Automation will require Retraining and Reskilling

4/5 (1)

4/5 (1)

As technology continues to permeate all aspects of business and influence how employees execute their roles, there is a growing need for more technologically proficient employees to quickly become future-ready. To enable this, organisations need to develop strategies and support parameters to reskill and upskill their workers.

Why the Need to Retrain and Reskill

Retraining and reskilling are nothing new, and industries have witnessed it several times in the past. The Industrial Revolution replaced many workers with mechanised tools and machinery. Workers who embraced change, and learned how to use the machines, replaced those who did not or could not. It is true that every new technology creates its own turbulence. What is unique this time is that the shift is happening faster as technology advances exponentially. So, people need to retrain and upskill quicker if they are to keep pace with the changing technology landscape.

Both manual and cognitive tasks are being empowered by machines and AI algorithms.  There is also a shortage of a skilled workforce with the right technical training.  Here is why the need for technological skills has increased exponentially over the last few years:

Productivity. Organisations are looking for ways to increase productivity and are spending time on identifying technologies that can help them compete in the future. The introduction of AI and automation is replacing legacy systems and displacing positions such as junior executives, administrative staff, customer service executives and so on. Chatbots and novel interactive robotic companions are offering better productivity with an ability to work 24×7 without taking breaks and can be updated and taught new skills with some minor changes in their algorithm. To understand and to work on AI and chatbot queries, employees require special training and skills. For instance, in a customer care team, if a chatbot is not able to respond to a query it can then be passed to a customer service executive who is able to work in tandem with the AI tool to solve the query.

Profitability. The primary reason why organisations look to technology-driven automation is to create an impact on their profit margin. While making the investment in the technology often requires an upfront cost, once the systems are in place there is a positive impact on productivity and hence more profits. Organisations should ideally invest part of the profits to implement more advanced technology and upskilling their current employees. The lower productivity workers often add up to the costs and reskilling them can save money and lead to more seamless workforce integration. For instance, Fintech is being used increasingly to automate decisions such as instant loan approval, KYC, fraud detection and other financial crimes.  This does not remove the need for subject matter experts and those that have experience and expertise in the domain – they can train those automated systems by feeding queries, analysing outputs and helping the organisation improve their automated process.

Avoiding being Obsolete. In this digital era, even individuals possessing decades of work experience might get outdated if they do not keep pace with an evolving landscape. Lacking an ability to understand and empathise with technology is often a consequence of improper training. Employees require training at regular intervals – if they do not have the expertise and cannot give the right feedback to these automated systems, there might be serious consequences.  As an example, most organisations planning to procure software will also evaluate SaaS solutions. This requires employees to be flexible to adapt to the Cloud environment and to look beyond the legacy systems that they are comfortable with.

Mergers and Acquisitions. In today’s competitive market, we witness mergers and acquisitions almost on a daily basis, with organisations wanting to gain skills, services,  technology and ultimately market share.  When a company is bought or merges with another, along with a change in leadership and organisational culture, there is also a change in technology used. If organisations want to retain the expertise of the newly acquired firm, retraining becomes essential. In these new set-ups, what will matter more than seniority is the ability of the employees to adapt to and learn the new technologies.

Being Competitive. Technology is seen as an enabler for business differentiation. Increasingly the twin focus areas for all organisations are customer experience (CX) and employee experience (EX) – how to retain and win both customers and employees.  When it comes to outperforming the competition, the technology used for the eCommerce platform, point-of-sale solution, back-office operations – virtually every part of the operation, can be a key component of the overall competitive edge. Having workers that are properly trained in the technology they use, and those who buy into the organisational culture will be a crucial advantage in this competitive world.


How to incorporate retraining and reskilling in your Transformation Journey

Organisations should follow best practices when embarking on reskilling initiatives, in order to rapidly drive ROI. It is always a good idea to invest in people who are invested in your organisation. Amit Gupta, CEO, Ecosystm interviewed Parry Singh, Chief Commercial & Digital Officer, Mediacorp where they discussed how emerging technologies such as AI,  are impacting the media industry, how to carry an organisation through the digital transformation journey and how to upskill employees for the future. The key takeaways for organisations looking to retain their valuable staff are:

  • Realise that learning is a continuous process. Companies should analyse technologies that will impact their business and their industry. But should also be aware that these technologies will evolve continually. To handle this learning should also be continuous. Walmart, for instance, has set up more than 100 “academies” in the US that provide continual classroom and hands-on training for various positions. Having the right talent in place is critical to the prospects of any organisation.
  • Make arrangements for just-in-time learning. Learning works best when people can apply their new-found knowledge and skills almost immediately. Organisations should identify the skills that employees will need in their immediate role. This also helps employees appreciate the value of the training and be open to future upskilling. NUS Business School offers a 3-day course, Leveraging Fintech for Business aimed at leaders and managers, to explore the business impact of Fintech – aimed at entrepreneurs and mid-career financial professionals who wish to upskill and those who are impacted by Fintech. Organisations can provide employees with on-demand learning tools and resources. Tools like mobile apps and online courses can help employees to learn and grow and allow them to engage with the program at their convenience and at their own pace instead of forcing them to adhere to a pre-planned schedule.
  • Partner if you do not have the right training process. An organisation cannot always be expected to have the right training resources available in-house – it might also prove to be expensive in the long run. Organisations that lack expertise or do not have enough resources to train and reskill employees, should partner with technology providers and dedicated external training programs. SkillsFuture in Singapore has partnered with IBM to train 2,500 Singaporeans on AI skills within the next three years, in a bid to help them apply AI in areas such as human resources, supply chain management, and media.


Technology-enabled automation will displace some workers while at the same time provide a platform for them to grow their careers and play a larger part in the success of the organisations. Companies can enable this transition through investments in training and education and provide a platform for workers to transition to new jobs. With the right tools, companies can continue to forge a long-term and mutually beneficial association with their employees in the face of rapid and increasing digital transformation.

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

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4.6/5 (13)

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

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

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

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

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

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

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


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

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

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

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

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

Ecosystm Snapshot: Salesforce Acquires Tableau

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5/5 (2)

In a move that feels “back to the future”, Salesforce has agreed to acquire Tableau Software Inc for US$15.3 billion in a deal that is expected to close in the third quarter of 2019. It seems all independent BI and analytics companies (except SAS!) eventually get snapped up – Business Objects by SAP, Hyperion by Oracle, Cognos by IBM. The move comes less than a week after Google acquired BI and analytics provider Looker.

Today, many businesses use Tableau (over 86,000), including a lot of Salesforce customers. They have chosen Tableau because it is easy to deploy and use, and like Salesforce own applications, it targets the ultimate decision maker – the business user – and sometimes even the consumer. Recent research into the BI systems integrators in Asia Pacific shows that Tableau is one of the leading analytics platforms for the partner community in the region – the big SIs have many people focused on Tableau. But that dominance is being challenged by a re-energised Microsoft, whose Power BI is also witnessing strong growth – and who is typically the price leader in the market.

For Salesforce customers, there is some overlap between products – their own Einstein Analytics tools do much of what Tableau can do – although Tableau helps customers see insights from data stored both on the cloud and inside their own data centres. It also moves Salesforce closer to the Customer 360 vision – the ability to get a view of customers across the Commerce, Marketing and Service Clouds. Salesforce customers not using Tableau today will get a better user experience by using Tableau as the visualisation platform.

History has shown that it is hard to make such acquisitions successful. Tableau was a huge success because it was independent. The same was for Business Objects and Cognos before their acquisitions. History has shown that when the large BI and analytics vendors are acquired, others move into that space. While Salesforce has announced they will run Tableau as a separate business, it will no longer be independent. Partners will need to be maintained and provided a growth path – and partners are the cornerstone of Tableau’s success. Some of these partners might have strong ties to other software or cloud platforms too such as SAP, Oracle, AWS or Google. Customers of Tableau might feel sales pressure to move to a Salesforce environment – and will likely see Salesforce integration happen at a deeper level than on other platforms.

Tableau’s independence will disappear. However keeping Tableau as a separate business may not be the long term goal for Salesforce – it might be to offer the best application and analytics solution in the market – to make the entire suite more attractive to more potential buyers and users. It may be to take Salesforce beyond the current users in their customers to many other users who may not need the full application but need the analytics and visualisations that the data can provide. If this is the case, then the company is onto a winner with the Tableau acquisition.


The long term goal is not analytics reports delivered to employees. It is not visualisation. It is automation. It is applications doing smart, AI-driven analysis, and deciding for employees. It is about taking the human out of the process. In a factory you don’t need a report to tell you a machine is down – you need to book a repair person automatically – or a service technician to visit before the machine has even broken down. And you don’t need a visualised report to show that a machine is beyond its life expectancy. You need the machine replaced before it fails catastrophically.

Too often, we are putting humans in processes where they are not required. We are making visualisations more attractive and easier to consume when, in reality, we just needed the task automated. While we employ humans, there will be a need to make decisions more effectively, and we will still require tools like Tableau. But don’t let the pretty pictures distract you from the main prize – intelligent automation.

If you would like to speak to Tim Sheedy or another analyst at Ecosystm about what the acquisition Tableau by Salesforce might mean to your business or industry, please feel free to schedule an inquiry call on the profile page.

Automation Versus AI – Building the Business Case for 70% Accuracy

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5/5 (2)

I ran several roundtables over the past few weeks speaking to business and technology leaders about their AI investments – and one factor came up many times – that it is hard to build a business case for AI because 70% accuracy was not good enough…

What this means is that companies have thousands of things to automate. Most of those automations in the short-medium term will deliver 100% accuracy using RPA and other simple automation tools. Every time you run that process you know the outcome.

Along Comes AI and Machine Learning

These dumb processes can now learn – they can be smart. But originally they won’t deliver 100% accuracy. They might only deliver 60-70% to start with – climbing perhaps to 90%. The benefits of these smart, learning processes can amaze – costs can fall, processes can improve, outcomes can accelerate. But traditionally we have built technology business cases delivering 100% accuracy and outcomes.

So we need a new way to think about AI and a different language to use about the way it works. The people who sign off on the business cases might not understand AI – they will come to the business case with the same lens they use for all technology investments (and evidently – all business investments). We also need to be better at selling the benefits to our leaders. CEOs and Managing Directors in the roundtables are surprised to hear that AI won’t deliver 100% accuracy – they said unless they know more about the capability, savings and outcomes that the solution might drive, they are unlikely to fund it.

Make Your Dumb Processes Smart

I take this as good news. It means we have moved beyond the hype of AI – the need to “do AI in our business” that drove many of the poorer chatbots and machine learning projects. It means that businesses review AI investments in the same way as any business investment. But it also means we can’t over-promise or under-deliver on AI. Woodside did this with their initial foray into AI, and they are still playing catch up today.

While there are many opportunities to use “dumb automation” and save money, reduce or redeploy headcount – or have employees focus on higher value activities or make real differences to customer experiences – there are as many opportunities to make dumb processes smart. Being able to automatically read PDF or paper-based invoices – processes usually done by humans – could be a huge saving for your business. OK – maybe you can’t redeploy 100% of the staff, but 70% is still a big saving. Being able to take human error out of processes will often help to save money at two steps on the process – automating the human input function up front and also getting rid of the need to fix the mistake.

Start Your AI Journey With The Low Hanging Fruit

Ecosystm’s Global Ongoing AI study has shown that most businesses are focusing their AI investments on internal initiatives – on reducing process time, cost savings and driving productivity – which makes the most sense today. They are the easier business cases to build and the easiest benefits to explain.


Perhaps AI is also a chance for businesses to acknowledge that “efficient” does not always mean “good”. Many of the processes we automated or coded to ensure 100% compliance don’t give customers or employees what they are looking for. And maybe making the customer happy 70% of the time is better than not making them happy at all…

If you’d like to dig deeper into Ecosystm’s reports exploring the data from our ongoing AI study – check them out here (you’ll need to register if you have not already – it is free to register, but some content is premium):

4 Vendors Emerge as Leaders: Understanding the AI Vendor landscape

Use Cases Drive AI Software Adoption: Understanding The Industry Landscape