AI in Accounting: What Actually Works in 2026

Everyone’s talking about AI in accounting now. The consultants have their slides ready. The vendors are rebranding their invoice OCR tools. LinkedIn is full of posts about “How I automated my finance tasks – comment FINANCE to get my n8n workflow.” Wrappers everywhere.

Here’s my honest take after working on the technical AI side of this stuff for a while: AI will absolutely change accounting. But not in the way most people think, and probably not as fast as the headlines suggest.

The terminology mess

Let’s start with the basics, because the language around this is a disaster. When people say “AI in accounting,” they could mean any of the following:

Rule-based automation: If invoice amount > 10,000, route to CFO. If “Intra-community supply under § 4 1b”, then “VAT_00”. Not AI. Just code with a marketing budget.

Machine learning classifiers: Trained models that categorize transactions based on patterns. These are actually useful and have been around for years. But they often don’t generalize well and are hard to keep up to date, because often they’re a black box.

OCR and document extraction: Reading invoices and pulling out vendor names, amounts, dates. This is common practice now. And in some instances it actually works.

Large Language Models: Our favorite brand new toy. GPT, Claude, Gemini. Can understand context, interpret messy inputs, handle edge cases. But also: can hallucinate numbers with absolute confidence.

Most “AI accounting” products today are really just ML classifiers with an LLM-powered chatbot stapled on top. Which is fine, but let’s be honest about what we’re dealing with.

Where AI already works today

Behind all the marketing talk, there are real wins happening right now:

Document capture and extraction. Modern systems can read invoices in any format, any language, any level of scan quality. The combination of vision models and LLMs has basically solved this problem. You still need human review for edge cases, but 80-90% straight-through processing is achievable. And if you get an invoice in a new format, it still works. Because AI.

Transaction categorization. For standard cases, ML models are excellent at learning your chart of accounts and applying it consistently. They don’t get tired on Friday afternoons. They don’t have “creative” interpretations of cost centers. Also: don’t forget that AI doesn’t necessarily means LLMs. We also have an awesome family of sub 1b parameter encoder only models that are wonderful at classification.

Anomaly detection. Spotting duplicate invoices, unusual amounts, vendors that suddenly changed bank details. Pattern recognition at scale is exactly what ML does well. This is genuinely useful for fraud prevention and audit prep.

Natural language queries. “Show me all marketing expenses over 50k last quarter” without writing SQL. This works now. It’s not magic, but looks like magic and really saves time. Why not chat with your business data 😉

The common thread? These are all tasks where being approximately right most of the time is valuable, and where humans can easily verify the output.

Where things get interesting (and dangerous)

Now for the hard part.

The moment you need AI to make a decision that has legal or tax implications, everything changes. Consider VAT determination on an incoming invoice. Sounds simple: it’s 19%, right?

Except when it’s not. Is the supplier in another EU country? Is this a service or a good? Does reverse charge apply? Is it construction-related (§13b in Germany)? Is the supplier even VAT-registered? Is it a triangular trade? Is there a pandemic with special vat rates?

I’ve written about this specific problem with tax codes in SAP before. The short version: there are dozens of edge cases, and getting it wrong means audit findings, back taxes, and possibly fraud allegations.

Here’s the uncomfortable truth: LLMs are very good at explaining what reverse charge is. In academic voice or as a sonnet. But they’re dangerously unreliable at determining whether a specific invoice should use it. The difference matters.

The hallucination problem is real. An LLM will confidently tell you that this invoice clearly qualifies for intra-community supply treatment. It might even cite the relevant EU directive. It might also be completely wrong, because it didn’t notice the supplier has a German VAT ID, or because the goods never actually left the country. I ran a couple of examples through different LLMs – and they were very opinionated about certain things. But not necessarily right. So right now, we’re creating a VATBench to get a better view of this.

When Claude or GPT makes a mistake in a creative writing task, you get a weird sentence. When it makes a mistake in tax determination, you get a six-figure assessment in your next audit.

The hybrid AI architecture that actually works

So where does this leave us? Not with “AI bad, humans good.” The answer is architectural. A pattern that really works magic combines three things:

LLMs for interpretation. Let the language model read the invoice, extract the relevant facts, classify the transaction type, identify the supplier’s jurisdiction. This is what they’re good at – information extraction!

Structured rules for decisions. Tax law is not creative. It’s a decision tree with many branches but clear logic. Once you have the facts, applying the rules should be deterministic. No creativity needed. No hallucination possible.

Transparent audit trails. Every decision needs to document why it was made. Which invoice fields were extracted. How the supplier was classified. Which rule determined the tax code. When the auditor asks, you need answers.

The key insight: don’t ask the LLM what the tax code should be. Ask it to extract the facts, then apply your rules. It’s not half as sexy as “our AI automatically handles everything.” But it works.

What this means for CFO offices and finance teams

A few practical conclusions:

You’re not getting replaced. The “AI will automate away accounting” takes are mostly written by people who’ve never closed a month-end.

Your job is changing. Less data entry, more oversight. Less manual matching, more exception handling. Less typing, more thinking. If you’re spending 60% of your time on tasks that could be automated, you should definitely talk AI.

You need to understand the tools. Not how to build an LLM from scratch (even this is super fun to do). But how they work, where they fail, what they can and can’t do. The finance leaders who thrive will be the ones who can evaluate AI vendors with real technical understanding.

Start with contained problems. Don’t try to “AI-enable the entire finance function.” Pick one painful process with clear success criteria. Invoice capture. Expense categorization. Intercompany matching. Get that working, learn from it, then expand.

The bottom line on AI in accounting

AI in accounting is real, useful, and overhyped all at the same time. The technology works for information extraction, pattern matching, and natural language interfaces. It doesn’t work—not safely—for unsupervised decision-making on anything with legal consequences.

The winning approach combines the interpretive power of LLMs with the precision of rule-based systems and the oversight of human experts. It’s less exciting than “fully autonomous AI accounting” but it’s what actually ships, actually works, and actually survives audits.

Evaluation-Set for every Customer

Today we launched a new feature in the Prompt-Tuning-Clinic – the “Evaluation Criteria” Section.

It’s one of the most annoying things in AI to hunt for the question whether a custom configured AI (ChatBot, Agent, Automation) is doing well or not. In most cases both suppliers and customers are treating it like this:

“Yesterday i did run this prompt against it, and it looked really well, good progress!” – or – “My boss asked it to do x and it gave a total wrong answer, we have to redo the whole thing!”

Its an inherent problem of AI to some extent, for one because of the universal capability of these systems and the fact that you can ask practically everything and will always get an answer. And – due to the non-deterministic architecture and functioning of these systems it is very hard to define what it is doing and what not.

We were a bit tired of this, and so we thought – why are we reading LLMarena (btw – we launched german LLM-Arena recently, try it here) and other rankings of new AI models every second day and dont apply similar mechanisms to our customer installations?

This is exactly what this new feature brings:

  • define a couple of test-prompts (you can upload some treatment material like your API-Documentation or an md-file of the Website and let the AI make proposals for test-prompts)
  • run these prompts against the current configuration of the bot
  • Evaluate them (can also be done with an LLM automatically)
  • Define correct answers for edge cases
  • Save those prompts that are important permanently
  • Give them thumbs up/down to create cases for Fine-Tuning and DSPy
  • Run them all to get a quality ranking

Once this is set up the game is changing drastically, because now we (both supplier and customer) do have a well defined test-set of intended behavior that can be run automatically.

This is not only good for initial setup of a system, but also for Improvements, Model-Updates, new Settings etc.

And: as we are also offering fine-tuning for our models and have integrated DSPy as automated Prompt-Tuning tool you can create training-data for these while creating your Evaluation-Set as well – just thumbs up/down on the answer creates an entry in the test-database for later.

Sign up for a free Account and try it out!

Business Intelligence in the AI Era in 2026: Opportunities, Risks, and the Architecture Behind It

Let’s be honest: Does your company have all business-relevant information available at the push of a button? Or is it also stuck in various data silos, largely unconnected – the ERP here, the CRM there, plus Excel spreadsheets on personal drives and strategy documents somewhere in the cloud?

If you’re nodding right now, you’re in good company. I regularly speak with CEOs and finance leaders, and the picture is almost always the same: The data would be there. But bringing it together to answer a specific question takes days – if anyone can do it at all.

Why This Is Becoming a Problem Right Now

The days when companies could rely on stable markets and predictable developments are over. Inflation, geopolitical tensions, disrupted supply chains, a labor market in flux – all of this demands a new discipline: Decisions must not only be good, they must be good fast.

Traditional business intelligence has a proven answer to this: dashboards, KPIs, monthly reports. But let’s be honest – these tools hit their limits as soon as questions get more complex. What happens to our margin if we switch suppliers? How does a price increase affect different customer segments? What scenarios emerge if the euro keeps falling?

Questions like these need more than static charts. They need a real conversation with your own data.

The Temptation: An AI Sparring Partner for Your Decisions

This is exactly where generative AI gets really exciting. The idea is compelling: An intelligent assistant that knows your company’s numbers, understands connections, and lets you explore strategic options – anytime, without scheduling, without someone having to build an analysis first.

“How did our top 10 customers develop last quarter?” “What if we reduced the product portfolio by 20%?” “Compare our cost structure with last year and show me the biggest outliers.”

A dialogue like this would democratize business intelligence. Not just the controller with their Excel expertise would have access to insights – every decision-maker could query the data themselves. I still find this idea fascinating.

The Problem: When AI Hallucinates, It Gets Really Expensive

But – and this is a big but – here’s the crux. Large Language Models are impressive at generating plausible-sounding answers. They’re considerably less reliable at delivering factually correct answers. Especially when it comes to concrete numbers.

An AI that misremembers a date in a creative text? Annoying, but manageable. An AI that invents a revenue figure or miscalculates a margin during a business decision? That can really hurt. The danger multiplies because the answers are so damn convincing. We humans tend to trust a confidently delivered statement – even when it comes from a statistical language model.

I say this from experience: A naive integration of ChatGPT with company data is a risk, not progress. Anyone who sees it differently has either been lucky or hasn’t noticed yet.

The Technical Challenge: Connecting Three Worlds

The solution lies in a well-thought-out architecture that intelligently brings together three different data sources:

Structured data via SQL: The hard facts – revenues, costs, quantities, customer histories – typically reside in relational databases. Here, the AI must not guess but query precisely. The system must generate SQL queries, execute them, and correctly interpret the results. No room for creativity.

Unstructured data via RAG: Beyond the numbers, there’s context – strategy papers, market analyses, internal guidelines, meeting notes. These documents can be accessed through Retrieval Augmented Generation: The system searches for relevant text passages and provides them to the language model as context.

The model’s world knowledge: Finally, the LLM brings its own knowledge – about industries, economic relationships, best practices. This knowledge is valuable for interpretation, but dangerous when mixed with concrete company figures.

The art lies in cleanly separating these three sources and making transparent where each piece of information comes from.

The Solution: Everything into the Context Window

Modern LLMs offer context windows of 100,000 tokens and more. This opens up an elegant architectural approach: Instead of letting the model guess which data might be relevant, we proactively load all needed information into the context.

A well-designed system works in several steps: It analyzes the user’s question and identifies relevant data sources. Then it executes the necessary SQL queries. In parallel, it searches the document base via RAG. And finally, the LLM receives all this information served up together – with clear labeling of sources.

The language model thus becomes an interpreter and communicator, not a fact generator. It can explain numbers, reveal connections, ask follow-up questions, discuss options for action – but it doesn’t invent data, because the real data is already in the context.

Transparency as a Design Principle

Such a system must build transparency into its DNA. Every statement about concrete numbers should cite its source. The user must be able to trace: Does this come from the database? Was it quoted from a document? Or is it an assessment by the model?

This transparency isn’t just a technical feature – it’s the prerequisite for trust. Anyone basing business decisions on AI-supported analyses must know what they’re relying on.

The Path Forward

Business intelligence with AI is neither utopia nor hype – it’s an architecture challenge. The technology is mature, the models are powerful, the interfaces exist. What many companies lack is a thoughtful approach that leverages the strengths of LLMs without falling prey to their weaknesses.

The future belongs to systems that intelligently connect structured databases, document knowledge, and language models – while always making transparent what is fact and what is interpretation. Companies that find this balance gain more than just another analytics tool. They gain a real sparring partner for better decisions in difficult times.

And yes – that’s exactly what we’re working on.

The Lean Revolution: Why Small Language Models will dominate 2026

Faster, cheaper, more controllable – and still powerful: Small Language Models are conquering the enterprise space.

While the world obsesses over GPT-5 and ever-larger models, something exciting is happening in the background: Small Language Models (SLMs) are evolving rapidly and becoming a real alternative for enterprise applications. In our latest webinar, we showed why – and demonstrated our own fine-tuned models live.

The Problem with the Big Ones

80-95% of all corporate AI projects fail. A sobering number that keeps making headlines. But why?

A major reason: Large language models like ChatGPT or Claude are often problematic for enterprise use. OpenAI recently switched off all legacy model variants when releasing GPT-5 – a nightmare for any corporate IT with running processes. Add data privacy concerns, unpredictable behavior, and dependency on American cloud services to the mix.

Small but Mighty: The Advantages of SLMs

Small Language Models (typically 1-20 billion parameters) offer tangible benefits:

⚡ Speed: Responses in milliseconds instead of multi-second waits. Once you’ve experienced the responsiveness of a local SLM, there’s no going back.

🔒 Privacy: Runs on your own servers, needs no internet connection, no data leaves your premises. Ideal for sensitive corporate data.

🎯 Control: No surprise model updates, no sudden behavioral changes. The model does exactly what it’s supposed to do.

💰 Cost: Significantly cheaper to operate than API calls to major providers.

🔧 Customizability: Through fine-tuning, SLMs can be precisely trained for specific tasks – with manageable effort.

The Secret Sauce: LoRA Fine-tuning

The game-changer is called LoRA (Low-Rank Adaptation). This technique makes it possible to customize models with surprisingly little data (from ~100 examples) and compute power. The principle: You only train a small “adapter” that’s layered over the model weights – no retraining of the entire model required.

The result? A model that not only gives the right answers but responds in exactly the right style. Anyone who’s tried to get ChatGPT to give shorter answers or avoid certain formatting through prompting alone knows how difficult that is. With fine-tuning, it works reliably.

Live Demo: Our Own SLMs

In the webinar, we showed three fine-tuned models, all based on LiquidAI’s LFM-2 with just 1.2 billion parameters:

  1. General German Model: Solid answers to everyday and technical questions
  2. Fritz Perls Therapy Bot: A model that perfectly imitates the confrontational conversation style of Gestalt therapist Fritz Perls
  3. Market Research Association Model: Analyzes implicit brand associations in professional market research style

The responsiveness is impressive – answers come practically instantly. And the best part: Everything runs on our own European servers.

The Future: Hybrid is King

Our vision at HybridAI: It’s all about the combination. Small, fine-tuned models for routine tasks, large models for complex queries – orchestrated by an intelligent control layer that recognizes which model is right for each situation.

This gives enterprises the best of both worlds: Fast, controllable, privacy-compliant answers for 80% of queries – and the power of large models when truly needed.

Want to Try It Yourself?

We’re making our SLM demo publicly available. Test for yourself how the small models perform – and contact us if you’d like to discuss custom fine-tuned models for your use cases.

🚀 HybridAI + N8N: Your AI Agent Just Got Seriously Agentic 🚀

Today marks a huge milestone for our HybridAI platform: we’ve fully integrated N8N – and it’s a game changer for anyone working with automation and intelligent agents.

What’s new?

🔗 Deep integration with N8N workflows
Every HybridAI user now gets free access to our dedicated N8N server. Even better: from inside any N8N workflow, you can now send a Function Call directly to your chatbot or agent – with a single click.

Example:
“Send a follow-up email to all leads from today.”
→ Your bot instantly triggers the corresponding N8N workflow.

Why does it matter?

Agentic AI means that your bot doesn’t just talk, it takes action. It can now handle complex workflows, launch services, update databases, and more – autonomously.

To achieve this, you need two things:

  1. A smart control center → your HybridAI agent
  2. A powerful action engine → N8N

Now you get both, perfectly connected.

What is N8N, anyway?

N8N is a no-code automation tool developed in Berlin. With it, you can:

  • Connect APIs and AI models
  • Read/write Google Docs
  • Send emails
  • Query or update databases
  • Build custom nodes for anything else

And now, your HybridAI chatbot can trigger it all seamlessly from any conversation.

How to get started?

If you have a HybridAI account, just go to your “AI Functions & Actions” section in the admin area and create a Function Call pointing to your N8N webhook. That’s it – your bot is ready to act.

🎯 Try it now and explore new levels of automation with HybridAI + N8N.

New IoT Integration: Real-World Data Meets Conversational Intelligence (2026 Update)

Update 2026: As of now, we also support MQTT sensor data. More importantly, we have connected our IoT sensor infrastructure to our BI solution. This means that data can now not only be read and reported, but also analyzed and evaluated in a multi-dimensional way.

We’re excited to introduce a powerful new feature on our platform: the ability to stream IoT sensor data directly into your chatbot’s context window. This isn’t about triggering an external API tool call—it’s about augmenting the bot’s real-time understanding of the world.

How it works

IoT sensors—whether connected via MQTT, HTTP, or other protocols—can now send live data to our system. These values are not fetched on-demand via function calls. Instead, they’re continuously injected into the active context window of your agent, making the data instantly available for reasoning and conversation.

Real-World Use Cases

🏃‍♂️ Fitness and Weight Loss

A health coach bot can respond based on your real-time activity:

“You’ve already reached 82% of your 10,000 step goal—great job! Want to plan a short walk tonight?”

Or reflect weight trends from smart scales:

“Your weight dropped by 0.8 kg since last week—awesome progress! Should we review your meals today?”

⚡️ E-Mobility and Charging

A mobility assistant knows your car’s charging state:

“Your battery is at 23%. The nearest fast charger is 2.4 km away—shall I guide you there?”

Bots can also keep track of live station availability and recommend based on up-to-date infrastructure status.

🏗 Accessibility and Public Infrastructure

A public-facing city bot could say:

“The elevator at platform 5 is currently out of service. I recommend using platform 6 and taking the overpass. Need directions?”

Perfect for people in wheelchairs or with limited mobility.

🏭 Smart Manufacturing and Industry

A factory assistant can act on process data:

“Flow rate on line 2 is below target. Should I trigger the maintenance routine for the filter system?”

This allows for natural language monitoring, error detection, and escalation—all in real time.

What Makes This Different?

🔍 Contextual Awareness, Not Tool-Calling
Sensor data is part of the active reasoning window—not fetched via a slow external call, but immediately available to the model during inference.

🤖 True Multimodal Awareness
Bots now reason not just over language but also over live numerical signals—physical reality meets LLM intelligence.

🚀 Plug & Play Integration
Bring your own sensors: from wearables to factory machines to public infrastructure. We help you connect them.

In Summary

This new feature unlocks unprecedented potential for intelligent agents—combining the power of conversational AI with a live, evolving understanding of the physical world. Whether you’re building a wellness coach, a mobility assistant, or an industrial controller, your agent can now think with real-world data in real time.

Reach out if you’d like to get started!

A practical view on agentic AI and why we think MCP is not solving a relevant problem.

Yes, in the current AI hype discourse this statement almost feels like suicide, but I want to briefly explain why we at HybridAI came to the conclusion not to set up or use an MCP server for now.

MCP servers are a (currently still “desired”) standard developed and promoted by Anthropic, which is currently gaining a lot of traction in the AI community.

An MCP server is about standardizing the tool calls (or “function calls”) that are so important for today’s “agentic” AI applications – specifically, the interface from the LLM (tool call) to the external service or tool interface, usually some REST API.

With the current ChatGPT image engine generated – I love these trashy AI images a little and will miss them…

At HybridAI, we have long relied on a strong implementation of function calls. We can look back on a few dozen implemented and production-deployed function calls, used by over 450 AI agents. So, we have some experience in this field. We also use N8N for certain cases, which adds another relevant layer in practice. Our agents also expose APIs to the outside world, so we know the problem in both directions (i.e., we could both set up an MCP server for our agents and query other MCPs in our function calls).

So why don’t I think MCP servers are super cool?

Simple: they solve a problem that, in my opinion, barely exists and leave the two much more important problems of function calls and agentic setups unsolved.

First: Why does the problem of needing to standardize foreign tool APIs hardly exist? Two reasons. (1) Existing APIs and tools usually have REST APIs or similar, meaning they already use a standardized interface. These are quite stable, which you can tell from API URLs still using “/v1/…” or “/v2/…”. They remain stable and accessible for a long time. Older APIs are often still relevant – like those of the ISS, the European Patent Office, or some city’s Open Data API. These services won’t offer MCP interfaces anytime soon – so you’ll have to deal with those old APIs for a long time. (2) And this surprises me a bit given the MCP hype: LLMs are actually pretty good at querying old APIs – better than other systems I’ve seen. You just throw the API output into the LLM and let it respond. No parsing, no error handling, no deciphering XML syntax. The LLM handles it reliably and fault-tolerantly. So why abstract that with MCP?

In reality, MCP adds another tech layer to solve a problem that isn’t that big in daily tool-calling.

The bigger issues are:

–> Tool selection

–> Tool execution and code security

Tool selection: Agentic solutions work by allowing multiple tools, sometimes chained sequentially, with the LLM deciding which to use and how to combine them. This process can be influenced with tool descriptions – small mini-prompts describing functions and arguments. But this can get messy fast. For example, we have a tool call for Perplexity when current events are involved (“what’s the weather today…”), but the LLM calls it even when the topic is just a bit complex. Or it triggers the WordPress Search API, though we wanted GPT-4.1 web search. It’s messy and will get more complex with increased autonomy.

Tool execution: A huge issue for scaling and security is the actual execution of tool code. This happens locally on your system. Ideally, at HybridAI, we’d offer customers the ability to submit their own code, which would be executed as tool calls when the LLM triggers them. But in terms of code integrity, platform stability, and security, that’s a nightmare (anyone who submitted a WordPress plugin knows what I mean). This issue will grow with more use of “operator” or “computer use” tools – as those also run locally, not at OpenAI.

For these two issues, I’d like ideas – maybe a TOP (Tool Orchestration Protocol) or a TEE (Tool Execution Environment). But hey.

Agentic Chatbots in SaaS – How HybridAI Makes Your App Smarter

SaaS platforms have long included help widgets, onboarding tours, and support ticket systems. But what if your app had a conversational layer that not only explained features – but also triggered them?

With HybridAI, this is now possible. Our system enables you to create agentic chatbots that speak your domain language, understand user intent, and call backend functions directly via Function Calls and Website Actions.

From Support Widget to Smart Assistant

Traditional support widgets are passive: they answer FAQs or forward tickets. A HybridAI bot, however, can do things like:

  • Trigger onboarding steps (“Show me how to create a new project”)
  • Fetch user data (“What was my latest invoice?”)
  • Execute actions (“Cancel my subscription”)

All of this is powered by safe, declarative function calls that you define – so you stay in control.

How It Works

  1. Define Actions: You provide a list of available operations (e.g. getUser, updateRecord, createInvoice) and their input parameters.
  2. Connect via API or Function-Call Interface: HybridAI receives these as tools it can call from natural language.
  3. Bot Instructs + Responds: The chatbot interprets the user prompt, selects a matching function, fills in parameters, and calls it.
  4. Real-Time Feedback: The user receives immediate confirmation or result, without ever leaving the chat.

Integration Benefits

  • No coding required to get started – Just define what your functions do.
  • Frontend or backend integration via JS events or APIs
  • Custom styling + voice – the bot looks like part of your product
  • Multi-language and context-aware – excellent for international SaaS

Use Cases

  • CRM assistants that update leads or pull sales data
  • Analytics bots that explain dashboards or alerts
  • HR bots that automate time-off requests
  • Support bots that resolve issues without agents

Ready to Try?

You can test HybridAI’s function-calling capability today with our Quickstart Bot – no sign-up required.

And if you’re ready to bring this into production, reach out to us – we’ll help you integrate HybridAI into your stack in days, not months.

Real-life use at school

This week we tested HybridAI for the first time in a real school environment. The students of Stadt-Gymnasium Köln-Porz had the opportunity to spend a German lesson with us under the guidance of Sven Welbers – on the wonderful topic: Grammar!

What could possibly be better!

It was genuinely exciting, as we configured HybridAI according to the teacher’s specifications to present a detective story that could only be solved step by step by completing grammar exercises. Since the stories were generated by the AI, each student had a unique version, with delightful variations even when new stories were generated.

Throughout the lesson, the bot provided feedback on progress and occasionally injected humorous messages.

Conclusion: The students certainly had a lot of fun! Not always guaranteed with such topics. The teacher was impressed by the educational quality of this lesson. Despite the dry material, the students appeared engaged and focused.

In the near future, we will develop further examples for the educational sector. The next session with a bot on the topic “Konjunktiv I and II” is already being prepared!

You can see the grammar bot in action here:

What to Expect from an AI Chatbot for Your Website in 2025

The world of AI chatbots is evolving at a rapid pace, and 2025 will mark a new era in intelligent, interactive website assistants. Businesses and website owners can now integrate AI chatbots that go far beyond simple scripted responses. These AI-driven assistants are more powerful, engaging, and action-oriented than ever before. Here’s what you can expect from the latest AI chatbot technology—and why it might be time to upgrade your website’s chatbot.

Core Features: The Must-Haves for 2025

  1. Function Calling: More Than Just Chat
    AI chatbots are no longer just answering questions—they are taking action. With function calling, chatbots can trigger automated processes, retrieve live data, and even control external applications. Imagine a chatbot that not only tells your customers their order status but also updates it in real-time. Or think of a system that can call several APIs in the background and integrate the results in the ongoing chat seamlessly.
  2. Rich Media Display: Images & Videos
    Websites are visual, and chatbots should be too. In 2025, AI chatbots seamlessly integrate with media libraries, displaying images, GIFs, and even videos within the chat. This is ideal for product demonstrations, interactive customer support, or guided tutorials. Your Chatbot should offer an interface to upload and manage media-files in a way that the LLM can understand and use them, when the conversation would benefit from it.
  3. Logging and Analytics: Know Your Users
    Keeping track of chatbot interactions helps businesses refine their strategy. AI chatbots now log conversations, analyze engagement trends, and provide deep insights into user behavior—all from a single dashboard. That is important as you are planning to offload one of the precioust things you have – the conversations with your customers – to the AI. The Chatbot should offer an easy interface to observe the conversations and maybe even refine them where necessary. A download of Logfiles is also something you should expect for further analysis, for instance if you want to compile some KPIs or dig deeper into the conversations.
  4. File Upload & Sharing
    Chatbots now support file uploads from both users and website owners. Whether it’s customers submitting documents for verification or business owners providing deeper insight material for the AI, this feature enhances workflow automation. As everyone is using Chat-GPT from time to time these days users are expecting this functionality and therefore your ChatBot should offer it.
  5. Live Streaming Responses
    Speed is key. AI chatbots now stream their responses in real-time, ensuring a more natural and engaging conversation flow. No more waiting for a full answer—users see it as it’s generated. And it underlines the feeling of magic when people interact with AI systems – a nice flowing streamed response creates the feeling to speak to something special and fascinates many users.
  6. Multiple AI Models for Maximum Flexibility
    Why limit yourself to one AI model? Hybrid chatbots allow businesses to use multiple LLMs (Large Language Models) for different tasks, choosing the best tool for each interaction. This ensures higher accuracy and better responses. Sometimes it is because of certain functionality, sometimes it can be speed, but LLM-models also vary in other aspects like restrictions, openness or recency of the training material.

Next-Level Features: The Competitive Edge

  1. Payment Integration: Monetize AI Conversations
    AI chatbots are not just support agents—they can be sales tools. With payment integration (e.g., PayPal, Stripe), customers can complete purchases, subscriptions, or donations directly in the chat. The ChatBot should support some ways of offering paid messages to the users.
  2. Emotion Detection: Smarter, More Human AI
    AI chatbots are becoming emotionally intelligent. By analyzing user sentiment, they can adjust their tone, prioritize urgent messages, and escalate issues when frustration is detected.
  3. Human Takeover: The Perfect AI-Human Blend
    Sometimes, AI isn’t enough. The best chatbots now feature smooth human takeover, allowing human agents to jump into conversations when needed. This seamless transition ensures customers get the best of both AI automation and real human support.
  4. Task Management: Keep the user in the loop
    As Chat-Bots are evolving more and more towards full-blown agents and personal assistants you should expect some sort of task-management built into your Chatbot so that a user can say “please remind me of this workout tomorrow morning”.

Final Thoughts

AI chatbots in 2025 will be more than just digital assistants—they’ll be action-oriented, multimedia-rich, and deeply integrated with business processes. Whether it’s automating workflows, displaying visual content, or handling transactions, the next generation of AI chatbots will redefine how businesses engage with their audience.

If you’re looking to integrate an advanced AI chatbot on your website, now is the time to explore the latest technology and get ahead of the competition!