Technology Talent: What’s Next?

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November has seen uncertainties in the technology market with news of layoffs and hiring freezes from big names in the industry – Meta, Amazon, Salesforce, and Apple to name a few. These have impacted thousands of people globally, leaving tech talent with one common question, ‘What next?’

While the current situation and economic trends may seem grim, it is not all bad news for tech workers. It is true that people strategies in the sector may be impacted, but there are still plenty of opportunities for tech experts in the industry. 

Here is what Ecosystm Analysts say about what’s next for technology workers.

Tim Sheedy, Principal Advisor, Ecosystm

Today, we are seeing two quite conflicting signals in the market: Tech vendors are laying off staff; and IT teams in businesses are struggling to hire the people they need.

At Ecosystm, we still expect a healthy growth in tech spend in 2023 and 2024 regardless of economic conditions. Businesses will be increasing their spend on security and data governance to limit their exposure to cyber-attacks; they will spend on automation to help teams grow productivity with current or lower headcount; they will continue their cloud investments to simplify their technology architectures, increase resilience, and to drive business agility. Security, cloud, data management and analytics, automation, and digital developers will all continue to see employment opportunities.

If this is the case, then why are tech vendors laying off headcount?

The slowdown in the American economy is a big reason. Tech providers that are laying of staff are heavily exposed to the American market.

  • Salesforce – 68% Americas
  • Facebook – 44% North America
  • Genesys – around 60% in North America

Much of the messaging that these providers are giving is it is not that business is performing poorly – it is that growth is slowing down from the fast pace that many were witnessing when digital strategies accelerated.

Some of these tech providers might also be using the opportunity to “trim the fat” from their business – using the opportunity to get rid of the 2-3% of staff or teams that are underperforming. Interestingly, many of the people that are being laid off are from in or around the sales organisation. In some cases, tech providers are trimming products or services from their business and associated product, marketing, and technical staff are also being laid off.

While the majority of the impact is being felt in North America, there are certainly some people being laid off in Asia Pacific too. Particularly in companies where the development is done in Asia (India, China, ASEAN, etc.), there will be some impact when products or services are discontinued.

Sash Mukherjee, Vice President, Content and Principal Analyst, Industry Research

While it is not all bad news for tech talent, there is undoubtedly some nervousness. So this is what you should think about:

Change your immediate priorities. Ecosystm research found that 40% of digital/IT talent were looking to change employers in 2023. Nearly 60% of them were also thinking of changes in terms of where they live and their career. 

Ecosystm research found that 40% of digital/IT talent were looking to change employers in 2023. Nearly 60% of them were also thinking of changes in terms of where they live and their career.

This may not be the right time to voluntarily change your job. Job profiles and industry requirements should guide your decision – by February 2023, a clearer image of the job market will emerge. Till then, upskill and get those certifications to stay relevant!

Be prepared for contract roles. With a huge pool of highly skilled technologists on the hunt for new opportunities, smaller technology providers and start-ups have a cause to celebrate. They have faced the challenge of getting the right talent largely because of their inability to match the remunerations offered by large tech firms.

These companies may still not be able to match the benefits offered by the large tech firms – but they provide opportunities to expand your portfolio, industry expertise, and experience in emerging technologies. This will see a change in job profiles. It is expected that more contractual roles will open up for the technology industry. You will have more opportunities to explore the option of working on short-term assignments and consulting projects – sometimes on multiple projects and with multiple clients at the same time.

Think about switching sides. The fact remains that digital and technology upgrades continue to be organisational priorities, across all industries. As organisations continue on their digital journeys, they have an immense potential to address their skills gap now with the availability of highly skilled talent. In a recently conducted Ecosystm roundtable, CIOs reported that new graduates have been demanding salaries as high as USD 200,000 per annum! Even banks and consultancies – typically the top paying businesses – have been finding it hard to afford these skills! These industries may well benefit from the layoffs.

If you look at technology job listings, we see no signs of the demand abating!

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Growing your Market Share in 2022

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Organisations have relied heavily on technology to survive and succeed over the last 2 years. 

Many tech providers have led the way – showing by example how strategies and technologies have to be shaped. They have also worked at improving their product and services offerings, introduced newer features and acquired companies to support market needs and grow their market share.

What should they do differently in 2022 to continue to succeed?

Ecosystm analysts think that a mere focus on products and features will not help. This is the time to focus on softer aspects such as skills, alignment with customer priorities, and an overhaul of channel programs.

Here is what Tech Providers should focus on in 2022 for continued success:

  • Build relationships with Business
  • Increase Automation to curb the effects of the Great Resignation
  • Syndicate Skills; not just Software
  • Focus on Channel Partners – and Pricing
  • Be Local and Industry-Specific

Read on to find out what Alan Hesketh, Darian Bird, Niloy Mukherjee, Peter Carr and Tim Sheedy have to say to Tech Providers.

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Click here to download Growing your Market Share in 2022 as a PDF.

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Ecosystm Predicts: The Top 5 Trends for Data & AI in 2022

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What Makes the Great Bounce Forward Different to the New Normal?

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One of the main questions that I have faced over the past week, since I wrote the  Ecosystm Insight – Welcome to the Great Bounce Forward – is “How is this different to the “New Normal”? Many have commented that the concept of the Great Bounce Forward is more descriptive and more positive than the term “New Normal” – but I believe they are different, and require different strategies and mindsets.

What makes the great bounce forward different to the new normal

This is a brief summary of some of the major differences between the New Normal and the Great Bounce Forward. I look forward with excitement and some trepidation towards this future. One where business success will be dictated not only by our customer obsession, but also the ability of our business to pivot, shift, change and adapt.

I can’t tell you what will happen in the future – a green revolution? Another pandemic? A major war? A global recession? Market hypergrowth? All the people living life in peace? Imagine that…

What I can tell you is what your organisation needs to do to be able to meet all of these challenges head-on and set yourself up for success. And to me, that won’t look like the new normal. There is nothing normal about these business capabilities at all.

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Welcome to the Great Bounce Forward

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As economies around the world are beginning to recover from the recessions and slowdowns caused by the pandemic, we are beginning to witness, what I like to call, the “Great Bounce Forward”.

Why the Great Bounce Forward? Because too many businesses, journalists and economists are talking about businesses “bouncing back”. But there is no bounce back. We are bouncing into the “economic unknown”. The trading conditions we see today are nothing like what they were at the beginning of 2020. While many people refer to the “new normal” I have heard few talks about how they are or will benefit from these new market conditions.

Bouncing back may not be relevant as we negotiate the economic unknown – it is time to evaluate how we can bounce forward!

Leaping Ahead Through Digital First

Customer interactions have changed – digital-first is now a requirement – and many customers expect a personalised and optimised experience. Many companies are starting to personalise experiences today – thinking they are “delighting customers” through personalised transactions and journeys. But you don’t delight customers by giving them what they want – you disappoint them if you don’t offer a true personalised experience.

Digital changes are coming thick and fast. For example, Australia Post has announced that online sales are currently 20% higher than what they were at the previous highest peak in December 2020. Yes – much of Australia is in a lockdown, but online sales are dwarfing what they were during lockdowns in 2020.

But it is not just about offering online sales. In the digital world, customers now expect to be able to track packages, get alerts when they are delivered, and have access to easy and free returns. Again – if you don’t do this today, you are creating poor customer experiences and are most likely losing business to those that offer great experiences.

Here is what organisations are witnessing:

The need to evolve their CRM solution. Salespeople expect the CRM to give them insights on who to sell to, why to sell to them and what approach will work best. CRM systems that don’t provide this analysis are letting businesses and salespeople down.

Analytics has to be turned into actions. More businesses are telling their analytics partners to stop telling them what to do, and just do it! Automating the outcomes of BI and analytics is beginning to be expected.

Ease of use has become essential. Interactions and processes need to be intelligent and easy to automate. We no longer throw teams of people at challenges – we automate the outcomes and use technology to deliver entirely new experiences without teams of employees pulling strings behind the scenes.

Process and technology changes happen quickly and seamlessly. We have been taught this by Zoom, Microsoft, AWS and Google. If you aren’t doing this today, you are behind the market and behind the expectations of your employees and customers.

Ecosystems are emerging to enable this agility and innovation. We can now innovate with a growing range of partners. Companies can partner for a single sale and move on. Start-ups are being embraced by dinosaurs, and competitors are becoming partners. More companies than ever are involving their own customers in their innovation processes. Ecosystems are changing the ability of technology and business teams to offer new and improved services to customers and employees.

Customer Experience Insights

Time for a Shift in Organisational Culture

Seemingly, the world changed overnight. But many of these changes have been in the works for years. It just took a global crisis to highlight how important they are and how much organisations need to change to embrace these opportunities. The only thing holding businesses back from thriving in the Great Bounce Forward are their people and culture. If you can embrace these changes, your businesses will move forward and emerge as different companies to the ones that entered the pandemic in early 2020. You’ll be more open, agile, innovative and digitally aware. You’ll be able to move in new, unheralded directions, driving improved customer, shareholder, or citizen value.

So stop thinking about how your business will bounce back. Make plans for it to bounce forward into the unknown.

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Ecosystm RNx: Top 10 Global AI & Automation Vendor Rankings

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Nuance Acquisition Strengthens Microsoft’s Industry & AI Capabilities

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Last week Microsoft announced the acquisition of Nuance for an estimated USD 19.7 billion. This is Microsoft’s second largest acquisition ever, after they acquired LinkedIn in 2016. Nuance is an established name in the Healthcare industry and is said to have a presence in 10,000 healthcare organisations globally. Apart from Healthcare, Nuance has strong capabilities in Conversational AI and speech solutions to support other industries. This acquisition is in line with Microsoft’s go-to-market roadmap and strategies.

Microsoft’s Healthcare Focus

Microsoft announced their Healthcare Cloud last year and this acquisition will bolster their Healthcare offerings and market presence. Nuance’s product portfolio includes clinical speech recognition SaaS offerings – Dragon Ambient eXperience, Dragon Medical One and PowerScribe One for radiology reporting – on Microsoft Azure. The acquisition builds on already existing integrations and partnerships that were in place over the years.

Microsoft Cloud for Healthcare offers its solution capabilities to healthcare providers using a ‘modular’ approach. Given how diverse healthcare providers are in their technology maturity and appetite for change, the more diverse the  ‘modules’, the greater the opportunities for Microsoft. This partnership with Nuance also brings to the table established relationships with EHR vendors, which will be useful for Microsoft globally.  

The Healthcare industry continues to struggle as the world negotiates the challenges of mass vaccination. But on the upside, the ongoing Healthcare crisis has given remote care a much-needed shot in the arm. Clinicians today will be more open to documentation and transcription services for process automation and compliance. The acquisition of Nuance’s Healthcare capabilities will definitely boost Microsoft’s market presence in provider organisations.  

However, Healthcare is not the only industry that Microsoft and Nuance are focused on. The Microsoft Cloud for Retail that was launched earlier this year aims to offer integrated and intelligent capabilities to retailers and brands to improve their end-to-end customer journey. Nuance has omnichannel customer engagement solutions that can be leveraged in Retail and other industries. As Microsoft continues to verticalise their offerings, they will consider more acquisitions that will complement their value proposition.

Microsoft’s Focus on Conversational AI

Microsoft already has several speech recognition offerings, speech to text services, and chatbots; and they continue to invest in the Conversational AI space. They have created an open-source template for creating virtual assistants to help Bot Framework developers. In February, Microsoft announced their industry specific cloud offerings for Financial services, Manufacturing, and Non-Profit, and also introduced a series of AI and natural language features in Microsoft Outlook, Microsoft Teams, Microsoft Office Lens and Microsoft Office mobile to deliver interactive, voice forward assistive experiences.

“There is no slowing down in this space and the acquisition clearly demonstrates the vision that Microsoft is building with Nuance – a vendor that has made speech recognition, text to speech, conversational AI the foundation of the company. This is a brilliant move by Microsoft in the Conversational AI space and a win-win for both companies.

This move could also mark further inroads for Microsoft into the contact centre space. With Teams now being integrated into contact centre technologies, working with large customers using speech and conversational AI, Dynamics 365 could herald the start of more acquisitions for Microsoft to bolster a wider customer engagement vision.

The Conversational AI war is heating up and various other cloud vendors such as Google and AWS are starting to get aggressive and have made investments in recent years to enhance their Conversational AI capabilities. Google Dialogflow has been seeing rapid uptake and they now have deep partnerships with Genesys, Avaya, Cisco and other contact centre players. Microsoft coming into the game and acquiring a company with years of history and IP in the speech space, demonstrates how the cloud battle and the war between Google, Microsoft and AWS is heating up in the Conversational AI. All of a sudden you have Microsoft as a powerhouse in this game.”


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Building Trust in your AI Solutions

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In this blog, our guest author Shameek Kundu talks about the importance of making AI/ machine learning models reliable and safe. “Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes.”

Building trust in algorithms is essential. Not (just) because regulators want it, but because it is good for customers and business. The good news is that with the right approach and tooling, it is also achievable.

Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes. With the increased use of Artificial Intelligence/ Machine Learning (AI/ML), today’s algorithms are also more powerful and difficult to understand.

A false dichotomy

At this point in the conversation, I get one of two reactions. One is of distrust in AI/ML and a belief that it should have little role to play in regulated industries. Another is of nonchalance; after all, most of us feel comfortable using ‘black-boxes’ (e.g., airplanes, smartphones) in our daily lives without being able to explain how they work. Why hold AI/ML to special standards?

Both make valid points. But the skeptics miss out on the very real opportunity cost of not using AI/ML – whether it is living with historical biases in human decision-making or simply not being able to do things that are too complex for a human to do, at scale. For example, the use of alternative data and AI/ML has helped bring financial services to many who have never had access before.

On the other hand, cheerleaders for unfettered use of AI/ML might be overlooking the fact that a human being (often with a limited understanding of AI/ML) is always accountable for and/ or impacted by the algorithm. And fairly or otherwise, AI/ML models do elicit concerns around their opacity – among regulators, senior managers, customers and the broader society. In many situations, ensuring that the human can understand the basis of algorithmic decisions is a necessity, not a luxury.

A way forward

Reconciling these seemingly conflicting requirements is possible. But it requires serious commitment from business and data/ analytics leaders – not (just) because regulators demand it, but because it is good for their customers and their business, and the only way to start capturing the full value from AI/ML.

1. ‘Heart’, not just ‘Head’

It is relatively easy to get people excited about experimenting with AI/ML. But when it comes to actually trusting the model to make decisions for us, we humans are likely to put up our defences. Convincing a loan approver, insurance under-writer, medical doctor or front-line sales-person to trust an AI/ML model – over their own knowledge or intuition – is as much about the ‘heart’ as the ‘head’. Helping them understand, on their own terms, how the alternative is at least as good as their current way of doing things, is crucial.

2. A Broad Church

Even in industries/ organisations that recognise the importance of governing AI/ML, there is a tendency to define it narrowly. For example, in Financial Services, one might argue that “an ML model is just another model” and expect existing Model Risk teams to deal with any incremental risks from AI/ML.

There are two issues with this approach:

First, AI/ML models tend to require a greater focus on model quality (e.g., with respect to stability, overfitting and unjust bias) than their traditional alternatives. The pace at which such models are expected to be introduced and re-calibrated is also much higher, stretching traditional model risk management approaches.

Second, poorly designed AI/ML models create second order risks. While not unique to AI/ML, these risks become accentuated due to model complexity, greater dependence on (high-volume, often non-traditional) data and ubiquitous adoption. One example is poor customer experience (e.g., badly communicated decisions) and unfair treatment (e.g., unfair denial of service, discrimination, misselling, inappropriate investment recommendations). Another is around the stability, integrity and competitiveness of financial markets (e.g., unintended collusion with other market players). Obligations under data privacy, sovereignty and security requirements could also become more challenging.

The only way to respond holistically is to bring together a broad coalition – of data managers and scientists, technologists, specialists from risk, compliance, operations and cyber-security, and business leaders.

3. Automate, Automate, Automate

A key driver for the adoption and effectiveness of AI/ ML is scalability. The techniques used to manage traditional models are often inadequate in the face of more data-hungry, widely used and rapidly refreshed AI/ML models. Whether it is during the development and testing phase, formal assessment/ validation or ongoing post-production monitoring,  it is impossible to govern AI/ML at scale using manual processes alone.

o, somewhat counter-intuitively, we need more automation if we are to build and sustain trust in AI/ML. As humans are accountable for the outcomes of AI/ ML models, we can only be ‘in charge’ if we have the tools to provide us reliable intelligence on them – before and after they go into production. As the recent experience with model performance during COVID-19 suggests, maintaining trust in AI/ML models is an ongoing task.

***

I have heard people say “AI is too important to be left to the experts”. Perhaps. But I am yet to come across an AI/ML practitioner who is not keenly aware of the importance of making their models reliable and safe. What I have noticed is that they often lack suitable tools – to support them in analysing and monitoring models, and to enable conversations to build trust with stakeholders. If AI is to be adopted at scale, that must change.

Shameek Kundu is Chief Strategy Officer and Head of Financial Services at TruEra Inc. TruEra helps enterprises analyse, improve and monitor quality of machine


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Policy Making in a Pandemic: Use of AI in SupTech

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Artificial Intelligence (AI) is becoming embedded in financial services across consumer interactions and core business processes, including the use of chatbots and natural language processing (NLP) for KYC/AML risk assessment.

But what does AI mean for financial regulators? They are also consuming increasing amounts of data and are now using AI to gain new insights and inform policy decisions. 

The efficiencies that AI offers can be harnessed in support of compliance within both financial regulation (RegTech) and financial supervision (SupTech). Authorities and regulated institutions have both turned to AI to help them manage the increased regulatory requirements that were put in place after the 2008 financial crisis. Ecosystm research finds that compliance is key to financial institutions (Figure 1).

Drivers for Cybersecurity and Regulatory Investments

SupTech is maturing with more robust safeguards and frameworks, enabling the necessary advancements in technology implementation for AI and Machine Learning (ML) to be used for regulatory supervision. The Bank of England and the UK Financial Conduct Authority surveyed the industry in March 2019 to understand how and where AI and ML are being used, and their results indicated 80% of survey respondents were using ML. The most common application of SupTech is ML techniques, and more specifically NLP to create more efficient and effective supervisory processes.

Let us focus on the use of NLP, specifically on how it has been used by banking authorities for policy decision making during the COVID-19 crisis. AI has the potential to read and comprehend significant details from text. NLP, which is an important subset of AI, can be seen to have supported operations to stay updated with the compliance and regulatory policy shifts during this challenging period.

Use of NLP in Policy Making During COVID-19

The Financial Stability Board (FSB) coordinates at the international level, the work of national financial authorities and international standard-setting bodies in order to develop and promote the implementation of effective regulatory, supervisory and other financial sector policies. A recent FSB report delivered to G20 Finance Ministers and Central Bank Governors for their virtual meeting in October 2020 highlighted a number of AI use cases in national institutions.

We illustrate several use cases from their October report to show how NLP has been deployed specifically for the COVID-19 situation. These cases demonstrate AI aiding supervisory team in banks and in automating information extraction from regulatory documents using NLP.

De Nederlandsche Bank (DNB)

The DNB is developing an interactive reporting dashboard to provide insight for supervisors on COVID-19 related risks. The dashboard that is in development, enables supervisors to have different data views as needed (e.g. over time, by bank). Planned SupTech improvements include incorporating public COVID-19 information and/or analysing comment fields with text analysis.

Monetary Authority of Singapore (MAS)

MAS deployed automation tools using NLP to gather international news and stay abreast of COVID-19 related developments. MAS also used NLP to analyse consumer feedback on COVID-19 issues, and monitor vulnerabilities in the different customer and product segments. MAS also collected weekly data from regulated institutions to track the take-up of credit relief measures as the pandemic unfolded. Data aggregation and transformation were automated and visualised for monitoring.

US Federal Reserve Bank Board of Governors

One of the Federal Reserve Banks in the US is currently working on a project to develop an NLP tool used to analyse public websites of supervised regulated institutions to identify information on “work with your customer” programs, in response to the COVID-19 crisis.

Bank of England

The Bank developed a Policy Response Tracker using web scraping (targeted at the English versions of each authority/government website) and NLP for the extraction of key words, topics and actions taken in each jurisdiction. The tracker pulls information daily from the official COVID-19 response pages then runs it through specific criteria (e.g.  user-defined keywords, metrics and risks) to sift and present a summary of the information to supervisors.

Market Implications

Even with its enhanced efficiencies, NLP in SupTech is still an aid to decision making and cannot replace the need for human judgement. NLP in policy decision is performing clearly defined information gathering tasks with greater efficiency and speed. But NLP cannot change the quality of the data provided, so data selection and choice are still critical to effective policy making.  

For authorities, the use of SupTech could improve oversight, surveillance, and analytical capabilities. These efficiency gains and possible improvement in quality arising from automation of previously manual processes could be consideration for adoption.

Attention will be paid in 2021 to focusing on automation of processes using AI (Figure 2).

Digital Focus for 2021 in Financial Services

Based on a survey done by the FSB of its members (Figure 3), the majority of their respondents had a SupTech innovation or data strategy in place, with the use of such strategies growing significantly since 2016.

Summary

For more mainstream adoption, data standards and use of effective governance frameworks will be important. As seen from the FSB survey, SupTech applications are now used in reporting, data management and virtual assistance. But institutions still send the transaction data history in different reporting formats which results in a slower process of data analysing and data gathering. AI, using NLP, can help with this by streamlining data collection and data analytics. While time and cost savings are obvious benefits, the ability to identify key information (the proverbial needle in the haystack) can be a significant efficiency advantage.


Singapore FinTech Festival 2020: Infrastructure Summit

For more insights, attend the Singapore FinTech Festival 2020: Infrastructure Summit which will cover topics tied to creating infrastructure for a digital economy; and RegTech and SupTech policies to drive innovation and efficiencies in a co-Covid-19 world.

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