Intelligent Automation enters the Industrial Age
As earlier uncertainties about the viability and effectiveness of robotic automation, or even its potential threats to humanity are disappearing, the 2020s herald a new age. Both the sector and the technology itself are maturing to a degree of ubiquitous adoption and rising benefits. In this brief article we examine implications in the Private Wealth sector and key trends at the start of the decade.
Just a few short years ago the Private Banking and Wealth Management industry was oblivious of the arrival of new process automation paradigms and enabling tools. Focused on transactional efficiency, more traditional forms of automation like STP were valued and sought after. Synpulse was among the first proponents of RPA - but even in our early events on the subject a subtitle said: «Nirvana or Apocalypse?». Back then fear of the unknown combined with inertia and uncertainty of returns were the barriers to early adoption. Luckily the compact and agile nature of RPA tools allowed pioneers to find ‘proof of the pudding in the eating’ - through compact and rapid prototypes and pilots.
Automating «at scale»: industrialisation
Nowadays, even in the conservative Private Wealth sector, everyone has an RPA pilot: just ask an IT or Operations executive, and they have done it (or are doing it right now). This is very encouraging, and we have been delighted to deliver many of those pilot projects.
Prototypes and pilots, however, have a very limited ROI potential and it is widely acknowledged that real scale of deployment is required to reap the full benefits. In a global study with 1,500 C-level executives, Accenture found that 3 out of 4 struggle to scale their early-days projects, and 4 out of 5 believe they will not achieve their growth objectives without scaling the intelligent automation. A significant number also fear that in 5 years they may be out of business if they fail to scale.
Another convincing finding in favour of scaling shows that the success rate of RPA and AI project doubles with scaled deployment compared to standalone pilots, and the ROI nearly triples (32% for prototypes vs 86% for implementations at scale, on average across sectors and geographies).
Narrowing the focus to the Wealth Management sector and to the UK, there is similar consensus about the importance of automation for continued growth and profitability. In a recent quest for the «Wealth Management Firm of 2025» (a series of round tables run by Compere with 20 top executives representing combined AUM of £105bn and 3,000 employees), automation and AI were among top trends and priorities, along with related concepts like Digital Engagement and Cloud use.
The question many decision makers are facing is neither «why» or «whether» - but «how». How to achieve the scale from an as-is state and isolated small pilot, to an enterprise-wide adoption across numerous processes, their functional domains and supporting systems?
The question many decision makers are facing is neither «why?» or «whether?» - but «how?»
Studying the experience of successfully scaling adopters, a clear pattern has two parallel components:
This ultimate state, when everything that benefits from automation works together in a single environment, is referred to as industrialisation.
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The evolutionary path to the industrial level is typically described three main phases:
Studies find 80-85% of companies at the first level, 15-20% at the second, and only fewer than 5% at the top. These are global and cross-industry numbers; in our observation, for the UK Private Banking and Wealth Management industry they are notably lower. Which - with the unstoppable advancement of intelligent automation - only means a good growth opportunity.
Opportunities and challenges
A critical success factor on the journey to industrialised Intelligent Automation is the prioritisation of deployment areas, with clear viability and business cases. A prime target initially is the back office where operational efficiency can be boosted by automating repetitive manual tasks. As the technology matures and evolves, more intelligent decisioning can be introduced in complex processes with a significant cognitive component. Similarly, usage of AI is already expanding into the front office, helping to reduce risks and enhance client experience. Any of those use cases needs to be carefully evaluated for the technical viability of realistic outcomes, and the benefits assessed against the comprehensive costs of implementation.
Apart from operational efficiency, current candidates for the more intelligent deployments are Risk Management, Trading and Investment (alpha generation and assurance), and CX (client experience enhancement through real-time personalisation). Risk is already overtaking operational efficiency as automation priority: a Refinitiv study found it no.1 (ahead of Costs) at 82% of companies globally, and at 100% of US respondents.
Other applications currently being addressed are managing and enhancing market data, alternative data sources (including unstructured, e.g. web scraping social media, satellite imagery), data biases detection and correction with cognitive processing, etc.
A major obstacle to most of the above is the recognised (and ongoing) suboptimal Data Quality. Much more effort and investment is required to cleanse, organise and responsibly govern companies’ data before convincing success can be achieved with data science and intelligent automation.
Another challenge is skills demand and availability - where significant retraining will be required for the human workforce to collaborate effectively with intelligent automation beyond creating and deploying it.
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Synpulse will be addressing all these issues with top industry representatives and experts at the next Senior Executives Forum on the subject, on the 26th of March in London. For details:
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2yVladimir, thanks for sharing!