AI Agents: Real Power Begins with Interaction Data.

The buzz around AI agents has shifted from sci-fi projections to practical implementations in enterprises across sectors. While we have seen AI evolve from simple task automation to more sophisticated roles in business functions, there's an entirely new frontier awaiting discovery: AI agents enriched by digital interaction data.

Let’s dive into what AI agents are doing today, and how they can revolutionize work with the right data at their fingertips.

The Current State of AI Agents: From Taskmasters to Adaptive Assistants

Today’s AI agents are more than glorified taskbots. They're designed to handle a variety of roles, such as:

1. Customer Support Assistants: Responding to customer queries, suggesting solutions, or escalating issues to human agents, often with context-aware recommendations.
2. Sales and Marketing Assistants: AI agents can suggest next-best actions for sales reps, auto-fill RFPs, draft email sequences, and perform account research—all in real-time.
3. HR Assistants: Screening resumes, coordinating interviews, and even managing onboarding tasks, allowing HR teams to focus more on strategic initiatives.
4. Finance and Ops Assistants: From processing invoices to monitoring cash flow trends, AI agents assist in keeping the financial health of the organization in check.
5. Software Developers’ Allies: These agents help developers troubleshoot, generate code snippets, or debug existing codebases, accelerating the development cycle.

In essence, AI agents have become multi-faceted assistants that reduce manual work, but their effectiveness is currently limited by the data they can access—mostly structured data from databases, shared drives, CRMs, or ERPs. This limitation is also their ceiling.

The True Potential: AI Agents Powered by Digital Interaction Data

Enter the world of interaction data, the unstructured yet invaluable footprint of how people work. This data encompasses everything from mouse clicks, keystrokes, and scrolling patterns to screen navigation, chat messages, and email exchanges. If AI agents gain access to such data, the landscape of work will transform dramatically.

1. Contextual Personal Assistants:

• Today: AI agents prepare future meeting notes or summarize past interactions based on CRM logs or virtual meeting app recordings.
• Tomorrow: With interaction data, AI agents can understand the 'why' behind an employee's workflow, not just the 'what.' Imagine an AI agent that, knowing how you navigate through documents or CRM screens, preemptively prepares talking points for your next meeting, offers dynamic agenda suggestions, or even drafts a pitch based on your previous conversations.

2. Proactive Workflow Optimizers:

• Today: AI agents detect patterns based on historical task data to suggest improvements.
• Tomorrow: Armed with digital interaction data, AI agents can identify inefficiencies in real-time, down to micro-details like how many clicks it takes to complete a process or which app navigation habits waste the most time. This will allow them to propose not only optimizations but also auto-complete mundane tasks that usually drain employees’ productive hours.

3. Always-On Learning Coaches:

• Today: AI agents serve up generic training videos or modules based on roles or previous certifications.
• Tomorrow: They’ll offer tailored, microlearning opportunities by analyzing employees’ actual screen behavior. An AI agent might recognize that a user struggles with a specific software feature and can offer immediate, bite-sized guidance based on interaction patterns.

4. Customer Experience Engineers:

• Today: AI agents respond to customer queries in a reactive manner, based on FAQ banks or pre-programmed knowledge bases.
• Tomorrow: With digital interaction data, AI agents can tap into real-world user journeys to understand customer frustration points more intuitively. This enables them to suggest product changes, guide customers seamlessly, or even preemptively suggest a fix before a problem is reported.

5. AI Sales Enablement:

• Today: AI agents source information from connected knowledge bases like Slack channels, CRMs, or Google Drive, helping sales teams locate relevant resources and documents.
• Tomorrow: With digital interaction data, AI agents can dive deeper into unstructured data sources—like email threads, shared folders, and collaborative documents—pulling out context-rich insights. Imagine an AI agent that not only finds answers to RFPs but also pulls in context from related documents, past deal conversations, and sales call notes. This deeper level of understanding enables sales teams to craft personalized pitches, respond to RFPs more accurately, and engage prospects with better insights, leading to higher conversion rates.

6. Decision-Making Support:

• Today: AI agents analyze structured reports to assist decision-making, often limited to what is manually inputted.
• Tomorrow: AI agents can read between the lines—quite literally. With access to email threads, chat conversations, and user clicks, they can deliver a more nuanced understanding of team sentiments, potential pitfalls, or hidden dependencies that might impact decision-making.

Key Enablers for the Next-Gen AI Agents

While the possibilities are vast, achieving them requires overcoming several hurdles:

1. Capturing Interaction Data: Capturing and making sense out of this messy, unstructured, and scattered data fabric across different systems and applications needs powerful AI models. This is the crucial first step in unlocking the potential of next-gen AI agents.
2. Ethics and Privacy: AI agents must ensure compliance with data privacy laws (e.g., GDPR, CCPA) and maintain transparency in data usage. Employees should be aware of what data is being captured and how it’s being used to enhance workflows.
3. Real-Time Processing: Realizing the potential of interaction data requires AI agents to process vast amounts of data in real-time, ensuring they provide value when needed the most.
4. Organizational Change Management: Companies need to ensure employees are comfortable working alongside AI agents that have deeper insights into their digital behavior, establishing trust and showing how AI enhances rather than threatens their roles.

What’s Next? The Intelligent Digital Workspace

The future of AI agents lies not just in automation, but in augmentation—making employees exponentially more efficient, proactive, and empowered. We are on the brink of an intelligent digital workspace where AI agents don’t merely respond to queries but anticipate needs, adapt workflows, and evolve alongside human partners.

Picture this: an AI agent, like a true digital work twin, that not only helps you navigate daily tasks but transforms into a trusted advisor that grows and learns with you, constantly uncovering ways to improve productivity, performance, and potential.

Digital interaction data isn’t just a data source—it’s the fuel for a smarter, more capable AI. It's the difference between an assistant that can act and one that can think. And as this evolution unfolds, it won’t be about whether AI agents can keep pace with us, but whether we can keep up with them.

Interesting space to be in. Let me know what you think.

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Good reads on AI Agents:

• AI Agents: Broad Trend Of Digital Transformation – DATAFOREST
• Companies Look Past Chatbots for AI Payoff – The Wall Street Journal
• Agents are the future AI companies promise—and desperately need – The Verge

Also check out the work Soroco is doing to unlock the power of interaction data, visit soroco.com.

Learn all about Digital Interaction Data driven Intelligence from this Everest Group playbook