October 21, 2025

AI in Business: Solving the Dirty, Dull, and Difficult with Markaaz

By Matt Shubert, SVP Data Analytics & Research, Markaaz

AI in Business: Tackling the Dirty, Dull, and Difficult with Markaaz

Over the past three years, artificial intelligence (AI) has become a central topic in business and technology circles, driven by the rapid release of Large Language Models (LLMs). These tools have begun to augment and automate parts of our daily lives, especially low-risk tasks like online searches, finding recipe ideas, or helping our kids with math homework.

Instead of manually searching and scrolling through countless sources, we now receive condensed, synthesized overviews. This shift has introduced a new level of efficiency, but also a new set of concerns.

Accuracy: The Barrier to High-Risk AI Adoption

Despite its promise, AI still struggles with accuracy. Confident but incorrect outputs occur more frequently than we see in human decision-making. As a result, most users limit AI to low-risk tasks. For high-value or high-risk decisions, such as legal or medical advice, trust in AI remains low.

But what happens when AI becomes more accurate than humans? If human accuracy is 87% and AI reaches 95%, will we accept a 5% error rate from machines over a 13% error rate from people? We have been led to distrust machines and their red glowing eyes in the movies, but business leaders must ask: is a machine better?

The answer likely lies in a hybrid model, where AI enhances human critical thinking and supports, not replaces, decision-making.

The Right AI Use Cases: Dirty, Dull, and Difficult

AI is best suited for tasks that are:

  • Dirty – Messy, complex, or unstructured (e.g., cleansing large datasets)
  • Dull – Repetitive and monotonous (e.g., translating business names)
  • Difficult – Time-consuming or cognitively demanding (e.g., identifying ownership structures)

The Right Human Use Cases: Define, Diagnose, and Decide

Humans are best suited for:

  • Define – Humans frame the problem, provide context, and shape the desired outcome. This step sets the direction and ensures AI is solving the right challenge.
  • Diagnose – People validate AI outputs, apply domain expertise, and offer feedback to refine future iterations. This ensures quality, accuracy, and relevance.
  • Decide – Ultimately, humans make the final call—aligning results with compliance, ethics, and business goals. AI supports the process, but people own the outcome.

Working together – AI provides throughput and scale, humans ensure accuracy, intricacy, and nuance. It’s a continuous learning cycle and powerful relationship.

Business Data Is Dirty, Dull, and Difficult, Markaaz Is Built for That

Business data so often falls into these three categories. It can be messy, repetitive, and complex which reduce the accuracy and speed to decisions. That’s where Markaaz comes in.

Markaaz uses AI to:

  • Cleanse and unify disparate business data from 65,000 global sources
  • Streamline onboarding by reducing manual entry and friction
  • Constantly connect multi-country and multi-ownership hierarchies

Whether it’s translating business names, validating registration details, or surfacing risk signals, Markaaz applies AI where it delivers the most value, on the tasks that are dirty, dull, and difficult to accelerate time to decision and maximize onboarding before your competitors.

Smarter Business Decisions Start with Smarter Data

AI is not just a consumer tool; it’s a business imperative. By applying AI to the right use cases, organizations can reduce operational burden, improve accuracy, and accelerate workflows.

Markaaz is leading this transformation by combining verified data with intelligent automation. We help financial institutions, marketplaces, and service providers onboard businesses faster, smarter, and with greater confidence. Harnessing the value of AI requires a trusted partner to help automate the dull, dirty, and difficult tasks that constantly slow progress.

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Source: Harvard Business Review – The Irreplaceable Value of Human Decision-Making in the Age of AI

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