The Rocks in Your Backpack: How AI Carries the Weight of Manual Processes

In most companies, you’ll find an operations department, housing employees completing mission-critical tasks. Ranging from data entry to answering customer service emails, these tasks are necessary burdens that a business carries, but they consume human capital (and actual capital) making it difficult to scale efficiently. We call these tasks “rocks,” repetitive, manual workflows requiring human-level inference.

Rocks hinder a company’s ability to evolve, where hiring and turnover complexity become the bottleneck to servicing new revenue or extracting operating leverage. We’ve observed that businesses seek help carrying their rocks when faced with cost pressures or an inability to scale.

The Evolution of Rock Management

Historically, companies have tried to lighten their backpacks through offshoring - delegating these rocks (tasks) to teams in other countries where labor costs are lower. As most executives know, offshoring has limitations. It works well for reducing costs but falls short on quality and consistency. The success of an offshoring lift largely hinges on time and effort invested in teaching the foreign staff how to meet operational standards (or teaching someone else how to then teach them). Given language constraints, time zone barriers, and process complexity, companies often find the work they tried to offshore is back on their shoulders as they clean up errors to maintain service quality.

Technology was another avenue pursued to reduce operational load, commonly through robotic process automation. RPA was touted as the “automated worker,” intended to reshape how businesses operate by reducing mundane, time-consuming tasks. But the original promise of RPA has gone unfulfilled. RPA simply replicates pre-mapped clicks and keystrokes that a human would make on top of user interfaces (the screens that humans use to interact with software). These rigid, linear workflows are dependent on non-changing UIs. They aren’t scalable when deployed to legacy applications, which run on individual machines. And they fail when faced with complex use cases that have infinite outcome permutations. Cost is also prohibitive to RPA solutions. Utilizing RPA requires detailed mapping and proactive upkeep of each workflow by expensive consultants. Until now, offshoring to cheaper (but still human driven) providers has reigned as the most reliable form of ops management.

However, with the introduction of LLMs (large language models, a form of AI), intelligent automation systems can now produce human-level inference and output. Tasks that were previously out of reach for technology and thus sent off to cheaper labor are eligible for AI-powered automation systems. Businesses no longer need to look beyond borders for affordable, reliable solutions – in fact, they don’t need to leave their own operating systems. Intelligent automation typically works within the company’s OS via an integration layer and is trained on business-specific historical data. This yields higher quality and more consistent outputs than offshoring, and for far less money than RPA.

Solving Manual Processes with AI

Here are operational tasks where we’ve identified AI systems can match, or often outperform, humans:

• Data entry from disparate sources that previously required human collection and extraction.

• Document extraction for unstructured data that requires human processing or training OCR software for automated extraction, such as images or PDFs.

• Call center interactions, which require real-time responses and human level problem solving.

• Transferring information from unstructured to structured format, that previously required extensive human review and manual translation.

• Turn-based communication that requires creative responses and dynamic workflows, such as answering email inquiries.

These rocks exist across many industries, but we’ve found the heaviest backpacks in supply chain, logistics and transportation, human resources, manufacturing, finance and accounting, insurance, government, automotive, distribution, and healthcare.

While not an exhaustive list, we’ve experienced firsthand how AI automation systems quickly reduce dependency on human staff in these industries to create a meaningful operational and financial lift. They convert human-level processing and understanding into dynamic workflows that autonomously learn over time, trained on patterns from the business’ historical data. We’d go as far to say AI automation systems are revolutionary, enabling companies to effectively manage labor costs and grow efficiently.

Rocks can be challenging to manage, but that doesn’t mean you have to carry them alone. Theary helps businesses build, implement and manage AI for high-touch, regulated processes. If your company manages manual workflows, we’d love to discuss. info@theary.com