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How to Use an Asset Management System to Automate Key Business Processes?

Asset Management System

Automation evokes polarized opinions from both leaders and employees. For some, it is critical as it ensures business continuity, cost-effectiveness and business efficiency. For others, it may create feelings of insecurity and panic due to the perception of the impending loss of jobs. 

In fact, according to a McKinsey report31% of organizations are worried about labor displacement and job loss stemming from job automation and artificial intelligence (AI). No matter which side of the spectrum businesses lie on, to advance the trifecta of growth–People, Process, and Technology, automation is essential.

Why is Automation Necessary?

Automation refers to a wide range of technologies that aim to lower human intervention in processes without compromising on the quality of the final output. The business benefits of automation are aplenty.

From empowering decision-makers to make quick, informed decisions to accelerating business processes, the benefits cover the entire spectrum of operational efficiency. A few data-backed advantages of using an asset management system to drive end-to-end automation within an enterprise include:

How Can the Firm’s Culture Support Automation?

Establishing a cross-organization cultural shift requires a change in employee and leadership mindset. To start with, there needs to be greater reinforcement of three key messages that redefine automation the right way:

To drive automation success, the idea of automation needs to be effortlessly woven into the cultural DNA of the organization. This can be achieved by:

Building an automation-first company requires organizations to inculcate an automation-first culture and mindset. In simpler words, involving the right people-assets becomes the foundation on which a winning automation program rests.

The Role Talent Plays in Successful Automation

An automation-first company, in essence, should intrinsically build a culture of innovation, creative problem-solving, and productivity. Additionally, it should foster a culture of collaboration and communication. However, automation brings with itself the primary challenge of an ever-widening skills gap–one where the employees do not have the requisite soft and technical skills required to make the most of the automation software. In fact, a recent McKinsey survey found that “addressing potential skills gaps related to automation/digitization” was a Top-10 priority for 66% of participants today.

The proposed solution lies in leveraging the right people–and empowering them with the right knowledge–to augment the automation initiatives instead of viewing automation as a replacement to human talent, skills, and knowledge.

Businesses can address the automation-skills gap by:

Long story short, there’s a growing need for talented, automation-savvy workers if businesses wish to extract the maximum potential of automation processes and systems, and thrive in a competitive landscape.

How can data be stored in an automation-friendly way?

The final leg of adapting your organization to the automation journey is understanding:

  1. How can data be stored in an automation-friendly way?
  2. Why should the cloud be used to store data automatically via third-party processes?

Around 52% of business leaders believe they can automate 10-30% of their daily workload. This means that tremendous amounts of data get generated as the business processes get automated. Storing said data in a physical space can create issues of costs, lack of infrastructural bandwidth, etc. This is where cloud data storage comes into play.

Here’s how to develop a data automation strategy:

Step 1: Develop a well-conceived data automation strategy.

Before zeroing in on a robust cloud tool, focus on developing a data automation strategy by asking the following questions:

Automation-related questions to ask:

Data-related questions to ask:

As a best practice, set clear goals and realistic expectations for the automation process and ensure that the cross-functional teams are always involved in the planning process. Other important points to keep in mind include:

Step 2: Think about investing in a robust, enterprise-grade, and low-code cloud storage tool.

Once you have a comprehensive data automation plan in place, it is time to consider a relevant cloud storage solution–one that is dependable, well-supported, and user-friendly. Also consider whether the solution will be relevant in the long term and whether it has the flexibility to grow as your organization’s needs change.

It bodes well for an organization to invest in a cloud-based data storage tool. Here’s why:

Step 3: Outline the measures for data transformation with respect to time and resources needed, and engage in old-fashioned project management to maintain transparency and automate sustainably.

The next logical step in the data automation journey is outlining the necessary steps for data transformation basis the functional challenges and clearly-identified gaps within the tech stack and the specific business needs. Also think about creating a dedicated team of automation ‘champions’ who can lead the charge, prioritize sub-projects/tasks as needed, and ensure that the data automation project stays on track.

Step 4: Verify for success, test as often as needed, and calculate the ROI of automation.

Once the process is underway and the data is stored, make sure to verify that the dataset was successfully loaded, devoid of any roadblocks throughout the procedure. Ideally, aim to iterate, test, and develop once an automated procedure has been prototyped. As a thumb rule, schedule the dataset to be updated on a regular basis and keep tracking the time versus cost progress to gauge what’s working (and what’s not). Aim to prioritize processes that save time and can offer higher ROIs for best results.

Creating an automation-friendly firm starts with establishing an automation-friendly culture, keeping the employees at the forefront of the change. An automation-first firm can benefit from increased productivity, reduced operational gaps in the tech stack, increased revenue growth, and scalable as well as sustainable customer retention.

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