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Bringing AI into an Enterprise: Winning the race

Attributed to: Sumir Bhatia, President, Asia Pacific, Data Center Group

It’s no secret that organisations are increasingly looking towards AI. In fact, IDC predicts the region’s cognitive and AI spending to reach US$1.0 billion in 20181.

Yet, while organisations recognise the important role AI plays in supplementing business today, many still struggle to implement sound AI strategies due to a lack of know-how. For example, a common misconception is that AI presents a one-size-fits-all solution to otherwise complex and layered problems—but the reality is that AI can be adopted in a multitude of ways, depending on organisational needs and business intelligence (BI) insights derived from data gathered. From mining social data, to driving engagement in customer relationship management (CRM), the flexibility of AI provides organisations with the freedom of choice to best address their most pressing needs.

As such, bringing AI into an organisation can often be a daunting task—much like how cooking can be stressful for those who do not know their way around a kitchen. However, understanding the technology-specific building blocks needed can help organisations kick-start their journey of AI adoption, just as how using a recipe to understand the cooking process can often increase the success rate of amateurs in the kitchen.

Identifying specific areas where AI can value add

Organisations need to prioritise thinking about where and how AI capabilities can help enhance existing products and services. Beyond just that, organisations need to understand the inherent value that exists in identifying necessary goals and targets in specific instances where AI could solve a business problem or generate adequate value. This thus allows for a better formulation of appropriate and efficient strategies to attain these goals.

Case in point, AI solutions are often not generalisable and require specific data resources and training, which means that organisations should place a greater emphasis on activities that have the greatest potential business impact. In essence, by developing a keen understanding of the different aspects of AI, be it machine learning, deep learning, or natural language processing, organisations can then be able to utilise the various facets of AI—cohesively or on their own—to better plug the necessary gaps within the system.

Understanding the challenges

Successfully adopting AI is more than just about technical compatibility—some AI applications have the capacity to be adopted much quicker than others, despite possessing comparable, relevant technical requirements. For instance, broader solutions can go a long way in making sure that a company’s portfolio of AI initiatives can generate meaningful value in the short-term, while also presenting opportunities in the long-term.

Here are three factors that organisations should consider to help streamline decision-making:

 One-time costs. Analyse the initial capital set-up involved in implementing a new AI solution, versus pay-as-you-go “AI as a service” platforms.
 Costs involved in switching solutions. Evaluate the costs associated with replacing existing solutions with a desired AI solution. This means more than just monetary costs in replacing legacy systems (which might also mean wholesale changes to other parts of the IT ecosystem), but includes cultural and political elements as well.
 Ecosystem requirements. Determine if an integrated solution will require any complementary technologies. For example, an AI solution that must be integrated with innovative IoT sensors and emerging robotics technology will be more complex to adopt.

 

Aim for short and long-term goals

Organisations should also plot their AI goals based on their existing capabilities, as there is often a gap
between organisational goals, and what can actually be executed and accomplished within a specific period
of time. It is important to first assess benefits—such as improved marketing or brand identity despite the
fact that it may or may not be less quantifiable. This helps organisations avoid falling into the trap of seeking
immediate monetary gains and better sets them up for tangible success across the long-term as well.
Alternatively, organisations can also look to pursue small-scale plans that deliver small-scale payoffs,
similar to a pilot programme before aiming for larger implementations. While this process could potentially
be longer, this helps ensure well-established processes are set up. Utilising these small-scale projects can
help address any vital missing links and helps identify the processes and solutions that need to be acquired
or improved before actually beginning to implement AI systems. This also helps address the point earlier
on there being no one-size-fits-all method when it comes to AI implementation; as it all comes down to
individual needs and requirements.

Work with a trusted partner to pioneer the movement

With the Asia Pacific landscape evolving at such a rapid pace, we have learnt that our partnerships with
industry-leading businesses are essential to helping our customers succeed. A classic example of this
would be our recent work with the University of Adelaide. By developing a greater understanding of their
needs, we were able to work with them to build, test, and deliver a system for research-use in just six weeks.
It is absolutely crucial to note that after ensuring the organisation is primed to implement AI (both
technologically and organically), it is important to have trusted partners who are experts in the field that can
help provide the much needed perspective around AI implementation. A partner can help:

 Set realistic, achievable goals.
 Keep a tight timeframe and lean team to ensure goals are streamlined and focused.
 Get a clearer picture of what can or needs to be done moving forward.

Yes, there are certain rulesets to follow and necessary measures to be well-versed with when implementing
AI, but is it all worth it? According to research done by Accenture on the impact of AI in 12 developed
economies, the impact of AI technologies on business is projected to increase labor productivity by up to
40%, allowing people to make more efficient use of their time2. There has never been a better time for
innovative, forward-thinking leaders to adopt a disciplined, bold and well-thought-out strategy in
implementing AI to better drive organisations of today – the time is certainly now.

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