Insights

Why AI and Cognitive Computing Don't Work On Their Own

Diego Cepeda, Senior Product Marketing at UST SmartOps

Cognitive computing is a flashy term that promises intelligent technology, built on a foundation of AI. It refers to tools like software that can mimic human cognition — in other words, it's the technology that executes AI functions. This includes things like deep learning and machine learning programs.

But there's a hard truth to this exciting tool:

AI and cognitive computing can't stand alone.

Without intelligent automation, AI and cognitive computing programs are essentially just smart humans who are really good with data and can do a couple basic tasks.

You'd still need a human supervising them to:

1. Feed the program data,
2. Determine what to do with the insights it discovers, and
3. Create rules that tell the software how to execute on those decisions.

That means that if you feed data into a cognitive computing program, it'll provide strong analysis — but it won't do anything with it. It can predict trends — but it can't make changes to capitalize on those trends. It can create a model — but can't recognize biases within that model.

What AI and Cognitive Computing Do: Intelligent Analysis

Cognitive computing is incredibly powerful. It's essentially a blanket term for AI technology that can mimic human capabilities. This concept isn't new: you may have heard of the Turing test, where the ultimate goal of AI is to mimic human activity so completely that the program can fool a human into thinking the bot was just another human.

Here's how one industry expert views cognitive computing:

"Cognitive computing process uses a blend of artificial intelligence, neural networks, machine learning, natural language processing, sentiment analysis and contextual awareness to solve day-to-day problems just like humans."

Similarly, IBM defines cognitive computing as:

"Systems that learn at scale, reason with purpose and interact with humans naturally."

Cognitive computing therefore refers to programs that are great at:

• Analyzing data
• Applying intelligent insights to real-life situations
• Identifying business opportunities or inefficiencies
• Transcribing information
• Much, much more

These programs are particularly powerful because they can learn and scale their learning to broader pools of data, gaining deeper and more informed insights as they go.

But here's the key. Notice anything missing from these definitions? They make no mention of the implementation of cognitive programs, from gathering data to adapting to unique environments. They also don't refer to the execution of intelligent tasks, or how these programs can take actions based on the insights they discover.

What AI and Cognitive Computing Don't Do: Data Ingestion and Process Automation

To understand why AI and cognitive computing fall short of their promise, we need to take a step back and examine the purpose of cognitive computing programs. What are these programs used for? What outcomes do they aim to deliver?

If you implement a cognitive computing program with the goal optimizing business processes, you're going to be disappointed. That's because cognitive computing is just one component of the broader process of business optimization. Business optimization requires you to:

1. Discover problems and acquire business data (data ingestion)
2. Analyze data and create models that draw insights from that data (cognitive computing)
3. Execute and automate decisions based on those insights (process automation)

To get the most out of AI and cognitive computing, you'll need to also utilize data scientists who understand statistical models, programmers who can code sophisticated algorithms, data acquisition tools like document processing or Optical Character Recognition, human oversight and computer programs that automate tasks like Robotic Process Automation. Each of these tools is powerful in its own right, but they're all required components to optimize business processes and drive outcomes.

Crucially, AI and cognitive computing don't run these processes themselves. They need outside support, in the form of a human or additional software, to cover the trifecta of business optimization.

The Solution: One Tool for Business Optimization

Luckily, there's a way to capture the myriad benefits of cognitive computing and combine them with the other necessary components:

Intelligent Process Automation.

If cognitive computing is a blend of numerous AI functionalities, IPA is a blend of cognitive computing with numerous data processing and automation capabilities. IPA achieves the goal of true cognitive automation and creates a solution that's more than the sum of its parts.

IPA is, in other words, an orchestration framework that enables you to draw on the benefits of data ingestion, cognitive computing and process automation. This holistic approach fulfills the promise of cognitive computing.

Don't have enough data within your system for cognitive computing? IPA can extract it and then apply cognitive computing. Want great insights from your programs but don't have the man-hours to execute on them? IPA can deliver the same insights and learn the best way to automate those tasks.

The point is, intelligence simply isn't as useful without automation. IPA solves that problem by bringing both functions to bear on any business process.

At the same time, intelligence and automation aren't as useful without a human. IPA is designed to augment, rather than replace, human ingenuity. To understand a business process, you need a manager who has an expert understanding of how an entire system of tasks come together to deliver business outcomes.

AI, automation and a human understanding of how to deliver value in business is an unbeatable combination. That's what IPA delivers.

Learn more at ustsmartop.ai!