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).
#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.
Written with contributions from Ravi Pattamatta and Ratnesh Prasad
Moving from a product or regional focus to an industry focus appears to be the “strategy du jour” for many technology vendors today. For some it is a new strategy – with the plan to improve customer focus and increase growth; for others it is the pendulum moving back to where they were five or ten years ago as they bounce from being industry-centric to product-centric to geography-centric and back again.
Getting your industry focus right is much harder than it seems – and has to be timed with client needs and market opportunity. The need to focus on the industry varies for different technology products, services and capabilities. For example, most technology buyers want their vendors to understand what their business does and how they add value to customers – that is a given and industry-aligned Sales teams make a lot of sense. Many tech buyers also want certain software functions to align directly to their processes – there is little appetite to customise ERP and financial suites to specific industry needs and processes – and tech vendors should support these out-of-the-box or cloud needs.
Industry Solutions May Not Drive Competitive Advantage
If the industry solution you are selling is the same as what any of their competitors can buy from you, then organisations get the exact same benefit as the market – no more, no less. For example, about 10-15 years ago, large telecom providers around the globe made significant investments in CRM platforms (often from Siebel) – bringing in one of a few large global systems integrators to deploy their standard processes and systems. These CRMs were supposed to provide business and customer benefit, and drive competitive advantage. And while they did deliver positive change (often at SIGNIFICANT cost!) when every telecom provider was using the same solution with the same or similar processes, any competitive advantage was lost.
Industry Solutions are Often the Sign of a Mature Market
The widely accepted hypothesis is that the technology innovation and adoption happens in waves. The market has 5-7 year waves of innovation, followed by 5-7 year waves of deployment, adoption and consolidation.
The Innovation Phase. In this stage new companies emerge, new products or services are launched and leading/bleeding edge companies embrace these new technologies to drive competitive advantage and business growth. They experiment with new technologies that drive new business capabilities – sometimes failing, but always pushing the envelope for business innovation and forging the path for mass market adoption. In this stage there is often little demand for industry solutions – as both the providers and buyers of the solutions are still working out where the business benefit is; where the technology might be able to drive change or help them get ahead of competitors. If you examine the growth of a company such as Salesforce, you see that the early stage products are targeted towards a generic market – customers are expected to customise the solution based on their needs and individual requirements. In 2002 I worked for a challenger telecom provider that had deployed a traditional Peoplesoft CRM capability, and I was part of the team that brought Salesforce into the business – and as a cloud-based solution, we saw the competitive advantage was the pace at which we could customise the product (by excluding IT teams and processes). However, the solution was a “one-size-fits-all” product. The innovation stage is typically characterised by high growth of smaller vendors and technology service providers who challenge the status quo.
The Deployment, Adoption and Consolidation Phase. This stage of market growth is when the mass market starts to adopt these solutions. Many of these buyers walk the paths that have been forged before them by the more innovative, leading edge businesses. This stage typically sees less innovation, less experimentation, and more standard deployments. To make the solutions more palatable and easier to sell to the mass market tech vendors typically pre-configure or customise the solutions to specific needs – for business teams, roles or industries. It is usually in this stage of market growth and deployment that the industry solutions see significant interest and adoption. This is where the mass market gets access to the business benefits the more innovative businesses received many years earlier (and often profited from in this time). In my example of the Salesforce deployment in 2002, over the following years many partners started to create industry solutions, and eventually Salesforce themselves sold industry-specific solutions – or at least targeted certain products and capabilities at specific industries and provided accelerated deployment models to drive advantage at a faster rate. The deployment and consolidation stage of market growth is typically characterised by steady, slow growth across the entire market as benefits are being driven to all providers (product vendors and solutions or implementation providers). Legacy providers either play catch up or suffer declining business as they realise the solution they sell no longer provides the business and customer the benefits that it used to.
Industry Focus Should be Aligned to Customer Segments, Solution Type and Geography
The decision to sell industry-focused solutions should be driven by the type of solution you are selling; the business benefit you are promising; and the type of business you are targeting the solution towards. Businesses that are more innovative will still buy some pre-configured, industry-specific solutions that don’t differentiate their business or drive competitive advantage. But where they expect competitive advantage, they need to stand apart – to be the only business with that capability.
It is also worth understanding that an innovation in one market might be standard practice in another (and vice-versa). Countries across the globe and specifically here in Asia Pacific have different approaches to technology and innovation. China and parts of Southeast Asia are often innovators – pushing the boundaries of new and emerging tech to do things we never thought possible (in the same way Silicon Valley traditionally has done). Australia and India are traditional markets that adopt industry solutions after they have been tried and tested by others. Innovation in Japan seems to happen in stages and at pace but only once every 10-15 years or so. New Zealand and Singapore are generally more nimble economies where businesses often have to be innovative to gain global competitive advantage quickly.
Evidence indicates that the rate of innovation is increasing across the entire region – even in the less innovative economies. The window for industry solutions is much smaller regardless of location – as the next new innovation is just around the corner. Even the large, traditionally less agile businesses are driving innovation programs – for example, many of the big financial services “dinosaurs” such as DBS and Commonwealth Bank often win tech innovation awards and offer market-leading customer experiences.
Use this lens to better develop your industry approach. The depth of your industry solution or capability will dictate the opportunities that you will drive based on the type of customer and technology stage. Do you want to drive innovation or efficiency in your clients? Do you want to win the big “safer” deals – but be thought of as a technology solution provider; or win the smaller deals in companies that will become the market leaders of tomorrow – and be considered a market leader and king maker? Understanding your own business goals, the current sales and delivery capabilities, and the capacity to change will help your company create a go-to-market strategy that suits your current and future customers and will likely dictate the growth rate of your business over the next 5-7 years.
Keep yourself abreast with the latest industry trends
Ecosystm market insights, data, and reports are jam-packed with industry analysis and digital trends across several industry verticals to help you keep tabs on the fast-paced world of tech.
The ongoing global crisis is expected to drive more investments in FinTech. Blockchain adoption, in particular is expected to lead to a more open and interconnected economy that is borderless, transparent and does not need counter-party trust to operate. One particular area where Blockchain has been piloted is in smart contracts. Financial contracts involve legal work, document handling, sighting, signing, and sending them to the right people. All of this involves both time and people – and proves to be an expensive option eventually. Blockchain can speed this process up in a secure (with no failure points), interoperable and risk-free environment.
While smart contracts are expected to increase efficiency, there are questions being raised with respect to interpretation and technical capacity. The Law Commission in the UK is conducting a detailed study to analyse how current law applies to smart contracts and to highlight any uncertainties or gaps in relation to enforceability, interpretation and so on. The World Bank is looking at the role smart contracts could play in improving financial services in poorer nations – especially in insurance and short-term unsecured loans. Initiatives such as these are a positive step towards adoption.
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.
Digital Currency
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
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.”