Dynamic technological progress, especially in the field of artificial intelligence, prompts us to rethink how companies operate and deliver services. The application of modern technologies has been and increasingly will be an important factor in competitiveness, enabling process optimization, revenue growth, cost reduction, and the creation of personalized solutions.
Digital transformation is not just about computerizing processes; it often involves a fundamental change in the approach to organizational management. One could say that we are experiencing another wave of transformation, this time centred around AI.
In this article, we will examine the process of effective digital transformation using AI in companies and institutions. I will attempt to answer key questions concerning the benefits of AI, preparing an organization for transformation, the main stages of this process, and the associated challenges and risks.
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AI as a Tool, Not a Goal
Implementing AI in organizations can bring tangible benefits and impact operational efficiency, the quality of decision-making, and competitiveness. Research by PwC (“AI Jobs Barometer 2024”) showed that 84% of CEOs noticed an increase in employee productivity after implementing AI. 17% of them believe that over the next three years, AI will significantly change how their companies “create, deliver, and capture value.”
Despite numerous benefits, AI should be treated as a tool supporting business goals, not a goal in itself. The key to success is precisely identifying areas where AI can bring real value. Sometimes, a conscious decision not to implement AI can be… better. In some cases, excessive automation can lead to a usually temporary decline in quality or increased system maintenance costs. Therefore, a thorough analysis of potential benefits and risks is crucial.
I am an advocate of taking actions that provide long-term value. This way of thinking is also close to me when it comes to AI.
From my observations, some organizations abandon full AI implementation after the pilot phase, concluding that the technology does not bring the expected benefits. In such cases, companies should verify whether the pilot was properly conducted (e.g., whether it was accompanied by training and whether employees genuinely utilized AI’s potential). If so, resources should be focused on areas where AI has real potential to improve efficiency or process quality. In other words, narrow the scope of transformation.
The Process of Digital Transformation Using AI
Digital transformation using AI is a complex process requiring careful planning and execution. The most important stages are:
1. Diagnosis of the Current State
A detailed analysis of existing processes, technologies, and human resources. Identification of areas where AI can bring the greatest benefits.
Failure to fully diagnose processes and existing systems before implementing AI can lead to not optimal results. For this reason alone, it’s worth approaching the moderate successes of other companies with caution.
2. Creating an AI Utilization Strategy
Developing a comprehensive strategy that includes clearly defined, measurable business objectives.
The strategy should consider the criteria for selecting appropriate AI tools, risk analysis (technological, operational, financial), and legal and ethical aspects. An essential element is also training, to which I will devote more space later in the article.
3. Defining Tools
Selecting technologies, applications, models, and methods (organizational, operational, procedural, etc.) that will be used to implement AI in the organization, including data organization and processing.
Regarding the AI tools and models themselves, companies and institutions can use one or both approaches:
- Using existing models and tools – choosing ready-made solutions can be faster and cheaper but requires greater adaptation of the organization (because the tools won’t adapt to us!). It’s worth remembering that AI components are also present in applications not directly associated with artificial intelligence (accounting programs, office suites, graphic applications, etc.). This is a step I recommend starting with, and companies usually do so. Proper training of employees in using new tools is also important.
- Creating own solutions – alternatively, organizations can develop dedicated AI systems better suited to their needs. This is more time-consuming and costly but provides greater flexibility. In the public sector, creating proprietary AI systems may be the only option to maintain data confidentiality and adapt solutions to specific regulations.
On a side note, when implementing AI, it is also worth considering RPA solutions for process automation.
4. Pilot Implementation and Testing
Conducting pilot implementations on a small scale to test new technologies in controlled conditions, identify problems, and assess AI’s impact on processes.
Based on the results obtained, it will be easier to convince others of further changes. It will also be possible to verify earlier assumptions and prepare a more rational implementation plan.
But beware—the AI implementation plan should not be a document created once and for all; it’s worth adopting an agile approach and revisiting earlier steps when necessary.
A good strategy is to use several tools from a given category during the pilot phase and thus choose the best one.
5. Wider Integration with Existing Systems and Processes
Implementing the finally selected tools and reorganizing processes with the intention of utilizing AI’s potential.
After the pilot phase, the biggest challenge begins—the execution of the final implementation plan. Why is this the biggest challenge? It results from several reasons:
- Often, systems are closed and/or outdated—sometimes they need to be changed or modified, which can be costly.
- Legal and licensing issues may arise related to changes and transferring data on which AI would operate.
- Current staffing is tailored to existing needs, which may necessitate changing job roles and, ultimately, layoffs.
- Employees have their habits and a certain level of knowledge, so the transformation may involve the need for them to acquire and use new skills.
- It is a process that may be spread over years, bringing changes that go far beyond the IT area.
Many organizations experience difficulties in integrating AI with existing systems, which often leads to delays or strategy changes. It’s worth being prepared for this.
6. Evaluation and Optimization
Monitoring the effects of the transformation systematically and with the ability to compare results, followed by further refining the transformation plan.
Work doesn’t end upon completing the initial plan. Continuous monitoring of results and optimization of implemented solutions are necessary. Evaluation should include measuring operational efficiency indicators and assessing the impact on the quality of products or services. It’s also worth asking employees about AI’s impact on their work and their needs.
To assess the outcomes of the implementation, it is worthwhile to select appropriate KPIs, systematically monitor the results, and strive to improve them.
Training and Skill Development
Digital transformation requires employees to adapt to new realities. Training and developing their competencies translate into greater effectiveness of AI implementations.
When preparing a training program, it’s worth considering a range of competencies that will help in the transformation process and effective use of AI. In my opinion, the following are useful:
- Training in using AI at work and in business – this training is essential and includes, for example, tool operation, prompt engineering, or ways to optimize and automate work. I conduct such training daily, and it significantly increases the effectiveness of using AI tools. All employees who will be affected by the change and will work with AI should participate.
- Strictly technical training – this concerns tool integration or building own AI models and is intended for people who will implement artificial intelligence and integrate it with previously used systems.
- Change management training – helps managers effectively introduce changes, minimizing employee concerns.
- Development of soft skills – skills such as collaboration, creativity, and problem-solving are still important. Training prepares employees for effective use of AI and adaptation to a changing business environment.
Training cannot be treated as a one-time action. Digital transformation is an ongoing process, and AI technologies will continue to develop dynamically, which means that employees must be ready for continuous upskilling. Organizations should implement competency development programs as an integral part of their business strategy, regularly updating and adjusting them to changing needs. This is especially important in the era of widespread use of AI.
Interesting fact: Several participants asked me whether, given the scale of changes, they should participate in my training every year. In fact, that would be great. But I’m working on another solution, and interested parties are invited to subscribe to the newsletter.
Challenges and Risks
Integrating AI with existing IT systems is one of the main challenges. Often, these are closed solutions and, not infrequently, outdated. Additionally, AI implementation may bring the need to modernize infrastructure (computer hardware, networks, etc.). As a result, it may be necessary to increase the budget allocated for transformation, and delays may occur.
Data management in the context of AI is also quite a challenge. Often, data is hard to access or unstructured (e.g., information from the same category is stored in different formats, not always in appropriate fields, sometimes in different datasets/databases). To cope with this problem, AI is sometimes used during the transformation to organize collected data, including structuring previously unstructured data. Often, the outcomes of the transformation include procedures or guidelines on how to maintain data quality in the future.
Organizing data streams is extremely important in relation to AI implementation and process automation.
Even without the above-mentioned problems, implementing AI involves significant financial investments. It’s not only about purchasing tools or programming and maintenance work. Additional costs result from infrastructure modernization, employee training, and change management. As a result, such a transformation can involve costs ranging from tens of thousands of dollars for small companies to millions for large organizations.
It’s worth remembering that the most important recipients of almost every change are people. In organizations, there is often a significant disparity in technical skills, which can lead to difficulties in adapting to new technologies. During training sessions and discussions about transformation projects, I observe that many employees feel uncertainty related to introducing AI, which can affect their motivation and the effectiveness of the process.
Moreover, employees’ habits regarding previous work methods can be associated with a significant barrier in the transformation process. Resistance to change, fear of job loss, and lack of understanding of the benefits of AI can lead to serious difficulties in effectively implementing AI. Therefore, it is necessary to conduct extensive informational activities and training that will help employees understand and accept the changes.
Employee concerns and some resistance are understandable. It is important to ensure that the AI implementation process is transparent and accompanied by training.
Introducing AI can lead to temporary operational disruptions, which in turn can result in a temporary drop in productivity. The result? For a moment, we have increased expenses, and problems and delays in operational activities can affect revenues.
Implementing AI brings challenges related to legal regulations (there is still much uncertainty in this area) and ethics. Organizations must ensure compliance with regulations (e.g., GDPR, AI Act) and consider ethical aspects of AI use, such as algorithm transparency and avoiding discrimination.
There’s a lot to consider, right? However, one should not be discouraged—awareness of these problems and choosing a company that helps to sensibly carry out the transformation will prevent many issues and quickly deal with those that will inevitably occur.
The Future of AI in Business and Institutions
IDC experts predict that global spending on AI will reach $300 billion annually by 2026 (they estimated $154 billion for 2023). It looks like a lot of work ahead! Organizations must invest in IT and employee skill development and build a data-driven culture. I am convinced that companies continuously investing in their talent achieve higher returns on AI investments.
Digital transformation using AI is an opportunity for significant development and strengthening of market position; it’s a chance to streamline processes. It requires careful planning, clearly defined goals, and investment in tools and employee skill development. In the case of (new?) companies that will be built with an AI-first approach, benefits can be counted in hundreds of percent compared to traditional operating models.
Thanks to AI and the next wave of digital transformation, your company can accelerate and outpace the competition.
The key is not only to recognize AI’s potential but also to consciously and responsibly use it, ensuring continuous skill development, risk management, and an ethical approach to implementing new technologies. Despite many challenges and opportunities in disguise, I think it’s worth taking this risk. If the first step is still ahead of you, it’s worth taking it today!
I encourage you to explore the training offerings and subscribe to the newsletter, which will allow you to receive notifications about subsequent articles. We would also be glad to discuss transforming your company—feel free to contact Oxido. Finally, I recommend a text on how to talk to employees about AI.
