A framework to understand AI.
The term 'AI' has become an umbrella for a variety of technologies that serve
different purposes. Understanding the different flavours of AI is important in order to
optimise your technology portfolio. Here is a simple framework to
demystify
AI, which you can use to evaluate the investments in AI technology needed to support a
particular strategy:
A framework to understand AI.
Most modern organisations already use Business Intelligence (BI) solutions to generate
interactive dashboards and reports. In essence, these are digital versions of
paper-based reports, which we would not strictly classify as 'AI'. Traditional BI
solutions need
humans to
define rules about the data to use, how to transform it, and how
to present it - and they rely on people to do something with the information that they
provide.
Be that as it may, the BI software companies are embedding machine
learning
technology into their products, evolving them into Advanced Analytics platforms (which
we will cover in the next section). For example, Microsoft has added AI features to its
Power BI software that allow it to understand questions in natural language and
to generate a narrative summarising key insights from the data. Other BI software
companies such as Qlik and Tableau are similarly upgrading their products.
If you need AI capability,
we recommend looking at your existing BI software
before acquiring a new product.
Advanced Analytics (AA) and BI solutions have a similar purpose - to inform decisions
made by humans. However, AA solutions use machine learning algorithms to detect patterns
in data that are hard for humans to find - including unstructured data such as audio,
images and text. Applications of AA
include:
In recent years, Robotic Process Automation (RPA) has emerged as a tool to automate
simple tasks. RPA
needs people to define rules for the software to follow so they lack the
'intelligence' part of AI. Nevertheless, RPA has been applied successfully and widely by
large organisations to improve the efficiency of back-office functions such as Finance
and HR.
The largest RPA software companies include UIPath, Automation Anywhere, and BluePrism.
You can also find vendors that specialise in one or more business functions or industry
verticals, such as Pega. Similarly to BI software, the RPA companies are adding AI
capability to enable their products to learn rules from experience and work with
unstructured data - they may refer to this capability as 'Intelligent Automation'.
If you need to connect one or more of your back-office systems, you might be able to use
RPA to
reliably copy data between them without making expensive software changes.
Cognitive Computing (CC) systems have both intelligence and the ability to take action.
They are trained - based on example - to decide by themselves
how to respond to specific
events. Examples include:
We have examined four types of AI technology that have distinct business applications. We
used a simple framework to define these categories, which you can also leverage to classify
AI
products based on autonomy and intelligence. You can analyse your organisation's AI
portfolio by overlaying investments on top of this framework.
At Cognis, we are passionate about protecting the future of community-oriented organisations by enabling them to effectively engage with stakeholders in the digital economy.
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