The Winning Formula – Achieving Success with AI

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Last week I wrote about the need to remove hype from reality when it comes to AI. But what will ensure that your AI projects succeed?

It is quite obvious that success is determined by human aspects rather than technological factors. We have identified four key organisational actions that enable successful AI implementation at scale (Figure 1).

Keys to Unlock AI Nirvana - Enabled by Upskilling

#1 Establish a Data Culture

The traditional focus for companies has been on ensuring access to good, clean data sets and the proper use of that data. Ecosystm research shows that only 28% of organisations focused on customer service, also focus on creating a data-driven organisational culture. But our experience has shown that culture is more critical than having the data. Does the organisation have a culture of using data to drive decisions? Does every level of the organisation understand and use data insights to do their day-to-day jobs? Is decision-making data-driven and decentralised, needing to be escalated only when there is ambiguity or need for strategic clarity? Do business teams push for new data sources when they are not able to get the insights they need?

Without this kind of culture, it may be possible to implement individual pieces of automation in a specific area or process, applying brute force to see it through. In order to transform the business and truly extract the power of AI, we advise organisations to build a culture of data-driven decision-making first. That organisational mindset,  will make you capable implementing AI at scale. Focusing on changing the organisational culture will deliver greater returns than trying to implement piecemeal AI projects – even in the short to mid-term.

#2 Ingrain a Digital-First Mindset

Assuming a firm has passed the data culture hurdle, it needs to consider whether it has adopted a digital-first mindset. AI is one of many technologies that impact businesses, along with AR/VR, IoT, 5G, cloud and Blockchain to name a few. Today’s environment requires firms to be capable of utilising a variety of these technologies – often together – and possessing a workforce capable of using these digital tools.

A workforce with the digital-first mindset looks for a digital solution to problems wherever appropriate. They have a good understanding of digital technologies relevant to their space and understand key digital methodologies – such as Customer 360 to deliver a truly superior customer experience or Agile methodologies to successfully manage AI at scale.

AI needs business managers at the operational levels to work with IT or AI tech teams to pinpoint processes that are right for AI. They need to make an estimation based on historical data of what specific problems require an AI solution. This is enabled by the digital-first mindset.

#3 Demystify AI

The next step is to get business leaders, functional leaders, and business operational teams – not just those who work with AI – to acquire a basic understanding of AI.

They do not need to learn the intricacies of programming or how to create neural networks or anything nearly as technical in nature. However, all levels from the leadership down should have a solid understanding of what AI can do, the basics of how it works, how the process of training data results in improved outcomes and so on. They need to understand the continuous learning nature of AI solutions, getting better over time. While AI tools may recommend an answer, human insight is often needed to make a correct decision off this recommendation.

AI Research and Reports

#4 Drive Implementation Bottom-Up

AI projects need alignment, objectives, strategy – and leadership and executive buy-in. But a very important aspect of an AI-driven organisation that is able to build scalable AI, is letting projects run bottom up.

As an example, a reputed Life Sciences company embarked on a multi-year AI project to improve productivity. They wanted to use NLP, Discovery, Cognitive Assist and ML to augment clinical proficiency of doctors and expected significant benefits in drug discovery and clinical trials by leveraging the immense dataset that was built over the last 20 years.

The company ran this like any other transformation project, with a central program management team taking the lead with the help of an AI Centre of Competency. These two teams developed a compelling business case, and identified initial pilots aligned with the long-term objectives of the program. However, after 18 months, they had very few tangible outcomes. Everyone including doctors, research scientists, technicians, and administrators, who participated in the program had their own interpretation of what AI was not able to do.

Discussion revealed that the doctors and researchers felt that they were training AI to replace themselves. Seeing a tool trying to mimic the same access and understanding of numerous documents baffled them at best. They were not ready to work with AI programs step-by-step to help AI tools learn and discover new insights.

At this point, we suggested approaching the project bottom-up – wherein the participating teams would decide specific projects to take up. This developed a culture where teams collaborated as well as competed with each other, to find new ways to use AI. Employees were shown a roadmap of how their jobs would be enhanced by offloading routine decisions to AI. They were shown that AI tools augment the employees’ cognitive capabilities and made them more effective.

The team working on critical trials found these tools extremely useful and were able to collaborate with other organisations specialising in similar trials. They created the metadata and used ML algorithms to discover new insights. Working bottom-up led to a very successful AI deployment.

We have seen time and again that while leadership may set the strategy and objectives, it is best to let the teams work bottom-up to come up with the projects to implement.

#5 Invest in Upskilling

The four “keys” are important to build an AI-powered, future-proof enterprise. They are all human related – and when they come together to work as a winning formula is when organisations invest in upskilling. Upskilling is the common glue and each factor requires specific kinds of upskilling (Figure 2).

Upskilling needs vary by organisational level and the key being addressed. The bottom line is that upskilling is a universal requirement for driving AI at scale, successfully. And many organisations are realising it fast – Bosch and DBS Bank are some of the notable examples.

How much is your organisation invested in upskilling for AI implementation at scale? Share your stories in the comment box below.

Written with contributions from Ravi Pattamatta and Ratnesh Prasad

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NAB Embraces Multicloud, Partners with Microsoft

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5/5 (1) The National Australia Bank (NAB) and Microsoft announced a strategic partnership last week, to develop and architect a multicloud environment to be used by both NAB and its New Zealand counterpart, Bank of New Zealand (BNZ).

The five-year partnership will involve Microsoft and NAB sharing development costs and investments to migrate around 1,000 out of 2,600 applications from the NAB and BNZ stacks, on Microsoft Azure. By 2023, NAB aims to run 80% of its application on the cloud, build a robust cloud foundation, and enable customers to access applications and services on the cloud.

The partnership aims to support NAB’s commitment to continuous improvement and innovation, leveraging the Microsoft global engineering team. It also involves setting up of the NAB Cloud Guild program, where Microsoft will train 5,000 NAB and BNZ technologists to equip them on cloud and allied technology skills.

NAB and Microsoft have previously collaborated to improve the experience for NAB customers, through cloud-based applications. NAB’s cloud-based AI powered ATM was the result of a proof-of-concept (PoC) developed on Microsoft Azure’s cognitive services, in 2018. It involved general ATM security captures along with facial biometrics to enable customers to withdraw cash without a card or a phone.

Besides the partnership with Microsoft, NAB also uses Google Cloud for multicloud workloads as well as AWS for its AI competencies and resources across platforms. In February, NAB launched an AI-based voice service to boost the bank’s contact centre experience along with AWS.

Ecosystm Comments

Ecosystm Principal Advisor, Tim Sheedy says, “If ever there was a sign that multicloud is the predominant approach for businesses, this is it. NAB is a big AWS client – in Australia and New Zealand. They lead the way for businesses in training thousands of employees on AWS technologies through their Cloud Guild. But now Azure is also developing a strong foothold in NAB – the public cloud services market is not a one-horse race!”

“Many businesses that have standardised on – or preferred – a single cloud vendor will find that they will likely use multiple cloud environments, in the future. The key to enabling this will be the adoption of modern development environments and architectures. Containers, microservices, open-source, DevOps and other technologies and capabilities will help them run their applications, data and processes across the best cloud for them at the time – not just the one that they have used in the past.”

Sheedy thinks, “NAB’s competitive advantage will not come from whether they are using AWS or Azure – it will come from the significant time and effort they are investing in giving their employees the skills they need to take advantage of these environments to drive change at pace. Too many businesses are increasing their cloud usage without making the necessary investments to upskill their employees – if you know you are planning to spend more on the cloud, then start now in reskilling and upskilling your staff. There is already a real shortage of cloud skills and it is only going to get worse.”

 

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