Solving AI Adoption Conundrum

Ever since the term 'artificial intelligence' was coined in 1956, we have achieved several staggering milestones in the technology, all aimed at making human lives easier.

Thanks to the recent advances in machine learning and neural networks, AI is powering consumer technologies such as search engines, OTT platforms, dating apps, and many IoT devices. With heavy reliance on data collection hardware, such as smart devices, virtual assistants and fitness bands; consumer platforms now rely on AI for processing enormous volumes of data in milliseconds and provide an enriching customer experience.

However, despite keen interest and opportunities for innovation in every industry, AI hasn't been as widely adopted outside of the consumer technology space. In Malaysia, according to a report, 26% of local businesses have embarked on AI initiatives with there being a larger market potential for AI adoption.

In this article, we will delve into one potential business unit — the IT department — that presents a huge range of opportunities for organizations to get started with AI and resolve the AI conundrum that arises when companies’ interest in AI is high, but their implementations of AI are low. 

The AI-readiness factors

Leveraging AI requires a certain level of readiness and involves acknowledging the common problems plaguing organizations. 

For companies to implement AI in their daily operations, they require large volumes of data, backed by an enormous amount of time required to build and train machine learning models to derive meaningful correlations from data dependencies, and provide actionable recommendations.

Even with data integrations and algorithms in place, very few AI-based systems are completely autonomous. For instance, machine learning algorithms still require humans to interact with the systems, known as Interactive Learning, to continuously gauge their responsiveness in dynamic ecosystems. 

There are two key factors to AI success: 1) continuous learning of diverse and evolving business use cases, and 2) strict monitoring and governance by subject matter experts to ensure the algorithms' factual and contextual accuracy while providing desired recommendations. Organizations will need to build a culture that encourages experimentation and ensures that these systems aid humans, not replace them.

These factors contribute to an organization's preparedness for AI. Companies also require a sizable amount of budget, time, and supporting talent to propel the technology, which might not be their immediate priority amongst other pressing requirements.

AI in a post-crisis world

COVID-19 has brutally forced many organizations to go remote overnight. While this could be a boon or bane, depending on organizations' preparedness, IT teams are the first to bear the brunt of an unprecedented emergency such as equipping the workforce with remote-ready technologies, tools and practices.

This situation presents AI and machine learning algorithms with more diverse and critical use cases, including bandwidth requirements, remote connections and communications, logs from applications, networks and servers, service desk requests, and so on for further learning.

Such real-time data coupled with historic information can enable these models to predict near future business requirements and proactively notify IT teams, which can then equip themselves to ensure digital resilience in the longer run by adapting to business trends. Hence, IT could just be that ideal place where organizations can kick start their AI experiments.

From providing real-time insights about potential security incidents, offering conversational assistance for efficient management of help desk requests, and using predictive analysis to provide pre-emptive solutions to users’ problems — AI has a lot to offer to IT.

In fact, AI in ITOps will aid in correlating real-time data, periodically analyzing cause and effect, normalizing multi-dimensional data, prioritizing incidents based on severity, and building pre-defined response plans to anticipate and mitigate future events.

AI will also prove its worth orchestrating incident-response automation. It can contribute significantly to zero-day threat detection by drastically reducing cyberattack response times to milliseconds. With self-learning response algorithms in place, organizations get advantage to stay on top of attacks by anticipating, identifying and thwarting them on time.

This intrinsic capability to process data at lightning speed and provide actionable insights will not only pave the way for automating mundane and repetitive tasks. It will also mitigate the risk of operation and maintenance fatigue, saving costs for bigger needs.

Embracing the new normal

Soon, AI will no longer be a "nice to have" technology.  Prioritizing AI-powered innovations and solutions will enable organizations to adapt to evolving trends, with impressive data processing and workflow automation capabilities that require minimal human intervention.

AI adoption is a continuous process and requires massive volumes of real-time data to build and train learning models. Given the time and budget constraints, it would be prudent for organizations to begin with one potential vertical, which presents a huge scope for AI-enabled decision-making.

However, if enterprises are even remotely inclined towards adopting AI, they should first foster a culture that is open to experimentation, appreciates data-driven decision-making, and does not let innovation to arbitrarily replace humans.

Let's face reality. Organizations will need to make the most of the technologies that can help them lead through the new normal and retain their competitive advantage during uncertain times. It all starts with a "let's do it!" attitude to take the first step towards embracing the change.

Srilekha Sankaran, product consultant at ManageEngine, authored this article. 

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Photo credit: iStockphoto/metamorworks