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​Why knowledge is power for AI-focused partners

​Why knowledge is power for AI-focused partners

Artificial intelligence, machine learning and smart things promise a new future for the channel, but how can partners capitalise?

In the year just gone, Alexa and Siri became pseudo personal assistants that put AI into everybody’s pocket.

Yet in 2017, despite Amazon and Apple advancing the market, the vendor landscape will remain heterogeneous and fragmented, with use cases still scarce.

Rather, the next 12 months will see CIOs partner with marketing and CX colleagues to reap the benefits of AI, as it will increasingly be integrated into existing architectures and applications as a way to drive innovation throughout the year.

“Previously, any talk of AI almost always meant robots performing human tasks,” ManageEngine vice-president, Sridhar Iyengar, recalled. “There was no talk about how such smart machines would be built.”

In the 1990s, Iyengar said the AI conversation started to take the form of a “software brain” that could use data mining techniques to leverage data and discover meaningful patterns.

“But conversations are now focused around deep learning and machine learning,” he observed. “Apart from discovering patterns, AI can make recommendations that businesses can use to strategise and win customers.”

With AI set to become “commonplace in everyday life” by 2020 - Iyengar believes adoption will be driven by three key factors.

Firstly, data is not scarce anymore, with the copious amounts of information available helping to build quantitative models that make use of algorithmic techniques such as machine learning, deep learning and predictive analytics.

Secondly, computing power has become very affordable in the modern era, meaning that the once-expensive AI techniques are now in the reach of ordinary businesses.

Thirdly, Iyengar believes businesses are primarily motivated by providing the best customer experience possible, with AI capable of reacting and responding in real-time to meet changing requirements.

In short, data is the fuel for machine learning, with adoption driven by the ability to automate systems for recurring routine tasks, which translate to major productivity improvements.

“The shift is enabled by improved processing power, better algorithms, and the availability of big data - structured and unstructured - which is fundamentally changing the way humans and machines work together,” SAP Innovation Centre Singapore vice-president, Christian Boos, said.

In addition, Boos said most use cases of AI in business fall into two categories - automating transactional knowledge work in shared services environments, and developing cognitive platforms simplifying employees’ everyday lives and allowing them to focus on higher value tasks.

“Businesses should want to deliver enterprise software systems that can learn how to fully automate business processes at unprecedented levels, react to real-time changes and provide the best possible results for today’s digital businesses,” Boos added.

Customer demand pushes AI agenda

Across Australia, CIOs will have a greater need for speed in 2017, with customer demand expected to push businesses to the limit in the coming year.

Stemmed from increased end-user appetite, organisations are adopting new technology faster than ever before, buying new products or services quicker than previously, triggering the rise of AI, the Internet of Thing and augmented reality.

Irrespective of technology however, success forever hinges on its ability to meet the changing demands of a customer, a customer that is becoming better informed, less patient and fickler by the day.

With enterprise AI adoption still maturing, partners must now focus on providing relevant information at relevant times to build trust across the industry.

“It’s more about building a trusting human/ machine partnership where users and machines work in tandem with each other to amplify business possibilities,” Oracle vice-president of product and data science for the Adaptive Intelligence Program, Jack Berkowitz, said.

Depending on the business function, Berkowitz outlined the different applications capable of meeting different customer needs:

- Finance professionals can negotiate best supplier terms, while optimising cash flow needs and balancing costs

- Human resources recruiters can automatically identify best-fit candidates in the shortest time, while HR managers can create job descriptions to find well-suited positions

- Marketing and commerce managers can drive higher conversion rates, lift, repeat purchases, and ultimately, revenue, with smart, contextual offers and recommended actions for individual consumers

- Supply chain managers can automatically find the best options to distribute goods around the world, while optimising costs and price for both the buyers and the transporters.

Channelling intelligence

Despite an increased appetite to innovate, businesses remain challenged by a lack of knowledge within the field of artificial intelligence, creating opportunities for partners to capitalise on advisory and consultancy roles.


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Tags dataanalyticsartificial intelligencecscmachine learningDXC Technology

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