Companies face challenges every day. Some are obvious, others are hidden behind symptoms. But it is crucial to describe the problem correctly before discussing solutions. This is exactly where the new AI feature in kyro comes in. The AI is integrated directly into Open Challenges (OCL, list of measures). It guides users through problem definition, root cause analysis, and action planning. Step by step, with clear questions and an automatic summary of the answers. The goal: To create clarity, identify causes, and derive measures—all without complicated tools or lengthy discussions.
1. Problem Description: Clarity From the Start
Many process optimizations fail because the problem is not described correctly. The AI in kyro therefore asks simple but effective questions, such as:
- What is the main problem?
- Where does the problem occur?
- When does it occur?
- Who is involved?
- Why is it a problem?
- What makes it so important?
- How often does it occur?
- What impact does it have?
These questions encourage kyro users to take a closer look. Instead of vague statements, a clear picture emerges. An example of a real-world problem: “We have too little engagement on the LinkedIn company page.” The AI asks questions. Who is responsible for LinkedIn? How often does the problem occur? What are the consequences? In the end, the AI summarizes all the answers in a structured problem definition. In addition, the AI in kyro quantifies the problem and asks how much time is spent on each problem. This information is particularly helpful later on when prioritizing problems. Thanks to the AI feature, you save time when describing the problem and stay focused. The feature also ensures that everyone involved has the same understanding of the problem.
With the help of AI in kyro, the following problem description was developed for the problem “Insufficient reach on the company’s LinkedIn posts”:
Example Problem Description
The company’s LinkedIn posts have insufficient reach, which affects the visibility and engagement of the audience. The communications department is responsible for creating and publishing LinkedIn posts. These reach problems are constant. The low reach means that fewer people become aware of the company and therefore fewer leads can be generated. One reason for the low reach is that LinkedIn has designed the algorithm for company pages to favor personal profiles. The company page publishes new content about three times a week, with each post taking about 10 minutes to create and publish. The success or reach of the posts is measured by the impressions shown in LinkedIn statistics.
Quantification of the Problem / Calculation
- Average time per post: 10 minutes
- Posts per week: 3
- Post creation and publication: 3 x 10 minutes = 30 minutes per week
- Annual time spent on posts: 30 x 52 = 1,560 minutes = 26 hours per year
Without optimizing its reach, the company loses 26 hours per year creating content that does not generate the desired number of leads.
2. Root Cause Analysis: From Assumptions to Facts
Once the problem is clear, it’s time to look at the causes. There are often many assumptions about what the causes of a problem are. But not every cause is really relevant. The AI in kyro helps with specific questions, such as:
- What are the suspected causes?
- How can these causes be confirmed?
- Which causes have what impact?
- Which causes can we influence—and who can do so?
This creates a structured picture. Users can see at a glance which causes are likely, how strong their impact is, and where to start. AI helps to focus on the confirmed causes. This prevents teams from wasting time and energy on side issues. Instead, they work specifically on the points that really have an impact.
The Root Cause Analysis From Our Example:
The insufficient reach of the company’s LinkedIn page is due to the communications department’s lack of capacity to develop and implement strategies specifically tailored to and focused on the company page. Although personal profiles performed better and were therefore the focus of attention, the company page was neglected. This neglect continued despite constant reach problems, as there were insufficient resources to develop innovative solutions to increase visibility on the company page.
3. Action Planning: From Insights to Action
In the end, what matters is that something happens. AI also accompanies this step. It uses root cause problem solving to generate suggestions for short-term, medium-term, and long-term measures. These must then be reviewed, evaluated, and prioritized by the responsible team.
AI uses key performance indicators (KPIs) to highlight which measures require little effort but have a big impact. At the same time, the suggestions make it easier to assess which steps are risky or expensive. This allows implementation to be specifically controlled and later reviewed.
In our example, “Insufficient reach on the LinkedIn channel,” the AI feature in kyro developed the following suggestions for measures and actions:
Short-term Measures: Improve the Visibility of the Company Page Through Immediate Measures
- Increase posting interval – Increase the posting frequency to 5 posts per week under the direction of the communications department using existing resources. KPI: Increase weekly impressions by 20%.
- Content repurposing – Use existing content from personal profiles. Reorganize the content curation team to tailor content specifically to the company page. KPI: 10% increase in interaction rate.
- LinkedIn analytics workshop – Train the communications department in the use of LinkedIn statistics to make data-driven decisions. KPI: Improve engagement by 15%.
- Hashtag optimization – Introduce new hashtag strategies on LinkedIn to increase organic reach. KPI: Increase daily impressions by 10%.
Medium-term Measures: Development of a Sustainable Strategy to Increase the Reach of the Company Page
- Creation of a content calendar – Establishment of a structured content plan that covers thematic priorities and time distribution. Responsible: Communications department. KPI: 30% more structured content.
- Expand network with influencers – partnerships with relevant industry influencers to increase the reach of content. KPI: 25% growth in impressions.
- Activate employees – training for employee engagement on LinkedIn to use their personal profiles to expand the reach of the company page. KPI: 20% increase in overall engagement.
- Strengthen internal cooperation – Collaboration between marketing and PR to create improved content. KPI: 15% more content with high reach.
Long-term Measures: Institutionalization of LinkedIn Strategy and Automation of Content Management
- Automation of posts – Introduction of a content management system (CMS) to plan and automate regular posts. KPI: 40% reduction in manual effort.
- Integration of analytics tools – Implementation of integrated analytics tools to monitor reach metrics in real time. KPI: 30% increase in data analysis efficiency.
- Regular strategy workshops – Quarterly workshops to adapt and innovate LinkedIn strategies. KPI: 15% improvement in engagement per quarter.
- Promoting corporate culture – Development of programs to promote awareness of the importance of corporate communication on LinkedIn. KPI: Measurable 25% increase in employee participation.
Why the AI Feature Is So Valuable
The integration of AI into the Open Challenge list offers several advantages:
- Structure instead of chaos: The questions follow a clear logic. No aspect is left unaddressed
- Speed instead of stagnation: The answers flow directly into a structured summary. No one has to write minutes
- Focus instead of distraction: AI directs attention to causes and measures that really matter
- Transparency instead of misunderstandings: Everyone involved sees the same picture. Discussions are based on facts
- Learning curve instead of repetition: Teams learn with each analysis because the AI consistently applies the same logic
With the new AI feature, kyro becomes even more of a daily tool for continuous improvement.
AI in kyro: Practical Application
Whether in marketing, production, or administration—the principle is the same. A team describes a problem. The AI asks questions, summarizes, moves on to the causes, and finally to the measures. The result? Clear, actionable improvements.
The example illustrates the support provided by AI in concrete terms: The communications team notices that the company’s LinkedIn posts have little reach. The AI provides support through analysis. The problem is described precisely, the cause (lack of capacity) is identified, and concrete measures such as building an influencer network or content planning are derived. In this way, a vague observation is turned into a structured improvement process. Of course, the proposed measures must be revised and adapted to the specific needs of the company.
The AI feature in kyro is not a gimmick. It is a real companion for teams who want to understand and solve problems. Through targeted questions, clear summaries, and a consistent structure, every challenge becomes an opportunity. The combination of lean methodology, digital platform, and artificial intelligence makes kyro unique. And it shows what modern process optimization can look like: simple, clear, and effective.
Are you ready to understand and solve your business problems faster? Get in touch with the kyro experts!
Note: The AI in kyro is a closed “Large Language Model” and is trained by the kyro software itself. Like any AI model, the AI in kyro can make mistakes or respond differently to similar questions. You should always carefully check information from AI models, including information from the AI feature in the kyro software.
