Throughout the last decade of digital transformation, and even over the last couple of years of refinement of generative artificial intelligence (GenAI), businesses have been largely focused on point solutions. Whether they are deploying tools to improve research and data extraction, risk assessment and regulatory reporting or analytics and forecasting, most businesses have thought about technology in vertical silos based on a specific function or range of functions.
While innovation has still flourished in that environment, it has been limited in many ways by compartmentalization. When we think about using a piece of technology to perform a professional task, like assessing reporting requirements or evaluating future business risks, for example, most of us still think about the steps: developing the right prompts to start scanning information; opening up a specialized legal research tool to analyze relevant case law; accessing a drafting program to summarize insights.
But what if those artificial boundaries between different information sources and workflow management solutions didn’t exist? What if we had a network of agents we could tap to go out and access all of those different tools and plumb the depths of those information resources without us having to manually toggle between functions and different interfaces? How would professional work change in that scenario? And how much more could we get done in a day?
How Agentic AI is Transforming Workflows
That is the future that’s currently being unlocked by agentic AI. Unlike GenAI, which focuses on generating content such as text, images or code based on prompts, Agentic AI can interact dynamically with its environment, make decisions independently, and adapt based on feedback, which enables more complex problem-solving. The simplest way to think about it is within the context of the legal research example I discussed above. Instead of going through each individual step and bouncing back-and-forth between software programs to get an answer, professionals using agentic AI would be able to tell their AI agent what they want to achieve, and that agent would then interact with a network of sub-agents to access the information and organize it for the end user.
That simple flip – from having to go out and collect information to being able to assign an agent to collect, organize and report back – changes everything about how large corporations are starting to think about the way they tackle compliance in everything from legal to tax to risk management functions.
Unlocking a Proactive Approach to Risk Management
Let’s take the initial due diligence process for a possible M&A transaction as an example. Historically, if a CEO or CFO entered the General Counsel’s office and mentioned they were considering an acquisition in Malaysia, the General Counsel, while digging up information on Malaysian employment law, would cooperate with the HR team to develop employment policies, the procurement team to understand relationships with suppliers and partners in the region, the environmental, health, and safety team to understand local manufacturing requirements, and so on down the list, compiling notes and reports along the way.
That same General Counsel using agentic AI solutions would be able to cover all of that ground in a matter of minutes by using AI agents to access specialized sources of information and the company knowledge base to create a dashboard of key risks and opportunities associated with acquiring a manufacturing company in Malaysia.
Where Compliance Meets Commerce
That prospect is so exciting to so many companies because it is about much more than just streamlining processes and delivering better answers faster. Those are obvious benefits, but the real game changer here is that the technology puts the compliance function of a big business in a position to be far more strategic. When it doesn’t take weeks and dozens of staff just to gather the information, corporate legal, tax and accounting and risk and compliance professionals can afford to spend a lot more time analyzing complex risks and advising the C-suite on their overall strategy.
In the Malaysian M&A example, for instance, the AI-driven research could turn up a rule limiting foreign ownership of domestic manufacturers in the region. Instead of simply presenting the C-suite with that challenge as a conclusion, the in-house legal team could instead focus their time on proposing a joint venture agreement that would help streamline the transaction.
Countless examples like this are being unlocked every day as AI-driven technology development continues to evolve. While so much of that evolution has been focused on efficiency and time-savings, the real value is coming in the form of new opportunities being unlocked by those efficiencies.
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