{"id":226863,"date":"2026-07-23T13:40:44","date_gmt":"2026-07-23T13:40:44","guid":{"rendered":"https:\/\/www.9senses.ai\/?page_id=226863"},"modified":"2026-08-27T08:16:42","modified_gmt":"2026-08-27T08:16:42","slug":"transformation","status":"publish","type":"page","link":"https:\/\/www.9senses.ai\/de\/transformation\/","title":{"rendered":"KI Transformation"},"content":{"rendered":"
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Thriving in Times of AI<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n

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AI Business Transformation<\/h1><\/div><\/div>\n\n

AI is changing every business, without anyone ever making a decision. A company that goes slow or \"no\" on AI will still have AI tools coming in as part of the software and equipment it uses, and by employees running AI chats on their devices without authorization, training and documentation. For some companies, competitors suddenly might have an edge because they embrace the technology. Escaping AI is impossible - the only choice is how to engage. How a company responds to the AI exposure it already has defines its AI Identity<\/strong> - and with it, its AI Strategy.<\/p>\n<\/div><\/div>\n\n

\u201cSir, my need is sore.
Spirits that I\u2019ve cited
my commands ignore.\u201d<\/p><\/blockquote>\n

The Sorcerer\u2019s Apprentice, Johann Wolfgang von Goethe (1797\/98)<\/span><\/p>\n<\/div><\/div>\n<\/div>\n\n

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What is your AI Identity? Take a 5-minute test<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n
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Your AI Identity: beyond AI strategy<\/h2>\n

9senses has defined three archetypes of AI identities companies can have. Learn more by opening one of the tiles below, or take our 5-minute test to find out where your company stands<\/a>.<\/span><\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n

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AI Hesitant<\/h3>

The AI Hesitant does not actively engage in AI, but AI is still creeping in through third-party tools and unauthorized AI use by employees.<\/p>\n<\/div><\/div><\/div><\/div>\n\n

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AI Sprinter<\/h3>

The Sprinter embraces AI without hesitation - and might reap some short-term benefits but might endanger control over the business long term.<\/p>\n<\/div><\/div><\/div><\/div>\n\n

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AI Wise<\/h3>

An AI Wise company finds the right balance between adopting AI and maintaining control. Its foundations are sound, and its investments are targeted.<\/p>\n<\/div><\/div><\/div><\/div>\n\n

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From the inside, the Hesitant position feels safe. No budget is burned on hyped AI that is more likely to fail than to succeed, no risky projects are started, and employee use of AI chats is blocked.<\/p>\n

Unfortunately, the defining property of AI is that it does not wait for an invitation. It just arrives. The software you already pay for adds AI features release by release, and your people are ahead of you: in the largest global study on workplace AI to date<\/a>, only 40% of employees said their workplace has any policy or guidance on generative AI, while almost half admitted to using it in ways that break company rules, including feeding sensitive company information into free public tools. 57% said they hide their AI use altogether.<\/p>\n

There isn't a \"no-AI\" option<\/h3>\n

While it is possible to decide nothing on AI or even actively say \"NO\", you are still fully exposed. The EU AI Act is in place, and a purchased product or an illegitimately used chatbot can put you in scope for rules you never read.<\/p>\n

If you contacted us<\/a> tomorrow, our advice would not be an AI project. It would be to build the AI Foundation and AI Baseline to cover you. It would mean training your senior management to build some basic understanding of AI. It would mean an inventory of what is already in your company, estimating the exposure, defining rules and boundaries, and setting up minimal training and support for employees who are very likely already using AI behind your back.<\/p>\n

Take the AI Identity test and find out which company type yours is - or schedule a 15- minute call to learn more about how we can help.<\/p>\n<\/div><\/div>\n<\/div>\n\n

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The price
AI Hesitants pay<\/h3>\n

You carry the exposure without most of the benefits. You pay for AI inside your tools whether it is governed or not.<\/p>\n

One pasted contract, one confidently wrong answer sent to a customer, and the incident lands on an organization with no rules, no owner and no routine. But full responsibility. And when engaging becomes unavoidable, you start under pressure instead of by choice.<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n

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FAQs for AI Hesitants<\/h3>\n<\/div><\/div>\n\n
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We deliberately decided against AI. Isn't that a valid strategy?<\/h4>

Deciding against your own AI applications is entirely legitimate, and we often advise companies to do just that. Deciding against AI, while it creeps in through tools and illegitimate use by employees, is impossible. The exposure exists whether you engage or not; the only choice is whether it is covered.<\/p>\n<\/div><\/div>\n\n

Can't we just block the tools?<\/h4>

Blocking is the worst possible decision. It doesn't remove tool use, it just shifts it outside the visible area. Teams or individuals that are under pressure to deliver output will simply switch to AI on private devices, which often means lower-quality models, privacy and confidentiality issues, and a complete loss of control over what is going on.\u00a0 Approved alternatives, combined with training and clear rules, reduce unauthorized use far more effectively than a ban.<\/p>\n<\/div><\/div>\n\n

What is the very first step?<\/h4>

The first step is learning about AI<\/a> in order to understand what it can do, and what it cannot. This is followed by measuring: creating an inventory of where AI already lives in your organization and how it is governed. This takes days, not months. Everything else builds on this.<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n

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The Sprinter is visibly the innovator, ahead of the curve, using the latest technology and possibly scoring some quick wins. Some of those gains are real; maybe well-used AI increases staff efficiency and reduces hiring needs.<\/p>\n

But still, a large number of generative AI pilots produce no measurable return<\/a>, and, <\/span>increasingly, companies are abandoning AI initiatives<\/a>. The pattern behind these numbers is always the same: applications are added faster than the foundations that keep them controllable, ranging from appropriate testing to governance.<\/span><\/p>\n

Sprinters often steal from their own future<\/h3>\n

The deeper cost compounds: in a 2026 BCG study, C-level interviewees<\/a> observed deskilling among their employees who heavily rely on AI-generated output and complained about glossy work that was shallow in content. That's a key AI feature. Managing this, while steering the company towards efficient AI use, is hard work.<\/p>\n

At the same time, some savvy companies reduce entry-level hiring, while managing AI use productively. They will pay the price in the future, when nobody in the organization has learned how to do things at the core of the business, and how to review and judge AI outputs. We call it the AI Apprenticeship Debt Curve<\/a>. Quick gains now, losses arriving later.<\/p>\n

\"\"<\/p>\n

If an AI Sprinter contacted us<\/a> tomorrow, our advice would be to pause new AI rollouts, bring the AI Foundation up to strength before the next launch, and establish a sound AI Baseline that guides everyday AI use and adoption.<\/p>\n<\/div><\/div>\n<\/div>\n\n

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The price
AI Sprinters pay<\/h3>\n

Every AI-based application added on this footing raises exposure faster than oversight and control can catch up. Incidents land on an organization that is fast but unprepared.<\/p>\n

The risks include a high cost for abandoned AI projects, uncertainty about AI impact on the business, and possibly a loss of human capability through deskilling.\u00a0<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n

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Questions AI Sprinter might ask<\/h3>\n<\/div><\/div>\n\n
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We are seeing real productivity gains. Why change anything?<\/h4>

The gains are not the problem, but the uncovered exposure underneath is. Reducing that exposure doesn't mean giving anything up; it means being able to keep what you have gained and build a solid foundation that outlasts the short-term wins.<\/p>\n<\/div><\/div>\n\n

Won't governance slow us down?<\/h4>

The evidence points the other way. Only a scarily small number of AI initiatives generate measurable long-term value: the scoped ones with a defined purpose, clear success criteria, early fail gates, and good governance. Building the harness that doesn't let you cede control to AI is a small cost compared to the risk you're otherwise taking.<\/p>\n<\/div><\/div>\n\n

Which of our running applications should we review first?<\/h4>

The highest risk lies in generative AI and Machine Learning applications where the output doesn't undergo regular human validation, and where continuous testing isn't a built-in feature. That is precisely where AI confidently makes the errors nobody discovers.<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n

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Being AI Wise doesn't mean a choice on how much AI a company adopts. It rather means that it is prepared for AI. The starting point is to ensure that all employees - from top management to everyone with potential exposure - understand AI well enough to see its benefits, limitations, and risks.<\/p>\n

Further, it means understanding where AI already lives, understanding the risks and managing them, particularly when it comes to general use of generative AI to solve business problems or agents that process data and make decisions. It is better to provide solid tools rather than have employees use AI anyway under the table, with all the risks this entails. Lastly, there is a solid governance layer required, ensuring that AI can't quietly damage the business, and that AI applications are tested the way they should be - continuously.<\/p>\n

Balance is maintained, not reached<\/h3>\n

Being AI Wise has no finish line. AI models change, vendors add features, their prices and terms move, regulations change, people come and go. With each of those changes the exposure drifts, so coverage has to be re-measured and grown with every new application. That is why we describe AI transformation as a control loop rather than a journey. An AI Wise company is staying in that loop.<\/p>\n

Let us know<\/a> if you want to become AI Wise - it's easier than you think.<\/p>\n<\/div><\/div>\n<\/div>\n\n

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What AI Wise companies win<\/h3>\n

AI Wise companies engage with Artificial Intelligence, but in a way that ensures their business foundation and their long-term success are never in danger.<\/p>\n

By laying a solid AI foundation and defining a baseline for AI use, they ensure proper adoption and governance, making most AI initiatives fail early or succeed.<\/p>\n

It is based on knowledge about AI, its benefits and risks, and a clear governance framework around it.<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n

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Questions we get asked<\/h3>\n<\/div><\/div>\n\n
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Is AI Wise just a polite word for slower?<\/h4>

AI Wise says nothing about adoption speed. A company can deliberately introduce AI in places where it is meaningful or stay away from AI outside of generic employee support. Both can be wise. It's the choice you have.<\/p>\n<\/div><\/div>\n\n

Do we need a Chief AI Officer?<\/h4>

Not necessarily. But you need AI literacy on all management levels, and you need clear decision-making authority about AI initiatives across the organization. The setup is secondary, but competence and agility matter.<\/p>\n<\/div><\/div>\n\n

How do we stay on top of AI change?<\/h4>

This is challenging, as AI models change almost daily under the hood, and AI capabilities are introduced in more and more products, from software to equipment. These changes need monitoring and regular reviews, as well as a completely different testing approach: with AI, every day is \"release day\".<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n

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To become AI Wise<\/strong>, where you're neither run over by AI changing your reality nor among those who aimlessly engage, it is imperative to develop a thorough understanding of Artificial Intelligence, its benefits and its limitations, and begin applying it cautiously and methodically, with the necessary guardrails and a way to rapidly adjust as it changes.\u00a0<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n

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AI Wise building blocks<\/h2>\n

Three layers of engaging with AI. The first two are essential for all organizations, even if they don't start their own specific AI initiatives. The third one - AI Substance - is optional.<\/span><\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n

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AI Foundation<\/h3>

The AI Foundation keeps you on solid ground. It defines your strategic position, governance and ownership, rules and boundaries, and the evaluation of your AI exposure and risk.<\/p>\n<\/div><\/div><\/div><\/div>\n\n

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AI Baseline<\/h3>

The AI Baseline makes everyday AI use manageable across the organization. It defines approved tools, AI literacy, usage rules and oversight, including ongoing risk management as AI use and conditions change.<\/p>\n<\/div><\/div><\/div><\/div>\n\n

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AI Substance<\/h3>

AI Substance is entirely your choice. It consists of the specific AI applications you introduce into products or business processes. These initiatives are selected one by one, where AI can create measurable value.<\/p>\n<\/div><\/div><\/div><\/div>\n\n

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Four key elements are at the core of an AI Foundation.<\/p>\n<\/div><\/div>\n\n

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AI Strategy<\/h3>\n

This decides where AI belongs in your organization and where it doesn't. It is built based on the AI literacy of senior management and defines what strategy the company wants to follow on AI, and how this strategy is continuously revised as the technology evolves. It defines the guiding principles of AI adoption.<\/p>\n<\/div><\/div>\n<\/div>\n\n

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Governance and ownership<\/h3>\n

This mandatory layer defines the rules for engaging with AI on an organizational level. It defines where the decisions about \"yes\" and \"no\" sit, and how they are managed. There is no hiding behind \"the AI did it.\" Courts have already confirmed that.<\/p>\n<\/div><\/div>\n<\/div>\n\n

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Rules and boundaries<\/h3>\n

The regulatory framework - for example the EU AI Act and data protection laws - is only the floor of what matters. Most boundary decisions are business decisions: which data may go into what tools, which AI uses and decisions need to be documented; what is off-limits.<\/p>\n<\/div><\/div>\n<\/div>\n\n

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Risk evaluation<\/h3>\n

Risk evaluation starts with understanding exposure - an inventory of the AI that has quietly entered your business without you asking for it - and a basic assessment of what each finding could do to the business and its risk profile. This evaluation is what the strategic position and the boundary rules are built on.<\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n

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The 9senses
AI Foundation Program<\/h3>\n

We have developed a comprehensive, structured approach to building a company's solid AI foundation in less than two months.\u00a0<\/p>\n

It consists of 3 distinct elements:<\/p>\n

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  1. An AI inventory with AI exposure review - identifying all current visible and hidden exposure and evaluating its potential impact on business and risk.<\/span><\/li>\n
  2. The AI Foundation Day - a structured off-site meeting for senior leadership that aims at the following:\n