GCCs employ around 1 million Indian professionals and has an immense impact on the economy, contributing an estimated USD 30 billion. US MNCs have the largest presence in the market and the dominating industries are BSFI, Engineering & Manufacturing, Tech & Consulting.
GCC capabilities have always been evolving
The journey began with MNCs setting up captives for cost optimisation & operational excellence. GCCs started handling operations (such as back-office and business support functions), IT support (such as app development and maintenance, remote IT infrastructure, and help desk) and customer service contact centres for the parent organisation.
In the second phase, MNCs started leveraging GCCs as centers of excellence (CoE). The focus then was product innovation, Engineering Design & R&D. BFSI and Professional Services firms started expanding the scope to cover research, underwriting, and consulting etc. Some global MNCs that have large GCCs in India are Apple, Microsoft, Google, Nissan, Ford, Qualcomm, Cisco, Wells Fargo, Bank of America, Barclays, Standard Chartered, and KPMG.
In the post-COVID world, industry boundaries are blurring, and business models are being transformed for the digital age. While traditional functions of GCCs will continue to be providing efficiencies, GCCs will be “Digital Transformation Centres” for global businesses.
The New Age GCC in the post-COVID world
On one hand, the pandemic broke through cultural barriers that had prevented remote operations and work. The world became remote everything! On the other hand, it accelerated digital adoption in organisations. Businesses are re-imagining customer experiences and fast-tracking digital transformation enabled by technology (Figure 1). High digital adoption and rising customer expectations will also be a big catalyst for change.
In last few years, India has seen a surge in talent pool in emerging technologies such as data analytics, experience design, AI/ML, robotic process automation, IoT, cloud, blockchain and cybersecurity. GCCs in India will leverage this talent pool and play a pivotal role in enabling digital transformation at a global scale. GCCs will have direct and significant impacts on global business performance and top line growth creating long-term stakeholder value – and not be only about cost optimisation.
GCCs in India will also play an important role in digitisation and automation of existing processes, risk management and fraud prevention using data analytics and managing new risks like cybersecurity.
More and more MNCs in traditional businesses will add GCCs in India over the next decade and the existing 1,700 plus GCCs will grow in scale and scope focussing on innovation. Shift of supply chains to India will also be supported by Engineering R & D Centres. GCCs passed the pandemic test with flying colours when an exceptionally large workforce transitioned to the Work from Home model. In a matter of weeks, the resilience, continuity, and efficiency of GCCs returned to pre-pandemic levels with a distributed and remote workforce.
A Final Take
Having said that, I believe the growth spurt in GCCs in India will come from new-age businesses. Consumer-facing platforms (eCommerce marketplaces, Healthtechs, Edtechs, and Fintechs) are creating digital native businesses. As of June 2021, there are more than 700 unicorns trying to solve different problems using technology and data. Currently, very few unicorns have GCCs in India (notable names being Uber, Grab, Gojek). However, this segment will be one of the biggest growth drivers.
Currently, only 10% of the GCCs in India are from Asia Pacific organisations. Some of the prominent names being Hitachi, Rakuten, Panasonic, Samsung, LG, and Foxconn. Asian MNCs have an opportunity to move fast and stay relevant. This segment is also expected to grow disproportionately.
New age GCCs in India have the potential to be the crown jewel for global MNCs. For India, this has a huge potential for job creation and development of Smart City ecosystems. In this decade, growth of GCCs will be one of the core pillars of India’s journey to a USD 5 trillion economy.
The views and opinions mentioned in the article are personal. Anupam Verma is part of the Senior Leadership team at ICICI Bank and his responsibilities have included leading the Bank’s strategy in South East Asia to play a significant role in capturing Investment, NRI remittance, and trade flows between SEA and India.
Intellibot is the latest in a string of purchases by ServiceNow that reveals their intention to embed AI and machine learning into offerings. In 2020, they acquired Loom Systems, Passage AI (both January), Sweagle (June), and Element AI (November) in addition to Attivio in 2019. These acquisitions were integrated into the latest version of their Now Platform, code-named Quebec, which was launched earlier this month. As a result, Predictive AIOps and AI Search were newly added to the platform while the low-code tools were expanded upon and became Creator Workflows. This means ServiceNow now offers four primary solutions – IT Workflows, Employee Workflows, Customer Workflows, and Creator Workflows – demonstrating the importance they are placing on low-code and RPA.
ServiceNow was quick to remind the market that although they will be able to offer RPA functionality natively once Intellibot is integrated into their platform, they are still willing to work with competitors. They specifically highlighted that they would continue partnering with UiPath, Automation Anywhere, and Blue Prism, suggesting they plan to use RPA as a complementary technology to their current offerings rather than going head-to-head with the Big Three. Only a month ago, UiPath announced deeper integration with ServiceNow, by expanding automation capabilities for Test Management 2.0 and Agile Development projects.
Expansion in India
The acquisition of Intellibot, based in Hyderabad, is part of ServiceNow’s expansion strategy in India – one of their fastest growing markets. The country is already home to their largest R&D centre outside of the US and they intend to launch a couple of data centres there by March 2022. The company plans to double their local staff levels by 2024, having already tripled the number of employees there in the last two years. The expansion in India means they can increasingly offer services from there to global customers.
“Buyers will find that many of the automation capabilities that they currently purchase separately will increasingly be integrated in their enterprise applications. This will resolve integration challenges and will be more cost-effective.”
The cloud hyperscalers are also likely to play a growing role in the RPA market over the next year. Microsoft and IBM have already entered the market, coming from the angle of office productivity and business process management (BPM), respectively. Google announced just last week that they will work closely with Automation Anywhere to integrate RPA into their cloud offerings, such as Apigee, AppSheet, and AI Platform. More interestingly, they plan to co-develop new solutions, which might for now satisfy Google’s appetite for RPA rather than requiring an acquisition.
Here are some of the trends to watch for RPA, AI and Automation in 2021. Signup for Free to download Ecosystm’s Top 5 AI & Automation Trends Report.
There is a lot of hope on AI and automation to create intellectual wealth, efficiency, and support for some level of process stability. After all, can’t we just ask Siri or Alexa and get answers so we can make a decision and carry on?
Automation has been touted as the wonder formula for workplace process optimisation. In reality it’s not the quick fix that many business leaders desire. But we keep raising the bar on expectations from automation. Investments in voice technologies, intelligent assistants, augmented reality and touchscreens are changing customer experience (Figure 1). Chatbots are ubiquitous, and everything has the potential to be personalised. But will they solve our problems?
100 percent automation is not effective
Let’s first consider using automation to replace face-to-face interactions. There was a time when people were raving about the check-in experience at some of the hotels in Japan where robots and automated systems would take care of the check-in, in-stay and check-out processes. Sounds simple and good? Till 2019, if you checked into the Henn-na Hotel in Japan, you would be served and taken care of by 243 robots. It was viewed by many as a template for what a fully automated hotel could look like in the future.
The hotel had an in-room voice assistant called Churi. It could cope with basic commands, such as turning the lights on and off, but it was found to be deficient when guests started asking questions about places to visit or other more sophisticated queries. It was not surprising that the hotel decided to retire their robots. In the end it created more work for the hotel staff on-site.
People love the personal touch when they are in a hotel; and talking to someone at the front desk, requesting assistance from hotel staff, or even just a short chat over breakfast are some of the small nuances of why the emotional connection matters. Many quarantine hotels today use robots for food delivery, but the hotel staff is still widely available for questions. That automation is good, but you need the human intervention. So, getting the balance right is key.
Empathy plays a big role in delivering great Customer Experience
Similarly, there was a time when many industry observers and technology providers said that a contact centre will be fully automated, reducing the number of agents. While technologies such as Conversational AI have come along where you can now automate common or repetitive questions and with higher accuracy levels, the human agent still plays a critical role in answering the more complex queries. When the customer has a complicated question or request, then they will WANT to speak to an agent.
When it reaches a point where the conversation with the chatbot starts getting complicated and the customers need more help there should be the option – within the app, website or any other channel – to escalate the call seamlessly to a human agent. Sometimes, a chat is where the good experience happens – the emotional side of the conversation, the laughter, the detailed explanation. This human touch cannot be replaced by machines. Disgruntled customers are happier when an agent shows empathy. Front line staff and human agents act as the face of a company’s brand. Complete automation will not allow the individual to understand the culture of the company. These can be attained through conversations.
Humans as supervisors for AI – The New Workplace
Empathy, intuitiveness, and creativity are all human elements in the intelligence equation. Workers in the future will need to make their niche in a fluid and unpredictable environment; and translating data into action in a non-replicable way is one of the values of human input. The essence of engineering is the capacity to design around human limitations. This requires an understanding of how humans behave and what they want. We call that empathy. It is the difference between the engineer who designs a product, and the engineer who delivers a solution. We don’t teach our computer scientists and engineering students a formula for empathy. But we do try to teach them respect for both the people and the process.
For efficiency, we turn to automation of processes, such as RPA. This is designed to try to eradicate human error and assist us in doing our job better, faster and at a lower cost by automating routine processes. If we design it right, humans take the role of monitoring or supervisory controlling, rather than active participation.
At present, AI is not seen as a replacement for our ingenuity and knowledge, but as a support tool. The value in AI is in understanding and translating human preferences. Humans-in-the-loop AI system building puts humans in the decision loop. They also shift pressure away from building “perfect” algorithms. Having humans involved in the ethical norms of the decision allows the backstop of overly orchestrated algorithms.
That being said, the astute use of AI can deepen insights into what truly makes us human and can humanise experiences by setting a better tone and a more trusted engagement. Using things like sentiment analysis can de-escalate customer service encounters to regain customer loyalty.
The next transformational activity for renovating work is to advance interactions with customers by interpreting what they are asking for and humanising the experience of acquiring it which may include actually dealing with a human contact centre agent – decisions that are supported at the edge by automation, but at the core by a human being.
Ecosystm research shows that process automation will be a key priority for technology investments in 2021 (Figure 2).
With AI and automation, a priority in 2021, it will be important to keep these considerations in mind:
Making empathy and the human connection the core of customer experiences will bring success.
Rigorous, outcome-based testing will be required when process automation solutions are being evaluated. In areas where there are unsatisfactory results, human interactions cannot – and should not – be replaced.
It may be easy to achieve 90% automation for dealing with common, repetitive questions and processes. But there should always be room for human intervention in the event of an issue – and it should be immediate and not 24 hours later!
Employees can drive greater value by working alongside the chatbot, robot or machine.
Ecosystm Predicts: The Top 5 Customer Experience Trends for 2021
Download Ecosystm’s complimentary report detailing the top 5 customer experience trends for 2021 that your company should pay attention to along with tips on how to stay ahead of the curve.
#1 The New Decade of the ‘Empowered’ Consumer Will Propel Green Finance and Sustainability Considerations Beyond Regulators and Corporates
We have seen multiple countries set regulations and implement Emissions Trading Systems (ETS) and 2021 will see Environmental, Social and Governance (ESG) considerations growing in importance in the investment decisions for asset managers and hedge funds. Efforts for ESG standards for risk measurement will benefit and support that effort.
The primary driver will not only be regulatory frameworks – rather it will be further propelled by consumer preferences. The increased interest in climate change, sustainable business investments and ESG metrics will be an integral part of the reaction of the society to assist in the global transition to a greener and more humane economy in the post-COVID era. Individuals and consumers will demand FinTech solutions that empower them to be more environmentally and socially responsible. The performance of companies on their ESG ratings will become a key consideration for consumers making investment decisions. We will see corporate focus on ESG become a mainstay as a result – driven by regulatory frameworks and the consumer’s desire to place significant important on ESG as an investment criterion.
#2 Consumers Will Truly Be ‘Front and Centre’ in Reshaping the Financial Services Digital Ecosystems
Consumers will also shape the market because of the way they exercise their choices when it comes to transactional finance. They will opt for more discrete solutions – like microfinance, micro-insurances, multiple digital wallets and so on. Even long-standing customers will no longer be completely loyal to their main financial institutions. This will in effect take away traditional business from established financial institutions. Digital transformation will need to go beyond just a digital Customer Experience and will go hand-in-hand with digital offerings driven by consumer choice.
As a result, we will see the emergence of stronger digital ecosystems and partnerships between traditional financial institutions and like-minded FinTechs. As an example, platforms such as the API Exchange (APIX) will get a significant boost and play a crucial role in this emerging collaborative ecosystem. APIX was launched by AFIN, a non-profit organisation established in 2018 by the ASEAN Bankers Association (ABA), International Finance Corporation (IFC), a member of the World Bank Group, and the Monetary Authority of Singapore (MAS). Such platforms will create a level playing field across all tiers of the Financial Services innovation ecosystem by allowing industry participants to Discover, Design and rapidly Deploy innovative digital solutions and offerings.
#3 APIfication of Banking Will Become Mainstream
2020 was the year when banks accepted FinTechs into their product and services offerings – 2021 will see FinTech more established and their technology offerings becoming more sophisticated and consumer-led. These cutting-edge apps will have financial institutions seeking to establish partnerships with them, licensing their technologies and leveraging them to benefit and expand their customer base. This is already being called the “APIficiation” of banking. There will be more emphasis on the partnerships with regulated licensed banking entities in 2021, to gain access to the underlying financial products and services for a seamless customer experience.
This will see the growth of financial institutions’ dependence on third-party developers that have access to – and knowledge of – the financial institutions’ business models and data. But this also gives them an opportunity to leverage the existent Fintech innovations especially for enhanced customer engagement capabilities (Prediction #2).
#4 AI & Automation Will Proliferate in Back-Office Operations
From quicker loan origination to heightened surveillance against fraud and money laundering, financial institutions will push their focus on back-office automation using machine learning, AI and RPA tools (Figure 3). This is not only to improve efficiency and lower risks, but to further enhance the customer experience. AI is already being rolled out in customer-facing operations, but banks will actively be consolidating and automating their mid and back-office procedures for efficiency and automation transition in the post COVID-19 environment. This includes using AI for automating credit operations, policy making and data audits and using RPA for reducing the introduction of errors in datasets and processes.
There is enormous economic pressure to deliver cost savings and reduce risks through the adoption of technology. Financial Services leaders believe that insights gathered from compliance should help other areas of the business, and this requires a completely different mindset. Given the manual and semi-automated nature of current AML compliance, human-only efforts slow down processing timelines and impact business productivity. KYC will leverage AI and real-time environmental data (current accounts, mortgage payment status) and integration of third-party data to make the knowledge richer and timelier in this adaptive economic environment. This will make lending risk assessment more relevant.
#5 Driven by Post Pandemic Recovery, Collaboration Will Shape FinTech Regulation
Travel corridors across border controls have started to push the boundaries. Just as countries develop new processes and policies based on shared learning from other countries, FinTech regulators will collaborate to harmonise regulations that are similar in nature. These collaborative regulators will accelerate FinTech proliferation and osmosis i.e. proliferation of FinTechs into geographies with lower digital adoption.
Data corridors between countries will be the other outcome of this collaboration of FinTech regulators. Sharing of data in a regulated environment will advance data science and machine learning to new heights assisting credit models, AI, and innovations in general. The resulting ‘borderless nature’ of FinTech and the acceleration of policy convergence across several previously siloed regulators will result in new digital innovations. These Trusted Data Corridors between economies will be further driven by the desire for progressive governments to boost the Digital Economy in order to help the post-pandemic recovery.
AI Will Move from a Competitive Advantage to a Must-Have
The best practices and leading-edge technology-centric implementations, over the years gives a very good indication of market trends. In 2018 and 2019 AI-centric engagements were few and far between – they were still in the “innovation stage” as trials and small projects. In 2020, AI was mentioned in most applications, showcased as best practices. AI is currently a competitive advantage for businesses. CIOs and their businesses are using AI to get ahead of their competitors and highlighting these practices for external recognition.
That also means that it is a matter of time before AI becomes a standard practice – processes are smart “out-of-the-box”; intelligent applications are an expectation, not the exception; systems learn because that is how they were designed, not as an overlay. If your competitors are using AI today to get ahead of you, then you need to also use AI to catch up and keep up. In 2021, having a smart business will not get you ahead of the pack – it will move you into it.
AI Will Thrive in Areas where the Cost of Failure is Low
While organisations will be forced to adopt AI to remain competitive, initial exploration of AI solutions will be in areas that they consider low risk. The Financial Services, Retail, and other transaction-oriented industries will use AI to drive improved personalisation, increase customer retention, and improve their ability to lower risk and combat fraud. These are process-driven areas, where manual processes are being enhanced and enriched by AI. Although machine learning and other AI technologies will help improve the speed and quality of services, they will not be a replacement for many of the more complex business practices that companies and their employees frequently overlook to automate. The ‘low hanging fruit’ to add AI to will come first, with various degrees of success.
There will be industries and processes where organisations will be more skeptical about adopting AI. If Google finds a wrong translation or gives a wrong link, it is not a big concern, unlike a wrong diagnosis or wrong medication. In areas that are crucial to our well-being – such as healthcare – AI does not yet have the trust for acceptance of society. There are still questions around ethics and algorithm concerns.
Technology Providers Will Stop Talking about AI
Technology vendors highlight what they consider their key differentiators, that show that they are ahead of the game. When every piece of software and hardware is intelligent, vendors will stop talking about the fact that they are intelligent. This may not fully happen in 2021 – but ENOUGH technology will be intelligent for those who have not yet made their software smart to understand that they cannot talk about its intelligent capabilities as that just shows they are behind the market.
The good news is that the less we hear about AI, the more intelligent applications will become. AI is quickly becoming a core capability and a base expectation. Systems that learn and adapt will be standard very soon – but be wary, as significant market changes can break these systems! Many companies learned that the pandemic broke their algorithms as times were no longer “normal”.
Enterprises Will Seek Hyperautomation Solutions
RPA will increasingly become part of large enterprise application implementations. Technology vendors are adding RPA functionality either organically or through acquisitions to their enterprise application suites. RPA often works in conjunction with major software products provided by companies such as Salesforce, SAP, Microsoft, and IBM. Rather than having an operative enter data into multiple systems, a bot can be created to do this. Large software vendors are taking advantage of this opportunity by trying to own entire workflows. They are increasingly integrating RPA into their offerings as well as competing directly in the RPA market with pureplay RPA vendors.
As the RPA offerings continue to mature, enterprises seek to scale implementations and to automate non-repetitive processes, which require more intelligence. They will seek to automate more processes at scale. They will demand solutions that process unstructured data, handle exceptions, and continuously learn, further increasing productivity. Intelligent automation typically incorporates AI, particularly voice and vision capabilities and uses machine learning to optimise processes. Hyperautomation turbo charges intelligent automation by automating multiple processes at scale – and will become core to digital transformation initiatives in 2021.
Businesses Will Put “Automation Targets” in Place
2020 was the year that many businesses started seeing some broad and tangible benefits from their automation initiatives. Automation was one of the big winners of the year, as many businesses took extra steps to take humans out of processes – particularly those humans that had to be in a specific location, such as a warehouse, the finance team, the front desk and so on (because of the pandemic, they were often working at home instead). Senior management is seeing the benefits of automation, and they will start to ask their teams why more processes are not automated Therefore we will start to see managers put targets around a certain percentage of tasks automated in an area – e.g. 70% of contact centre processes will be automated, 90% of the digital customer experience for a certain outcome will be automated and so on. Achieving these numbers may not be easy, but the targets will change the mindset of people designing, implementing, and improving processes.
Ecosystm Principal Advisor, Dr Alea Fairchild says, “Education administration budgets are not increasing, but the pressure for quick response and more personalised interaction for students, means that administrators need to focus on interaction as the core competency. This requires institutions to automate as much of the volume back-office activity as feasible. The challenge is that individualised course structures mean more complex billing configurations.”
Dr Fairchild, who is active in international education in Belgium, says, “Individual study paths, including Erasmus exchanges, create a need for an audit trail on transfers, exemptions and completions.”
Ecosystm research finds that educational institutions are focused on adopting emerging technologies mainly to improve student services (Figure 1). The processes are being automated to reduce risks, errors and turnaround times for results and application processing, while also removing repetitive tasks so administration can focus on more value-add student-facing activities.
University of Staffordshire Embraces Digital Transformation
The University of Staffordshire is a “connected university” with an emphasis on industry connections and graduate employability. At the heart of Stoke-on-Trent and a regional hub for healthcare education, the university has six schools as well as a well-known degree in computer games design.
The UK-based University has over the years built a reputation for being keen on embracing digital as a way of better management, offering better student services, and serving the larger community. In 2018, Staffordshire University announced plans to build a multi-million-pound apprenticeship hub at its Stoke-on-Trent campus supported by tech giants including Microsoft to equip students with digital skills and to deliver more than 6,500 new apprenticeships over the next decade.
Last year, the University implemented a digital assistant, called Beacon, hosted on Microsoft Azure Cloud that provides support to their students on their learning and on-campus activities, including monitoring their emotional well-being and providing recommendations on groups and societies that they might be interested in. Beacon aims to ease the life of a university student, acting as a digital coach, and to minimise drop-outs due to stress and uncertainty.
The University of Staffordshire, recently implemented robotic process automation (RPA) as part of its digital transformation plan. Talking about the role of RPA in Education, Dr Fairchild says, “This is a recent trend in higher education, with other new initiatives seen at the University of Auckland and University of Melbourne. RPA as a tool is used in Education to achieve the service levels required to meet both students’ and potential students’ expectations. This includes downloading student applications, processing language waiver requests, and entering academic results. These are all rule-based, high volume applications where automation increases speed and reduces errors.”
The University is using Blue Prism Cloud to access the RPA software and has plans for a automation-led digital transformation roadmap. Dr Fairchild says, “Blue Prism is based on Java and uses a Top-Down approach. It offers a visual designer with no recorders, scripts, or any intervention. Blue Prism is based on process diagrams that utilise core programming concepts and create the operational process flows to analyse, modify and scale business capability.”
The Staffordshire Digital team initially implemented RPA in the Finance department, as it involves a lot of administrative and back-office operations such as management of finance, records, tuition fees details and more. The University’s emphasis is to free up personnel and make them focus on more productive areas. This is beneficial for both the administrative staff’s feeling of personal contribution as well as student service satisfaction levels. “Using RPA gives the opportunity to universities to revisit, redesign, and improve their existing processes in line with expectations from digital native students. For prospective students, the next wave of RPA integration is intelligent machine learning algorithms to help route emails and integrate chatbots to address questions on course selection,” says Dr Fairchild.
Gain greater visibility into the key adoption trends in Robotic Process Automation (RPA) solutions and industry best practices
The market is growing rapidly with large global RPA specialists such as UiPath, Automation Anywhere, Blue Prism and AntWorks experiencing high rates of growth in the region.
RPA vendors in Asia Pacific, are typically addressing immediate, short-term requirements. For example, healthcare companies are automating the reporting of COVID-19 tests and ordering supplies. Chatbots are being widely used to address unprecedented call centre volumes for airlines, travel companies, banks and telecom providers. Administrative tasks increasingly require automation as workflows become disrupted by remote working.
Companies can also be expected to scale their current deployments and increase the rate at which AI capabilities are integrated into their offerings
RPA often works in conjunction with major software products provided by companies such as Salesforce, SAP, Microsoft and IBM. For example, some invoicing processes involve the use of Salesforce, SAP and Microsoft products. Rather than having an operative enter data into multiple systems, a bot can be created to do this.
Large software vendors such as IBM, Microsoft, Salesforce and SAP are taking advantage of this opportunity by trying to own entire workflows. They are increasingly integrating RPA into their offerings as well as competing directly in the RPA market with pureplay RPA vendors. RPA may soon be integrated into larger enterprise applications, unless pureplay RPA vendors can innovate and continually differentiate their offerings.
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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 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.