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Businesses understand that automation can help organisations free up critical human resources and improve overall efficiency. Still, such benefits can only be achieved when a company takes a truly holistic approach to introduce machine learning-powered automation, according to Bikram Singh, CEO at EZOPS.
“Automating white-collared functions can be fraught with irrational fears by workers who view it as a threat to their jobs. However, with careful planning and execution, it can also be turned into a fulcrum to upskill and re-deploy these workers to roles that maximise their potential and consequently drive the greatest business value for employers. Automation is here to stay, and its effects will only intensify in the coming years,” says Singh.
“The critical question is how organisations can fully utilise AI, or technically-speaking machine learning, in a meaningful way, to drive business value and innovate across different business verticals. This is when we can see the real value for these types of intelligent process automation platforms.”
Automation as a team sport
Automation in the workplace can help simplify routine tasks that can lead to higher productivity levels for the entire workforce. According to Mercer’s 2020 Global Talent Trends survey, 61% of employees believe their employers are preparing them for the future of work, with 78% of employees reporting that they are ready to learn new skills and use AI to streamline solutions and help boost productivity.
And while every organisation has its own set of data and rules that they operate by, many still struggle to streamline solutions and gain the most effective results from their in-house analytics and datasets.
“Global financial institutions with large datasets face an even bigger problem to automate the data life-cycle using machine learning. As data flows across a myriad of platforms and functions, it is often untagged, making implementing machine learning more difficult,” says Singh.
“These firms have hundreds of systems managing data, which are very tightly governed by internal policies and regulators. Many regulatory issues emanate from poor data quality and stewardship. Consequently, regulators are increasingly demanding transparency into the drivers of algorithmic decisions, and explainable AI is a pre-condition for any AI system today.”
So, what’s the solution? Singh believes that using a singular platform that provides end-to-end data and process automation can help clients deal with all of those issues holistically, saving firms the excruciating pain of stitching together solutions to achieve enterprise-wide automation. The key to success is a well-calibrated approach amongst critical stakeholders. It is imperative to set clear goals, quantifiable milestones, and align expectations and risks at the onset. Then draw up a practical implementation plan that democratises decision-making and mutualises risk. While tempting on the surface, a top-down approach may not always be a winning strategy. The people most affected by machine learning should be brought in as equal partners as they are vital to driving successful outcomes. The fear of disintermediation or relevance in a post automation ecosystem must be addressed upfront with clear direction and options for upskilling the workforce to channel them into more value-added functions at these firms. A new paradigm is unfolding with machine learning, and firms that can manage fear and chaos with minimal disruption will emerge as leaders in the new automation race.
“AI-driven automation must be viewed as a team sport,” he says. “This is especially critical if an organisation is spread across different regions with varying degrees of comfort around disintermediation of the workforce.”
Setting the groundwork
For several businesses, the mention of automation and AI in a workplace can be met by either enthusiasm or nervousness.
While there is much to discuss about technology and automation taking people’s jobs, it’s essential to recognise that technology is a powerful tool that will reduce mundane and repetitive tasks, freeing employees to focus on driving value and working on more strategically important tasks.
“AI is a powerful tool to drive operational efficiency, reduce cost and increase transparency in the business,” says Singh. “AI can provide a clear roadmap of understanding of how to capture value, which can lead to fewer manual processes and cost reduction and, therefore, an increase in productivity.”
Setting the foundations of how automation will be used within the workforce is the first step to ensure that these technologies are being utilised effectively.
“The first step is clarity of vision – this is extremely important. A shift in business mindset is also imperative,” says Singh. “Find the right alignment of what your organisation is driving towards, and then utilise the technology to get there,”
For technology to be transformative within the workforce, business leaders need to understand their current working landscape to ensure that the business objectives are well defined right from the onset.
“AI by itself is not going to be the be-all and end-all for every single operational challenge. It has to be brought in in a way that actually makes sense as part of the day-to-day operations. Once a business has figured out where the gaps are, they can understand the areas that automation can drive value and take predictive analytics,” adds Singh.
“The idea is not to replicate or replace the human process. It’s to understand how AI can find opportunities where there are gaps, take over manual processes that can be done at scale, especially when you’re dealing with vast, complex datasets.”
The benefits of a single automation platform
Organisations dealing with a large dataset will quickly recognise the benefits of working with automated technologies to streamline workflows and improve data quality and analytics.
“This, in effect, can help organisations help detect data anomalies across large datasets, which would typically take humans a lot of time, and free up those critical resources to be more effective elsewhere in the business,” says Singh.
Each business should also understand its existing ecosystem and the processes that drive the most value. The technologies that need to be introduced must be scalable with an open architecture to easily be integrated with existing processes and existing legacy applications.
“There needs to be an upfront analysis in order to understand how data is moving and how people are interacting with that data,” says Singh. “Ask your business – what is our business process? Where are the pitfalls, and where are the areas that can actually be automated? The complexity of each of these steps has to be fully understood upfront. And then it has to be future proof.”
For financial institutions, the most significant challenges are usually within the back-office operations and compliance. These operations are critical to support the business development and help meet the business’s growth ambitions.
“Financial services need a lot of help in their middle and back-office operations and for their regulatory reporting,” says Singh. “These operations are tied to different business verticals, such as capital markets, wealth management or payments, and this is where there’s a lot of value for intelligent automation processes to step in and help streamline solutions.”
However, the critical question here is how many financial firms can utilise the benefits of AI in a meaningful way without being stuck in the proof-of-concept stage.
“The people who will make AI most effective are the people who are in day-to-day operations. The eventual goal is perhaps the disintermediation of certain processes that are reliant on human beings,” says Singh. “However, in doing so, there is an immediate opportunity for retraining the workforce to drive greater business value.”
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