The Digital Revolution: Impact on Enterprises

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While talking about the paradigm shift that we are seeing in the industry today, I like to use the analogy of space exploration. The aim is to make space an extension of the earth through its use in communication, tourism and for mineral exploration. This is an entirely new paradigm on how all the investments in space technology and transportation are being made – by private industries as well as by governments of major economies.

Similarly, technology is no longer a business enabler – it is now a catalyst to create and capture new value streams and to create sustainable differentiation. The relationship between customers and enterprises is now a two-way street. Customers are not just using products and services but feeding information and critical insights back to the enterprises on a real-time basis. Now, even B2B enterprise businesses must be seen more as a B2B2C.

This new paradigm has brought technology to the core of every business and is forcing enterprises to transform the way they run their businesses.

 

The Multiplier Effect

Enterprises have to look at technology with a fresh new lens now. So far, they have had to cope with any emerging technology in isolation. Now they are being hit by a number of technologies – agile network, cloud, data analytics, intelligent technologies –  each of which has its impact. But what makes it transformational is the interplay of these technologies. Big Data analytics, AI and IoT has taken in isolation can definitely impact an organisation. But when the three work together, they have an exponential impact on an organisation and on it’s Digital Transformation (DX) journey. This has two clear impacts on businesses.

 

Impact on Enterprises

 

Expanding Role of Business Functions. As organisations embark on their DX journeys, they are likely to increase technology spend. Even though some budget would go into upgradation of IT infrastructure, a larger portion of the budget is likely to go towards digitisation, business applications, cybersecurity, and intelligent technologies. These projects would require the involvement of multiple stakeholders. And consequently, a higher proportion of technology spend will be controlled by the business functions – and this proportion will only get. Let us take AI as an instance. The global Ecosystm AI Study shows that only about a fifth of AI projects now are being funded by IT. Multiple other stakeholders are involved in emerging technology projects – the key department being where the solutions are being deployed.

Building a Transformation Roadmap. Ad-hoc implementation of technology will no longer be a viable option. What enterprises will need is an interdisciplinary model of work to attain the most value out of their transformation through technology. This should lead to a clear roadmap – that melds technological capabilities with business requirements. And mind you, each organisation’s roadmap will be distinct and unique.

Wider organisational functions will have to be engaged and aligned. Managing budgets and new business models simultaneously comes with a set of its own complexities. In my opinion, a Balanced Scorecard and Lean Approach is the way ahead for organisations as they approach Transformation.

 

Irrespective of the size of your organisation, you have to focus on the three main building blocks necessary to implement transformation:

  • Defining measurable business objectives that are aligned with the organisational goals and aspirations
  • Assigning transformation owners for each of the key functions identified for Transformation
  • Using structured program management using Lean principle and the Balanced Score Card approach

Understandably, an interdisciplinary model of work and a structured program may not be easy to implement. I discuss the complexities of introducing the new wave of technology in my report.

If your organisation is embarking on a DX journey or if you wish to discuss your ongoing DX roadmap, leave a comment below or connect with me on the Ecosystm platform.

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The acquisition of CloudCherry will bolster Cisco’s contact centre offerings

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Cisco announced their plans to acquire CloudCherry to bolster their contact centre portfolio. Launched in Chennai in 2014, CloudCherry is a customer experience (CX) management startup that helps organisations understand the various factors influencing CX. CloudCherry has employees in Chennai, Bengaluru, Singapore and Malaysia, besides the US and their team of around 90 employees will join Cisco’s contact centre solution practice as part of the acquisition

Using artificial intelligence (AI) as the underlying solution to CloudCherry’s open API platform allows for various customer data sets from  CRM systems to other communication touchpoints in the contact centre to be analysed in real-time for the organisation to deliver a personalised CX. When agents can understand what is taking place in real-time and when the contact centre team has one integrated point of data injecting analytics, improving the ability to drive greater loyalty and eventually higher revenues.

Some of CloudCherry’s offerings are:

  • Measuring customer journeys. CloudCherry provides the opportunity to follow the customer across 17 different channels, driving contextual real-time conversations with customers on the channels they choose. It is important to understand the micro journeys  – for example, their customer PUMA sells products online and in physical stores and may have two micro journeys in addition to an overall customer journey map for:
    • Online customers
    • In-store customers
    • Blended customers
  • Predictive Analytics. Their predictive engine is based on customer feedback, their actions and their purchasing data. With advanced predictive analytics, CX teams can derive what is needed to increase the ROI.
  • Questionnaire builder. They have the capability to respond to feedback collected from surveys in real-time. There are set conditions for survey questions so that when triggered by customer response, the concerned employee or department is quickly notified regarding it. For instance, when a customer gives a low rating on store cleanliness or staff behaviour, an alert can be immediately sent to the concerned employee to follow up and take corrective action. At the same time, even positive feedback can be noted in order to recognise and reward employees.
  • Sentiment Analysis. This helps organisations tap into machine learning and deep learning to identify customer sentiment associated with open-text responses and brand conversations.

These are just some of the applications and tools the CloudCherry platform offers to their customers.

Leading with data will be critical to driving personalised CX

CX decision-makers and buyers of contact centre and CX technologies have an important role to play in the next few years to look at re-inventing how they view CX. This means re-looking at the ways they have been running contact centres in the traditional way and making investments towards the cloud, machine learning and predictive analytics.

  • By having rich analytics, mobile conversation through the app can be richer. A Mobile-led CX approach is key in today’s world where most people spend hours on a phone.
  • Issues can be prevented before they happen if in-store transactions are monitored and dissatisfied customers can be identified.  The organisation has the ability to reach out to the customer through any touchpoint to mention proactively that they are aware of the issues that just happened and what they can do to help solve the negative experience. Proactive notifications demonstrate how a brand takes it, customers, seriously
  • Leveraging AI as the underlying platform to understand customer behaviour is going to be the next battleground for CX vendors. The challenge so far has been that many organisations have invested in several data and CRM tools from various vendors. When agents have to view customer information, they are dealing with data in an unsynchronised format. This explains why when we contact a contact centre, we sometimes have to repeat ourselves and state the problem we are facing. Or worse than that, the agent has no idea that we had a problem a week ago and spoke to 2 agents. These frustrations are real and still happen today.
  • Contact centre of the future will not be reactive but proactive in helping understand customer sentiment in real-time to make the necessary adjustments and actions needed to solve the issue the customer is facing. The deep analytics platform for CX also means that agents will be empowered with information and bots can be placed to help agents say the right things or make suggestions to customers. The use cases to help deliver personalised CX are enormous.

 

Ecosystm comment

This is an important and good acquisition for Cisco. Cisco has a vast set of customers globally and in the Asia Pacific region in the collaboration, voice and contact centre space. This acquisition marks how they are investing in enhancing their existing contact centre portfolio to use machine learning, cognitive and predictive analytics to alleviate their offerings. The contact centre is a key part of Cisco’s larger collaboration portfolio.

According to the company, Cisco products support more than 30,000 contact centre customers and more than 3 million contact centre agents around the world. Vasili Triant, VP and GM of Cisco Contact Centre solutions mentioned in a blog recently that the acquisition will augment Cisco’s contact centre portfolio with advanced analytics, journey mapping and sophisticated survey capabilities whether their customers are using Webex Contact Centre in the cloud or their hosted and on-premises solutions.

The market for predictive analytics and customer analytics in the contact centre and across the CX segment will be big and we are at the beginning of a new era of organisations using data as the platform to deliver a new way of engaging with customers. CloudCherry offers a CX management platform that uses predictive analytics to derive insights for contact centre agents.  The market for deep analytics is becoming an important area of investment for organisations as a way to decrease customer frustration. It is by applying analytics before, during and after the call that will allow contact centres to deliver a personalised CX as was mentioned in my last blog. This is the reason why a data-driven culture will be key to driving rich outcomes for the contact centre. Contact centres will have to lead with analytics so that every experience across every single touchpoint the customer has with the brand is analysed and observed in real-time. We will see many contact centre vendors and players in the CRM space acquire companies with capabilities like what CloudCherry offers.

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5 Misconceptions about Artificial Intelligence

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Innovation is often fuelled by the evolution of technology, which unlocks greater potential for businesses. Artificial Intelligence (AI) is often considered the trendiest of today’s emerging technologies, viewed as the real enabler of innovation. Many businesses are adopting AI and investing in AI-based solutions. However, every emerging technology faces the same stumbling block – trust. AI has been no different and despite the growing adoption, not everyone is ready to jump on the AI bandwagon yet.

The other major stumbling block for AI has been the inability of the common man to grasp the implications of the technology and differentiate it from what has been depicted in Science Fiction and dystopian novels. There is also confusion in the mind of technologists on the definition of AI. Some experts will say that only Deep Learning with non-linear algorithms is AI, while most vendors promote their automation tools like AI. A few misconceptions have hence arisen regarding AI, its uses and its impact on the workforce and society.

 

#1 AI is a new technology

Despite recent hype around the technology, AI is not a new technology and not a product of this century’s innovations. The beginnings of AI can be traced to the middle of the 20th century and then it gained pace. During the second world war, an English mathematician and computer scientist, Alan Turing documented his ideas on creating an intelligent machine. Turing’s test theory proposed that if a machine could engage in full conversation with no detectable variances from a human, the machine could be deemed as a thinking machine. Turing worked to crack the German military’s encryption, the ‘Enigma’ code.

Later in 1956, American computer scientist John McCarthy organised the Dartmouth Conference, where the term ‘Artificial Intelligence’ was first used.

Today, AI is a much broader term and refers to a range of technologies from Automation to Deep Learning.

AI-Timeline

 

#2 AI can replace the human brain – completely

Humans have evolved over millions of years from being hunter-gatherers to agricultural societies to a modern-day man who can succeed in secondary and tertiary industries. We have adapted, evolved and became good at surviving in the real world. Despite this many people hold an opinion that AI will replace the driving force of one of the most complex machines on this planet – the human brain.

AI has clearly come a long way. Its ability to learn vast amounts of data, recognise patterns, and produce results is improving us in countless ways. However, the problem with achieving true AI is also its greatest strength – that it does not learn like a human. The technology behind AI is scientific and complex and building a competitive AI from scratch requires expensive specialised talent. For instance, a successful image recognition solution is more accurate than most humans, but the same coding cannot address another type of problem.

AI cannot replace a complex structure of neurons and humans will continue to use their intelligence for more innovation. Humans do and will continue to, play a major role in most AI applications, especially critical ones in research and medicine. Each of our innovations has made the race more productive, and that is what AI will further add to the human race.

 

#3 AI poses a threat to security & privacy

While the benefits of AI and Big Data technologies are being felt, people also consider them as a threat to their anonymity and privacy.

With online social accounts, digital identities and other digital data gathering entities – both private and government – privacy has become a pertinent question. With the emergence of sophisticated AI systems, these privacy concerns have been aggravated. AI brings the ability to fetch, combine, and analyse a huge quantity of data from varied sources.  The impression is that AI can perform these designated operations with no supervision and there is fear that humans will lose control of a system entirely.  Instances of data and privacy breaches heighten this fear.

This fear may be unfounded. Today, AI systems can simplify user privacy policies on websites, which most of the users do not bother to read and simply click on the ‘Accept’ box. Polisis is an AI-powered automated analysis tool for privacy policies. Polisis pulls a website’s privacy policy and takes around 30 seconds to interpret it, display a summary, and present a flowchart of the policy with highlights on how the online service will handle user data. An AI-powered chatbot called PriBot, answers questions about the privacy policy of any company.

AI Generated Visualisation
Polisis’ AI-generated visualization of the privacy policy for Pokemon Go. Image: PRIBOT

 In reality, AI technologies are being utilised to create a safer and more secure society. AI brings speed, scale, and automation to computing and is changing the way we work, live, and interact. We are guiding AI capabilities for better healthcare provision, citizen safety, research accuracy, and even enhanced cybersecurity. Very often, the data used by these algorithms are aggregated and anonymised.

 

#4 AI will replace jobs

There is an abundance of fear, uncertainty, and doubts about the risk and opportunity of AI. Will it create jobs or destroy them?

There is no doubt that AI is poised to transform jobs and will change the face of employment. It is easier to see existing jobs disrupted by new technology than to envision what new jobs the technology will enable. AI is poised to replace tasks, not jobs. Some functions – and sometimes all the functions – of an individual or team might be automated. Employees with no plans or desire to re-skill should be concerned, but those who are continuously improving and changing their skill sets need not be too concerned that automation will put them in an unemployment queue.

“While businesses will face pain, as they adjust to new lower cost and higher productivity expectations – and employees will need to continually update their skills – the overall assessment is for jobs growth. It is just that the jobs created will be different to the jobs that exist today”, as Tim Sheedy (Principal Analyst, AI & Automation, Ecosystm) puts it in his report Automation Will Transform Jobs – Plan for Change Now

Read Report – https://www.ecosystm360.com/#/link?type=report&id=155b38c6-3764-4715-a9ad-1ec6447260ac

A few businesses today are creating Automation Teams or Centres of Excellence – banks, telecommunications providers and utilities are leading this push. With continued effort, AI will eventually become intelligent enough to understand the tasks and make them easier for the workforce. Employees need to trust, use and maximise the full potential of the technology, and see its benefits for scaled implementation.

The NAB Cloud Guild is a good example of how organisations should provide training to not just technology staff but to any interested employee, on emerging technologies to equip their business for future demands.

 

#5 AI is implemented only by large vendors

AI is driving many Digital Transformation (DX) projects and large vendors, especially with platform and enterprise capabilities, have had the first movers’ advantage in AI deployments. Businesses are striving to make their systems more intelligent for better process automation and customer retention. After 40 years of automating manual tasks using enterprise applications (such as ERP, SCM, and CRM), intelligent systems will make many of these systems redundant – or at the least reduce business reliance on them.

One of the big challenges for large businesses – and their IT teams – today is to customise their AI to their organisations’ DX requirements. Many companies have made their first foray into the world of AI – often starting with technologies such as RPA, IoT sensor analytics, and chatbots. They are now looking to go beyond evolving their RPA solutions into Smart Process Automation (SPA) solutions. They are also going beyond basic chatbots/ virtual assistants to implement NLG and semantic computing, as their customer focus deepens. For these large enterprises, integration of AI solutions with internal systems and other AI solutions is the key challenge, and they often prefer to partner with their existent enterprise vendor or systems integrator for their AI implementations.

However, smaller organisations and start-ups are equally leveraging AI. Several tech start-ups also exclusively focus on AI and are developing a niche, industry-specific solutions. These smaller solution providers will probably be integrated into larger vendors’ partner ecosystems, as their capabilities deepen, and their customer base grows. Organisations need not look to only larger, established vendors for their AI implementations.

 

AI is still an emerging technology and it might take some time for AI to be trusted. The truth, however, is that AI opens up immense possibilities for individuals, enterprises, and governments.

 

Do the supposed threats outweigh the benefits of AI? We would very much love to hear your suggestions, ideas, and thoughts on this subject.

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VendorSphere: AI Gets Real – Ramco’s Vision Is To Make Your Systems Work For You

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I recently attended a briefing with Ramco Systems – if you haven’t heard of them, they are one of an emerging group of software vendors who are challenging the big application software companies – SAP and Oracle. They put innovation at the centre of their business – aiming to constantly drive improvement for their customers, and bringing companies the benefits of systems that consumers see in their web-based and mobile apps but have been sorely missing from the enterprise application market. To be honest they are a breath of fresh air in a market that needs it – and their endeavours are seeing results both in plaudits from analyst firms and new customer wins.

At the briefing, Ramco demonstrated some of the AI capabilities they have been weaving into their software platforms. And in doing so they have shown the gap between today’s systems and systems that actually work for their clients. ERP, HR, Payroll and other enterprise applications are data sinks – they demand constant input, and while they do a good job in automating business processes, they could do so much more.

Within Ramco they have moved away from email completely for employee inquiries – all interactions now happen with their transactional chatbot, including scheduling meetings, checking leave balances, discovering and understanding personal achievements, raising a travel request and claiming travel expenses – as well as understanding company policies and supporting employees with speculative queries. This same bot is available for clients as they aim towards a zero-UI interface – no more logging onto systems and interrogating applications, running searches. Now you ask a question and get an answer – using an IM client or a voice interface (such as Google Home or Amazon Alexa devices). This is the way systems should serve employees.

Like other enterprise application vendors, they have added an AI capability to their platform – but they are taking the extra step to make that AI work out of the box (or the cloud). For example, with all the information in your HR systems (employee skills, time and attendance, incentives, expenses, payroll) they are looking at making that information accessible and actionable for potential users – creating systems that understand the context and anticipate needs.

In your finance or ordering systems, they are applying machine learning so it understands that ‘client A’ tends to order specific items from specific locations – so ordering agents are guided towards those options versus having to scroll through long lists.

(see images for an example of that in the process)

They are recommending where costs should be allocated or validating inputs based on historical learnings. The systems can catch a mistake, errors or even fraud – saving the business significant amounts of money and of time in error correction or re-work.

 

Ramco’s vision is that agents only have to manage exceptions in enterprise applications – not every single detail. Complete automation is still an unrealistic expectation, but businesses should aim for 85% automation, with 12% of processes needing intervention for mild intervention and 3% needing deep intervention. In Ecosystm’s experience speaking to businesses that have automated to such a degree, an 85% automation does NOT lead to an 85% saving – as you typically automate the easier cases anyway. But the savings should be real and measurable – up to 50% time saving for accounts receivable or payable teams, for payroll teams, for help desks or for other highly manual processes should be achievable.

And while the business case can be built on the saving, the pay-off also comes in happier and more engaged employees who have the information right at their fingertips to make better business decisions or drive smarter business processes.

So why highlight Ramco’s AI capabilities? For a number of reasons:

  1. For AI to be widely adopted, it needs to be easy and accessible – Many other vendors (the big cloud players in particular) are making AI tools and assets available for customers, but they still have to do the hard work – find a business problem, gather the data, train the algorithm, deploy the algorithm and then train users on the new process. There are hundreds – or even thousands of examples of processes in business that can be made smarter and easier through the use of machine learning and AI – and vendors should be building these capabilities into the products and platforms. Ramco is doing that – they are by no means alone – but they are a good example of a software vendor that is disrupting a market by focusing on helping their customer succeed.
  2. I believe there is a bigger trend going on in the way businesses buy software (and look out for an upcoming report on this topic). More and more I see businesses adopt the best solution for their needs – NOT the one that does 80% of what they want. And the best software is often built by smaller, more agile companies. They build for specific business needs and specific niches – and they focus on providing exactly what customers want. I am seeing a general move away from the big platform providers towards the smaller ISVs. Partly because they cost less (I regularly hear companies say they saved up to 90% by using a specialist provider!) – but also because they provide the best solution – and businesses can no longer compromise when it comes to driving the best customer and employee experiences. Again, Ramco is a part of this change.

You should demand more from your applications provider – an AI platform is not enough. They need to make your actual application smart – they need to be able to automate processes you are already doing. If you have data the system should be able to learn, they need to focus on making the system work for you, your employees and your customers – not the other way around (as is too often the case). AI needs to be a core component of your business applications, not a bolt-on.

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InsureTech: Transforming the Insurance Industry

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The global insurance industry today faces several challenges  – starting from the shift in the demographic patterns and the disease burden, to managing an ever-growing agent ecosystem,  to responding to customer expectations. The advancement in technologies and their adoption is creating opportunities for insurance companies to modernise and reinvent themselves through new product and services offerings and by evolving their business models.

Drivers of Transformation in the Insurance Industry

  • Global Competition. Over the last few years, leading insurance providers have been looking for a share of the global market and are no longer content with their traditional domestic markets. They especially want to get into markets where there are fewer players and/or larger population. The Indian insurance industry, for example, has seen a number of new private entrants over the last decade, attracted by the large population base and by a high percentage of young population. Many of the leading global insurance providers have partnered with Indian counterparts for a presence in the market. The story is similar in several emerging economies. While the presence of insurance providers is good for the future sustainability of a country, the market is extremely competitive. Investing in technology can be the key differentiator in capturing a larger share of the pie.
  • Customer Expectations. Today’s customers are tech-savvy and expect a certain level of service and at their fingertips too. Moreover, easy access to the internet equips them to do basic research to evaluate their best options. The Fintech revolution also impacts the customer base, as they expect services such as instant approval and prefer to purchase items only when they require them. This ‘on-demand’ market has fueled the microinsurance industry and opened the gates for smaller providers.
  • Regulatory Requirements. In the aftermath of the financial crisis of the previous decade and with new entrants in several countries, regulatory authorities are working on an overdrive to bring better accountability to the insurance market. Moreover, in most countries the regulations have incorporated market conduct guidelines aimed at consumer protection.  Reporting, service level and fraud prevention requirements will see an increased uptake of technologies that can assist in fulfilling compliance requirements.

Key InsureTech Technologies

  • IoT. The auto insurance companies were the first to leverage IoT and telematics to enhance navigation, safety and communication features that could help customise the premiums payable. The home insurance sector has already leveraged it using sensors and connectivity to assess and reduce risks to the properties they insure  – large providers such as Allianz, Aviva and AXA have been working on their IoT ecosystem. This has immense potential for ‘usage-based’, personalised product and premium offerings in the health and life insurance industries (provided they work within the purview of compliance requirements).  Ultimately sensors are not the most important technology in an IoT solution – the analytics solutions that can derive intelligence from the sensor data are. IoT+AI will give that much-needed edge to insurance companies.
  • AI – Machine Learning. AI and machine learning make it possible for insurance companies to mine both structured and unstructured data. The use cases range from underwriting, claims management and personalised offerings through behavioural data and sentiment analysis. There are examples of early adopters in the auto industry – but again there are obvious and wider use cases, that can benefit risk modelling, pricing, customer acquisition, and agent and channel efficiency.
  • AI – Virtual assistants/Chatbots. This falls right in with managing customer experiences. As customers expect more self-service (yes, the future will see less agents!) several insurance providers are using chatbots at several customer touchpoints, covering departments such as Sales and Claims. This will increasingly be the norm as smart phone (and app) penetration increases and the target base becomes younger. There are online-only insurance providers where clients interact with chatbots services and they are able to cater to a larger, untapped, mass market. There are more advanced adoption examples such as USAA’s use of intelligent personal assistant equipped with an NLP engine that have been trained with a deeper knowledge of policies. Virtual insurance agents will become more of a norm in the near future.

Which brings us to the important question on how insurance companies are planning to leverage InsureTech. Multiple stakeholders could benefit from InsureTech adoption. The Claims department appears to be a key stakeholder, focused both on fraud prevention and automation when it comes to transaction and processing. Sales and Customer Service appear to be next in line, where personalisation of product offerings would equip the teams better for a competitive market.

Challenges of AI Adoption in Insurance

It is obvious that the insurance companies are still at a nascent stage of adoption of AI and InsureTech. While cybersecurity is a recurrent concern (as it should be), it is a common concern across any technology area. The biggest challenge that the insurance industry faces in adoption of AI and other data-driven technologies is the actual data management – from access to integration. The industry may be data-intensive, but the data exists in silos. In the end an InsureTech implementation should benefit multiple departments – Underwriting, Claims, Sales and so on.

Several insurance companies will look to consulting firms and systems integrators to create a roadmap to their transformation journey and enable the data integration – especially as technologies evolve and when internal IT lack the right skills to manage these projects.

The technology that will be the key component of InsureTech and transform the insurance industry is AI. In spite of the challenges of adoption, the industry will be forced to transform to survive in the highly competitive market. Companies in emerging economies will especially benefit from investing in AI – in fact, India and especially China will see a surge in InsureTech investments.

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Global Initiatives to Support AI Governance and Ethics

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Any new technology that changes our businesses or society for the better often has a potential dark side that is viewed with suspicion and mistrust. The media, especially on the Internet, is eager to prey on our fears and invoke a dystopian future where technology has gotten out of control or is used for nefarious purposes. For examples of how technology can be used in an unexpected and unethical manner, one can look at science fiction movies, Artificial Intelligence (AI) vs AI chatbots conversations, autonomous killer robots, facial recognition for mass surveillance or the writings of Sci-Fi authors such as Isaac Asimov and Iain M. Banks that portrays a grim use of technology.

This situation is only exacerbated by social media and the prevalence of “fake news” that can quickly propagate incorrect, unscientific or unsubstantiated rumours.

As AI is evolving, it is raising some new ethical and legal questions. AI works by analysing data that is fed into it and draws conclusions based on what it has learned or been trained to do. Though it has many benefits, it may pose a threat to humans, data privacy, and the potential outcomes of the decisions. To curb the chances of such outcomes, organisations and policymakers are crafting recommendations about ensuring the responsible and ethical use of AI. In addition, governments are also taking initiatives to take it a step further and working on the development of principles, drafting laws and regulations. Tech developers are also trying to self-regulate their AI capabilities.

Amit Gupta, CEO, Ecosystm interviewed Matt Pollins, Partner of renowned law firm CMS where they discussed the implementation of regulations for AI.

To maximise the benefits of science and technology for the society, in May 2019, World Economic Forum  (WEF) – an independent international organisation for Public-Private Cooperation – announced the formation of six separate fourth industrial revolution councils in San Francisco.

The goal of the councils is to work on a global level around new technology policy guidance, best policy practices, strategic guidelines and to help regulate technology under six domains – AI, precision medicine, autonomous driving, mobility, IoT, and blockchain. There is participation of over 200 industry leaders from organisations such as Microsoft, Qualcomm, Uber, Dana-Farber, European Union, Chinese Academy of Medical Sciences and the World Bank, to address the concerns around absence of clear unified guidelines.

Similarly, the Organization for Economic Co-operation and Development (OECD)  created a global reference point for AI adoption principles and recommendations for governments of countries across the world. The OECD AI principles are called “values-based principles,” and are clearly envisioned to endorse AI “that is innovative and trustworthy and that respects human rights and democratic values.”

Likewise, in April, the European Union published a set of guidelines on how companies and governments should develop ethical applications of AI to address the issues that might affect society as we integrate AI into sectors like healthcare, education, and consumer technology.

The Personal Data Protection Commission (PDPC) in Singapore presented the first edition of a Proposed Model AI Governance Framework (Model Framework) – an accountability-based framework to help chart the language and frame the discussions around harnessing AI in a responsible way. We can several organisations coming forward on AI governance. As examples, NEC released the “NEC Group AI and Human Rights Principles“, Google has created AI rules and objectives, and the Partnership on AI was established to study and plan best practices on AI technologies.

 

What could be the real-world challenges around the ethical use of AI?

Progress in the adoption of AI has shown some incredible cases benefitting various industries – commerce, transportation, healthcare, agriculture, education – and offering efficiency and savings. However, AI developments are also anticipated to disrupt several legal frameworks owing to the concerns of AI implementation in high-risk areas. The challenge today is that several AI applications have been used by consumers or organisations only for them to later realise that the project was not ethically fit. An example is the development of a fully autonomous AI-controlled weapon system which is drawing criticism from various nations across the globe and the UN itself.

“Before an organisation embarks on the project, it is vital for a regulation to be in place right from the beginning of the project. This enables the vendor and the organisation to reach a common goal and understanding of what is ethical and right. With such practices in place bias, breach of confidentiality and ethics can be avoided” says Ecosystm Analyst, Audrey William. “Apart from working with the AI vendor and a service provider or systems integrator, it is highly recommended that the organisation consult a specialist such as Foundation for Responsible Robotics, Data & Society, AI Ethics Lab that help look into the parameters of ethics and bias before the project deployment.”

Another challenge arises from a data protection perspective because AI models are fed with data sets for their training and learning. This data is often obtained from usage history and data tracking that may compromise an individual’s identity. The use of this information may lead to a breach of user rights and privacy which may leave an organisation facing consequences around legal prosecutions, governance, and ethics.

One other area that is not looked into is racial and gender bias. Phone manufacturers have been criticised in the past on matters of racial and gender bias, when the least errors in identification occur with light-skinned males. This opened conversations on how the technology works on people of different races and genders.

San Francisco recently banned the use of facial recognition by the police and other agencies, proposing that the technology may pose a serious threat to civil liberties. “Implementing AI technologies such as facial recognition solution means organisations have to ensure that there are no racial bias and discrimination issues. Any inaccuracy or glitches in the data may tend to make the machines untrustworthy” says William.

Given what we know about existing AI systems, we should be very concerned that the possibilities of technology breaching humanitarian laws, are more likely than not.

Could strong governance restrict the development and implementation of AI?

The disruptive potential of AI poses looming risks around ethics, transparency, and security, hence the need for greater governance. AI will be used safely only once governance and policies have been framed, mandating its use.

William thinks that, “AI deployments have positive implications on creating better applications in health, autonomous driving, smart cities, and a eventually a better society. Worrying too much about regulations will impede the development of AI. A fine line has to be drawn between the development of AI and ensuring that the development does not cross the boundaries of ethics, transparency, and fairness.”

 

While AI as a technology has a way to go before it matures, at the moment it is the responsibility of both organisations and governments to strike a balance between technology development and use, and regulations and frameworks in the best interest of citizens and civil liberties.

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Singapore Government Promoting Tech Adoption in the Legal Industry

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Singapore is encouraging the adoption of technology in the legal sector for higher efficiencies. In May, the Ministry of Law (MinLaw), Enterprise Singapore, the Infocomm Media Development Authority (IMDA) and the Law Society of Singapore (LawSoc) announced the launch of a new SmartLaw Guild to encourage law firms to adopt technology.

The SmartLaw Guild brings together case studies from the legal industry and organises  knowledge sharing sessions. Speaking at the launch of SmartLaw Guild, Communications and Information Minister S Iswaran, said that the majority of legal practices in Singapore are catered to the SME sector given that 90% of organisations in Singapore fall under the category.  The Government is making an effort in the evolution of technology to support the SME legal practices. Mr. Iswaran also encouraged practicing lawyers to take advantage of the skills training provided by the IMDA’s Techskills Accelerator initiative in areas such as cybersecurity, AI and data science.

 

Why have Law Firms been Slow in Tech Uptake?

A LawSoc survey held in 2018 showed that the adoption of technology helps in the delivery of legal services but only an estimated 12% of law firms in Singapore appears to have adopted digital technology till date. Hence, to encourage digitalisation of the legal industry, legal firms in Singapore will benefit from the SGD 3.68 million fund that has been set aside, to provide them with funding support for adopting technology solutions.

Commenting on the announcement, Ecosystm VP & General Counsel, Nandini Navale said “Across jurisdictions, law firms are bound to licensing and regulatory conditions and have to follow strict standards of professional ethics, confidentiality, and care to clients. This could be a possible reason for their ‘abundantly cautious’ approach towards the adoption of new technology and digitalisation. A glitch or even a minor fault in the technology could result in the loss of license to practise, breach of regulatory obligations, reputational damage or can compromise the interest/privacy of clients. Therefore, AI and technology in systems and processes will have to be proven reliable and fail-safe as a condition for the implementation in the legal sector.”

Law has been a conservative industry. This is fast changing, however with the “BigLaw” in countries investing heavily in technology and looking to implement AI to help their legal staff perform due diligence and research, provide additional legal insights and in process automation in legal work.

Advanced technology solutions powered by AI are enhancing business capabilities and the adoption of AI in the legal industry can help in a quicker resolution of disputes and more consistent outcomes. “AI is capable of transforming the legal sector. The technology could be used to sift through volumes of case law and litigation history, and help lawyers to interpret, prepare and support their positions. Legal issues spotters are being utilised in the contract due diligence and review, legal-tech being deployed for routine and low-value work. Applications for time trackers, billing and invoicing, and legal data analytics are also being adopted” says Navale “The Singapore Government is indeed walking the talk – an example of this is the introduction of the Venture Capital Investment Model Agreements (VIMA) documentation.” The initiative was launched in 2018 by the Singapore Academy of Law (SAL) and the Singapore Venture Capital & Private Equity Association (SVCA) which comprises a set of standard documents that improve the process of structuring a deal and transactions for venture capital firms, start-ups, and SMEs. The core working group for the initiative adopted technology and created a questionnaire that guides through the documentation with auto-versioning and customisation to save time, cost and effort.

 

How have some Disruptive Technologies Impacted the Legal Industry?

Amit Gupta, CEO, Ecosystm interviewed Matt Pollins, Partner of renowned law firm CMS where they discussed the legal implications of AI as well as the uptake of new technologies in the legal industry.

What do you think are the implications of technology adoption in the legal industry?

Let us know in your comments below.

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VendorSphere: NEC’s Facial Recognition Capabilities

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I was invited recently by NEC to attend their briefing where Walter Lee, their Evangelist and Government Relations Leader presented to analysts and journalists about how they are winning large contracts across various sectors in the areas of biometrics and surveillance. Biometrics is not just used as a way to drive greater security, but is also helping increase speed in processing times, reducing waiting period in queues and used as a way to drive efficiency and reduce costs which was highlighted by Lee through the various projects NEC had won recently.

NEC’s Artificial intelligence (AI) engine, NeoFace’s strength lies in its tolerance of poor-quality images. The NeoFace solution can match images with low resolutions down to 24 pixels between the eyes and this has allowed it to demonstrate the matching accuracy which is hard to achieve for most vendors offering Facial Recognition solutions. It is its ability to work across various challenges around low resolution, light and images that has allowed NEC to be one of the leading suppliers of Facial Recognition solutions globally.

Key Case Studies Presented

In 2018 Delta Airlines launched the first ‘biometric terminal’ in the US at the international terminal in Atlanta’s Hartsfield-Jackson airport. The biometric push according to Lee replaces tickets and customers now check in by using their face. The system recognises their face and they are checked in. Customers no longer need to use their passports to get through checkpoints around the airport.  Lee emphasised on how it takes 9 minutes to board an international flight. Apart from driving identification and security, this use case highlights how airports around the world can increase efficiency in their overall check in and boarding processes at airports. Other core benefits derived from this implementation include better security for border control, seamless service, speed of boarding (savings of 9 minutes per flight). Privacy issues were addressed with regards to where the data was residing and how long the data would be kept for and in this case the data was kept for only 24 hours.

According to the global Ecosystm AI study of current and planned Facial Recognition adoption by industry, the transportation industry is leading the number of deployments globally.

Adoption of Facial Recognition by Industries

Another case study presented is the upcoming 2020 Summer Olympic and Paralympic Games in Tokyo., for which NEC will provide the Facial Recognition solution. The solution will be used to identify over 300,000 people at the games including athletes and officials. It is the first time that Facial Recognition technology will be used for this purpose at an Olympic Games. The NEC solution will allow the matching of tens of thousands of faces in a nano second according to  Lee.

The Tokyo 2020 implementation will involve linking photo data with an IC card to be carried by accredited people. NEC says that it has the world’s leading face recognition tech based on benchmark tests from the US’s National Institute of Standards and Technology (NIST).

Ecosystm Comment.

NEC has years of experience in biometrics and Facial Recognition. Not many vendors have solutions that can capture vast amounts of images in a nano second. Their solutions are used by some of the largest organisations in the world. NEC has also perfected the art of handling low resolution images which if not analysed accurately can lead to unintended consequences. The ability to process low resolution images with speed and accuracy is not something that is easily achievable. Security and the rise of terrorism are some of the needs as to why Facial Recognition is important. Additionally, speed and efficiency in administrating passenger boarding at airports whilst ensuring that the security and identity checks have been made is important. The Delta Airlines case study is a great example of how there can be a savings of 9 minutes per flight. NEC continues to gain traction in the market and the Ecosystm AI study has them as one of the top vendors being evaluated for planned implementations for Facial Recognition globally.

The benefits of Facial Recognition solutions are huge – however there must be greater scrutiny around the possible outcomes of AI. Whilst regulation on AI is still at its infancy, 2019 and 2020 will see greater scrutiny and regulation around AI implementations. These will be directed towards protecting individual’s data but also there will be greater emphasis on addressing issues around privacy, ethics and bias in AI implementations. Feeding the machine with the right data (unbiased and ethical) and measuring the various outcomes before the project goes live must be looked at with greater diligence.

2 weeks ago, San Francisco became the first US city to ban the use of Facial Recognition technology by the police and local government agencies. One of the reasons for the ban was with regard to bias. When designing the systems, if technology specialists feed the wrong information for example recognising only a certain skin colour, then the problem of making the wrong and unwanted assumptions start arising. The ecosystem of players in the AI industry ranging from government, academia right down to vendors have a greater role to play in ensuring ethics and bias issues are addressed from the onset of the project. There are consultants in the market as I highlighted in my recent Ecosystm report, that prepare companies for the impact of ethics, fairness and bias. We can expect more of such consultancies and specialist agencies to grow in the market.

NEC has taken this into consideration and published a set of principles for the application of biometrics and AI.  The “NEC Group AI and Human Rights Principles” will guide the company along the lines of privacy and human rights. These initiatives were led by the Digital Trust Business Strategy Division, in collaboration with several other divisions within the company, as well as industry stakeholders including industry experts and non-profit organisations.

 

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How IoT and AI will transform the sports business?

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This is an abstract of my presentation in Dubai on 23rd April 2019. I want to convey my special thanks to Dr. Eesa Bastaki, President of the University of Dubai for inviting me on the occasion. It was a magnificent experience delivering at such a great University.

In the year 2016, I considered Rio as the first Internet of Things (IoT) Olympic games in my article “The future of “The Internet of Olympic Games”. In Rio, we saw how athletes, coaches, judges, fans, stadiums, and cities benefited from IoT technology and solutions which transformed the way we see and experience sports. Next year we will have another opportunity to validate my predictions for the upcoming Tokyo 2020 Summer Olympics. Therefore, we may designate Tokyo as the first Artificial Intelligent (AI) Olympic Games.

During my presentation at the University of Dubai, I explained to the audience how incredible IoT and AI technologies are and to what extent they are impacting our sports experience. I elaborated on IoT and AI’s significant role in health management, improving aptitude, coaching, and training. These technologies are enabling athletes to improve performance, coaching for better preparation, fewer judgment errors, and a better experience for spectators. I also commented on the importance of IoT and AI to enhance the security of teams, audience, stadium, and cities altogether.

With the use of IoT and AI we are creating a world of smart things transforming sports business where every thousandth part of a second is crucial to predict the outcomes of a race, a match or a bet. I cited various examples on how different sports are utilising IoT and AI, and not in the least I shared a vision of the future that’s like 10-15 years onwards from the present – Can you envision a world of a real and virtual world of sports integrated together? Can you visualise robots and humans or super-humans playing together?

On the other side, speaking of the challenges involved with AI, IoT, and machine learning models for sporting, I conveyed the dark side of these technologies. We cannot forget the fact that the sports industry is a market and therefore enterprises, Governments, and individuals may make erroneous uses of these technologies.

In summary, it in this session I shared my point of view on-

  • How IoT and AI will transform coaches, athletes, judges, and fans.
  • How IoT and AI will attract the audience to the stadiums
  • How IoT and AI will transform the Industry?
  • How AI is changing the future of sports betting?

How IoT and AI will transform athletes, coaches, judges and fans?

Athletes

While the true essence of a sport still lies in the talent and perseverance of athletes, it is often no longer enough. Therefore, athletes will continue to demand increasingly sophisticated technologies and cutting-edge training techniques to improve performance. For example, we may see biomechanical machine learning models of players to predict and prevent potential career-threatening physical and mental injuries or can even detect early signs of fatigue or stress-induced injuries. It can also be used to estimate players’ market values to make the right offers while acquiring new talent.

Coaches

Coaches are consuming AI to identify patterns in opponents’ tactics, strengths and weaknesses while preparing for games. This helps coaches to devise detailed game plans based on their assessment of the opposition and maximise the likelihood of victory. In many leading teams, AI systems are used to constantly analyse the stream of data collected by wearables to identify the signs that are indicative of players developing musculoskeletal or cardiovascular problems. This will enable teams to maintain their most valuable assets in prime condition through long competitive seasons.

Judges

We tend to think that technology is helping us to make decisions in sports more accurate and justified. That´s why we look at the inventions such as from Paul Hawkins – creator of Hawk-Eye, a technology that is now an integral part of the spectator’s experience when watching sport live or more recently VAR in soccer.

The use of technology is allowing the decision makers to experience the game with multiple cameras angles in real-time combined with the aggregated data from various sensors (stadiums, things, and athletes) thus making them make more objective and accurate decisions.

We as spectators or fans need more transparency about the exercise’s difficulty, degree of compliance and final score. And we have the technology to do it.

The IoT and AI technology don’t claim to be infallible – just very, very reliable and judges also need to be adapted to new technologies.

Fans

Without fans, sports would find it difficult to exist. It is understandable companies are also targeting fans with IoT and AI to keep them engaged whether in the stadium or at home.

How IoT and AI will attract the audience to the stadiums?

The stadiums, sports clubs and many leagues across the globe are incorporating technologies both inside and outside the stadium areas to boost the unique experiences for fans and not only during the gameplay.

The challenge is how to combine the latest technologies with old-school stuff to please supporters from both newer and older gen. people looking forward to witnessing a game in a stadium?

How will the stadiums of the future be? I read numerous initiatives of big clubs and leagues, but I am excited about the future stadium of Real Madrid. I wish the club would allow me to advise them how to create a smart intelligent Global environment to provide each fan with an individual experience, know who is in the crowd, learn fan behaviors to anticipate their needs.

How IoT and AI will transform the Industry?

“As long as sports remain a fascination for the masses, businesses will always have the opportunity to profit from it. As long as there is profiting to be gained from the world of sports, the investment in and incorporation of technology for sports will continue.”

I went through an article warning about an entirely new world order that is being formed right now. The author explained how 9 companies are responsible for the future of AI. Three of the companies are Chinese (Baidu, Alibaba, and Tencent, often collectively referred to as BAT), while the other six are American (Google, Amazon, IBM, Facebook, Apple, and Microsoft, often referred as the G.Mafia). The reason is obvious, as far as AI is about optimisation using the data that’s available, these 9 companies will manage most of the sports data generated in the world.

Collaboration is needed now to stop this threat and to address the democratisation of AI in sports. It is important that companies and Governments around the globe work together to create guiding principles for the development and use of AI and not only in Sports. This means we need regulations but in a different way. We do not want AI power to lie only in a handful of lawmakers, renowned and smart people who lack skills in IoT and AI.

Will AI change the future of sports betting?

The impact of technology on sports cannot be specifically measured, but some technological innovations do raise questions about fairness. Are we still comparing apples with apples? Is it right to compare the speed of an athlete wearing high-tech running shoes to one without?

Whether we like it or not, technology will continue to enhance the athlete’s performance. And at some point, we will have to put specific rules and regulations in place about which tech enhancements are allowed.

There is a downside to advanced technology being introduced to sports. Nowadays, Machine Learning models are routinely used to predict the results of games. Sports betting is a competitive world itself among fans, but AI can substantially tilt that playing field.

I am afraid that IoT and AI companies may spoil the result predictions but more concerned about the manipulation of competitiveness that AI algorithms could bring with the Terabytes of data collected with IoT devices and other sources like social media networks, without the permission of the users.

The sports industry is already generating billions of dollars every year and without control and awareness, we could find the future generation of ludopaths and a small number of service providers controlling the game.

Let me know what else would you like to see in my future posts. Leave your comments below.

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