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Dan Adika is CEO of WalkMe, digital adoption leaders empowering every organization to realize the promise of its technology.
Generative AI is life-altering technology that is already changing the way we work, build, communicate and understand the world. One of the biggest questions in front of leaders—amid tremendous investment—is how to get meaningful value out of AI.
That requires respecting the cardinal rule for new technologies: It must solve real-world problems.
While everyone’s talking about how AI will change the world of work, it’s not some amorphous one-and-done shift. Instead, AI’s capacity to continuously optimize work environments and specific workflows is the real game changer, where adaptation to new circumstances and data is automated—and where change doesn’t create disruption.
In other words, AI must solve new and existing everyday work problems quickly and without friction as they arise. So what are some of these everyday problems? And how does AI need to behave to help? Let’s take a look.
Invoicing And Billing Disputes
Accounting and finance teams, across verticals, handle invoicing and billing questions and disputes daily. AI capabilities that are always active in the background and backed by domain-specific knowledge and data can assist employees in reviewing disputes and identifying the next best actions.
AI is most effective when it considers complete context, including buyer-seller interactions and past resolutions. AI-assisted reading can help staff analyze each dispute holistically, recognizing patterns and recommending actions. Based on conditions, AI can identify the next best action, either triggering automation for approvals or rejections or notifying a human for intervention. If there are open-text fields on a form, AI smart tips can help sort and interpret what’s needed.
Triaging At A Medicare Call Center
Medicare contact centers handle critical issues, requiring agents to accurately triage and quickly assess incoming calls. AI can help human agents navigate vast knowledge bases and changing regulations to resolve issues more efficiently.
AI-assisted reading can evaluate call transcripts in near real-time while also extracting relevant information, determining eligibility and suggesting next best actions. This improves customer experiences and agent productivity and regulatory compliance.
AI can also act as a “Guardian Agent,” monitoring inputs in real time, flagging HIPAA violations, validating eligibility requirements and providing ongoing quality assurance and compliance with key regulations. This would reduce compliance risks, protect patient data and speed up resolution times. Gartner predicts that 40% of CIOs will demand AI “Guardian Agents” by 2028.
Prepping Financial Advisors For Better Service
The financial sector is ever evolving—from creatively packaged assets to cryptocurrencies and complex regulations. Even with this changing landscape, financial advisors are required to be experts and conduct accurate financial analyses for all clients and map them to the most suitable financial products.
AI can help by ensuring advisors are comprehensively and efficiently prepped for client meetings. AI can recognize the exact nature of upcoming meetings and suggest the best actions to help the advisor understand client histories, finances and needs.
Financial advisors also navigate complex computing applications. Ideally, AI can perform that navigation and pull and consolidate the right data from different apps. AI tools can then assist in drafting notes and consuming reports. With all of this data, AI can trigger automation to create comprehensive quotes tailored to a particular client, reinforced by evidence and sound financial advice.
AI Grasping Context And Purpose
In each of these use cases, AI that sorts through appropriate, clean datasets—and grasps the full context of the worker’s role, functions and intentions—is the differentiator in deriving quantifiable value.
Understanding context is the base from which AI can leap into the next domain of AI agents. Let’s look at what enables proactive contextual assistance:
• A Data Model Trained On User Interactions: This uses a vast dataset that captures user actions across applications and workflows.
• An Intent Recognition Engine To Understand User Actions: This interprets user actions, whether they involve inputs, queries or requests. It identifies the underlying intent behind what a user wants to achieve.
• An Orchestration Engine For Intelligent Execution: After ascertaining user intentions, this engine executes next steps on a user’s behalf. It can automate complex workflows that span applications, generate text, create new UI elements and make API calls to connect and interact with external systems.
With these capabilities, AI agents can understand purpose and autonomously plan and take actions to meet defined goals. We’re quickly heading for a future where AI will be expected to offer proactive assistance or get the job done on its own without prompting or app-switching with a conversational interface that users can effortlessly tap into on demand.
For companies to achieve these goals, AI must be designed with transparency, governance and human oversight in mind. Businesses must cultivate a workforce that understands AI’s capabilities and limitations, ensuring employees are equipped to work alongside AI rather than be displaced by it. This means fostering a culture of AI literacy, providing hands-on training and integrating AI solutions that enhance—not replace—human decision-making.
While AI agents will automate routine and repetitive tasks, humans will play a crucial role in oversight, strategy and high-stakes decision-making. Employees will need to embrace and master working with AI, not just as an information source but as an integral part of their workflows. Businesses that proactively prepare for this shift—by empowering their employees with the right guidance and governance using AI—will be best positioned to harness AI’s full potential.
Ultimately, AI and humans will work together to power the adoption of technology across industries in unseen ways and spur productivity across the globe—all in service of real-world problem-solving and matching innovation with human needs. Companies that embrace this evolution thoughtfully will lead the way in defining the future of work.
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