Role of Artificial Intelligence in Business Intelligence

Robin
Jun 18, 2019

Artificial intelligence is a replica of human intelligence. It is called artificial because its drivers are machines and software-man-made things. Typically, it starts by churning through the big data. Simply say, the big data is put into the funnel of software, wherein intelligence algorithms tap patterns or models.

These models are driven by filtering through the sublime subsets of AI:

Artificial Intelligence Components

Machine learning: Machine learning is defined as an analytical modeling process to track patterns or hidden insights. A series of methods, consisting of neural networks, statistics, operations research, and physics, feed it with patterns. The coolest thing is that this modeling assists in filtering insights from the big data without being explicitly programmed.

A neural network: Modeled on the human nervous system, it is a kind of machine learning. Several interwoven neurons configure models upon responding to external inputs. These models relay intelligence or decisions between each unit of neurons.

Deep learning: Simply say, it’s a colossal network of neurons. Multiple layers of these neurons perform an array of functions to and fro until they achieve the intended learning models. These models internally improve the cognitive computing. These learning models include speech recognition and image scanning.

Cognitive computing: It aims at developing instinctive recognition & interpretation of the audio and visuals in the real time in response to a human command.

Computer vision: It determines the artificial assistance that sticks around deep learning to recognise audios and visuals in the real time.

Natural language processing: It empowers machines to look up the queries with analytical eyes for sensing the proposition and coming back with a relevant response in human language. In simpler words, it transforms electronic language into human speech while carrying out tasks.

Together with aforementioned elements, the AI figures out queries, comprehends, analyzes and comes with a relevant response. In the meantime, it gets unprecedented support of graphical processing units, IoTs, advanced algorithms and APIs. This is how Google Home or Apple’s Siri produces relevant output against what you command to. Moreover, what you get is in the voice of a human being. It is evolved with the view to simplify our complexities. Gradually, it is penetrating into almost every walk of life.

What can AI be used for in business?

Role of AI in Business Intelligence

The AI works like a robot. Being a machine, it needs a human to interfere with its learning. Therefore, the data scientists principally stick to ‘Observe, Understand and Learn’ fundamentals.

Observe: This step unearths the insight. Upon coming across the nuts and bolts of the business, the data analyst classifies the insight consisting of groups into segments using computer vision. It uses it artificial intelligence to drill down the related data from other resources for competitive analysis. This is how the correct information about where the business is comes up front.

Understand: It determines anomalies or oddities that are the grass root reasons of flaws in business operations/activities. The machine compares redundancies together with standard processing via performance databases. A stringent analysis unveils errors. This is how the AI utilizes its cognitive capabilities on the basis of fed patterns.

Learn: It assesses the accurate way to behave according to customers’ intention and trends in the business. Therefore, the data analysts draw relevant patterns that can feed hierarchical knowledge into current business situation. Being the outcome of analytical research, these patterns help in removing errors.

Shortcomings of Human Interference:

Human interference is gradually minimizing. You should be indebted to AI for it. However, computers and many other machines have been simplifying complexities in your life. Despite this fact, human intelligence is needed.

Let’s say, you have millions of emails, comprising personal and official electronic messages. If you want to split both kinds of messages under their respective categories, you have to manually recognise each and every mail. Thenceforth, you could move further. This is the pothole that needs to be repaired.

The AI is coming with patches, which tend to shrink human penetration into understanding, analyzing and finally, making decisions. This is how the turnaround time or TAT could be minimized.

Changing Outsourcers:

Let’s consider a scenario wherein a data management company frequently sought assistance from the data entry service providers. Being budget constraint, it had to change the outsourcing partners twice. An array of difficulties would surround it. Entering into a new agreement with another outsourcing partner would bring it back to the square one. Now, processing training would be a can of worms. Frankly speaking, it would be impractical to recall guidelines that are in the bits and pieces.

But, the AI is evolved to such an extent that the algorithms will automatically ingest massive datasets. With the help of machine learning, those sets could be analyzed and pulled out intelligence. This intelligence could be helpful in recalling the data from any point in the future.

Frequent Circulation of Authority:

What if the authority to access data frequently shifts from person to person?

Its best example is the company where attrition rate is quite high. The work flow of such organisation certainly paralyzes for a while when an employee surrenders his job. The new employee, who fills the vacuum, takes time to learn the work and responsibilities. Sometimes, weeks and months pass by.

However, the titles and keywords could insert convenience in learning. They carry the associated information in detail. Just a glimpse could let the newly appointed workforce sift through hundreds of documents prepared by predecessors. But, the absence of such provisions stabilizes further progress in the productivity.

This stability could be removed with AI. It could be trained to retain the learning from the history, which opens opportunities to start performing relatively quicker and efficiently.