Becoming a cloud-centric technology company is a given nowadays for companies
that consider themselves future-ready. The question is hence, not whether a
company is operating in the cloud, but what level of sophistication they have
reached in their cloud endeavors. This is because the cloud is being enriched
by incorporating other emerging technologies, especially machine learning.
There is no doubt that contemporary cloud networks will be more intelligent
than ever. And companies must harness the power of the intelligent cloud to
realize value.
Machine
intelligence or artificial intelligence (AI) is one emerging technology in the
enterprise space. Research-centric organizations are exploring ways and means
to monetize it and add incremental value to businesses. The machines’
self-learning capabilities open up new avenues for customer engagement, sales
lead generation, operational efficiency and so on. A potent combination is seen
in the confluence of machine learning with cloud computing, both deriving from
each other to help organizations develop a competitive edge across functions.
Some AI applications that help us in day to day corporate operations include
the following areas:
Business
Intelligence: Machine learning is a
useful input for BI, and bringing machine learning closer to enterprise data
warehouse will help data-backed decision-making. These AI-driven insights will
add value across functions such as CRM,
ERP, SCM, MRP, HR, sales, and finance. Companies
are foregoing traditional enterprise platforms and embracing these.
Internet
of Things (IoT): The IoT revolution
is taking a new turn towards data-driven cloud platforms, where machine
learning capabilities help make sense of the massive amounts of data. For
example, machine learning will find extensive application in Industrial IoT,
wherein predictive maintenance is an important use case. The idea is to use
Multiple Machine Learning algorithms in parallel to create the best
intelligence capabilities for smart industries. Microsoft Azure IoT Suite and
IBM Watson IoT are the two dominant solutions in this field.
Personal
Assistants: With advanced machine
learning capabilities, voice-based personal assistants are set to become more
powerful than ever. These are equipped with the intelligence to learn quickly
and effectively from past interactions, tracking usage trends and thereby
offering a customized experience to the user. Some of the popular examples of
AI-based personal assistants are Amazon Alexa, Apple Siri, Google Assistant,
and Microsoft Cortana.
Cognitive
computing: This field involves adding
sensory capabilities to intelligent systems i.e. the ability to see, listen,
talk, and take decisions and so on. The basis for this is a number of hi-tech
arenas such as natural language processing, face detection, visual recognition,
text to speech, speech to text, video analytics, language translation,
sentiment analysis etc. The basis for all these capabilities is machine
learning (ML). Developers build cognitive APIs to create these man-to-machine
interactions. Current market examples include Amazon AI, IBM Watson, Google
Cloud and Microsoft Cognitive API.
Bots-as-a-Service: Move over mobile engagement, businesses are now
turning to interactive bots to engage with customers. The conversational experience
is delivered by a machine learning-driven API which allows them to learn from
previous interactions and respond effectively. What’s more, today’s bots are
available across a wide range of platforms such as WhatsApp, Facebook
Messenger, and Slack. The next best thing is their being available as a
service. Market offerings that exist today are API.ai, IBM Watson Botkit and
Microsoft Azure bot.
