Today, the GenAI frenzy has seemingly calmed at least marginally. Many companies are still facing the same questions they were a year ago: How can they take advantage of the promised cost savings and substantial efficiency gains that GenAI allegedly offers? How do they actually go about putting it into their business and functional use cases?

Many companies are struggling. There are a few reasons for this.

First, many businesses, large and small, are still grappling with how to integrate traditional AI such as rule-based algorithm and machine learning into their operations. At best, they are in an exploratory phase with traditional AL, and at worst they’re simply feeling lost.

Second, GenAI is far more complex and is geared to serve specific purposes. While it is able to write a 5,000-word report in no time, it cannot, for example, do a basic data entry task, like extracting and classifying regulatory or clinical data, that traditional AI can do easily.

Third, the longer-term implications of adopting GenAI such as the long-term costs and the impacts of current and future regulation are still uncertain.

Here’s how companies can get their bearings and figure out what to do next.

Key Considerations to Take Advantage of GenAI

Given this current state of affairs, how could businesses onboard GenAI? Here, we would like to offer a few suggestions:

Choose performance over novelty.

In our long experience working with GenAI, its performance doesn’t stem from human-like text responses in a conversational manner or a model that is trained on a vast amount of data. To get the best out of GenAI, you must ask whether it’s the right technology for a particular task or goal.

In other words, instead of unquestioningly embracing the latest AI technology, companies must understand the business problems that they are trying to solve and find the most suitable AI tool based on both the strengths and weaknesses of each of available options.

Combine GenAI with the power of vector database.

This is a new form of database that specializes in retrieving the closest matching records to best answer specific queries (as opposed to traditional databases that merely hold the records). Companies can use an GenAI such as ChatGPT to break down users’ queries, and then use a vector database to look for the best answers that match those parameters.

Put differently, GenAI by itself may not be sufficient. Depending on the problems to be tackled, it can only be half of the technology solution. The need for vector database to make GenAI truly useful means companies should expect to face even more complexity and long lead time when putting the solution together.

Never forget human-in-the-loop.

As ever, no matter how powerful AI technologies seem to be, their abilities are only as good as how much humans are involved. This is no different for GenAI. Humans play a critical role in guiding GenAI toward business goals, managing interactions within IT systems, designing the actions required for data going to and coming out of AI models as well as mitigating hallucinations the made-up or outright false information produced by GenAI that remains a major problem of GenAI today.

Have realistic expectations.

GenAI is a fast-traveling ship with a lot happening below deck. It is hard to know exactly what, how much, and how quick GenAI companies can realistically achieve. Believing with conviction that it can yield immediate results and outstanding financial returns will most likely lead to disappointments. Leaders must recognize that the exploratory and experimental journey of GenAI will likely be a long one.

The utilization of GenAI technologies in business operations transcends a mere technological investment; it’s fundamentally a business imperative. Hard as it is as an undertaking, to onboard GenAI in company operations is to understand the nuances of the current GenAI developments and have a keen awareness of the challenges presented. Yet, for those businesses that can successfully make use of GenAI to reach their business goals, the rewards can only be both promising and huge.

Cost savings occur in two general areas:

1. Reduced effort during content creation on the client’s part
2. Significantly reduced billable time from translation companies
3. Reduction of Review and QC cycles

General benefits of REGai for cost-effective localization and translation

For decades, many enterprises have used “flat” document structure in products like Microsoft Word. When content is converted to digital structure, it is possible to assemble “documents” from sub-document structure.

The following are widely recognized benefits of digital structuring for content that is translated and localized:

• Separation of content from formatting styles, fonts, etc., either come from external style sheets or a component of the publishing solution separate from the document content
• Country-specific publishing
• Consistent document structure the number of editing and formatting decisions made by teams are dramatically reduced when digital structure is used
• Content reuse linking and effective usage of content repository

Basically, the author “writes once” and “uses many times.” If one instance of a Caution or Warning is linked 200 times in a product, it only needs to be edited or translated once, and all instances are instantly corrected.

Although conversion to digital structure in and of itself will eventually lead to time and cost savings in localization and translations, the savings can be more dramatic when REGai is used as a solution. In short, REGai accommodates for the “human factor” of your Resources/Teams and key stakeholders who may have minimal time to come up to speed on a new solution.

In today’s demanding Labeling work stream, we are faced with more types of content to publish in more combinations for delivery to more countries. With that challenge, REGai will be highly appreciated by your team for “hiding” unnecessary complexity when you just need to concentrate on creating good, solid content.

Remote Monitoring

To conduct the ongoing clinical trials remotely, monitoring and management of data and document is necessary. There is no fixed method that can be implemented and hence files are generally shared in the form of email, fax or such other cloud-based tools. This leads to compliance risk issues if not kept under proper surveillance. Such file sharing systems are generally used as they are low in cost but they fail to be compliant with the authority regulations. Moreover, fax and mailing services require huge manual work and provide greater scope of error in updating the retrieved data. In areas of clinical trial such minute errors too can have severe impacts.

Technology Driven Automation

Automation is of great help in current situation as it allows collaboration and a seamless exchange and upgradation of data. Access to Electronic Medical Record (EMR) tends to be of great help in addition to SaaS based tools. These being cloud based, perform the tasks automatically and hence there is less of manual task and lesser scope for errors. This advanced technology comes with a central repository for the storage of bulk data which gets analysed regularly and in real time, thereby easing out much of the task.

Technological transformations especially in the field of clinical trials have already started. Automation has provided greater control, access to vast data, accountability, lesser compliance risks and scope for continuous communication. This allows in performing remote clinical trials with added visibility and helps more people to come forward and adopt the change.