How to Start with AI in Your Company: Process Audit and Identification of Automation Opportunities

The business world is undergoing a revolutionary change, driven by Artificial Intelligence (AI). It is no longer just a futuristic vision but an essential tool for companies aiming to remain competitive and efficient. However, many companies in Slovakia ask themselves: "How do we actually start with AI?" The answer lies in a systematic approach, beginning with a thorough audit of existing processes and identifying genuine opportunities for automation. This article, created by experts from ABRA Consulting, will provide you with a complete guide.

Why is AI Crucial for Every Company Today?

Artificial intelligence is not just about robotics or autonomous vehicles. In the business environment, its primary contribution lies in data processing and analysis for decision-making (according to MIRRI, up to 70% of its utilization) and in process automation (63%). These two areas are fundamental for increasing efficiency, reducing costs, and unlocking new growth opportunities.

Today's era is characterized by an enormous amount of data. Companies that can effectively utilize this data gain a significant advantage. AI can analyze data at a scale and speed impossible for humans, uncovering hidden patterns, predicting trends, and optimizing operations. This is why preparing for AI transformation is crucial, as emphasized by Grant Thornton. It's not just about partial improvements, but about strategically moving towards a "total AI company," where routine tasks are performed by AI, and people can focus on innovative, strategic, and creative roles.

Emil Fitoš, President of the IT Association of Slovakia, pointed out at the ITAPA AI conference that AI and other modern intelligent automation tools are indispensable for non-production processes. This means that office work, customer service, marketing, and HR can also be significantly transformed. However, with increasing digitalization and AI deployment, there are also risks of cyberattacks that companies face, as SCDI warns. Therefore, it is essential to take these risks seriously and ensure trustworthy AI (KPMG).

Step 1: In-depth Audit of Existing Processes

Before you start thinking about specific AI tools or solutions, you need to understand how your company operates today. Without a thorough audit of existing processes, any AI deployment is risky and likely ineffective. This step is the foundation upon which your entire AI strategy rests.

Identification and Mapping of Key Processes

The first step is to identify all important processes within your company. You don't have to start with every single process; focus on those that are critical to the company's operation, revenue generation, or are particularly time- and resource-intensive. As stated by UCM Trnava, the principle is that processes should be verbally described and mapped.

How to do it?

1. List of processes: Create a comprehensive list of all internal processes, from employee recruitment, through order processing, financial statements, marketing campaigns, to customer support.
2. Process owners: Assign a "process owner" to each process – a person or department responsible for that process and possessing the best information about it.
3. Detailed mapping: Together with process owners, create visual maps (e.g., flowcharts) for each identified process. Record:
   • Inputs: What data or information is needed to initiate the process?
   • Steps: What are the individual activities and decisions within the process? Who performs them?
   • Outputs: What is the outcome of the process? What data or information does it generate?
   • Dependencies: Which other processes does it affect or depend on?
   • Tools used: What software, systems, or physical tools are used?

The goal is to gain a clear and detailed picture of how processes currently operate. Even at this stage, you will often uncover inefficiencies, duplications, or unnecessary steps. The analysis of SME needs also emphasizes that digitalization should be understood in the context of digitizing existing processes and manufacturing products. Expanding digital capabilities is essential.

Data Collection and Analysis Related to Processes

Mapping processes is just the beginning. To effectively identify opportunities for AI, you need quantitative data.

What data to collect?

• Time: How much time does it take to perform each step or the entire process? What is the average time, minimum, and maximum?
• Costs: What are the direct and indirect costs associated with the process (salaries, licenses, materials)?
• Error rate: What is the error rate in the process? How many errors occur, and what is their impact (financial, reputational)?
• Volume: How many times is the process performed per day, week, month?
• Complaints/Problems: Collect feedback from employees and customers about "pain points" in the processes.
• Data availability: What data is already in digital form? What is structured and unstructured? Where is it stored?

This data will help you quantify the potential benefits of automation. For example, if you find that invoice processing takes 20 hours per week and has a 5% error rate, you have a strong argument for deploying AI for automation. Improving existing products and processes is key, and this includes identifying project contributions to transformation, as stated by the Research and Innovation Strategy for Smart Specialization.

Step 2: Identifying Opportunities for AI Automation

With a clear picture of your processes and available data, it's time to identify where artificial intelligence can bring the greatest value.

Finding "Low-Hanging Fruit" and High-Potential Areas

Focus on processes that meet the following criteria:

1. High frequency and volume: Processes that are repeated often and in large volumes (e.g., processing a large number of documents, answering common customer questions).
2. Repeatability and structuredness: Processes that have clearly defined steps and minimal need for human judgment.
3. High error rate: Processes where the human factor often leads to errors.
4. Time-consuming: Processes that take up a lot of employee time, which could be used for more strategic tasks.
5. Data access: Processes that generate or require access to large amounts of data that AI can analyze.

This is where the concept of Robotic Process Automation (RPA) comes into play, which, as Hostragons explains, is a technology that allows robots (software) to mimic human interactions with digital systems. In combination with AI and Machine Learning (ML), as Dominanta.sk emphasizes, RPA can automate even more complex and less structured tasks.

Examples of areas for automation:

• Customer service: Chatbots and virtual assistants that can answer 80% of common questions, redirect more complex cases, and gather information before human interaction.
• Finance department: Automation of invoice processing, payment matching, expense reporting, fraud detection.
• HR: Automation of resume screening, answers to common candidate questions, form processing.
• Marketing: Content personalization, automation of email campaigns, customer sentiment analysis, ad optimization. A marketing audit is an ideal preceding process, as stated by UCM Trnava.
• IT/Operations: System monitoring, predictive maintenance, automation of security operations.
• Logistics: Route optimization, warehouse inventory management, demand forecasting.

Intelligent Automation of Non-Production Processes

As Emil Fitoš mentioned at ITAPA AI, AI is ideal for the intelligent automation of non-production processes. This is not just about simple clicks or data copying, but about AI's ability to "understand" context, learn from data, and even make autonomous decisions.

Examples:

• Processing unstructured data: AI can extract relevant information from emails, PDF documents, voice recordings, or images and then process them or move them to the next step in the process.
• Predictive analytics: Predicting demand, employee turnover, investment risks.
• Recommendation systems: In e-commerce, for internal supplier selection, or project management.
• Quality audit: KPMG, in its 2024 transparency report, highlights its Trustworthy AI for providing quality audit. AI can assist in data verification, anomaly detection, and structuring audit processes.
• Energy audit: Identifying opportunities for AI integration can also lead to more efficient energy audits and optimized energy consumption, which is also relevant for production processes, as stated by SCDI.

When identifying opportunities, it is crucial not to see AI merely as a replacement for human labor, but as a tool to empower and enhance human work. The goal is to free employees from repetitive tasks so they can focus on innovative, strategic, and higher-value activities.

Step 3: Evaluation and Prioritization of Opportunities

After identifying potential areas for AI automation, it is important to evaluate and prioritize them. Not every opportunity is equally valuable or easily implementable.

Evaluation Criteria

When evaluating individual opportunities, consider the following factors:

1. Potential ROI (Return on Investment): What will be the financial benefit of automation (cost savings, revenue increase)? What is the estimated payback period for the investment?
2. Implementation complexity: How challenging will the implementation of the AI solution be? Will you need external experts, new technologies, integration with existing systems?
3. Data availability and quality: Do you have sufficient and high-quality data to train AI models? Insufficient or low-quality data can significantly increase complexity and costs.
4. Impact on employees and company culture: How will the change affect employees? Is retraining necessary? What are the potential concerns, and how will you address them? The goal should be work transformation where people perform more creative tasks while AI handles routine, as suggested by Grant Thornton's "total AI company" vision.
5. Risks: What are the potential risks associated with implementation (cyberattacks, ethical issues, technology failure)? Identifying potential risks and opportunities for a business is essential, as stated by Dominanta.sk. In the context of increased cyberattack risk faced by all companies, it is important to take them seriously and implement thorough security measures, as emphasized by SCDI.

Creating Pilot Projects

To start, it is advisable to select 1-3 pilot projects that have high ROI potential and relatively low complexity. A successful pilot project can serve as a proof of concept, build confidence in AI within the company, and provide valuable experience for broader deployment.

Key aspects of pilot projects:

• Clear goals: Define what you want to achieve with the pilot project (e.g., reduce processing time by X%, reduce error rate by Y%).
• Measurable results: Establish key metrics that you will track to evaluate success.
• Small scale: Start on a smaller scale, for example, with one department or one specific process.
• Collaboration: Involve employees from the relevant department in the project. Their feedback is invaluable.
• Flexibility: Be prepared that the first attempt may not be perfect. Learn from mistakes and adapt.

Step 4: Preparation for Implementation and Cultural Change

Successful AI integration requires more than just technology. Organizational structures and company culture also need to be prepared.

Data and Infrastructure

AI models are data-dependent. Ensure your data infrastructure is robust, secure, and that data is accessible and of the required quality. This may involve investing in cloud solutions, data lakes, and data management tools. Data protection and compliance with regulations (e.g., GDPR) are also important.

Building "Trustworthy AI"

KPMG emphasizes the concept of "Trustworthy AI," which is ethically responsible, transparent, and secure. This includes:

• Ethical principles: Creating internal guidelines for the ethical use of AI.
• Transparency: Understanding how AI makes decisions (model interpretability).
• Security: Protecting AI systems from cyberattacks and manipulation. Cybersecurity is a priority for all companies, as stated by SCDI.
• Regulatory compliance: Ensuring that AI solutions meet all relevant laws and regulations.

Education and Change Management

Many employees have concerns about AI, which may be due to a lack of information or fear of job loss. It is crucial to communicate the vision, educate employees about the benefits of AI, and retrain them for new roles. As Grant Thornton states, in a "total AI company," people are exclusively focused on managerial, strategic, and creative tasks, while operational activities are performed by AI. This transformation requires a shift in mindset and skills. Offer training in AI literacy, new technologies, and skills that will be important in the future (e.g., analytical thinking, problem-solving, creativity).

Conclusion

Starting with Artificial Intelligence in your company doesn't have to be daunting. The key to success is a systematic approach, beginning with a thorough audit of existing processes, followed by the identification and prioritization of automation opportunities. Focus on data for decision-making (70%) and process automation (63%), as emphasized by MIRRI. Don't forget pilot projects, which allow you to gain experience with low risk.

Remember that AI is not a one-time project but a continuous process of transformation and adaptation. Prepare your company for the future by investing in data infrastructure, trustworthy AI, and especially in educating your people. With ABRA Consulting, you gain a partner who will guide you through every step on the path to an efficient and innovative company powered by artificial intelligence.

Want to find out how AI can transform your business? Contact ABRA Consulting experts today to schedule a consultation, and let's identify your best opportunities for AI automation together!