Why Automated Data Entry Still Needs a Human Touch to Ensure Accuracy
This is the age of automation and businesses are investing
hugely in automating data entry as it can speed up the process of data entry.
It is believed that by leveraging the power of AI, you can increase workplace
efficiency, reduce business costs and experience other additional benefits. We
are often made to think that machines can outsmart people and this will leave
many people out of jobs. However, it is important to realize that the AI that
we use is weak AI or niche AI, which is not strong enough to create sentient
robots. Moreover, AI systems have no intrinsic needs and without humans they
have no intrinsic purpose and goals. Therefore, it is not possible to eliminate
human assistance and completely automate. As a company providing data entry services to diverse
customers, we know that a collaboration between machines and humans is
necessary to maximize productivity, innovation, efficiency, and increase
profits.
This collaboration can be made successful by defining
clear roles, expectations and rules-of-engagement for everyone involved. So,
here is how it is done:
- Digitization of data: The first step is to find out what both parties can offer. Businesses are spending on digitization ($2.3. trillion a year) as it is more efficient and faster to conduct business operations. Huge volumes of data flow into the organization and if all these datasets are collected, aggregated and analyzed, you can draw valuable data insights that are required to make the right business decisions. When it comes to machines, they will scour through these data, process and analyze it and then tell you whatever their rules say they should tell you. It creates massive volumes of information, but is waiting for a user to come to pull it out from a dashboard or a report. However with AI, they can create models for identifying and making recommendations on existing, and even new, business issues like evolving market trends, supply chain optimization, hidden sales opportunity and so on. Machine are great at pulling data from ERP and CRM and they can find out the patterns and trends easily. But there are things that humans can do better. This is because humans are instinctual and make connections intuitively with experience and sense which machines lack. Machines fail to identify objects or animals, and they can only make decisions depending upon a set of data; and if there is any inaccuracy in the data, then the results become useless.
- Taking action based on the data: The related disadvantages discourage people from trusting machines and their decisions completely. Although machines can deal with transactional activities with the help of AI, it is important to have human assistance to cross check and ensure that all data and insights are completely reliable. It is important to realize that, just as we need the machines, the machines also need us. So, it is vital to have the right set of people who can work with the machines and also put together decisions. It is also important to have a cognitive automation platform, to keep interactions clean, fluid and on track and also serve as a bridge between digital systems and their human counterparts.
- Learning from decisions: Both machines and humans learn from experimentation and explanation. But compared to machines, people learn very quickly. Machines need a healthy feedback loop, if they are to learn. This loop involves two types of information, namely, responses from people to system prompts that ask them to accept, ignore, reject, or override a recommendation; and data collected by the computer about the results of decisions made by it or human counterparts as regards expected goals, scenarios, metrics, or outcomes. Getting feedback in the early stage of a machine’s development helps both machines and humans to shape how they make decisions.
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