AI in solving business problems: strategies and applications

nShortcuts

• Artificial intelligence (AI) as a strategic problem-solving tool: from definition to transformational impact on today’s organizations
• The spectrum of business problems addressed by AI: from operational optimization and customer experience personalization to product innovation and risk management
• Key AI technologies and approaches in the service of problem solving: from machine learning and NLP to expert systems and generative AI
• Step-by-step process for implementing AI solutions in an organization: from problem identification and data strategy to implementation, monitoring and scaling
• Each of these stages requires close collaboration between business teams, data analysts, AI/ML engineers and IT specialists.
• Building an “AI-ready” organization: from data-driven culture and competency development to technology infrastructure and ethical governance (ethical AI)
• The role of leadership and managers in driving AI transformation: from strategic vision to supporting experimentation and managing change
• Challenges, measuring success and the future of AI in solving organizational problems: a strategic partnership with EITT

nArtificial intelligence (AI) in solving organizational problems: a strategic guide to applications, implementation and building competitive advantage

nIn today’s highly complex and dynamic business environment, organizations are constantly looking for new ways to increase efficiency, drive innovation, optimize processes and make more accurate decisions. Artificial Intelligence (AI) is emerging as one of the most transformative technologies of the 21st century, offering unprecedented capabilities to solve a wide range of organizational problems, from everyday operational challenges to complex strategic dilemmas. It is ceasing to be merely the domain of research labs or niche applications, and is becoming an increasingly accessible and practical tool that, if deployed wisely, can become a powerful engine for driving growth and building sustainable competitive advantage. Understanding the potential of AI and skillfully using it to address specific problems is becoming a key competency for leaders and managers at all levels.

nThe purpose of this article is to comprehensively discuss the role of artificial intelligence as a problem-solving tool in organizations. We will look at what categories of business problems can be effectively addressed by AI, what key AI technologies and approaches are applicable here, and what the process of implementing such solutions looks like - from identifying the problem to scaling and monitoring the effects. We will also delve into how to build an “AI-ready” organization that is ready to harness the potential of this technology in a responsible and ethical manner. EITT, as a partner supporting companies in their digital transformation and development of strategic competencies, wants to provide you with the knowledge to not only understand how AI can help solve your company’s specific challenges, but also how to consciously shape your AI adoption strategy to maximize its benefits and support your long-term business goals.

Artificial intelligence (AI) as a strategic problem-solving tool: from definition to transformational impact on today’s organizations

nArtificial intelligence in the context of organizational problem solving refers to the application of computer systems and algorithms capable of performing tasks that would traditionally require human intelligence - such as analyzing data, recognizing patterns, learning from experience, understanding natural language, making decisions or generating new solutions - to identify, analyze and solve specific business or operational challenges. The idea is not to create a one-size-fits-all “artificial awareness,” but to use specialized AI tools to address specific, well-defined problems where they can deliver measurable value. AI thus becomes not an end in itself, but a powerful means to achieve business goals.

nThe evolution of AI from theoretical concepts to practical business applications is driven by several key factors: the tremendous increase in the availability of data (Big Data), which is the fuel for machine learning algorithms; significant advances in computing power and algorithm development (especially in the area of deep learning); the growing availability of cloud platforms offering AI/ML (AI/ML as a Service), which lowers the barrier to entry for many companies; and increasing competitive pressures and the need for continuous innovation.

nThe transformative impact of AI on today’s organizations is already evident and will continue to deepen. AI allows companies to automate routine and time-consuming tasks, freeing up human potential for more creative and strategic action. It enables deeper and faster analysis of data, leading to a better understanding of customers, markets and internal processes, resulting in more informed and fact-based decisions (data-driven decision making). AI stimulates product and service innovation, such as through the creation of personalized offers, intelligent assistants or predictive systems. It also optimizes operational processes, reducing costs, improving quality and increasing efficiency. Finally, AI can support risk management, fraud detection or regulatory compliance. A strategic approach to identifying and solving problems using AI is thus becoming a key element in building a resilient, agile and innovative organization.

The spectrum of business problems addressed by AI: from operational optimization and customer experience personalization to product innovation and risk management

nThe potential of artificial intelligence to solve business problems is extremely broad and covers virtually every aspect of a modern organization. When properly selected and implemented, AI solutions can bring significant improvements and new value in many key areas.

• Operations and Supply Chain Optimization: AI is used for demand forecasting, inventory optimization, production planning, logistics and transportation management (e.g., route optimization, delivery scheduling), and predictive maintenance (predictive maintenance), where algorithms analyze machine sensor data to predict failures and plan maintenance activities, minimizing downtime. Business process automation (Robotic Process Automation - RPA, often augmented with AI) eliminates manual, repetitive tasks in areas such as accounting, order processing and HR.
• Improving Customer Experience (CX): AI is revolutionizing the way companies interact with customers. Intelligent chatbots and virtual assistants provide round-the-clock service, answering questions and solving simple problems. Recommendation and personalization systems tailor offers, marketing content and user interfaces to individual customer preferences and behaviors. Sentiment analysis based on social media reviews or surveys allows companies to better understand customer sentiment and expectations. AI also supports the prediction of customer churn (churn prediction), enabling proactive retention efforts.
• Decision Process Support and Risk Management: AI algorithms can analyze vast amounts of data in search of patterns, trends and anomalies, providing managers with valuable insights to support strategic and operational decision-making. AI is being used to forecast sales, analyze credit risk, detect fraud and abuse, optimize investment portfolios or model the impact of various factors on business performance. In the area of cyber security, AI helps identify and neutralize threats in real time.
• Product and Service Innovation and Research and Development (R&D): AI can accelerate R&D processes by automating scientific data analysis, simulation, generating new product concepts or optimizing their design (e.g., in materials engineering, biotechnology, drug design). Generative AI opens up new possibilities in content creation, graphic design or even code generation.
• Human Resource (HR) Management and Talent Development: AI supports HR departments in automating recruitment processes (e.g., resume pre-selection, recruitment chatbots), identifying talent, personalizing development paths and training programs, analyzing employee engagement and satisfaction (employee sentiment analysis), and strategic workforce planning.
• Resource Optimization and Sustainability: AI can help optimize the use of energy, water and other natural resources in production processes or building management (smart buildings), supporting sustainability goals. nnThis list is only illustrative and shows what a versatile problem-solving tool artificial intelligence can be, as long as it is applied thoughtfully and targeted to specific business goals.

Key AI technologies and approaches in the service of problem solving: from machine learning and NLP to expert systems and generative AI

nBehind the ability of artificial intelligence to solve complex business problems is a wide variety of technologies and approaches, which are selected depending on the specifics of the task and the available data. Understanding these fundamental AI concepts, at least at a general level, is important for managers and leaders to be able to consciously participate in discussions about potential applications of AI in their organizations.

nMachine Learning (ML) is currently the most widely used branch of AI in business. It includes algorithms that can learn patterns and relationships directly from data, without the need for explicit programming for each case. Distinctions include:

• Supervised learning (supervised learning): Algorithms learn from historical data, where both inputs and desired outcomes (labels) are known. Used, for example, in classification (e.g. spam detection, customer segmentation) and regression (e.g. sales, price forecasting).
• Unsupervised learning (unsupervised learning): Algorithms analyze data without prior labels, independently discovering hidden structures, patterns or anomalies in the data. Used, for example, in clustering (clustering) customers, detecting unusual transactions or reducing the dimensionality of data.
• Reinforcement learning: AI agents learn optimal action strategies by interacting with the environment and receiving rewards or punishments for their decisions. Used, for example, in process optimization, recommender systems or games. Deep learning, a subfield of ML, uses multi-layer neural networks to analyze highly complex patterns in large data sets and has been spectacularly successful in image and speech recognition, among others.

nNatural Language Processing (NLP) is a field of AI that deals with the interaction between computers and human language (spoken and written). NLP technologies enable machines to understand, interpret and generate natural language. Applications include chatbots and virtual assistants, sentiment analysis in texts, automatic translations, document classification, information extraction or summary generation, among others.

nComputer Vision focuses on enabling machines to “see” and interpret visual information (images and video). It is used, for example, in quality control in manufacturing, object recognition, medical image analysis, surveillance systems or autonomous cars.

nExpert Systems are computer programs that emulate the decision-making process of a human expert in a narrow field of knowledge. They are based on a knowledge base (containing facts and rules) and an inference mechanism. While they were popular in the past, they are now often integrated with ML-based approaches.

nOptimization and Planning Algorithms are used to find the best possible solutions to complex decision-making problems under given constraints. They are used, for example, in logistics (route optimization), production planning, resource allocation or project portfolio management.

nGenerative Artificial Intelligence (Generative AI), including large language models (LLMs) and diffusion models, is the latest breakthrough in AI that enables the creation of new, original content - text, images, code, music, video - based on patterns learned from massive data sets. It has the potential to revolutionize many areas, from marketing and software development to scientific research and the arts.

nChoosing the right AI technology depends on the specifics of the problem, the availability and quality of the data, and the goals the organization wants to achieve. Often a combination of several different approaches yields the best results.

Step-by-step process for implementing AI solutions in an organization: from problem identification and data strategy to implementation, monitoring and scaling

nImplementing artificial intelligence-based solutions to solve organizational problems is a complex project that requires a strategic approach, interdisciplinary collaboration and iterative improvement. It is not just a matter of purchasing technology, but a transformational process that should be carefully planned and managed. Several key stages in this process can be distinguished:

• Identifying and Defining the Business Problem: The first and most important step is to precisely define the problem you want to solve with AI, and identify the expected business benefits and measurable objectives (KPIs). Questions need to be answered: what specific problem are we addressing? What are its causes and effects? How can AI help solve it? Is solving this problem strategically important to the company? It is important to involve business representatives who best understand the context of the problem in this stage.
• Feasibility Assessment and Data Strategy & Assessment: Next, it is necessary to assess whether the application of AI is technically feasible and whether we have adequate data to train and validate models. It is crucial to audit the available data for quality, quantity, relevancy and compliance with privacy requirements (e.g., RODO). A strategy for acquiring, preparing and managing data for the AI project should be defined. Lack of adequate data or poor data quality is one of the most common causes of AI project failures.
• Selecting or Developing an AI Model: Based on the defined problem and available data, a suitable AI model or algorithm must be selected or developed. This may mean using off-the-shelf AI services offered by cloud providers, adapting existing open-source models, or building your own dedicated solution, but this requires specialized expertise in data science and machine learning.
• Model Training, Testing and Validation: The selected AI model must be trained on properly prepared historical data and then tested and validated for its accuracy, reliability and ability to generalize to new data. This is an iterative process that often requires multiple adjustments to the model parameters and training data.
• Pilot Deployment and Feedback Collection: Before an AI solution is deployed on a large scale, it is recommended that a limited pilot project be conducted to test its performance under real-world conditions, collect feedback from users and identify any problems.
• Full-Scale Deployment and Integration with Business Processes: After successful piloting, the AI solution is deployed in the target environment and integrated with existing business processes and IT systems. Extremely important at this stage is the change management aspect (change management) and proper user training.
• Monitoring, Maintenance and Continuous Improvement: AI solutions are not static. It is necessary to continuously monitor their performance, accuracy and business impact, and to regularly update models in response to changing data and market conditions (MLOps). The learning and improvement process should be continuous.
• Scaling the Solution: If the implementation of AI brings the expected results, plan to scale it to other areas of the organization or expand its functionality.

Each of these stages requires close collaboration between business teams, data analysts, AI/ML engineers and IT specialists.

Building an “AI-ready” organization: from data-driven culture and competency development to technology infrastructure and ethical governance (ethical AI)

nIn order for an organization to fully and responsibly harness the potential of artificial intelligence to solve problems and create value, it is not enough just to implement individual AI projects; a deeper transformation is required to become an “AI-ready” organization. This means building the right culture, developing the necessary competencies, ensuring adequate technology infrastructure, and establishing a solid ethical governance framework.

nA Data-Driven Culture is an absolute foundation. It means that decisions at all levels of the organization are made based on analysis of data and facts, not just intuition or opinion. This requires promoting data literacy among all employees, providing them with easy access to relevant information, and building trust in analytical systems. Leaders must lead by example, actively using data in their daily decisions.

nDeveloping competence in AI and data analytics is another key element. Organizations need to invest in upskilling and reskilling programs for their employees to prepare them for new technologies and new roles. This includes not only IT professionals and data scientists, but also managers and business employees who need to understand how AI can support their work. Acquiring external talent in the AI/ML field is also important, but developing internal capacity is equally important.

nThe right technology infrastructure is essential for the effective implementation of AI solutions. This includes platforms for collecting, storing and processing large data sets (e.g., data lakes, data warehouses), tools for modeling and training AI/ML algorithms (e.g., MLOps platforms), and scalable cloud environments that often offer turnkey AI services.

nAn extremely important, and often underestimated, aspect is the establishment of a robust ethical governance framework for artificial intelligence (Ethical AI Governance). As AI becomes more deeply embedded in decision-making processes and people’s lives, it is essential to ensure that the solutions used are fair, transparent, accountable and free of discriminatory bias (bias). Organizations should develop internal codes of ethics regarding AI, implement mechanisms for oversight and auditing of algorithms, and ensure the privacy and security of data used by AI systems. Responsible implementation of AI builds trust both inside and outside the organization.

nFinally, top management commitment and support are absolutely critical to the success of the transformation to an “AI-ready” organization. Leaders must not only understand the strategic importance of AI, but also actively promote cultural change, allocate the necessary resources, and create an environment conducive to innovation and experimentation with new technologies.

The role of leadership and managers in driving AI transformation: from strategic vision to supporting experimentation and managing change

nTransforming an organization to effectively use artificial intelligence to solve problems and create value is a complex process that requires not only advanced technology and competent professionals, but most importantly strong, visionary and committed leadership at all levels of management. Managers play a key role as architects, catalysts and guardians of this change, shaping strategy, building culture and supporting their teams to adapt to new realities.

nAt the highest level, leadership is responsible for defining a strategic vision for the use of AI in the organization. Leaders must answer the questions: how can AI help us achieve our business goals? In what areas can AI bring the greatest value or competitive advantage? What are the potential risks and how do we manage them? The vision should be clearly communicated to the entire organization, inspiring and mobilizing employees to take action. It is also essential to ensure adequate resources (financial, human, technological) and remove organizational barriers that could inhibit the implementation of AI initiatives.

nMiddle managers act as the bridge between strategy and operations. Their job is to translate the overall AI vision into specific projects and initiatives in their areas of responsibility. They must be able to identify business problems that can be effectively solved with AI, work with technical teams to design and implement appropriate solutions, and monitor their effectiveness and impact on results. It is also crucial to support their teams in acquiring new AI-related competencies and adapting to changed work processes.

nAn extremely important aspect of the role of managers is to build an organizational culture that encourages innovation and experimentation with AI. This means creating a safe space where employees are not afraid to propose new ideas, test unusual solutions and learn from possible mistakes. Managers should encourage interdisciplinary collaboration between business teams, data analysts and AI specialists. Promoting data literacy and critical thinking skills in the context of AI is also key.

nChange management is another fundamental responsibility of leaders in the AI transformation process. Implementing new technologies often involves employee concerns about the future of their roles or the need to learn new skills. Managers must proactively communicate the benefits of AI, address concerns, provide appropriate support and training, and engage employees in the change process, building their sense of shared responsibility for the success of the transformation. A leader who is himself an enthusiast and user of AI solutions becomes the best ambassador for this change within his team and the entire organization.

Challenges, measuring success and the future of AI in solving organizational problems: a strategic partnership with EITT

nDespite its enormous potential, implementing artificial intelligence to solve organizational problems is not without its challenges. Organizations often struggle with issues related to the quality and availability of data, which is essential for training effective AI models. The shortage of skilled AI and data science professionals is another significant barrier. Integrating AI solutions with existing IT systems (legacy systems) can be complicated and expensive. Costs associated with the purchase or development of AI technologies, as well as the construction of the relevant infrastructure, can also be significant. One should not forget about ethical issues, such as the risk of bias in algorithms, the lack of transparency in some models (“black box”), or concerns about data privacy and AI’s impact on the labor market. Finally, measuring the actual return on investment (ROI) of AI projects and proving their business value can be difficult, especially for more complex or long-term initiatives.

nTo effectively measure the success of AI implementation, organizations should define clear performance indicators (KPIs) that reflect both technical aspects (e.g., accuracy of models, processing speed) and, more importantly, the impact on specific business outcomes (e.g., cost reduction, revenue growth, improved customer satisfaction, reduced process time). It is also important to monitor the impact of AI on employees, e.g. through engagement surveys or assessing the development of new competencies.

nThe future of AI in solving organizational problems looks extremely promising. We can expect to see further progress in the development of more sophisticated yet easier-to-use AI tools, including a growing role for generative AI in automating complex tasks and creating innovative solutions. There will be an increasing emphasis on so-called “explainable AI” (XAI) to better understand the decision-making processes of algorithms and increase trust in them. Democratizing access to AI tools, such as through low-code/no-code platforms with built-in AI functions, will enable an even wider range of employees to benefit from the technology’s potential. Issues of ethics, responsibility and sustainability of AI will play an increasingly important role in shaping future applications.

nAs a trusted partner in the field of digital transformation and strategic competency development, EITT offers comprehensive support to organizations seeking to consciously and effectively leverage the potential of artificial intelligence to solve their key business challenges. We help our clients in:

• Conducting an AI maturity diagnosis (“AI readiness assessment”) and identifying areas with the greatest potential to apply the technology.
• Develop a coherent AI implementation strategy that is integrated with overall business objectives and takes into account technological, process, human and ethical aspects.
• Will select appropriate AI tools and platforms and in data architecture planning.
• Designing and implementing training and development programs (upskilling and reskilling) for employees at all levels, building competencies necessary for the AI era (e.g., data literacy, AI/ML basics, AI ethics, AI project management).
• Facilitating workshops and strategy sessions on identifying specific problems to be solved with AI and designing innovative solutions.
• Support in the cultural change management processes associated with AI implementation and in building an internal ethical governance framework for artificial intelligence. Our goal is not only to help implement AI technologies, but more importantly to support building an organization’s sustainable ability to innovate and solve problems based on an intelligent, data-driven approach.

nIn summary, artificial intelligence is revolutionizing the way organizations approach problem solving, offering unprecedented opportunities for automation, optimization, prediction and new value generation. Properly implemented and managed, AI can become a powerful ally in the pursuit of strategic goals, increasing efficiency and building competitive advantage. While the path comes with challenges, a strategic and responsible approach to AI adoption is key to success in an increasingly complex and dynamic business world.

nIf your organization is facing the challenge of harnessing the potential of artificial intelligence to solve specific business problems, is looking for support in developing an AI strategy, or wants to prepare its teams for the coming era of smart technologies, we warmly invite you to contact EITT. Our experts are passionate and committed to helping you navigate the world of AI and turn its potential into real successes. Together, we can design the future of your organization, powered by intelligent solutions.

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Frequently Asked Questions

What types of business problems are best suited for AI solutions?

AI delivers the strongest results on problems that involve large volumes of data, require pattern recognition, or demand consistent and repeatable analysis. Operational optimization, demand forecasting, customer segmentation, and anomaly detection are particularly well-suited. Problems that require purely creative judgment or operate with very limited data are less suitable for AI alone.

How long does a typical AI implementation project take from start to measurable results?

A focused pilot project addressing a single well-defined problem can deliver initial results within two to three months. Full-scale implementation, including integration with existing systems, team training, and process redesign, typically takes six to twelve months. The iterative nature of AI projects means that models continue to improve in accuracy and value over time.

What is the most common reason AI projects fail in organizations?

The most frequent cause of failure is starting with technology rather than a clearly defined business problem. Organizations that purchase AI tools without first identifying a specific challenge, assessing data readiness, and defining measurable success criteria end up with solutions that do not connect to real business outcomes and struggle to justify continued investment.

Does an organization need to build an in-house AI team to benefit from artificial intelligence?

Not necessarily. Many organizations start successfully with ready-made AI-as-a-Service solutions and cloud-based platforms that require no specialized in-house expertise. As AI maturity grows, building internal competencies in data analysis and AI project management becomes increasingly valuable, but external partners and consultants can bridge the gap effectively in the early stages.