Gone are the days of simple scripts. Discover intelligent conversational systems that are becoming a key component of both CX and sales strategies. Artificial intelligence is no longer a vision of the future; it is a fundamental element of today’s business reality. Currently, companies that fail to implement a coherent AI strategy risk falling behind, with little chance of catching up to the competition. AI-powered solutions have stopped being merely productivity tools, becoming true operational reformers and business models. The shift from seeing AI as a “novelty” to a “standard investment” signals technological maturity and measurable return on investment. Companies no longer ask “if” they should invest, but “how” to do it strategically to gain a competitive advantage.
It is important to note at the outset that AI is an extremely broad concept encompassing various technologies – practically used in the market for over a decade. Today, artificial intelligence includes everything from simple automation systems to sophisticated agents based on large language models (LLMs) with highly specialized functions. AI-supported solutions in sales and customer service can be applied to a wide range of activities, from message personalization and sentimet analysis to advanced lead scoring and sales funnel forecasting.
Such a broad approach to AI has led to a 1.8-fold increase in global AI deployments between 2024 and 2025 alone. In this dynamic landscape, Poland stands out in the region, being the most AI-friendly market in Central and Sutheastern Europe. According to the latest research conducted by the Human+AI Institute and Campus AI in April 2025, only 18% of Polish companies have not invested in this technology, compared to 43% across the region. Moreover, 62% of Polish organizations have increased their AI spending, and deployment leaders achieve results up to 45% faster than their competitors. This is proof that Polish businesses are moving from testing to actual implementation at a pace faster than the global average.
Poland’s openness to new technologies can once again become a technological advantage for the country. Technologies such as contactless payments via NFC, BLIK instant transfers, digital identity in the Obywatel app, or the digitization of healthcare through Moje IKP are solutions that are still not standard in the US or many Western European countries. AI could follow a similar path, as it is already a driving force behind growth that cannot be stopped.
AI in Customer Service. Revolution in Customer Experience.
AI is transforming the perception of customer service – from a costly, necessary support function to a strategic asset that strengthens customer relationships and differentiates a brand in the market. Rather than focusing solely on efficiency, the goal becomes building lasting, profitable relationships. Which AI-based solutions are most effective at optimizng the user experience?
Conversational chatbots available 24/7/365
Thanks to AI, chatbots no longer require human supervision. They use natural language processing (NLP) to conduct two-way, organic conversations, simulating interactions with a human agent. Being available 24/7/365 ensures immediate support, while for companies, it means reduced service costs and shorter wait times to reach a consultant. Importantly, these solutions are highly accurate and efficient. A well-structured knowledge base and FAQ enable them to assist customers effectively, providing a quality of service comparable to that of a human. Employees freed from chatbot management can focus on solving more complex problems or engaging in strategic work.
Examples: Sephora Virtual Artist demonstrates how AI can impact results – the chatbot contributed to an 11% higher conversion rate and a 50% increase in customer loyalty. Domino’s Dom reduced live agent costs by $500,000, while AI assistant Zoey at David’s Bridal generated $30,000 in dress sales within a few weeks. An example from Poland is Żabka with its AI chatbot Franek serving franchisees. The chatbot handled millions of messages, correctly categorized tens of thousands of inquiries, and, most importantly, saved over 2,000 hours of work just by clarifying issue details. It resolves 20% of cases without human involvement.
Customer Emotion Recognition
AI systems analyze voice tone, facial expressions, and text to assess a customer’s emotional state in real time. They enable proactive intervention before a negative experience leads to customer churn. These solutions can de-escalate conflicts with demanding clients by adjusting the tone of voice and the messages conveyed. They are also unaffected by human factors, always applying the appropriate strategy calmly. An example is Sprinklr which detects dissatisfied customers in real time and sends alerts to supervisors. Notably, Poland is a lader in developing AI-based customer emotion recognition solutions. Examples include VoiceLab.AI and Sentimenti.
Automatic Conversation Summaries
AI can automatically generate concise conversation summaries, capturing key points. This drastically reduces the time agents spend on post-call tasks such as note-taking and reporting, allowing them to focus on solving customer problems. Artificial intelligence can thus free employees from the tedious task of preparing meeting “minutes” and summaries. Dedicated AI agents using NLP continuously “record” the entire conversation in text form, then use large language models to create summaries, highlight the most important points, and even generate a follow-up task list after customer interactions.
Examples of solutions for automatic conversation summarization include Otter.ai, Fireflies.ai, and the AI Companion built into Zoom Communications. Similar functionalities are available in Gemini for Google Meet and Copilot for Microsoft Teams.
Personalized Communication and Consumer Need Protection
AI analyzes customer behavior data and interaction history (including sentiment analysis) to create personalized experiences and offers. AI systems enable proactive communication based on past customer actions. They can automatically generate personalized offers and even modify the appearance of services or websites to better match customer needs.
An example is L’Oréal ModiFace which achieved 3x higher conversion rates thanks to virtual try-ons. Starbucks, with its “Deep Brew” system, proactively suggests orders in the app, leading to higher purchase frequency. AI-driven personalization is no longer a luxury but a foundation – 71% of consumers now expect it. In Poland, major banks use such solutions to continously tailor their offers to individual clients, for instance, sending push notifications with preferential loan offers right after a large account transfer.
AI as an Organizational Knowledge Base
Artificial intelligence can be leveraged to build custom AI chatbots and agents for internal organizational use. Companies can either create their own solutions from scratch in an on-premise model or fine-tune popular open-source LLMs.
Properly implemented, AI serves as an assistant, supporting employees in real time by suggesting responses and providing information from the company’s knowledge base.
A prime example in Poland is mBank, which has implemented advanced AI-based tools to streamline the work of its advisors and call center agents, who handle thousands of customer interactions every day.
AI in Sales: Driving Revenue in Practice
Artificial intelligence is becoming an indispensable tool in sales departments for creating flexible strategies, enabling companies not only to optimize processes but also to directly increase revenue.
Lead scoring and contact prioritization
AI continously evaluates potential customers based on their likelihood of conversion, analyzing historical data and current behaviors.
Grammarly, using Salesforce’s Einstein AI, recorded a 30% increase in conversions and twice as fast deal closures. Progressive Insurance achieved over 90% accuracy in identifying “hot” leads, resulting in 3.5x higher conversion rates for top-rated contacts.
Real-time offer generation
AI solutions powered by large language models enable automatic creation of personalized offers based on live data, such as customer preferences and purchase history. This shortens response times and accelerates deal closures. Sales teams can use open chatbots or dedicated, in-house solutions integrated with other systems via API. This allows automation of the process and the generation of targeted communications without requiring manual edits from sales reps.
For several years, AI has been integrated into the most popular CRM systems such as HubSpot and Salesforce, and it is increasingly being implemented in smaller and more specialized platforms as well.
Dynamic pricing
The vast amount of data AI can process from various sources allows for price optimization in e-commerce, adapting to demand, competitor pricing, stock levels, time, and even weather.
A prime example is Amazon, which changes product prices an average of 2.5 million times per day, contributing to an estimated 25% profit increase. Similarly, Uber and Lyft have long used dynamic pricing, adjusting fares based on real-time demand. It’s no coincidence that rides on New Year’s Eve or national holidays are noticeably more expensive – when demand rises, customers are willing to pay more.
Cross-selling and up-selling during conversations
AI can also act as an intelligent salesperson, suggesting complementary products (cross-selling) or higher-value options (up-selling) based on purchase history. This solution, familiar from e-commerce platforms, recommends items “Frequently Bought Together” or offers bundle discounts for complementary products. Today, these systems work fully automatically without the need to manually program each offer.
Generative AI as a sales content copywriter
AI agents automate the creation of content such as product descriptions, ad copy, and visual assets, significantly reducing production costs and time. Properly configured, AI can serve as a highly specialized copywriter – faster and far more efficient, dramatically shortening the time from idea to execution.
Some of the world’s biggest brands have embraced AI to power their advertising campaigns. Cadbury‘s “Not a Cadbury Ad” campaign used AI to create thousands of localized ads, resulting in a 32% increase in engagement. Lexus leveraged IBM Watson to script a commercial which boosted perceptions of the brand’s innovativeness by 13%. The Japanese premium carmaker was also among the first to produce an AI-generated TV ad – back in 2018, four years before OpenAI’s ChatGPT was released to the market.
Challenges and pitfalls of AI implementation
Despite its tremendous benefits, implementing AI comes with a range of challenges and potential pitfalls.
- Risk of over-automation: Pushing automation too far can lead to a loss of human oversight and control, increasing the risk of errors and reputational damage. Overly complex processes may also cause organizations to lose control of operations and face internal management issues.
- Job displacement: The growing integration of AI raises concerns about replacing human jobs. It’s worth noting, however, that AI is unlikely to take jobs away in the coming years. Instead, it will transform the nature of work, creating a need for reskilling and upskilling employees.
- Data protection and ethics: The use of AI brings critical concerns around compliance with regulations such as GDPR, as well as ethical issues like “scoring” individuals without their knowledge. This makes it particularly important to develop Edge AI solutions that process sensitive data within a company’s own IT infrastructure, without relying on external servers.
- Energy challenges for AI development in Poland: AI infrastructure, particularly data centers, is highly energy-intensive. The current state of Poland’s energy sector – still largely dependent on coal (56.7% in 2024) – poses a fundamental challeng to the sustainable growth of AI in the country. In fact, energy demand for AI in Polish data centers surged by an impressive 94.3% in August 2024.
AI trends for the coming years
The future of AI in business points to an era of deep integration, advanced predictive capabilities, immersive experiences, and the rise of no-code models—enabling the deployment of new solutions without specialized knowledge or programming skills.
- AI integration with omnichannel: Combining data from physical stores and online channels to create a unified, comprehensive view of the customer.
- Advanced predictive analysis: Forecasting customer behavior with greater accuracy – such as service cancellations – allowing companies to proactively address their needs.
- AI meets VR/AR: The convergence of AI with virtual and augmented reality (VR/AR) is set to deliver a new generation of immersive experiences, including virtual showrooms.
- Global and Polish perspectives: Poland, as a leader within the Baltic states consortium, plans to build the Baltic AI Gigafactory, a €3 billion initiative aimed at creating the infrastructure needed to develop and train advanced AI models.
Summary and recommendations for companies
As we can see, solutions leveraging elements of artificial intelligence are not just a trend – they are a strategic necessity for maintaining competitiveness and driving growth. Companies that implement AI strategically can expect increased revenue, optimized operational costs, and a competitive edge.
Recommendations:
- Invest in comprehensive AI strategies rather than individual tools.
- Prioritize data and its quality, as AI effectiveness depends directly on it.
- Focus on responsible AI implementation: ethics, data privacy, and human oversight are crucial for building customer trust.
- Develop employee skills by investing in reskilling to prepare staff for effective collaboration with AI.
- Monitor trends and adapt, as the AI landscape is evolving rapidly.
