AI-Ready or AI-Native? 7 Common Questions About Using AI in Companies
Many companies already use ChatGPT, Claude, Microsoft Copilot or other AI tools. But does that automatically mean they use AI meaningfully and productively? Do they use these technologies deliberately and purposefully, or are they simply following a current trend?
There is an important difference between AI-Ready and AI-Native.
There is an important difference between AI-Ready and AI-Native.
1. What does AI-Ready mean?
An AI-Ready company has the basic prerequisites for using artificial intelligence. These include, above all, the AI skills of its employees, known as AI Literacy, suitable technologies and IT infrastructure, and access to relevant data.
Equally important are clear rules for the responsible use of AI, compliance with legal requirements such as the General Data Protection Regulation (GDPR) and the EU AI Act, and a realistic understanding of the necessary investments and ongoing costs.
In short: the company is prepared to use AI deliberately, meaningfully, safely and cost-effectively.
Equally important are clear rules for the responsible use of AI, compliance with legal requirements such as the General Data Protection Regulation (GDPR) and the EU AI Act, and a realistic understanding of the necessary investments and ongoing costs.
In short: the company is prepared to use AI deliberately, meaningfully, safely and cost-effectively.
2. What does AI-Native mean?
In an AI-Native Organization, AI is not just an additional tool. It is integrated into processes, products and decision-making from the very beginning.
The key difference can be described with two terms: "Bolted-on" vs. "Baked-in". With a Bolted-on approach, AI is added to existing processes, whereas with a Baked-in approach it is part of the way of working and creating value from the very beginning.
An example: instead of using AI merely to summarize meetings, companies can redesign the entire process of information processing and decision-making. AI then supports not just individual tasks, but the entire workflow.
Our way of thinking changes as well. Instead of only asking, "How can AI help me complete my existing tasks faster?", we ask a different question:
What can we achieve together with AI that was not possible before?
AI is thus seen not only as a tool, but also as a thinking partner that helps us develop new ideas, take on different perspectives and better prepare decisions.
The key difference can be described with two terms: "Bolted-on" vs. "Baked-in". With a Bolted-on approach, AI is added to existing processes, whereas with a Baked-in approach it is part of the way of working and creating value from the very beginning.
An example: instead of using AI merely to summarize meetings, companies can redesign the entire process of information processing and decision-making. AI then supports not just individual tasks, but the entire workflow.
Our way of thinking changes as well. Instead of only asking, "How can AI help me complete my existing tasks faster?", we ask a different question:
What can we achieve together with AI that was not possible before?
AI is thus seen not only as a tool, but also as a thinking partner that helps us develop new ideas, take on different perspectives and better prepare decisions.
3. What is the most important difference between AI-Ready and AI-Native?
AI-Ready means: we have the necessary prerequisites and skills to use AI meaningfully.
AI-Native means: we redesign our work with AI.
Both approaches are important. AI-Readiness creates the foundation, while AI-Native opens up new opportunities for innovation, efficiency and business models.
AI-Native means: we redesign our work with AI.
Both approaches are important. AI-Readiness creates the foundation, while AI-Native opens up new opportunities for innovation, efficiency and business models.
4. Does every company need to become AI-Native?
Not necessarily. The right approach depends on the company's strategy, business model and specific challenges.
What matters is not introducing as many AI tools as possible, but creating measurable value for customers, employees and the company.
It is worth looking at the success of AI use on three levels:
What matters is not introducing as many AI tools as possible, but creating measurable value for customers, employees and the company.
It is worth looking at the success of AI use on three levels:
Adoption
How many employees actually use AI, and how regularly?
Productivity
Are workflows becoming more efficient? Are we saving time, improving quality or reducing errors?
Business Value
What concrete economic benefit are we achieving in relation to our investments in technology, infrastructure, licenses and employee training?
The last question is especially important. A high usage rate alone does not mean that AI is creating real value for the company.
5. What role do leaders play?
Leaders do not need to be able to develop AI solutions or implement them technically themselves. There are specialized experts who support companies in introducing and integrating AI.
However, leaders should recognize potential, identify suitable use cases, understand the opportunities and risks of AI, and actively guide their teams through change.
The path to an AI-Native Organization means far more than introducing new technologies. It also requires a cultural shift that changes the mindset, collaboration and decision-making processes within an organization.
AI transformation is therefore not only a technical challenge, but above all an organizational and human one. Leaders play a crucial role in actively shaping this change process, building acceptance and supporting employees along the way.
However, leaders should recognize potential, identify suitable use cases, understand the opportunities and risks of AI, and actively guide their teams through change.
The path to an AI-Native Organization means far more than introducing new technologies. It also requires a cultural shift that changes the mindset, collaboration and decision-making processes within an organization.
AI transformation is therefore not only a technical challenge, but above all an organizational and human one. Leaders play a crucial role in actively shaping this change process, building acceptance and supporting employees along the way.
6. How do you get started?
Anyone who wants to introduce AI in their company should not start by selecting new tools, but by first understanding their own goals and potential.
A sensible start begins with five fundamental questions:
A sensible start begins with five fundamental questions:
Identify potential
Which business processes and challenges offer the greatest potential for using AI?
Use existing technologies
Which of our existing tools and systems already have AI features we can use?
Assess AI skills
Where do our employees and teams stand today in working with AI?
Identify expertise
Which skills and experience already exist in the company, and which do we still need to build or bring in externally?
Shape change
Which organizational and cultural changes do we need to integrate AI sustainably into the way we work?
It is worth reviewing the technologies and capabilities already available before investing in new AI solutions. Often, the first use cases can be implemented with existing tools, generating valuable experience.
On this basis, companies can plan their next steps in a targeted way and develop a suitable AI strategy.
On this basis, companies can plan their next steps in a targeted way and develop a suitable AI strategy.
7. The next step: How can AgileLAB help?
AgileLAB supports companies and leaders on the journey from AI-Readiness to an AI-Native Organization.
This is not just about getting to know new AI tools. We help companies identify concrete use cases, build the necessary skills and actively shape the associated change process.
Our training and consulting services are aimed both at employees who want to use AI meaningfully in their daily work and at leaders who guide organizational and cultural change.
Our training programs:
Funding opportunities for companies
As an authorized INQA coaching provider, AgileLAB also supports small and medium-sized enterprises in shaping digital and organizational change processes. Under certain conditions, up to 80% of eligible consulting costs can be covered as part of INQA coaching.
We will be happy to inform you about suitable funding opportunities and help you plan your next step.
Further information: AI-Native Trainings | Leadership & Change Management | INQA Coaching | Funding Opportunities
This is not just about getting to know new AI tools. We help companies identify concrete use cases, build the necessary skills and actively shape the associated change process.
Our training and consulting services are aimed both at employees who want to use AI meaningfully in their daily work and at leaders who guide organizational and cultural change.
Our training programs:
- AI-Native Foundations
- AI Value Architect
- Leading AI-Native Organizations
- FAI Training
- Change Management Trainings
Funding opportunities for companies
As an authorized INQA coaching provider, AgileLAB also supports small and medium-sized enterprises in shaping digital and organizational change processes. Under certain conditions, up to 80% of eligible consulting costs can be covered as part of INQA coaching.
We will be happy to inform you about suitable funding opportunities and help you plan your next step.
Further information: AI-Native Trainings | Leadership & Change Management | INQA Coaching | Funding Opportunities
Author
Nastassia Krupiankova
Managing Director, AgileLAB GmbH
Managing Director, AgileLAB GmbH
Sources and further reading
EU AI Act: Regulation (EU) 2024/1689 on Artificial Intelligence
GDPR: Regulation (EU) 2016/679 on the protection of personal data
Jellyfish AI Impact Framework: a model for assessing AI adoption, productivity and business outcomes.
GDPR: Regulation (EU) 2016/679 on the protection of personal data
Jellyfish AI Impact Framework: a model for assessing AI adoption, productivity and business outcomes.
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