8 Effective Approaches to Intelligent Automation Based on Operations Outcomes


8 Effective Approaches to Intelligent Automation Based on Operations Outcomes

Financial services companies are more inclined towards intelligent automation nowadays. Robotic process automation has set the bar real high. Howev

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Financial services companies are more inclined towards intelligent automation nowadays. Robotic process automation has set the bar real high.

However, Artificial Intelligence is driving a lot of interest for companies to look through the potential and the value of them. Organizations can detect the value i.e. the enhanced revenue, customer service and its improvement and last but not the least risk management.

Here are 8 effective approaches a company or an organization can adopt based on operation outcomes to enhance their business.

1. Take up a Joined-up opportunity assessment

There is plenty of evidence pointing out how automation technology can deliver substantial business advantages across a wide range of companies including those with the most out-of-date IT systems.

Nevertheless, performing a proper opportunity assessment is necessary as it unveils the optimum portfolio of processes.

Many companies target RPA right off the bat at a highly complex process which turns out to be a mistake. This is a huge responsibility that turns into a burden because the cost of the automation goes through the roof but on the other hand, the business drives very low ROI.

Companies can implement the efforts in a different way. For example, prioritizing less complex processes and automating them to drive more profit.

As a company do not fixate only on robotic process automation but also focus on digital and AI carry out a rapid opportunity assessment company-wide or unit-wide. Non-financial services such as compliance improvement or customer services are beneficial to go for alongside LEAN, system advancements, Six Sigma and BPR.

2. Employ Cloud Technology to support AI tool selection

You can find tons of tools available in the automation space and it is very difficult for organizations and businesses to identify which is more beneficial for their business.

When it comes to the selection of this kind, cloud-based services such as artificial intelligence or AI, digital and RPA, etc. are of great help.

These technologies help companies quickly to figure out whether a specific tool will suit their needs without fueling the upfront cost. When companies implement cloud-based services in their business, the deploy ability of the AI technology also improves.

3. Lab to live – Programme your automation pathway carefully

The development of machine learning is also complex and you need thorough processing throughout the process. Companies need to test them in live operation and follow careful planning.

Different tools operate differently though made for the same purpose which is why the process will also vary for each tool and its use case.

Developers learn to do this properly from their previous experience in RPA projects where the primary components include developing infrastructure, setting up control and governance while developing required skills.

However, there is one frustrating challenge with AI projects which is each AI project may involve multiple tools. The “lab to live” challenges are including new approaches quite often.

Here’s an example companies can now test distributed algorithms and test its operability on other devices such as computers and mobile.

4. Review your selected operating model

AI executions, like RPA, can reap the benefits of founding an operating model that involves a CoE or centre of intelligence.

A virtual workforce will thrive with a business-led CoE that is able to manage and enhance the workforce augmented by reading key skills such as risk, compliance and IT – in this case, it is best to migrate towards a CoE that comes under integrated automation.

This way you don’t need to develop different CoEs for every automation technology or technique. Companies need to conduct an integrated opportunity assessment to detect the benefits.

This’ll help design solutions across different automation technologies. If you are willing to alter and improve a virtual workforce especially if the workforce supplements read key skills, then business-led CoE is your best option.

5. Construct a talent plan

In the digital market of business, you’ll see an ever-growing demand for skilled talent in the area of RPA or the AI. The demand for skilled talent in the market of automation is extremely high but unfortunately the supply is below average.

Training a potential employee for a day or two won’t help you. On the contrary it’ll provide you low-quality product for your business.

Since the supply is low, hiring your way out seems unlikely as well. So, you need a combination of external assistance, organic growth and strategic hires. Organizations need to work on the foundation of getting at least 2 weeks of classroom training and after that, at least 2 to 3 months of hands-on project delivery.

The methods, under the supervision and coaching, will help develop analysts who will have the ability to deliver production-quality automation.

6. Operations control room is a necessity

Since the advancement of RPA technology, companies have understood the significance of implementing a control room to supervise performance. Businesses should monitor as well as manage NLP, OCR and similar adapters if they are the core of the processes.

A lot of errors and hand-backs to the customers would designate potential issues which require addressing. With the implementation of AI, the need for a control room arises as companies need to ensure the output of high-quality products. Other than this, additional supervision is also necessary.

If you put it practically, the possibility of partial influence in algorithms are most definitely what companies look out for because it can skew outcomes. This is the reason why, similar to CoE, companies endorse an integrated approach to the control room here.

7. Observe Impact

There are multiple benefits to implementing automation projects. The risks become less as the services improves and becomes more predictable while delivering on time without compromising the product quality. It also offers enhances capacity, decreased headcount and cost-efficiency.

Companies can identify desired advantages and effects when they perform the opportunity assessment at the beginning of the project. However, businesses should supervise the delivery of such performances.

This step is absolutely necessary to ensure that the investment in the automation project continues. To sum it up, au automation project will provide the mapped-out benefits to maintain the rollout.

8. Take on Risk and manufacture Controls earlier

Think of risk and control management as a priority when you are doing any kind of business. You need to address them early when you have the chance in the process of automation. Actively incorporate the 1st, 2nd and 3rd line from the moment you detect opportunities.

A sturdy focus on controls and risks is also necessary as worldwide there is a lot of attention on the regulatory and ethical issues brought up in the front by AI and robotics.


Digital and AI combined with RPA to generate IA or Intelligent Automation is potentially capable of transforming existing operations and even across legacy states enabling them to deliver end-to-end process transformation.

The potential of AI is enormous, no doubt about it, however, the actual implications for its delivery in the present day indicate its requirement for a well-targeted and prioritized plan.

Businesses need to get a clear picture of both the business processes and the ways of transformation so that they can utilize them to attain significant ROI from the AI processes.

Use these approaches to help you automate the most common tasks in your organization and see the immediate and long-term benefits.