Highly regulated industry of Banking has shown rapid adoption of Robotic Process Automation (RPA) in recent past. And with advent of Artificial Intelligence (AI) technologies, getting RPA & AI together is believed to transform not only banking / financial services but many other sectors with its novel application.
Views summarized are on aspects are beyond the process-level automation as these tech-solutions will have impact which is slightly wider. Idea is to evoke response from readers like you about what you feel about these points.
Hence, your comments, criticism, suggestions and exchange of ideas are most welcome!
RPA:
Mechanisms with similar objectives like RPA have been in existence and that’s how manual records have over a period have moved into automated / computer systems whereby helping enhance efficiencies. Such automations incl. controls / checks and balances / certain auto-transactions (esp. financial) were coded in the software solutions. Through RPA technology, this business process automation drive has been taken to outside stakeholders (customers/ clients) who consume the services of business through human interaction.
In service industry, these activities were repetitive in nature and usually bucketed in ‘customer-care’ vertical to keep the business moving forward. Current wave of application of this RPA technology is automating these repetitive activities by human resources by using ‘software robots’ to route the defined sequence of activities vis-à-vis set parameters.
There are two portions where automation has always been advocated – one, where series of activities related to any business operations are repetitive in nature and second, where governance needs to be enforced viz. implementation of business policies / rules to validate business transaction. In both scenarios automation is aimed to eliminate human subjectivity in decisions, make repetitive process more efficient in terms of time & total cost (humans don’t work 24×7 which robots would) and ensure 100% enforcement of business policies at base transaction to reduce cost of governance, oversight or corrective measures.
Current scenario in industry:
As IT solutions were adopted over a period, currently business & IT functions in almost every organization is working through complex web of software systems integrated into each other incl. the base ERP to achieve its objectives.
In pre-RPA scenario, automation had critical dependency on changing these base IT systems and variety of software products which were integrated with each-other. Which means project dependency was on those IT product companies whose products they were using to achieve such desired level of automation.
RPA has come to rescue where business and IT functions were unable to automate processes due to sheer cost of undertaking such effort in a ground-up manner and uncertainties of success of such business/ IT projects.
RPA has by-passed this problem altogether as it is system agnostic. This option has definitely given huge leverage to quickly realize the efficiencies in business operations but same time opened doors for some allied areas or points for consideration.
Some key business or commercial points for consideration in post – RPA scenario:
As RPA is system agnostic ‘bolt-on’ solution, current use of integrated software solutions would continue to be around. Which also means business will have to continue maintain this IT infrastructure to the extent required. In addition, with changing business and regulatory requirements, business will have to make changes in core workflows in the base IT systems.
Post implementation, business will have to ensure processes, which are automated through RPA mechanisms, are aligned to changes in core systems from time to time viz. enhanced KYC or disclosure norms in banking industry by the regulators. Which means RPA will need sustained effort.
As RPA has fuelled massive need of technical skills, the loss of jobs in many of these back – office IT services industry may have avenue to get employed with new skills. This change will drive major re-skilling drive to get this skill-deficit filled up quickly. Skilling same work-force with technical skills is going to be challenging as it will have its own dependencies.
Some of the key business & commercial points while thinking through RPA may be:
- Industry / businesses which are deploying RPA / AI tools will have to evaluate impact on its cost structures vis-à-vis realized efficiencies. Key question for business & finance leaders will be to evaluate what may be the quantum of savings through these efficiencies, how much of that will be needed to fund the RPA/ AI automation drive (implementation and cost to sustain it) and what tangible & intangible value is created at the end.
- Interesting business models will evolve in near future where smaller & larger IT services companies will have to evaluate options to partner with industry. Will it be “variable cost model” (cost per transaction automated) or “Composite cost model (fixed cost towards implementation plus cost linked with volume of business transactions)” or totally in – house for large conglomerates?
- It’s not end of the road for skilled resources working on conventional software / systems as they will continue to be in need for the industry. Given base IT systems / products are still needed at the core and are to be kept up to date, skilled force in those verticals will be needed.
- IT services industry will have to ensure steady support of IT professionals in both categories – base IT systems as well as RPA / AI tools. This will make some professionals to up-skill themselves and shifting in new domain with employable skills.
- Industry will have to assess how they will retain such diverse pool of IT talent to support not only the existing complex landscape of different software products but also the new tools RPA / AI. Having additional layer of expensive talent in-house may not be advisable in all scenarios. Finance team will have to play crucial role to help take appropriate decision keeping in mind long term.
- Businesses will have to eventually partner with IT services companies where high-cost talent is sourced from them to optimize utilization of these resources and optimize internal IT function’s budget on talent pool of new technologies.
- Same time, IT services companies will have a quick win to expand their billings to their clients through new skills which is available to be deployed giving rise to one more annuity revenue stream.
- Though RPA currently has major application / demand in banking sector, it will soon be adopted in other sectors too. This will give rise to surge in smaller IT services firms who will offer similar ‘talent-service’ with relative effectiveness. These small / boutique IT services firms will create downward pressure pricing power of large IT services firms. But, businesses will have to gauge their gains by evaluating cost saved in price negotiations vis-à-vis time-bound delivery of service considering desired business outcome through the automation.
RPA drive has undeniable impact on information security as well as privacy. Businesses will have to strengthen layer of governance by augmenting adequate mechanisms and investing in tools to mitigate those business as well as regulatory non-compliance risks.
In nutshell, businesses will have to adopt these changes by adding cost layers which should be funded by efficiencies in business through automations. Companies are looking at this next gen wave of IT automation to not only stay in the game but also be ahead of peers in this competitive environment.
Some food for thought – wider consideration:
RPA’s impact on current BPO sector will be key element and how work force will migrate from current job roles to new upon acquiring new technical skills is a large subject in itself and can be visited some other time.
Given industry has already started thinking & in some way implementing RPA coupled with AI, many less – complex ‘business decisions’ will move to these automated mechanisms which are currently taken by middle management and lower middle management members.
Assuming the solution is perfected in due course of time with relatively better success rate, RPA – AI tech solutions will then be applied to slightly more complex business decisions which currently are made by senior members. So, in near or distant future, will this AI enabled automation drive will leave business decision makers only to define parameters to be set in AI tools for even complex decisions whereby significant portion of business operations are driven in an ‘auto-pilot’ mode?
What will be the impact across regulatory scenario where significant portion of business operations is driven by AI / ML tools?
In case there is break-down of key validation step by ML tools resulting in incorrect AI judgment leading to erosion of significant business value or non-compliance, should we blame robots in future?
These are just some of the thoughts which may cross our mind and there would be many more as we keep diving deeper and feeling the actual impact as its implemented. Indeed interesting time ahead of us!
