How can you start AI adoption in B2B Services company in 2025?

Artificial Intelligence – or more precisely, Large Language Models (LLMs) and the interfaces built for them – has taken over the IT world. Globally, their potential spans a wide range of applications, particularly in enhancing employee productivity. Sales assistants, customer support bots, document generators – new tools promising business value are emerging everywhere. The main motivation behind using AI? Boosting productivity and automating processes to save time and reduce costs.

Key challenges in implementing AI in B2B services companies

• Hundreds of systems already supporting company operations
• Thousands of AI tools – with approx. 100 new ones released daily
• Lack of integration between leading AI assistants and existing software
• Unclear data security policies within AI products
• Limited education that can’t keep up with tech developments, reducing employee awareness

Where can AI realistically deliver value for B2B services businesses?

• 📄 Document, report, and proposal automation – essentially expert knowledge processing. Since the rise of ChatGPT, people have “stopped writing” in the traditional sense. Tools like Claude AI, Le Chat, and ChatGPT are now head-to-head.
• 🔍 Understanding market and client context – learning about market trends, competitors, or client challenges. Perplexity AI leads in this space.
• 🌍 Document translation – from fragments to full docs and presentations. DeepL is top-tier here.​
• 📚 Knowledge management and sharing – building specialized assistants or integrating AI into platforms like Notion, Microsoft Loop, or Confluence is reshaping how knowledge flows in companies.
• 📢 Content marketing – generating article ideas, posts, video scripts, or full media content.
• 🧠 Client problem-solving – redefining how we approach strategy, brainstorming, and critical thinking with the help of AI.
• 📨 Communication support – AI helps structure emails, reports, and presentations using formats like Amazon’s Narrative, the Problem-Solution-Results method, or the SUCCESS framework.
• 🎨 Visual identity creation – brand development, icon design, and even professional photoshoots enhanced by AI.

This Is Just the Tip of the Iceberg The list above highlights only a fraction of what's possible — the full landscape of AI product applications is extensive and constantly expanding.

Full screen, english version here

Let's move to one of the most common objection to AI Adoption: “I have ChatGPT — what else can I do?” Let’s bust the myth of this popular tool.

ChatGPT may boast over 300 million users, but it’s only one product among many that offer this type of experience. The real opportunity lies in choosing the tools that truly fit your company’s needs and processes. . An average of 100 new AI products are released daily. The key is not to use everything — but to control and curate what your company adopts.

Below is summary of short list with most common use-cases:

• 🗣️ Conversational AI – working with text, documents, notes, or voice: generate, enhance, and develop content.
• • US-based: ChatGPT, Claude AI, Gemini, Notebook LM
  • Europe-based: Le Chat, Bielik.AI, PLLuM
  • China-based: Kimi.AI, deepseek.ai
• 🖼️ Image Generation AI – creating iconography, presentation visuals: Recraft, Adobe Firefly, MidJourney
• Video AI – animation generation or editing existing video: RunwayML, Sora, Kapwing
• Audio AI – voice cloning, podcast creation, or real-time translation: ElevenLabs
• AI in Digital Workspaces – auto-creating presentations, document formatting, meeting or email summaries: Google Duet AI, Microsoft Copilot

This list isn’t exhaustive — and it’s changing fast. The above tools rank highly in our assessments due to their proven usefulness in business environments.

Check here more inspirations for AI usage

Implementing AI in professional services is a transformational process that requires gradual change and stakeholder adoption. To maximize the benefits while minimizing investment and resistance, we recommend the following four principles:

• “Start small and expand” we recognize that AI technology is complex, and not everyone fully understands the limitations of the software. That’s why we suggest starting small – build trust and competence around specific tools that deliver short-term wins, then scale toward long-term value.
• “You’re Not Ready for AI if Your Data Isn’t Ready” while AI assistants can be useful in everyday tasks, the real competitive advantage comes when your data ecosystem and use cases are well-prepared and aligned.

Check here sample report for AI readiness in B2B services

• “Education First, Then Implementation” don’t jump into tech without laying the foundation. Purchasing a license or custom AI solution won’t change anything if your employees don’t know how to use it. Empowering users is key.
• “Minimal Control, Maximum Value” AI needs guardrails. Begin with basic governance policies around tools, data, and risk management. Gradually evolve these into a broader AI governance framework.

Assess your company readiness here

These principles allow for an agile and comfortable AI rollout – both for company owners and employees.

In the short term, the greatest value of AI in professional services comes from purchasing specific tools and training employees to use them effectively. However, that’s just the beginning of the journey. There are three primary levels of AI implementation in B2B service companies:

1. AI products - as previously discussed, the company focuses on specific tools, leveraging their potential. It regularly monitors tool usage and changes in employee productivity.
2. AI automation - the company integrates large language models (LLMs) or other statistical models directly into business processes to replace manual work. Example use cases: Auto-generating newsletters, Monitoring legal/regulatory changes or  Auto-generating client offers Make.com, Zapier.com, Power Automate, Google Appsheet. These tools solve well-defined, logic-driven problems.
3. AI agents - here, the company delegates some decision-making to AI agents. These agents are assigned specific tasks and connect directly to systems, such as chat platforms or CRMs. Example tasks: Generating tasks from emails, Classifying incoming messages or  Handling new website inquiries. Leading platforms: n8n.io, Microsoft Copilot Studio, Vertex AI Agent or Amazon Bedrock Agents. 

🛡️ Now That You Understand the Different Levels of AI Implementation... 

…it’s essential to reflect on the risks – especially those tied to AI tools and how they are integrated within your company.

Adopting AI isn’t risk-free. The European Union is introducing safeguards through the EU AI Act, aiming to regulate the use of AI technologies responsibly. In parallel, international standards such as ISO 42001 (AI management systems) and ISO 23984 (AI risk assessment) are being developed to guide companies toward safe and ethical use. What’s most important is to understand your company’s role in the AI value chain and identify the key risks relevant to your operations. Assuming you are an AI consumer – meaning you're leveraging existing tools or models rather than building your own – it’s best to explain the implications with a practical example:

See examples of risks for AI product

As you've seen, even a single AI product can come with numerous challenges. To summarize, the key risks related to AI tools can be grouped into three main areas:

• Data privacy- many AI tools are developed by companies based in the United States, where user data is often stored outside the EU, making it non-compliant with GDPR.  Always review the tool’s privacy policy before implementation.
• Niche/unreliable software - with an average of 100 new AI tools launched daily, many come from unverified vendors or offer poor quality and limited support. We recommend verifying the legal entity behind any AI product before adopting it into your business environment.
• Incorrect predictions - Large Language Models (LLMs) can and do make mistakes. When integrating AI into daily workflows or automated processes, ensure you establish content validation mechanisms and limit fully automated decision-making.

Naturally, these aren’t the only risks — there are many more. Here, we’ve focused on the most critical ones to help you start with a risk-aware approach to AI adoption.

Implementing AI is a collaborative change process that involves key stakeholders and requires strong coordination. Based on our experience, we recommend structuring the rollout in four strategic phases:

• Week 1 – Strategy - define your company’s motivation for adopting AI. Identify initial use cases through process analysis and employee interviews. Flag any legal or industry-specific limitations, and obtain executive buy-in.
• Week 2 – Use Cases - prioritize 3 key processes where AI can bring immediate value. Select a leading AI assistant and define a capability-building plan. Conduct a review of training/advisory partners, and develop a use case matrix – outlining what’s permitted and restricted within your organization.
• Week 3 – Education - deliver training programs based on the strategy and prioritized use cases. Make key decisions: 
  - Will the work be done in-house or with a partner? 
  -  Is the team trained in both technology and business application? 
  -  Are data security measures in place? - Have you appointed a sponsor and leader to oversee the process?
• Week 4 – Pilot - select an area leader and form a dedicated task force with clear responsibilities, goals, and KPIs. Launch the pilot initiative, supported by tools to track progress and impact.

Click here to see detailed plan

What are the benefits from AI adoption in B2B services? Read below.