What is the IT function for and what will it become? The CIO as a strategic demand creator

Ade mccormack | May 6, 2016

article image
"As the CIO and IT functions evolve, tack towards the strategic end of the business-relevance sprectrum As more and more of our technology management moves into the cloud, and more and more of our budget goes to other functions, it raises the question as to what the IT function is for, or at least will become? Given the uncertainty around the future of the IT function, I would encourage you to tack towards the strategic end of the business-relevance spectrum. But as many CIOs, who having test-driven their best management consultant demeanour in the boardroom, have discovered, all roads lead back to new technology. So are we doomed to enter a vortex of operational obsolescence? Not necessarily. There is at least one way we can turn our technology branding into strategic currency. Think 'Three Ts'. Toys, Threats and Table-stakes. Let's look at these in turn."

Spotlight

SourceEdge Software Technologies

SourceEdge Software Technologies (SourceEdge), is a privately held company registered under Software Technology Parks of India (STPI). Founded in 2004, headquartered in Bangalore, the silicon valley of India, SourceEdge provides cost-effective software & IT related outsourcing solution to global companies.

OTHER ARTICLES

MTN Benin taps Ericsson AI in managed services extension

Article | March 25, 2020

The companies already partner on charging system operations managed services. The new deal, which is already being implemented, includes network operations center services and field services in radio, core and transmission technology. AI-driven intelligent and data driven operations are part of the deal. AI-related efficiency benefits, automation and data analytics will enable MTN Benin and Ericsson to run predictive operations to enable a shift from reactive to proactive IT and network management – boosting customers' experiences, as well as network quality and performance.

Read More

Why Humans Need Chatbots And Chatbots Need Humans

Article | July 1, 2020

Chatbots have come a long way in the past few years. The improvements in technology have enabled developers to expand on bot capabilities far beyond just functioning as a FAQ. Today, the automation of chatbots can process orders, perform financial transactions, make bookings, and much more. (Check out other intelligent functions here.) However, as intelligent as bots can be, no chatbot can handle and resolve all your customer queries. It simply cannot answer the infinite number of questions a human may throw at it. The technology is simply not there yet, and it may never truly get there. But perhaps more importantly, brands shouldn’t want a bot to manage every customer query. A bot working independently of human involvement won’t always deliver the best results for customer or agent. It’s the combination of chatbots and human agents that takes customer service to new heights. What you need is a smart and efficient way of translating your organization’s unique customer service philosophy into appropriate action so that every question is met with an answer in the best way possible – whether that be by bot, human agent, or a blend of both. To deliver this, you have to pay attention to the who, what, when, and where of customer engagement. You need to know who your highest-value customers are so you can always route them to a human agent, for example. You need to know what they need help with so a simple question can be managed by a bot. And the list goes on. Here’s why humans need chatbots, and chatbots need humans – and how you can achieve this perfect balance to deliver support that will exceed customer expectations and generate substantial ROI. Why humans need chatbots There’s no doubt that supplementing customer-facing roles with automation can yield fantastic results. The launch of McDonald’s self-serve kiosks is a great example of this. By giving customers the option of ordering their meal through a kiosk, or through a cashier, McDonald’s demonstrates the success you can achieve by combining automation with human. Here are just some of the benefits it brought to the customer and employee experience: 1. Automating large portions of simple queries so workers have more time to focus on other, more complex tasks 2. Reducing monotonous, repetitive queries to improve employee experience 3. Catering to customer preferences – choose quick automated service or deeper human engagement 4. Reducing queue times, in turn improving customer experience 5. Lessening the opportunity for human error 6. Generating ROI by reducing staff numbers These results almost identically mirror the benefits that intelligent chatbots can provide customer service teams. By implementing a bot, a large portion of frontline support can be automatically managed by the bot which: 1. Gives agents more time to handle complex questions 2. Reduces the monotony of answering repetitive questions 3. Allows customer to choose between chatting to a bot or an agent 4. Reduces wait time and queue length (through bot’s ability to handle infinite simultaneous conversations), in turn improving customer satisfaction through quicker resolution 5. Eliminates human error in data entry 6. Generates substantial ROI through lower service costs See how closely those benefits match? Recommended reading: Chatbot ROI Calculator Why chatbots need humans The relationship between bots and humans isn’t a one-way street. While agents need bots to provide more effective and efficient support, bots need agents to provide the personal, ‘human’ touch that many situations call for. In our latest 2020 Live Chat Benchmark Report, we found that chatbots handle 68.9% of their chats from start to finish – although an impressive stat, it still shows that many queries require an agent’s touch. Recommended reading – 2020 Live Chat Benchmark Report There are always going to be situations that call for human assistance: canceling a subscription, reporting a lost or stolen credit card, or registering a serious complaint. Or maybe the topic is sensitive, and your customer would feel more comfortable explaining their situation to an agent. Similarly, some (though increasingly less: stat?) people are still wary or reluctant to communicate with bots and prefer to only speak with a live agent. To cater to these customer preferences, it’s vital that these customers can be routed past or transferred from your chatbot to human agent without effort and without having to repeat themselves. It’s important to note however, that transferring from bot to agent isn’t always just in the interest of the customer – it can often benefit the customer service team too. This is because not all queries are equal. For example, if a customer reaches out asking about a bank’s opening times, this can be easily managed by a bot. However, when the same customer asks about a loan, this high-value interaction may dictate that – according to your unique customer service view – a human agent takes over immediately to ensure the customer receives the best experience and you close the deal as quickly and effortlessly as possible. If your chatbot can’t do this, turn it off and find a chatbot that can (we can help with that). How to create the perfect chatbot – human (agent) balance To begin creating the right balance between chatbot and human, you need a bot that’s widely accessible to today’s digital-first consumers; your bot needs to be where they are, wherever they are. Comm100’s AI Chatbot can serve customers on web, in-app, Facebook, Twitter, WeChat, WhatsApp for Business, and SMS. You also don’t need to build separate chatbots for each channel. Simply select the channels you want your bot to be available on (hint: all of them!) and you’re off. Although your customers will know they are speaking to a bot (and you should make this clear to them to set expectations), you need a bot that understands natural human language. Comm100’s AI Chatbot harnesses the world’s most advanced NLP engine so that it can understand your customers’ goals and provide the answers they’re looking for. Better still, add a large range of off-the-shelf integrations to this, and the Comm100 bot can begin performing actions on behalf of your customers – from tracking an order and paying a bill, to booking a flight. By resolving a large portion of your frontline customer service questions, your agents will have more time to focus on higher-value queries and customers that matter most to your bottom line. Recommending reading: Comm100 Chatbot Resolves 91% of Assigned Live Chats for Tangerine As we’ve discussed earlier, there will be times when you or a customer would rather connect with an agent than a bot. It’s crucial that your bot offers this flexibility. Firstly, your bot should be able to give the customer the option to speak to an agent at any time. Eighty-six percent of consumers believe they should always have the option to transfer to a live agent when dealing with a chatbot. You can easily set this option up within the Comm100 AI Chatbot. Next, you need a bot that can automatically identify the conversations that you want an agent to manage. This requires training your bot on the topics – ‘intents’, in bot lingo – that your customers will bring up. If there are specific intents that are of high value to you, you can tag them so when a customer mentions it, the bot recognizes it and automatically transfers the chat to the appropriate agent or department. The bot can also be trained to notify an agent or escalate the conversation when asked a question it can’t answer or if a visitor is clearly frustrated. As a failsafe, your agents should also be able to monitor bot conversations and take them over in these situations. Wrap-up Chatbots will never replace whole customer service teams, and nor should they. The ‘human touch’ is still essential to customer support, and we are a long way off until this changes. However, if implemented intelligently, bots can resolve a great portion of customer queries without any human involvement, allowing team sizes to reduce, or remain the same in the face of increased support volume. Take Tangerine, an Australian telecom company, for example. They experienced rapid growth, which in turn produced a surge in chat requests. By implementing Comm100’s AI Chatbot, up to 91% of assigned live chats were resolved by the bot without any agent involvement. As a result, Tangerine could manage the increase in chat volume without hiring and training more agents. And when high-value customers reached out, their agents were free to provide them with the best experience.

Read More

AI Adoption: an advanced digital transformation process

Article | May 17, 2021

Common view is that AI software adoption is 'on its way' and it will soon replace many jobs (example self-driving cars with drivers etc.) and the majority of companies are starting to embrace the efficiencies that AI brings now. Being a practitioner of AI software development and being involved in many projects in my company AI Technologies, I always found my direct experience in the field in contrast with what the media generally portraits about AI adoption. In this article I want to give my view on how AI projects affect the work dynamics into clients work processes and compare that with the studies available on the impact of AI and new technologies on work. This should help the reader, especially if he is an executive, to set the right expectations and mentality when he is assessing the potential investment into a new AI project and if his company is ready for it. To start with, any software development project, including AI, can be summarized into 3 stages: proof of concept (POC) when the prototype has been built, product development when the software is actually engineered at scale, live support/continuous improvements. It occurs often that projects in AI will not go pass the POC stage and this is often due to 1) not right IT/data infrastructure in place 2) not specialist people have been hired to handle the new software or digital transformation process has not been planned yet. Regarding point 2, the most difficult issue is around hiring data scientists or data/machine learning engineers because many companies struggle with that. In fact, in a March 2021 O’Reilly survey of enterprise AI adoption, it has been found that “the most significant barrier to AI adoption is the lack of skilled people and the difficulty of hiring.” And in 2019 software it has been estimated that there were around 144,000 AI- related job openings, but only around 26,000 developers and specialists seeking work. Of course hiring an internal data scientist, it is not the only problem in restructuring the workforce. Often a corporation has to be able to re-train entire teams to be able to fully benefit from a new AI software. I can give an example. As many readers know a sales process involves 3 stages: lead generation, q&a call/mails with potential clients and deal closing. Now, a couple of years ago AI Technologies had been engaged to automatize the q&a call stage and we build a ai bot to manage the 'standard questions' a potential client may ask (without getting into the details, using AI and technically word3vec encoding, it is very possible to automate mails/chatbot for 'standardized questions' like 'how much it cost?' 'how long is the warranty for' etc.). Using this new internal solution, it meant the team responsible for the q&a would have been retrained either to increase the number of leads or the number of closing. The company simply decided to not embark into the transformation process required to benefit the new AI adoption. This example, in various forms, it is actually quite common: companies unless they are really innovative prefer to continue with their corroborated internal procedures unless some competitors threat their profitability. This bring to the fact that actually AI is not an out of the shelves solution which can be plugged in with no effort. As the moment a POC is under development it should be a good norm to plan a digital transformation process within the company. Also it is worth mentioning that, it is unlikely that the workforce has to be dismissed or made redundant as many expected following AI adoption. Just following the example above, what the AI bot does actually is to get over the repetitive tasks (q&a) so people can do more creative work engaging more clients (lead generation) or convincing to buy ( deal closing). Of course, it means that some people have to be retrained but also means that with the same people, you can close/generate more sales. It is a misconception to think that AI solutions will make human work redundant , we just need to adapt to new jobs. My example resembles a classical example on adoption of ATMs. When ATMs were introduced in 1969, conventional wisdom expected the number of banking locations to shrink, but instead, it actually made it possible to set up many more of them, it became cost-effective. There were under 200,000 bank tellers in 1970, but over 400,000 a decade later. The other common problem to face when companies want to embrace AI adoption (point 1), it is their current infrastructure: databases, servers, and crm systems have to be already in place. To put it simply, any AI system requires data to work with so it naturally sits on top of data infrastructure in day to day business operations. In the last two years AI Technologies has been engaged to work with a large public organization (70,000 employees) to build a solution to automatically detect malicious behavior of its employees manipulating their data. To build the AI software we had also designed a system to stream data from each employee terminal into a central database for processing. This infrastructure was not present at the beginning of the project since before the need for malicious detection was arised, the organization never really realized the necessity to gather certain data: a simple login and logout time was all the needed to monitor the activity of their employees (which company folder/file they accessed etc. was not important). This is a common situation and most of the companies' infrastructure are usually not ready to be used directly with AI solutions: their current infrastructure was simply designed with other objectives in mind. For sake of completeness, most companies decide to invest their internal resources in other areas of the business rather than crm or expensive data structures. There is no blame on this choice, at the end any business has to be profitable and investing in infrastructure is not always easy to quantify the return of investment. If anything, this article should have given an idea of the major pitfalls approaching AI projects which can be summarized as follows: • AI solutions are not out of the shelves , ready made software that can be immediately put in use: they often require new skilled hires within the client organization and potentially a plan how to re-utilized part of the workforce. • It is often a myth that AI solutions will necessarily replace the employees although it is possible that they have to be retrained. • Any AI project works on data and infrastructure which are necessary to benefit the new solutions. Before embarking on AI projects an organization has to either budget in a new infrastructure or at the very least an upgrade of the one in use. In essence, due to the implication on both employees and infrastructure, AI adoption should be considered as a digital transformation process more than a software development project. After the overwhelming hype of attention of the recent years, I would expect that in the next 2-3 years more companies will start to realize what AI projects really are and how to best use them.

Read More

Empowering Industry 4.0 with Artificial Intelligence

Article | February 12, 2020

The next step in industrial technology is about robotics, computers and equipment becoming connected to the Internet of Things (IoT) and enhanced by machine learning algorithms. Industry 4.0 has the potential to be a powerful driver of economic growth, predicted to add between $500 billion- $1.5 trillion in value to the global economy between 2018 and 2022, according to a report by Capgemini.

Read More

Spotlight

SourceEdge Software Technologies

SourceEdge Software Technologies (SourceEdge), is a privately held company registered under Software Technology Parks of India (STPI). Founded in 2004, headquartered in Bangalore, the silicon valley of India, SourceEdge provides cost-effective software & IT related outsourcing solution to global companies.

Events