Agentic & task-oriented AI
This is the move beyond AI that just responds to prompts, toward AI systems that plan, take actions, coordinate workflows (so-called “agentic” AI) rather than purely reactive.
Agentic AI refers to AI systems that operate autonomously to achieve specific goals, taking initiative to plan, decide, and act without constant human intervention. Unlike traditional AI, which follows predefined rules or responds to direct commands, agentic AI is proactive and can handle complex, multi-step tasks by reasoning, using tools, and collaborating with other agents. This is enabled by integrating large language models (LLMs) with agent-based systems, which gives the AI the ability to reason and determine the best course of action.
Why it matters:
Agentic AI systems provide the best of both worlds: using LLMs to handle tasks that benefit from flexibility and dynamic responses, while combining these AI capabilities with traditional programming for strict rules, logic and performance. This hybrid approach enables the AI to be both intuitive and precise. Agents can autonomously perform tasks while adapting to new data or dynamic environments, something that’s challenging for static code. At the same time, critical processes (such as security or calculations) can rely on deterministic, traditional algorithms.
Agentic AI can be designed to search the web, called application programming interfaces (APIs) or query databases. Agents can fetch real-time information, retrieve updates or pull specific data points that are critical for decision-making. Agents can initiate and manage tasks such as data logging, real-time monitoring and trend analysis. They can proactively track and collect data streams from IoT devices, social media feeds or other systems, providing LLMs with fresh inputs for more informed decision-making and contextual responses.
Autonomous With the big brains of LLMs and the targeted capabilities of agents, agentic AI can operate independently and autonomously perform specific tasks without the need for constant human oversight. This enables continuous operation in environments where human supervision is limited or unnecessary. Autonomous systems can maintain long-term goals, manage multistep tasks and track progress over time.
For example, an agentic AI could be tasked with managing a marketing campaign, continuously monitoring performance, adjusting strategies and optimizing results based on feedback without the need for human input at every step.
Intuitively one can imagine many business functions currently performed with software as a service (SaaS) products being replaced or augmented by agentic systems, which enable workers to interact with data and perform tasks more efficiently with natural language inputs and simplified user interfaces.
For example, imagine a ticketing system that software developers use to track the progress of projects. Such a system requires many tables, tabs and workflows that aren’t always easy to understand at first glance. To find out useful information, users need to hunt for the right data, navigating a complex array of menus to get the information they need. Then, they might need to use that information to create a presentation.
What to watch:
AI Workflow
Artificial intelligence (AI) workflow is the process of using AI-powered technologies and products to streamline tasks and activities within an organization.
Recent advancements in AI-powered apps and tools and AI models have created new opportunities for businesses to improve how they handle workflows. As organizations embrace digital transformation, AI-driven workflows, powered by automation platforms and advanced templates eliminate inefficiencies caused by manual tasks and improve the partner, employee and customer experience.
Components of AI workflow automation
There are several AI technologies that organizations can use to improve their workflows.
APIs
Business process automation
Generative AI
Intelligent automation
Machine learning
Natural language processing
Optical character recognition
APIs
APIs, or application programming interface, are sets of rules or protocols that enable software applications to communicate with each other to exchange data, features and functions. APIs are a key component of AI workflows, as they drive the ability to connect services. For example, connecting from a website to your bank account to buy something online is an example of an API connection in use.
Business process automation
Business process automation (BPA) is a strategy that uses software to automate complex and repetitive business processes. It is typically used to automate simple tasks like processing orders or managing customer accounts that are integral for running the business, but better handled by automation than employee resources. BPA can easily handle employee onboarding, payroll and other manual tasks. A subset of BPA is robotic process automation (RPA). RPA uses intelligent automation technologies to perform repetitive office tasks. RPA powers data extraction, form completion, file movements and more.
Gen AI is a type of AI that creates original content—such as text, images, video, audio or software code—in response to a user’s prompt or request. Generative AI technologies like chatgpt can help companies identify ways to improve their workflows and create the right outputs. It can respond to users’ prompts or requests to create content, such as text, images, video, audio or software code. Gen AI can power so many AI workflows, from helping identify strategic goals and tactics to setting up meetings to offering feedback on marketing copy. McKinsey predicts gen AI might automate up to 10% of all tasks in the US economy.
Intelligent automation
Intelligent automation is a hallmark of any AI-driven workflows. It involves the use of automation technologies to streamline and scale decision-making across organizations. For example, an insurance provider can use intelligent automation to calculate payments, estimate rates and address compliance needs.
Machine learning
Machine learning (ML) is a branch of computer science that uses data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy. One such subset of ML is deep learning, which uses uses multilayered neural networks to simulate the complex decision-making power of the human brain
Natural Language Processing
Natural language processing (NLP) is a type of AI that uses machine learning to enable computers to understand and communicate with human language. Financial services organizations, for example, can use NLP to parse information from lengthy financial statements and other datasets to make smarter decisions on where to invest.
Optical character recognition
Optical character recognition (OCR), also known as text recognition, uses automated data extraction to quickly convert images of text into a machine-readable format. It can help organization take legacy information, such as books, decks and other printed information and digitize it to feed their modern knowledge management systems
Benefits of AI workflow automation tools
There are several key benefits to using AI-powered workflows.
Automate repetitive tasks
Drive cost savings
Eliminate human error
Enhance decision making
Improve the customer experience
Streamline and optimize processes
Automate repetitive tasks
AI workflows can eliminate the need for employees to focus on time-consuming tasks that are better automated. AI can handle these routine tasks and free up the human workers to spend more time with customers or partners and produce more business value.
AI can contribute to the “productivity paradox,” according to Rob Thomas, SVP Software and Chief Commercial Officer at IBM. Instead of taking everyone’s jobs, as some have feared, it might enhance the quality of the work being done by making everyone more productive.
Drive cost savings
Organizations that use AI workflows can save their employees from wasting time on unnecessary manual tasks. Those employees can focus on high-value projects and tasks that drive extra revenue. It also reduces friction and inefficiencies in information sharing, creating a smarter organization that makes decisions faster.
Eliminate human error
Team members might make mistakes, especially when doing complex tasks. For those activities that are better automated, AI technologies can accomplish those tasks quicker and with a higher degree of accuracy.
Enhance decision making
AI can remove bottlenecks by acting without needing human intervention. It can do real-time data analysis to make decisions that impact several business units. For example, marketers can use AI workflows to automatically optimize ad campaigns. AI workflows can alter spend to route funds to the highest-performing segments or social media posts.
Improve the customer experience
Organizations that created AI-driven, automated workflows are likely to be more efficient than those that rely on more manual processes. Organizations can use AI to create and start advanced chatbots and virtual assistants to streamline customer support to better assist customers when they have issues. For some customers, an AI-driven workflow that provides user-friendly chatbots helps them get answers without needing to talk to a human, therefore improving customer satisfaction. For example, Estee Lauder has started 6 a voice-enabled makeup assistant via a chatbot feature.
Streamline and optimize processes
AI-based automation software can easily manage many processes an organization depends on. Organizations want scalability and efficiency in their workflows so they can improve user experience. AI workflows can easily route information and processes across the organization so executives and employees have real-time information wherever they need to access it.
Conclusion
While many AI workflows can function without changing how employees work, some require employees to learn their processes. As such, organizations likely need to invest in courses training employees to use AI or license those training tools from others. This upskilling has several benefits, as those employees learn valuable skills. They also produce better and more efficient work.