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How to adopt AI at scale across the enterprise

by jcp

By Amit Gupta is Executive Vice President and Global Head of DRYiCE Software at HCL Technologies.

 

The phrase emerging technologies used to apply to AI, which was something of an optional add-on when the concept of digital transformation first hit the mainstream. Since then, AI has become a key driver of digital innovation, meaning it is now central to enterprise transformation plans. However, so far, it has proven difficult to scale these capabilities successfully across the enterprise.

There’s a drive from C-suiters to put AI right at the heart of their digital enterprises. This has resulted in four fifths of large businesses leaning on the assistance of some form of AI during the pandemic, according to KPMG. However, many business leaders fear that we’re moving too fast on AI, as it’s still a relatively new concept for most organizations. These concerns are understandable: it takes more than the flicking of a switch, or installing software on top of existing systems, to successfully adopt AI.

AI adoption depends on simplicity and time-to-value

It’s difficult to scale AI unless organizations find a way to remove its inherent complexities, by deploying it as part of a purpose-built solution that simplifies and solves challenges. If they don’t take the time to identify what those challenges are, and consider how AI can simplify them, businesses run the risk of doing the opposite, adding complexities for little-to-no return.

Runbook automation can be one of the most effective use-cases for AI, removing complexity by speeding up lengthy, time-consuming manual processes. For example, help desk enquiries, such as employees requesting equipment replacements, can be handled much more efficiently if some of the steps are managed through automated runbooks. AI can also enable self-service capabilities for many routine requests, taking the burden off skilled IT workers. For example, AI-powered cognitive virtual assistants can be used to enable employees to reset their own passwords, rather than needing the IT team to do it for them.

For AI to make lives easier, not harder, it must be applied in a way that is easy to understand and implement at scale, without constant expert attention. Although setting up these capabilities could take upwards of a year for an organization without the in-house expertise, it is achievable in 2-4 months with the right frameworks and knowledge in place.

Humanizing AI is key to its success

Some of us are still terrified that robots are going to take over the world, with talk of AI often raising images of dystopian futures portrayed in films like The Terminator. A ‘Big Bang’ adoption of AI is therefore usually not the best approach, as it can be difficult to build support amongst employees. Enterprises should build trust over time – moving from zero, to partial, and eventually full automation.

As they work to build this trust, IT leaders should consider that people are much more likely to accept AI if it is made as ‘human’ as possible. Natural Language Processing (NLP) makes it possible for AI to mimic human conversations, and over time it can even learn to understand human emotions. How much more accepting would employees be if AI started to display empathy when they had encountered a problem?

Leveraging AI for real-time business insights

Once enterprises have identified where and how they will put AI to use, it’s crucial to give it as wide a scope as possible to ensure the benefits are scalable. One of the most common approaches is using Robotic Process Automation (RPA) to simplify specific tasks within a complex business process. However, this only scratches the surface of what AI can achieve, because not all business flows follow a straight line – they jump from one set of applications to another, meaning RPA often requires manual intervention.

Business Process Observability (BPO) enables RPA to hit its full potential, enabling both IT management and operations teams to take proactive, effective action when there is a threat of business flow disruption. Taking a bank as an example, if an IT team was using RPA for payment processing, it won’t automatically be informed about problems that arise in that business flow. If a message is not passed on, a payment could fail, and the customer relationship be damaged as a result. BPO guarantees issues identified by AI are not overlooked, putting businesses in prime position to predict, prevent, and remediate issues.

Digital workspaces are the first frontier of digital transformation

AI can also be used to make digital workspaces run more smoothly and intelligently, which has been invaluable during the pandemic. Enterprises have been doing everything they can to provide the best user experience for employees working from home, and in the most immediate sense, this means giving them the best home office set-up possible.

This sudden stream of requests was difficult for many to deal with, but automated service exchanges used the power of AI to simplify the processes involved in requesting, ordering, and delivering home office supplies to employees. HR teams have also been reaping the rewards. AI-enabled Cognitive Virtual Assistants ease the burden, preventing staff from spending time answering the same questions multiple times when workers look for information about holiday processes and furlough scheme payments.

It’s now or never

If they work through the challenges, enterprises can rest assured there are already some great AI use cases to tap into. From now on, there can be no excuse for not putting AI at the heart of digital transformation. Those that fail to adopt AI at scale risk handing their competitors an advantage that could do significant damage over the longer-term. Those that harness it successfully will charge forward into the digital future and create untold opportunities to redefine their industries.

 

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