Businesses that want to take their AI technology to the next level are talking about AI agents, which can do much more than give organizations a technology boost. Unlike traditional computer programs that require explicit instructions for every step, AI agents have the autonomy to decide what actions to take based on the information provided to them. Using these agents offers organizations the opportunity to automate their more intricate workflows, but how can companies go from talk to action? Our AI team offers seven strategies to help develop successful AI agents.
Anyone who has onboarded a new employee quickly realizes how much institutional knowledge is required to effectively perform a role. Current employees – especially those with longevity at an organization or in a profession – inherently understand processes, roles, sources of information, and nuanced industry terminology. Much like training a new employee, building an AI agent requires that same transfer of institutional knowledge, using a two-step process:
When building an AI agent, it’s crucial to start with the smallest possible task, adjust for errors, and then build on the successes. The smaller and more specific the initial scope, the more efficiently and accurately the agent can perform. Scope creep is the enemy of effective AI implementation.
Following are a couple of real-life examples that show how our AI team put this strategy into practice for clients:
Not every AI agent has to start as a bespoke, custom-built solution to be successful. Starting with low-code solutions allows companies to test – and max out – capabilities before moving to agents that need more complex coding. This way, organizations can validate use cases, identify gaps, and build organizational experience before making larger investments. A variety of tools on the market offer a valuable on-ramp for experimenting with AI agents in a low-risk environment.
Following are a couple of real-life examples that show how our AI team put this strategy into practice for clients:
When training an AI agent, data quality is paramount. Throwing a vast amount of data at the agent will just confuse it – much like a human employee. So, how do companies know the right amount and type of data to provide?
Following are a couple of real-life examples that show how our AI team put this strategy into practice for clients:
The most crucial aspect of deploying an effective AI agent is integrating it into existing business systems because an AI agent’s true value is in how easily it integrates with emails, documents, databases, customer relationship management (CRM), ERP, and other core platforms. A variety of emerging technologies are simplifying AI system integration by allowing large language models (LLMs) to connect with various data sources and applications, but organizations still need to plan for integration by:
Following are a couple of real-life examples that show how our AI team put this strategy into practice for clients:
While AI agents offer immense potential to amplify productivity, it’s crucial to maintain human oversight and implement robust governance controls. Even as AI technology advances, AI agents can still make mistakes, hallucinate, or veer off course, which is why it’s vital to designate a human in the loop to help prevent mistakes and course-correct missteps. Blindly trusting an AI agent’s outputs without review could lead to a variety of issues, including:
Following is a real-life example that shows how our AI team put this strategy into practice for a client:
If thoughtfully deployed, AI agents have the potential to dramatically amplify human productivity. An iterative approach allows teams to identify and resolve edge cases, expand the AI agent’s scope responsibly, and progressively automate more complex workflows over time. Rather than trying to implement wholesale automation, AI agents are best used to augment human knowledge workers by managing tedious, repetitive tasks at scale, which can free up time for higher-value activities that require human judgment, creativity, and decision-making. To realize these productivity gains, organizations should:
Building an effective AI agent requires an appetite for exploration, a willingness to make upfront investments, and an acknowledgement of where internal knowledge and resources are lacking. For many companies, working with a third party – like Crowe – that offers deep expertise in AI technology and broad experience implementing AI across industries can be a worthwhile investment.
The most important thing companies can do? Get started on your AI journey now and continue to invest as the technology develops.
Our AI transformation team delivers tailored AI solutions – including AI agents – that can help companies solve problems and optimize performance. Contact us today to start your company’s AI transformation.
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