Xiaomi Corporation – a Chinese electronics giant often dubbed the “Apple of China”, with specialisation in smartphones and smart devices – is now planning to focus on non-smartphone segments for growth. Last week the Beijing based company announced a planned investment of more than US$7 Billion in the areas of 5G, Artificial Intelligence (AI), and Artificial Internet of Things (AIoT). The announcement builds on last year’s pledge to invest US$1.5 billion over five years in an “All in AIoT” (AI+IoT) strategy.
Setting the scene for AI, IoT and 5G
Facing stiff competition from the likes of Huawei (with 42% of China’s smartphone market share in Q3 2019), Xiaomi is aiming to sustain business growth in the face of a challenging smartphone market. Xiaomi Corp is well versed with 5G and not a newcomer in the AI space. They already make 5G devices, and AI has been embedded in Xiaomi’s technology ecosystem such as messaging, instore applications, smartphones, laptops, TVs, routers, speakers, and other smart devices. In fact, Xiaomi Corp claims to have the world’s largest consumer IoT platform with more than 213.2 million smart devices (excluding smartphones and laptops) connected to its IoT platform.
Commenting on the Xiaomi’s competitiveness amongst Chinese smartphone makers and market potential for AI, IoT and 5G globally, Ecosystm Principal Advisor, Tim Sheedy said “Xiaomi has had some recent successes as a smart device maker, with its robot vacuums and other devices quickly earning positive reviews and gaining market recognition. Xiaomi has been able to surpass many traditional players which demonstrates their capabilities of competing in the IoT/smart devices category.”
On the 5G capability aspect of Xiaomi Corp, Sheedy said, “it is early days for 5G – even in China – and the stated position of having many 5G devices in the market could put Xiaomi ahead of some of their competitors in relation to availability. Going forward, it will be interesting to look at the integration of AI, IoT and 5G by Xiaomi.”
Sheedy further added, “we can expect Xiaomi devices to get smarter and more interconnected. Xiaomi actually has the opportunity to take some of the home-centric device hubs out of the ecosystem as they connect straight to the 5G network, and are controlled by an intelligent cloud-based hub perhaps mostly accessed through Xiaomi mobile phones.”
Meeting the Cybersecurity standards
Cybersecurity concerns are common around Chinese device makers. Recently, Xiaomi wireless devices have been disabled from connecting to Google’s Nest devices due to security flaws. On top of this, there have been concerns over the last few years about how Chinese tech companies secure their products and who has access to the data.
“Privacy and security are concerns for consumers – but they are often trumped by convenience, features and off-course price. Xiaomi Corp is aiming to make some of the best devices in each category and as long as they respond quickly and openly to privacy issues, they are likely to continue to see success both inside and outside of China. Their focus on driving down costs in order to be a price leader will help them with this challenge too” said Sheedy. “Ultimately, the privacy and security issues will impact the entire market – not just Xiaomi or other Chinese vendors as the users will steer away from all “smart, connected devices” – not just those from specific suppliers.
Market Positioning Approach
Xiaomi Corporation’s philosophy is to do more business with smaller margins, which drove it to expand its smartphone market from China to other areas like India and Europe. The company is aiming to strengthen its positioning in the consumer electronics market with further tech investments.
Sheedy explains that “considering the fact that they are losing market share in handsets in China, I would imagine that they will double down their efforts to be better at what they do now – not enter entirely new markets. I expect that we will see them produce smarter, connected devices – getting into more of the home and consumer market rather than move into the enterprise market. The enterprise market requires different sales strategies and channels, different partners, different margins, different support mechanisms etc.”
Naturally, Xiaomi is not alone in investing heavily in next-generation technologies. For example, Huawei opened a dedicated IoT consumer lab at the end of 2018 and is continuing to commit resources to the development of this industry-changing technology.
“The 5G plus AIoT strategy is the right one for Xiaomi. They need to surround their competition and make their products better, smarter and more affordable. With the growth in 5G services, a product leadership in this space, beyond smartphones – in smart devices too – will set them up for continued success in what is a very competitive market” said Sheedy.
Artificial intelligence (AI) is perhaps the most electrifying and controversial of the so-called “disruptive” technologies. As AI becomes more sophisticated and the technology evolves, it will increasingly help to perform more complex tasks whether for personal or commercial use.
Today’s AI machines can replicate certain elements of intellectual ability and they are constantly striving to achieve more. This includes applications of autonomous vehicles, domestic and industrial robots, surveillance and security, automation, personal assistants, forecasting, data analysis and more.
The global Ecosystm AI study reveals top drivers of AI adoption. Organisations are trying to incorporate AI in their existing processes for better competitor analysis and insights, cost-effectiveness, deeper customer engagement to provide personalised service/product offerings, and for process redesign or automation.
AI supporting technologies
AI is driving important technologies and processes and driving better, faster and more accurate decisions which help processes run more effectively and efficiently. With AI insights, business strategies will be more information-driven, efficient and consistent.
There are certainly many benefits that organisations are deriving from AI. Ecosystm AI study reveals that organisations implementing AI are using it to drive various business solutions including billing management, supply chain optimisation, predictive maintenance, enhancing operations and more.
Below is a list of some hot technologies driven by AI.
Advanced Analytics
The proliferation of Big Data has led to the creation of massive data sets that can only be effectively analysed with AI tools and statistical models. AI can spot complex patterns in the data which is difficult for humans to understand. AI’s usefulness as an analytics tool is highly gaining importance in the use of predictive analytics and decision automation. Once sufficient data is available for use by AI, which is further filtered and processed thoroughly, the system can suggest actions or outcomes based on various parameters such as trends, patterns, historical information, frequency and more.
For example, the financial services industry is using advanced analytics to evaluate how customers earn, invest, spend and make financial decisions, which is useful for the organisations to customise their customers’ preference and offerings accordingly.
Natural Language Processing (NLP) and speech recognition
NLP involves the learning of languages by machine through the means of interaction between computers and unstructured speech/text. NLP requires massive processing power and complex algorithms to reinforce learning mechanisms. To help in NLP, AI generates models, which are further improved to create NLP and speech simulations. Nowadays, Natural language is being implemented and used in various conversational interfaces, such as those with bots, artificial learning agents that can generalise to new environments, and autonomous vehicles.
Cognitive processing
Otherwise known as semantic computing, refers to a digital processing that attempts to mimic the operation of the human brain. In general, semantics means the meaning and interpretation of words and sentence structure and how words relate to other words. So, how is semantic related and what is the semantic analysis used for in AI? Semantic technology processes the logical structure of sentences to identify the most relevant elements in the text to understand the topic. It is especially suited to the analysis of large unstructured datasets with high efficiency.
Vantagepoint has an artificial intelligence tool to improve their trading results. It has a patented tool which can forecast stocks, futures, commodities, Forex and ETFs and claims an accuracy of up to 86%. The tool can predict changes in market trend direction up to three days in advance thus enabling traders to get in and out of trades at optimal times with confidence.
Robotic Process Automation (RPA)
RPA has grown out of Business Process Automation (BPA) and refers to the use of AI to automate workflow and business processes. The advantages of RPA demonstrate it to be a solid tool in attaining higher quality output at lower costs which is much quicker than traditional methods. RPA can be used in IT support processes, back-office work, and workflow processes. The rules are programmed, and bots extract structured inputs from applications like Excel and enter them into other software such as CRM, SCM or accounting. A good example is the use of NLP to scan incoming emails and undertake the appropriate action, such as generating an invoice or flagging a complaint in an automated manner.
Machine Learning
Machine learning is an application of artificial intelligence (AI) which involves a combination of raw computing power and logic-based models to simulate the human learning process. Machine Learning is proving to be a successful approach to AI. When humans learn, they alter the way they relate information and the world, similarly when machines learn, they alter the data and form it into a piece of information.
An example, Image recognition is a popular application of machine learning in which images are fed into an algorithm, which attempts to recognise the contents of the image based on patterns. For instance, Yelp’s machine learning algorithms help the company’s human staff deal with tens of millions of photos to compile, categorise, and label the images more efficiently.
Chatbots and virtual assistants
Chatbots are robotic processes which simulate human conversation and automate functions. The technology is also used for so-called ‘virtual assistants’, which uses AI to interact with humans and aid with specific queries. They are increasingly being used to handle simple conversation and tasks in B2B and B2C environments. The addition of chatbots reduces human assistants and they can work throughout the clock. Chatbots and Virtual assistants improve with AI and can be trained to review conversations, past transactions and to draft a response based on context. If the user interacts with the bot through voice, then the chatbot requires a speech recognition engine.
Chatbots have been used in instant messaging (IM) applications and online interactive platforms. To exemplify, chatbots are deployed to assist online shoppers by answering noncomplex product questions, pricing, FAQ’s, order processing steps or forwarding information to human agents on complicated questions such as shipping delays or faults.
With AI technology evolving and improving so rapidly, many organisations are looking to use AI in their business, but there are still many questions to which adopters are seeking answers such as how to integrate AI into their existing systems, how to get access to data that will enable AI as well as the persistent technology concerns around cybersecurity and cost. The goal of many AI providers is to reach a stage where AI will support humans, control machines for us and automate repetitive tasks and processes.
Artificial Intelligence has come a long way and was one of the growing areas in 2019. Over the last few years, we have seen a growing number of AI based platforms, applications and tools that developers and scientists have worked to mimic a human brain. We believe that in 2020, AI will come out from the experimentation stage to the implementation and businesses will make deeper investments in AI to embed them in business applications.
This article presents the Top 5 Artificial Intelligence Trends for 2020 for the AI/Analytics market in 2020. It is based on the latest data from the global Ecosystm AI Study, and qualitative research by Ecosystm Principal Advisor Tim Sheedy.
The Top 5 Artificial Intelligence Trends for 2020
Here are the Top 5 Artificial Intelligence trends for 2020 that we believe will impact both businesses and consumers in 2020.
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Digital Transformation puts Analytics Back on Top of the Tech Priority List
In an effort to help the business operate faster, IT teams are looking to better analytics to drive functions and decisions more accurately. While many business teams deploy their own technologies and systems – only the IT team is in a higher position to gather data from multiple systems of record in order to create the detailed insights that business users demand. Getting a view across the entire customer journey means analysing data across many systems – both front and back-end. Business teams struggle to get these types of insights on their own, which is why IT excels at providing great analytics to help make better and faster decisions.
Just like in previous generations of BI, the analytics market is starting to consolidate. While the ability to display data visually will always be important, it is the analytics that drives automated decisions that will often be of the most business value.
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Automation will Lead Organisations to AI
RPA is increasingly moving beyond the usual task and process automation, to now being a business transformation lever. Additionally, there is an immense focus on incorporating AI/machine learning within RPA to make automation smart and intelligent. This allows software robots to mimic human behaviour and handle complex use cases, which was earlier not possible without human intervention.
Businesses will spend more money on their simple automation activities (RPA and analytics applications that do not learn) – but those that have already invested in automation are likely to want to take the next steps to AI.
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AI will Start to be Embedded in Most Business Applications
To date AI has been an overlay to most applications – data is extracted from processes, learnings are made, and then the process is altered based on those learnings. In 2020 we will see mass availability of self-learning intelligent applications. The standard ERP, CRM, SCM, knowledge management solution and other business applications will have embedded intelligence. This will make it easier and faster for businesses to get the benefits of machine learning and AI without the need to hire expensive data scientists, or the requirement to learn the tools and platforms required for creating smart applications.
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2020 will see the Democratisation of AI
Typically organisations required data scientists, AI coders, AI platforms and so on to do well in AI but with the increasing availability of AI in business applications, typical business users will begin to get a glimpse of what will be available at their fingertips in the next few years.
We expect templatised approaches to machine learning and associated technologies. Business users and data owners will be able to create algorithms that will improve business and customer outcomes. In some cases, we even expect AI to be available to consumers. We will start to see banking and finance applications that help better money management through learning – not just basic analytics, we will see more intelligent services in the market in 2020.
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More Businesses Will Require AI on the Edge
In the next decade or two, it is estimated that there will be 100 billion IoT devices generating and exchanging data into the cloud, without any human intervention. With so many IoT devices generating a huge quantum of data, decisions will need to be made in real-time and the current cloud environments will be a bottleneck in data processing due to latency rates, network speed and traditional data architectures. To overcome this, Edge Computing solutions will be essential to work with a variety of sensor and data input devices, information processing and decisions driven by machine learning and AI, and additionally work with cloud for the next level of analytics, decisions and management.
Ecosystm in partnership with SGInnovate, the government-backed organisation that promotes Deep Tech in Singapore, released a series of four reports covering areas of mutual interest: Cybersecurity, Artificial Intelligence, Cities of the Future and Healthtech. ‘Ecosystm Predicts: The top 5 Artificial Intelligence trends for 2020’ report is a part of this collaboration and is available for download from Ecosystm and SGInnovate websites.
Download Report: The Top 5 Artificial Intelligence Trends for 2020
The full findings and implications of the report ‘Ecosystm Predicts: The Top 5 Artificial Intelligence Trends for 2020’ are available for download from the Ecosystm website. Signup for Free to download the report and gain insight into ‘the Top 5 Artificial Intelligence Trends for 2020’, implications for tech buyers, implications for tech vendors, insights, and more resources. Download Link Below ?
++Update: A few days after we added this story, Australia released its AI roadmap focused on future investment in AI and machine learning, and Artificial Intelligence: Australia’s Ethics Framework.
Over the last few years, many countries have created plans to harness Artificial Intelligence (AI) for better citizen services. At this week’s SFFXSWITCH (Singapore FinTech Festival and Singapore Week of Innovation and TeCHnology), Singapore announced the next step in its Smart Nation initiative with the release of the “National AI Strategy”. The AI strategy will focus on social and economic benefits by providing modern infrastructure, intelligent services, and an excellent education system to citizens.
Commenting on Singapore’s AI adoption and implementation strategies, Ecosystm Principal Advisor, Tim Sheedy said “being a smaller country, Singapore is one of the few around the globe that can make investments into AI at a ‘country level’. Singapore has the luxury of having a progressive public sector that operates at the same speed as the private sector, and has the opportunity to become one of the leading economies in the world for both the deployment of AI in everyday services AND in ensuring the economy has the required skills.”
The key approach of the AI strategy
Singapore is aiming to position itself as a global hub for the development and implementation of AI solutions. National AI Office established under the Smart Nation and Digital Government Office (SNDGO) will be heading Singapore’s AI initiatives.
To begin with, the SNDGO will be initially working on key high-value sectors:
- Transport and Logistics: Intelligent Freight Planning
- Smart Cities and Municipal Services: Infra management and smart services
- Healthcare: Chronic Disease Prediction and Management
- Education: Personalised Education
- Safety and Security: Seamless border clearance operations
Speaking on AI strategies devised for transportation, smart cities, healthcare, education and safety and security in Singapore, Sheedy said “all of these investments are ones that you would make as an economy if you had the opportunity. They are all logical and will all benefit the lives of citizens and the success of the overall economy.”
Transport and Logistics: To optimise the freight and delivery services, a common data platform will be built. The aim is to bring in efficient transportation and logistics with intelligent AI systems in place by 2022 and further AI developments will scale deployment, enable optimised delivery processes and routing of services for freight planning.
Smart Cities and Estates: The country aims to launch AI powered chatbots by 2022 to record municipal issues and allocate them to the responsible authorities. AI powered services will be introduced to better serve the residents. By 2025, efforts will be established to optimise estate maintenance through AI and sensors and by the year 2030, data driven insights will be used to improve the infrastructure and living environment in Singapore.
Healthcare: An all-new AI system known as SELENA+ will be in place to screen and detect diabetic eye disease. SELENA+ is a first artificial intelligence algorithm which performs automated retinal photo analysis to detect retinopathy and systemic complications in diabetics. In addition to this, AI capabilities will also be used to predict cardiovascular diseases and create personalised chronic risk scores. All of these will help to detect diseases and take early preventive measures by the healthcare teams for the welfare of country and betterment of the economy.
“This could detect chronic issues early, and reduce the impact of them on individuals, families and the economy. Being able to make investments at a macro level – like this happening in Singapore – will make sure everyone benefits” said Sheedy.
Education: AI in education will bring the benefits of automated marking systems for English language in primary and secondary education. Further, AI-enabled learning systems will help students on learning and mastery of topics. The government has planned to expand automated AI and adaptive learning systems to more subjects at a later stage.
Sheedy said, “with the government being able to influence and change the syllabus in schools and universities, as well as the skills in the public and private sector, Singapore is uniquely positioned to drive real economic benefit from their investment into AI.”
Safety and Security: AI systems will help in border security and clearance procedures. AI will enhance travel experience with automated immigration clearance systems involving face and iris scans. The immigration processes will develop into seamless self-clearance systems and become faster.
“A few countries have the ability to drive this level of planning – most countries have many levels of government which make this planning – or execution of plans – difficult.” said Sheedy. “Many are also leaving the investment to the private sector, which means it will happen eventually, but may see many competing initiatives or different capabilities emerge that only benefit a single company, not an entire economy.”
Smart Nation drive
Singapore’s government is highly active in transforming the country into a smart nation. Singapore was working with the World Economic Forum’s Centre for Fourth Industrial Revolution (WEF C4IR) in order to come out with a framework for ethical and responsible AI adoption and deployment by the Asian governments.
Singapore is also set to invest US$ 360 million on AI and other digital technologies through 2020 and has invited Chinese and American companies to be a part of this.
According to Sheedy, there are many benefits of having deep investments in AI and AI capabilities in an economy.
It will make Singapore a more attractive investment location. If access to government and other services are seamless, then the barriers to entry to starting a new business or creating a new business capability will be much lower. It will mean that it is easier to build a business case for businesses to move to Singapore or start in Singapore – attracting investment funds and employment into the island state.
It will boost export capabilities – both for the skills that will be in demand through technology and business service providers and for the intelligent products and services that will likely emerge from the early AI investments. If Singapore can make more of the products and services that they produce “smart” – then these products and services will see increased demand – both locally and outside of Singapore.
It will make Singapore a better place to live and visit. With seamless government services, easier travel into and out of the country, and a government that anticipates the needs of its citizens, the quality of life for residents will increase.
The country can get ahead of the challenges and downsides of AI – and legislate or plan for these challenges, to ensure these challenges are understood and managed before they become problems.
In the escalating initiatives to become an AI superpower, Singapore has clearly indicated they are fully committed to leveraging AI to drive growth and citizen services.
++Update
An AI roadmap report was published by the Australian Government in November 2019, co-developed by CSIRO’s Data61 and the Department of Industry, Innovation and Science. The report identifies the opportunities and benefits of AI that Australia could capture.
The report classifies the strategies to help develop AI capabilities to boost the productivity of industry, generate jobs, bring economic growth, and enhance the quality of citizens’ life. To drive this, Australia has identified 3 key areas where it has the best opportunity to create new value-
Health, Ageing, and Disability – To develop AI to improve healthcare, aged care, and disability services while reducing healthcare costs.
Cities, Towns, and Infrastructure – To develop an AI system for the cities and infrastructure to provide better services, safety efficiency in a smart and cost-effective way.
Natural resource and environment management – Develop AI for better natural resource management and improve the productivity of agriculture, mining, fisheries, forestry, and environmental management.
Poly’s CEO Joe Burton was in Sydney recently to meet with staff, customers and partners. I had the privilege of interviewing him about the roadmap ahead for the company. Plantronics acquired Polycom for $2 billion at the end of March 2018 and earlier this year at Enterprise Connect, Poly was unveiled as the new brand – the coming together of Plantronics and Polycom. The company prides themselves on the strong engineering heritage they have across their product portfolio. Poly is playing in a large addressable market and these segments include unified communications (UC), video, headsets and contact centres.
Big news last week – Poly and Zoom partnership
At Zoomtopia last week, Zoom announced purpose-built appliances for their Zoom Rooms conference room system. These appliances are custom developed hardware that lets users gather room intelligence and analytics and will simplify installation and management of large-scale conference room deployments. One of the major partnerships for this was with Poly. Joe Burton was on stage with Eric Yuan the CEO of Zoom to unveil the Poly Studio X Series – The X30 (for smaller rooms) and the X50 (for midsize conference rooms).
What is promising about this offering is that the whole concept of launching a meeting by connecting to a screen has become simple. In a world where user experience is everything, simplicity and quality are what end-users expect. The Poly Studio X Series are all-in-one video bars that will simplify the Zoom Rooms experience and will feature Poly Meeting AI capabilities. Some of the features include advanced noise suppression to make it easier to hear human voices while simultaneously blocking out background noise.
For Poly, this is a great partnership given Zoom’s good growth in the Asia Pacific region. Poly is also increasingly deepening their relationships with other major players in the Video and UC market including Microsoft.
Flexible Workspaces and Contact Centres drives the headset market in Asia Pacific
According to JLL, the flexible space sector in Asia Pacific is expanding rapidly. From 2014 to 2017, flexible space stock across the region recorded a CAGR of 35.7% in Asia Pacific – much higher than in the United States (25.7%) and Europe (21.6%) over the same period. When you consider the changes in the modern workplace which include the rise of open flexible workplaces, remote and home working and the rise of freelancers, providing a seamless experience for the office worker will be important – it should be the same for a contractor as it is for full-time staff. As we move into more mobile and agile work practices and with the rise of open offices, headsets will play an important role for the office worker. More organisations across Asia are investing in headsets and whilst it may sound simple to just buy the headsets, it is more sophisticated than that. There is no one-size-fits-all headset and IT managers will have to invest in headsets to suit the persona of employees taking into account the role, workload, use of voice and video services and ultimately their comfort level. Vendors in the headset space are heavily investing in easy-to-use features, more automation, deep workflow integration and machine learning to deliver that experience. The opportunity for headsets does not stop there. In the contact centre space as agents spend long hours on calls, designing the right headset with feature rich AI capabilities will go a long way especially for training and coaching.
The one area Burton emphasised on is how AI and analytics is transforming this market and Poly investing in building these features into the headsets. Some of these examples include:
- Tracking conversations by using analytics to gain insights into long pauses of silence and “overtalking”. The analytics generated from these insights can help for training and coaching.
- AI can help track user behaviour patterns related to noise, volume and mute functions. These patterns can be used to detect problems during the call and could lead to possible training sessions for the agents. It is a great mechanism for supervisors to understand and work through where agents are struggling during the call.
Partnerships to expand their reach into the contact centre markets in the Asia Pacific region will be important. The market for contact centres is seeing a big shift and new entrants are making their presence felt in the Asia Pacific region. Poly will need to capitalise on this and expand their partnerships beyond the traditional vendors to expand their footprints across the contact centre markets.
Asia Pacific – an important growth theatre
Poly continues to win and have some large-scale deployments in Japan, China, India, and ANZ. They have also made several strides to develop what is best fit for the local market in terms of user requirements. With a deep understanding of the Chinese market, Poly released the Poly G200 in September this year which is tailor-made for the Chinese users with easy to use and collaborate solutions. The Poly G200 is the first and significant customised product launched in China, after Poly announced their ‘In China, for China’ strategy. This is a logical move given China is an important market and one that presents its own unique business dynamics.
Conclusion
The shift to mobility and the cloud has changed everything and is driving a new level of user experience. The ability to offer the same and frictionless experience when on the desktop, mobile device as well as other applications is what is driving fierce competition in the market. Users get frustrated when they cannot launch a video session instantly or when there is poor quality in audio. These may sound simple but addressing these frustrations are critical. Vendors in the UC, Collaboration and Video space are working hard to make sure that the experience is seamless when they are inside the office, out of the office and when they are working in open plan offices. Ultimately users want their daily office communication and collaboration solutions to work seamlessly and to integrate well into the various workflows such as Microsoft Teams.
On the contact centre front, Digital and AI initiatives are taking centre stage in nearly every conversation I have had with end-users. Company-wide CX strategy and customer journey mapping and analytics are what CX decision makers are talking about most. Poly is addressing that segment of the market by providing quality headsets coupled with AI to help in coaching and training by identifying trends and bridging the training gaps. There are new vendors starting to disrupt the status quo of some of the more traditional vendors in the contact centre market and hence deepening the partnerships with these new vendors in the contact centre space will be important.
Poly has a good addressable market to go after in unified communications and collaboration with their headsets and extensive range of video solutions. The most important part will be deepening the partnerships with the wide range of vendors in this space and engineering their products to be tightly integrated with their partner ecosystems’. The release of the Studio X series at Zoomtopia is a good example. I am confident that the road ahead for Poly is promising given the deep engineering capabilities the company invests in and how they are taking their partnerships seriously.
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.
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.
Click below to access insights from the Ecosystm Contact Centre Study on visibility into organisations’ priorities when running a Contact Centre (both in-house and outsourced models) and the technologies implemented and being evaluated
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.
#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.
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.
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:
- 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.
- 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.