While Generative AI applications like ChatGPT and DALL·E have dominated the conversation over the last few years, something much more powerful is around the corner. In 2025, AI agents look set to be next-level implementations of large language models (LLMs) because of their ability to automate a wide range of business processes, revolutionize customer services, and deeply integrate with other digital tools and platforms.
But how can your business ensure it’s well-positioned to take advantage of this productivity, cost-saving, and scalable automation? Digital Adoption Platforms (DAPs) hold the key.
What are AI agents?
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| Image by drobotdean on Freepik |
Unlike simple chatbots, these sophisticated tools can complete tasks and workflows without constant human supervision or direction. Instead of following a set of rules, AI agents are adaptable and responsive and can perform complex tasks thanks to a blend of qualities, such as:
Creating step-by-step plans by breaking down complex tasks into manageable chunks.
Storing and recalling data from past interactions that help shape their decisions.
Interacting with various tools like APIs to gather information or perform particular tasks.
Learning from outcomes or experiences.
While AI-augmented workflow automation tools have been around for a while, AI agents can go significantly beyond the capabilities of these process and rule-driven tools.
However, it’s worth noting that definitions in the world of AI are notoriously slippery. A leading AI firm, Anthropic — a recent recipient of a $1 billion investment from Google — uses the umbrella term agentic systems to describe this emerging technology but “draw an important architectural distinction between workflows and agents,” which are further explained in the screenshot below.
This distinction is useful when understanding how AI agents will shape the business world in 2025 and beyond.
If it still seems unclear, think about it this way.
Imagine you run a factory and you employ two interns.
Workflow Intern
For the workflow intern to be productive on your assembly line, you must give them a predetermined set of instructions for each product. They won't deviate from the process or make decisions about how to assemble the product.
They will faithfully complete their tasks as directed. However, if things go wrong, machines change, or new products are released, they will need your intervention or additional training.
Agent Intern
For the agent intern, you give them a clearly defined objective for assembling the product. From there, you show them best practices, let them watch and speak to other factory workers, and allow them to research and experiment the best and most efficient ways to assemble the product.
As products or machines change or operational problems occur, the agent intern can react and adjust. What’s more, they can also find new and more efficient ways of assembling the product.
Why AI agents will have an outsized impact in 2025
Advances in AI and ML and far greater integration with existing business tools means that AI agents will become much more powerful next year and beyond. Perhaps most importantly, these tools will lower the overall cost and complexity of implementing AI, making the technology more accessible to a broader range of businesses.
The best way to highlight the impact of AI agents is by using an example. Flo Crivello, the founder of the AI assistant platform Get Lindy, recently published a video on X to show an agent that “talks with customers of an energy company, schedules calls with them, and actually leads the calls itself to schedule solar panel installations fully autonomously.”
The implications here are immense and go far beyond, for example, Robotic Process Automation (RPA) tools augmented with AI decision-making abilities.Could be the craziest AI agent we've deployed yet — talks with customers of an energy company, schedules calls with them, and actually leads the calls itself to schedule solar panel installations fully autonomously
— Flo Crivello (@Altimor) January 17, 2025
People don't understand what is *already in prod* pic.twitter.com/xyvvDOig9J
How do AI agents work?
Each implementation of AI will have its own intricacies. However, the vast majority use LLMs with additional components for planning, memory, tool integration, and decision-making.
Here is a general overview of how AI agents work. Understanding this will help you implement AI agents across your workflows and business processes.
Goal setting
The first step for using AI agents begins with setting a clear objective about what you want it to do. For example, that could be:
Resolving complex customer service queries.
Schedule meetings, send reminders, and organize calendars.
Execute entire workflows, from data analysis to decision-making.
Data collection
The AI agent then gathers information that is relevant to its task. Some examples of these data include:
Customer data or information related to customer interactions.
Process logs or screen recordings of humans executing workflow tasks.
User behavior data or social media sentiment analysis.
Data Processing and Analysis
Once the AI agent has collected the data, it uses AI/ML algorithms to interpret the information and build effective models that are geared towards achieving their stated objective.
Decision making
With its objectives in mind, the AI agent chooses the best course of action to hit these goals.
Execution
The AI agent executes its designated task based on its analysis and decision-making.
Learning
As the agent performs tasks, it learns from outputs and through, in some scenarios, positive and negative feedback from humans.
As you can see, these AI agents can use a wide range of information to learn how to perform tasks. One of the most potent sources of information comes from workers themselves, as their actions show the agent what is required to complete a task.
How DAPs work with AI agents to unleash next-level productivity
There is no debate that AI agents will significantly impact businesses of many kinds in 2025. The only real question is about how your business can ensure the implementation of this next generation of automation tools goes as smoothly as possible.
In the section above titled How do AI agents work?, one of the most relevant points is about data collection. While this can refer to giving the AI agent access to data sets, customer interactions, user behavior data, and so on, when it comes to automating jobs to be done, employees and users themselves are one of the richest sources of information.
DAPs are primarily focused on improving things like product onboarding and adoption. These tools help by teaching users how to learn how to complete their jobs more quickly and efficiently. However, they can also be used to mine and understand different business processes.
The beauty of using DAPs to unearth and understand business processes is that they already contain information about frequent user activities that you can automate. For example, Usetiful’s smart analytics contains data and insights into user engagement and the effectiveness of product tours, which you can use to teach AI agents about the jobs that need to be done in your business. Additionally, it also tracks user behavior linked to jobs to be done in digital products, providing another rich source of data for your AI agents.
Advanced process mining
Another exciting way that Usetiful can help train AI agents is by analyzing user behavior patterns and unearthing frequent activities that are prime candidates for automation.
The real value here lies in Usetiful's understanding of user goals in B2B contexts, which provides valuable insights for developing AI agents tailored to very specific business needs. Understanding “why” tasks are performed, rather than just “what” the tasks are, will be instrumental in creating customized solutions for unique problems or inefficiencies that are holding certain businesses back.
Now, let’s take a look at some of the Usetiful smart analytics features that can help with AI agents.
Usetiful smart analytics that can help train AI agents
One of the key elements of DAP software is that it can record user interactions. Primarily, these features were developed because they generate data about the usability and adoption of software solutions. For example, which steps does a user take to perform a particular task, such as uploading a photo, downloading a document, and so forth?
Some of the data sources involved here include:
Event tracking: Logging specific user actions such as clicks, page views, and feature usage.
Feature adoption measurement: Measuring the user adoption of core features, most usually representing the set that differentiates the most successful users from non-successful ones.
Session Recording: Recording entire user sessions, providing a comprehensive view of how users navigate through the application.
Additionally, DAPs can analyze user behavior patterns to find areas where users struggle or experience confusion. However, where this gets interesting in an AI agent context is that you can also leverage this data to see how various jobs are performed with a whole host of different applications.
Step-by-step guide to using DAP analytics for training AI agents
OK, so now that you understand the theory and the concepts behind using DAP data for training AI agents, it’s time to put it all together.
STEP 1: Collect relevant data
Here are some of the ways you can use analytics data to guide your AI agent.
Track how users interact with your product tours, smart tips, and onboarding checklists. Additionally, you can also track how users interact with your digital product while adopting certain features or reaching certain goals.
Explore user profiles to granularly track user behavior, events, site visits, session data, and other user patterns.
Create segments based on user behavior so you can target particular roles or departments for hyper-targeted AI bot training.
STEP 2: Prepare data
For the sake of efficiency, you need to prepare this data so that it can be used to train your AI agents. Some steps you need to take here include:
Ensure the potential training data is accurate and free of errors. Remove any irrelevant or duplicate information to ensure clarity.
Depending on the AI models you use, it can be useful to structure the data into an easily readable format.
Find the most important elements, workflows, tasks, or interactions that will be useful for training your AI.
STEP 3: Train your AI
Now that you have prepared your data, you can train your AI agent to perform particular business processes.
Here are some of the things you’ll need to do:
Select the appropriate business process automation or AI agent tool.
Outline the specific human-performed task that you want to automate.
Train your AI on the prepared data. You can implement ML processes, such as supervised learning for user behaviors or reinforcement learning, to optimize process flows.
Continue to collect data from Useitful and use it within your AI agent system to achieve regular retraining and process changes.
Next time, we'll discuss how Usetiful works with AI agents in the context of specific integrations and partnerships. Stay tuned!
Best practices for using DAP analytics for AI bot training data
Using Big Data comes with big responsibilities. Here are some of the things you need to consider when compiling and using data to build intelligent automation bots.
Ethics are important when you collect and use data for AI training. So, be conscious of data privacy regulations and ensure you treat user data with utmost care and respect.
Test and compare your AI agent's performance regularly against human performance to ensure that your automation effort produces efficient and satisfactory outcomes.
While the whole point of automation is to handle repetitive tasks, it’s essential to embrace human-in-the-loop procedures where possible to ensure safety and quality stay at appropriate levels.
Develop strong governance and compliance frameworks to ensure the ethical use of AI in your business processes.
Final thoughts
AI agents will change the business landscape in 2025. However, their potential depends heavily on their access to useful training data. DAPs like Usetiful will have a big role to play here as they can help your business discover and record human-directed processes that the AI can learn from and mimic. Don’t get left behind.