Every role in an organization is a bundle of tasks. Some require judgment, creativity, relationship-building, or contextual understanding. Others are repetitive, predictable, and time-consuming: tasks that follow patterns and rules, executed the same way every time.

Synthetic resources can take over the second category entirely. What organizations do with the freed capacity (redeploy it, reduce it, or reinvest it) is a strategic decision that depends on their goals and circumstances. Our job is to identify where synthetic resources create value and implement them effectively.

The Task-Level View

Most job descriptions are fictions. They describe the role as it was conceived, not as it’s actually performed. When you break down what someone actually does in a week, you find a mix:

  • Time spent on strategic thinking, relationship management, creative problem-solving
  • Time spent on execution that requires judgment and adaptation
  • Time spent on repetitive tasks that follow predictable patterns
  • Time spent on administrative work that exists only because systems don’t talk to each other

That last category (repetitive, pattern-based, administrative) is where synthetic resources create value. These tasks don’t require human judgment; they require reliable execution.

Consider a typical professional services environment. An analyst might spend their week doing client research, synthesizing findings, building recommendations, and presenting to stakeholders. That’s the job description. The reality often includes hours of copying data between systems, formatting reports, chasing down information that should be readily available, and monitoring for updates that may or may not come.

The strategic work requires the analyst. The administrative work just requires something to do it reliably.

Identifying Synthetic Resource Candidates

Not every task is a good candidate for a synthetic resource. The best candidates share certain characteristics:

Repetitive and pattern-based. The task follows a predictable sequence. It doesn’t require novel problem-solving each time, just reliable execution of a known process.

Rule-governed. There are clear criteria for how the task should be performed. Decisions within the task can be codified: if X, then Y. Edge cases can be defined and handled.

Data-accessible. The information needed to complete the task exists in systems that can be accessed programmatically. If the task requires information that only exists in someone’s head, it’s not ready for automation.

Tolerance for latency. The task doesn’t require instant human judgment. There’s time for a synthetic resource to process, and for exceptions to be escalated.

Clear success criteria. You can define what “done correctly” looks like, which means you can validate that the synthetic resource is performing.

Tasks that meet these criteria are candidates for automation. Tasks that don’t (those requiring creativity, empathy, complex judgment, or real-time human interaction) are harder to automate with current technology.

Case Study: The Automated Competitive Intelligence Analyst

The Situation

A professional services firm doing consulting and advisory work across several industries needed to stay current on competitor activity, market developments, and client industry news. This intelligence informed business development conversations, proposal positioning, and strategic planning.

The firm had tried various approaches. For a while, a junior staff member was assigned to compile a weekly briefing. It was inconsistent: quality varied, things got missed, and it consumed hours of time that could be spent on billable work. They tried subscribing to news aggregation services, but the signal-to-noise ratio was poor and partners stopped reading the digests. They tried asking everyone to share relevant articles in a Slack channel, which worked for about two weeks before going quiet.

The underlying need was real: the firm needed competitive intelligence. But every solution they’d tried required ongoing human effort that eventually degraded or competed with higher-priority work.

The Challenge

The firm needed competitive intelligence that was consistent, comprehensive, and didn’t depend on someone remembering to do it. They wanted monitoring across specific competitors, client industries, and market themes, synthesized into briefings that partners would actually read. And they needed it to run reliably without becoming someone’s neglected side project.

The Approach

We designed a synthetic resource specifically for competitive intelligence: an agentic workflow that operates continuously without human initiation.

The workflow monitors a defined set of sources: competitor websites and press releases, industry publications, regulatory filings, news outlets, and social media channels relevant to the firm’s markets. It runs daily, identifying new content that matches relevance criteria defined by the firm.

But monitoring alone creates noise. The synthetic resource also synthesizes: grouping related items, identifying themes, distinguishing signal from noise, and producing structured briefings organized by competitor, by industry, and by strategic theme.

Weekly briefings are delivered automatically to partners’ inboxes every Monday morning. When significant developments occur (a competitor acquisition, a major client announcement, a regulatory change), the synthetic resource sends an immediate alert rather than waiting for the weekly cycle.

The firm can also query the synthetic resource directly. A partner preparing for a pitch can ask for a summary of everything relevant to that prospect’s industry in the last 90 days and get a synthesized response in minutes rather than assigning an analyst to spend half a day compiling it.

The Outcome

The implementation delivered several measurable results:

  • Consistent weekly briefings that actually get read (partners reported higher engagement than any previous approach)
  • Faster response to competitive developments, with alerts within hours rather than discovery days or weeks later
  • Research time for pitches and proposals reduced significantly; information that used to require manual compilation is now queryable on demand
  • No ongoing human effort required to maintain the monitoring and synthesis workflow

The Takeaway

Synthetic resources work best when they take over tasks that are necessary but don’t require human judgment: the kind of work that tends to degrade over time because it competes with higher priorities. Whether organizations use that freed capacity to take on more work, improve quality, or reduce costs is a strategic decision. The capability is the same regardless.

Is This Your Situation?

If your organization has tasks that are repetitive, rule-based, and time-consuming (tasks that tend to get neglected or compete with higher-priority work), those are candidates for synthetic resources.

The question isn’t whether AI can do the work. It’s whether you’ve identified the right tasks and built the workflow architecture to execute them reliably.

Our Synthetic Resources practice designs and implements AI workflows that automate tasks that don’t require human judgment, creating capacity your organization can deploy however it sees fit.