>\n    <p class=\"tn-aik-answer\">Quickly and profoundly. We see these changes as very positive.<\/p>\n    <p>Part of the work that once took up most of a consultant&#8217;s time, namely searching for, collecting and initially processing information, is now done much faster. This does not make consulting unnecessary; it changes where its value lies: in understanding the industry, correct interpretation, data verification and working with people.<\/p>\n    <p>For us, this means being able to do much more complex things in less time and to help clients achieve results in Europe faster. The tool has become more powerful. Responsibility for how it is used has stayed where it always was.<\/p>\n  <\/section>\n\n  <!-- FAQ -->\n  <section id=\"faq\" class=\"tn-aik-section tn-aik-faq tn-aik-reveal\">\n    <div class=\"tn-aik-num\"><b>07<\/b><span>FAQ<\/span><\/div>\n    <h2 class=\"tn-aik-h2\">Questions and answers<\/h2>\n    <details open>\n      <summary>Can a market entry strategy be built solely on data collected with AI?<\/summary>\n      <p>As a starting point, such data is useful: for an initial understanding of a market, standard models are usually enough. But a strategy needs verified data and correct interpretation, and interpretation is exactly where models often go wrong.<\/p>\n    <\/details>\n    <details>\n      <summary>Why does AI misinterpret data?<\/summary>\n      <p>A model sees a number but not how it came about: who counts it, what it includes, which rules sit behind it. Without an understanding of the industry&#8217;s structure, the same number can signal growing demand or a bottleneck, and the model often picks the wrong reading.<\/p>\n    <\/details>\n    <details>\n      <summary>Can AI be trusted on regulatory questions?<\/summary>\n      <p>As a starting point, yes; as a final source, no. Even specialized legal AI tools were wrong in 17-33% of answers in a Stanford study. Any rule should be checked against the current text of the law, taking its effective date into account.<\/p>\n    <\/details>\n  <\/section>\n\n  <!-- AUTHOR -->\n  <section class=\"tn-aik-section tn-aik-reveal\" aria-labelledby=\"tn-aik-author-title\">\n    <div class=\"tn-aik-author\">\n      <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/thenech.com\/wp-content\/uploads\/2026\/09\/dima-nechyporenko-doradca-b2b-portret.webp\" alt=\"Dima V. Nechyporenko, founder of the nech\" width=\"120\" height=\"120\" loading=\"lazy\">\n      <div>\n        <h3 id=\"tn-aik-author-title\">Dima V. Nechyporenko<\/h3>\n        <p class=\"tn-aik-role\">Founder of the nech<\/p>\n        <p>Over 18 years in strategy and business development in energy and B2B across the markets of Ukraine, Poland, the EU, Canada and the UAE. Led business development for EPC companies on utility-scale solar and conventional energy projects, and served as strategic director at RWDI, a Canadian engineering firm.<\/p>\n        <p><a href=\"https:\/\/www.linkedin.com\/in\/dmytronch\/\" rel=\"noopener\" target=\"_blank\">LinkedIn<\/a><\/p>\n      <\/div>\n    <\/div>\n  <\/section>\n\n  <!-- SOURCES -->\n  <section class=\"tn-aik-section tn-aik-sources tn-aik-end tn-aik-reveal\" aria-labelledby=\"tn-aik-src-title\">\n    <div class=\"tn-aik-num\"><b>08<\/b><span>Sources<\/span><\/div>\n    <h2 id=\"tn-aik-src-title\" class=\"tn-aik-h2\">Sources<\/h2>\n    <ol>\n      <li>Urz\u0105d Regulacji Energetyki, report on the activities of the President of URE for 2025; data from Agencja Rynku Energii and Ministerstwo Energii. As published by: <a href=\"https:\/\/www.gramwzielone.pl\/trendy\/20359525\/przylaczenia-oze-liczba-odmow-spadla-odrzucona-moc-wzrosla\" rel=\"noopener\" target=\"_blank\">Gramwzielone.pl, 11.05.2026<\/a>.<\/li>\n      <li>Magesh V., Surani F., Dahl M., Suzgun M., Manning C. D., Ho D. E. Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools. Journal of Empirical Legal Studies, 2025. <a href=\"https:\/\/arxiv.org\/abs\/2405.20362\" rel=\"noopener\" target=\"_blank\">arXiv:2405.20362<\/a>.<\/li>\n      <li>Proux C., Berger B. The Impact of AI Systems on Management Consultancy Work. Advanced Studies in Consulting Research and Digitalization, 2026. <a href=\"https:\/\/www.wi.uni-muenster.de\/de\/publication\/193019598\" rel=\"noopener\" target=\"_blank\">University of M\u00fcnster<\/a>.<\/li>\n    <\/ol>\n  <\/section>\n\n<\/div>\n<\/div>\n<\/div>\n\n<script>\n(function(){\n  var root=document.querySelector('.tn-aik');\n  if(!root) return;\n  var bar=root.querySelector('.tn-aik-progress');\n  function onScroll(){\n    var r=root.getBoundingClientRect();\n    var total=root.offsetHeight-window.innerHeight;\n    var p=total>0?Math.min(Math.max(-r.top\/total,0),1):0;\n    if(bar) bar.style.width=(p*100)+'%';\n  }\n  window.addEventListener('scroll',onScroll,{passive:true});\n  onScroll();\n  var items=root.querySelectorAll('.tn-aik-reveal');\n  var reduce=window.matchMedia&&window.matchMedia('(prefers-reduced-motion: reduce)').matches;\n  if(reduce||!('IntersectionObserver' in window)){\n    items.forEach(function(el){el.classList.add('is-in');});\n    return;\n  }\n  var io=new IntersectionObserver(function(entries){\n    entries.forEach(function(e){\n      if(e.isIntersecting){e.target.classList.add('is-in');io.unobserve(e.target);}\n    });\n  },{rootMargin:'0px 0px -8% 0px',threshold:0.08});\n  items.forEach(function(el){io.observe(el);});\n})();\n<\/script>\n\n","protected":false},"excerpt":{"rendered":"<p>AI in consulting speeds up data collection, but models often misinterpret what the data means. What this means for energy and engineering companies.<\/p>\n","protected":false},"author":1,"featured_media":4367,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[122,96,75],"tags":[],"class_list":["post-4373","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-expansion-into-foreign-markets","category-market-entry","category-strategy-planning"],"_links":{"self":[{"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/posts\/4373","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/comments?post=4373"}],"version-history":[{"count":1,"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/posts\/4373\/revisions"}],"predecessor-version":[{"id":4379,"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/posts\/4373\/revisions\/4379"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/media\/4367"}],"wp:attachment":[{"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/media?parent=4373"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/categories?post=4373"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thenech.com\/en\/wp-json\/wp\/v2\/tags?post=4373"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}