By Emmanuel Walckenaer, CEO of Yseop
Widespread acceptance of artificial intelligence is contingent upon presenting data in a way that empowers businesses and employees to trust it. We must leverage the best of AI and natural language processing without losing the human touch only our employees can provide.
Let’s face it: we’re drowning in data. Organizations across industries are seeing the volume of data growing exponentially, and the connectivity of that data is increasing so rapidly we simply cannot keep up. The issue isn’t how to store data, we’ve figured that out. And teams of highly skilled data scientists have been trained to analyze and process it. The problem many organizations face today is how to interpret large amounts of data to make it truly valuable.
This blend of human expertise working in harmony with technology to drive better outcomes is what’s referred to as the augmented workforce. This type of AI has long promised more efficient employees, consistency of output, increased volume of work, and a reduction of human error. In a 2021 survey of C-level and senior management professionals, 66% said they expect the rise of the augmented workforce to be the most disruptive technological trend in business this year and beyond. Even still, while the augmented workforce has received a lot of hype, it suffers from acceptability and explainability at its core. Many employees simply cannot accept what they don’t understand.
Making data accessible, acceptable, and explainable.
As the amount of data within an organization grows, so does the need to analyze and ultimately, use it. Think of data like a second language, only some have the knowledge and skillset to understand it. Now, imagine translating data into a language that everyone can understand. Suddenly, you’ve unlocked exponentially more value from the data that powers your business.
The key to seeing the rise of the augmented workforce is accessibility and acceptability. Understanding data cannot be limited to a team of highly trained data scientists. Insights must be unlocked across an organization. Back to the example of language, when you have a foreign language translated into your native language, suddenly understanding is tangible. Another barrier to AI is explainability. Many users cannot trust insights without fully understanding the process by which they were obtained. Clearly being able to explain the analysis and reasoning behind a recommendation is key to broad organizational success.
Accepting AI through natural language generation
The missing piece of many augmented workforces is natural language processing (NLP), and more specifically, natural language generation (NLG). NLG is software that writes like a human being from structured data, generating a high-quality narrative at rapid speed. Data that was once only accessible to data scientists through graphs or spreadsheets is translated with NLG software into a universal language that your entire organization understands, thus unlocking the full value of data and its insights. Applying NLG to structured data in order to create written narratives reduces the burden on professionals across the world by equipping them with the tools to create reports automatically. Not only does this save time, but automated teams are also proven to be more agile and accurate, which is a game-changer for a company’s staff.
While the benefits are hard to ignore, a common misconception in the discussion around AI is the myth that by implementing an augmented workforce, organizations are just trying to reduce overhead costs. In reality, organizations are reinforcing their workforce with a virtual team member to support them through periods of intense work such as financial controllers needing to close out a quarter or a team of medical writers under pressure to bring a new vaccine to market. Businesses aren’t repurposing people, rather they are reengineering their processes to fully leverage today’s technology with reliable virtual team members that essentially learn over time. Augmenting technology is helping financial institutions and pharmaceutical companies unlock benefits such as:
- Empowering analysts to focus on more strategic work by arming them with more insights to make better decisions.
- Improving accuracy and removing the risk of manual human errors in reporting ensures a high-quality standard across an organization.
- Increasing efficiency by streamlining processes and turning out high volumes of reports at a much faster pace.
Given these advantages and the immediate need to turn data into more powerful insights, organizations are starting to deploy the NLG technology to automate report and results writing. It’s no surprise that 98% of organizational leaders said that in order for AI to benefit the greater workforce, all employees must be able to communicate and learn from data through language. Thus, AI becomes the only viable way to translate interpretable data into a common language for large organizations.
If you have any questions about any of the concepts mentioned above, get in contact with Yseop here. We are happy to set up a time to discuss in more detail.
