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).
#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.
#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.
However, smart contracts are not the only area that financial institutions and governments have in mind when they pilot and adopt Blockchain – and there are several recent instances.
Many central banks have started identifying potential use cases for digital representation of fiat money that offers them unique advantages at various levels. According to Bank of International Settlements (BIS), 80% of the world’s central banks had already started to conceptualise and research the potential for central bank digital currencies (CBDCs), 40% are working on proofs-of-concept (POCs) and 10% are deploying pilot projects. The People’s Bank of China (PBOC) announced last month that it has processed more than three million digital yuan transactions since it began piloting its CBDC late last year. Transactions include bill payments, bar code scans, tap and go payments, and payments for transport and government services.
Singapore’s Project Ubin has successfully completed its fifth and final stage and is a step closer to greater adoption and live deployments of blockchain technology. The commercial applications of the payments network prototype include cross-border payments in multiple currencies, foreign currency exchange, settlement of foreign currency-denominated securities, as well as integration with other blockchain-based platforms to enable end-to-end digitalisation across many industries and use cases.
Crypto Exchange Ecosystems
A crypto exchange or digital currency exchange (DCE) makes it easier for buyers and sellers to securely store, buy, sell, or exchange crypto currencies. Various players across the financial industry have developed tools connecting the transactions, flow of funds, and financial instruments through crypto exchanges – including banks, digital payments and other FinTech providers.
In an effort to expand its retail presence, FTX acquired crypto app Blockfolio for USD 150 million in August 2020. Recently, FTX announced the launch of trade in the stocks of some of the largest global companies – Tesla, Apple, Amazon – by tokens against bitcoins, stablecoin and more.
In order to empower the emerging initiatives in the decentralised finance (DeFi) space, the world’s largest crypto exchange platform Binance announced the creation of a seed fund in September. Their USD 100 million accelerator fund added five new Blockchain projects – Bounce, DeFiStation, Gitcoin, JustLiquity and PARSIQ that will receive financial support from the fund.
PayPal has announced crypto buying and selling services through Paypal accounts. Paypal’s crypto service in partnership with Paxos is being rolled out in phases across the US. Outlining their plans for 2021, Paypal announced new crypto payments features including enhanced direct deposit, check cash, budgeting tools, bill pay, crypto support, subscription management, buy now/pay later functionalities and more with the integration of the capabilities offered by Honey – an internet browser extension and mobile app which PayPal bought for USD 4 billion in 2019.
It is expected that banks will join in as well – it has been reported that DBS Bank in Singapore is planning to launch a digital asset exchange platform to enable institutional and retail customers to trade cryptocurrencies.
Blockchain Enhancing Banking Features and Services
We are also witnessing several pilots and initiatives in banking industry functionalities such as settlements, identity management, security, transparency, and data management.
In theory, the bank reconciliation is simple, however, in practical aspects things may not work out so easily. The funding, lending, transfer, and transactions reconciliations is a complicated and time-consuming effort. in March 2020 the Spunta Banca DLT system promoted by the Italian Banking Association (ABI) and coordinated by ABI Lab was implemented across the Italian banking sector. Powered by R3’s Corda Enterprise blockchain, the solution streamlines and automates the reconciliation of transactions, provides real-time reconciliation process, handles technical elements with automated feedback and results in more transparent processes. Spunta has attracted broad interest from the Italian banking sector and since October, around 100 banks have been operating on Spunta to manage the interbank process and automate reconciliation of transactions.
Recently, in Spain, ten leading banks including Banco Santander, Bankia, BME, CaixaBank, Inetum, Liberbank, Línea Directa Aseguradora, Mapfre, Naturgy and Repsol, and the Alastria consortium have come together to build a self-managed digital identity (ID) solution dubbed as Dalion built on Blockchain technology. The project based on Alastria digital identity model (Alastria ID) aims to provide users with secure control on their digital information and personal data, making it easier for them to manage their digital identity. The project that was initiated in October 2019, has successfully completed the concept testing phase and is in its second phase, with the final solution expected to roll-out in mid-2021.
Grayscale, is the first digital currency investment vehicle to attain the status of a Securities and Exchange Commission reporting company. The digital assets management company is aggressively buying bitcoins and manages a total of USD 8.2 billion of cryptocurrency. Earlier this year, Singapore’s Matrixport, a financial services firm partnered with Simplex, an EU-licensed payments processing firm to enable buying of cryptocurrencies via VISA or Mastercard credit and debit cards with more than 20 supported fiat currencies.
As Blockchain matures we will see more large-scale adoption bringing collaborators together to form ecosystems that will give them a competitive edge. Solve some of their core challenges and empower their customers.
Singapore FinTech Festival 2020: Infrastructure Summit
Get more insights into the evolution of blockchain and its applications at the Singapore FinTech Festival 2020: Infrastructure Summit. The world’s largest fintech event will explore different uses of blockchain technology,trials being conducted, and the vast opportunities in the financial services industries
5/5 (1) In 2018, DBS Bank came together with AI start-up impress.ai to implement Jim – Job Intelligence Maestro – a chatbot that helps the bank shortlist candidates for positions in their wealth planning team. This is primarily for screening for entry-level positions. Apart from process efficiency, the introduction of AI in the recruitment process is also aimed at eliminating bias and objectively finding the right candidate for the right job. The DBS chatbot uses cognitive and personality tests to assess candidates, as well as providing them with answers to the candidates’ frequently asked questions. The scores are then passed on to actual recruiters who continue with the rest of the recruitment process. DBS claims that they have curtailed the initial assessment time of each applicant by an average of 22 minutes.
While some organisations have started evaluating the use of AI in their HR function, it has not reached a mass-market yet. In the global Ecosystm AI study, we find that nearly 88% of global organisations do not involve HR in their AI projects. However, the use cases of AI in HR are many and the function should be an active stakeholder in AI investments in customer-focused industries.
Telstra employs AI to vet Applicants
Last month, Australia’s biggest telecommunications provider Telstra announced its plans to hire 1,000 temporary contact centre staff in Australia to meet the surge in demand amidst the global pandemic. In response to the openings, Telstra received overwhelming 19,000 applications to go through and filter, with limited workforce. To make the recruitment process more efficient, the company has been using AI to filter the applications – and has been able to make initial offers two weeks from the screening. The AI software takes the candidates’ inputs and processes them to find the right match for the required skills. The candidates are also presented with cognitive games to measure their assessment scores.
Ecosystm Principal Advisor, Audrey William speaks about the pressure on companies such as Telstra to hire faster for their contact centres. “Several organisations are needing to replace agents in their offshore locations and hire agents onshore. Since this is crucial to the customer experience they deliver, speed is of essence.” However, William warns that the job does not stop with recruiting the right number of agents. “HR teams will need to follow through with a number of processes including setting up home-based employees, training them adequately for the high volume of voice and non-voice interactions and compliance and so on.”
The Future of AI in HR
William sees more companies adopting AI in their HR practices in the Workplace of the Future – and the role of AI will not be restricted to recruitment alone. “A satisfied employee will go the extra mile to deliver better customer experience and it is important to keep evaluating how satisfied your employees are. AI-driven sentiment analysis will replace employee surveys which can be subjective in nature. This will include assessing the spoken words and the emotions of an individual which cannot be captured in a survey.”
In the future, William sees an intelligent conversational AI platform as an HR feedback and engagement platform for staff to engage on what they would like to see, what they are unhappy about, their workplace issues, what they consider their successes and so on. This will be actionable intelligence for HR teams. “But for a conversational AI platform to work well and to encourage users within the organisation to use it, it must be designed well. While it has to be engaging to ensure employee uptake, the design does not stop at user experience. It must include a careful evaluation of the various data sets that should be assessed and how the AI can get easy access to that data.”
AI and Ethics
With the increased use of AI, the elephant in the room is always ethical considerations. While the future may see HR practices using conversational AI platforms, how ethical is it to evaluate your employees constantly and what will be the impact on them? How will the organisation use that data? Will it end up giving employers the right reasons to reduce manpower at will? These and allied issues are areas where stricter government mandates are required.
Going back to AI-assisted recruitment, William warns, “Bias must be assessed from all angles – race, education, gender, voice, accents. Whilst many platforms claim that their solution removes bias, the most important part of getting this right is to make sure that the input data is right from the start. The outcomes desired from the process must be tested – and tested in many different ways – before the organisation can start using AI to eliminate bias. There is also the added angle of the ethical use of the data.”