Top 5 business benefits of early AI-analytics adopters

AI can now be pre-trained for the life sciences

Pre-trained AI for life sciences opens endless possibilities where the application readily understands life sciences data sources, business terminologies, and key metrics. Out-of-the-box models allow for a seamless integration with enterprise applications and are easily deployed within weeks, and don’t demand any major investments in change management from the business or IT. Models that are pre-trained ensure one-sixth the time frame for deployment compared to legacy solutions or generic AI solutions.

Additionally, pre-trained AI requires no learning curve and allows commercial teams to simply start asking questions in natural language and get actionable insights on the fly.

Scalability to billions of data points

Counting on business users to extract insights from hundreds of reports and dashboards compromises the efforts in actually performing their core job. Cognitive insights platforms that are built ground up for life sciences have anticipated the data volumes and complexities and also accounted for the exponential growth.

Such platforms scale to billions of records and deliver a sub-second response to complex questions and calculations touching each data point necessary. This leap in processing power also reflects real-time updates to easily surface performance drivers and drill into any level of granularity with a single click. This volume of scalability allows enterprises to maintain a ‘single version of truth’ and put intelligence directly in the hands of business and does not limit the users by the amount of data that can be processed.

100% user adoption for enhanced agility and productivity

Central to any business intelligence (BI) platform is quick and easy access to insights. That is what BI ideally set out to achieve, but to date still struggles to deliver. Business intelligence is by design meant to serve the workforce making decisions on the frontline. Yet today these legacy systems are so complicated that business users are still dependent on data experts and IT for gathering key insights.

Cognitive insights platforms are fast, easy and contextual—and result in increased user adoption across the board. The natural language AI-trained on life sciences data already understands relevant business terminologies like NRx, TRx, Payer, Plans, Prescribers, TA and much more. This allows users to simply leverage voice or text to ask questions in the same way they would ask a colleague and get answers in seconds. There is no need to sift through complex dashboards anymore; get direct answers that allow for quick insight-driven decisions. This eliminates the need for any software training as cognitive insights platforms are as simple as using your favorite app.

Additionally, visualization AI readily delivers dashboards and reports in the best representation and needs no manual intervention. Contrast that with legacy BI products: after days of training, you still need to remember how to click eleven times in order to build a dashboard and then decide you are able to gather relevant insights. Lastly, pre-trained, out-of-the-box models allow easy configurations to be omnipresent across web, mobile, collaboration platforms, CRM and SMS. Teams in the field or office quickly make cognitive insights platforms, their go-to-solution to access insights anytime, anywhere.

Proactive contextual insights delivered when it matters most

Finding the most relevant insights from enterprise data is often a never-ending exercise of trying to find a needle buried deep in a haystack. It is not practical for a human to ask all relevant questions, let alone instantly identifying where business is gaining or losing and why. Now imagine if a cognitive insights platform that accesses billions of data points allows users to simply ask questions, instantly receive actionable insights, spot hidden trends and anomalies, and proactively push relevant and contextual insights in seconds.

What is humanly impossible to accomplish, e.g., instantaneously determining which healthcare providers are leading in new or total prescriptions at ZIP code level, is routine for AI. Furthermore, machine learning algorithms continue to learn users’ preferences and save the context that matters most, such as proactively pin-pointing stagnating sales, market share changes, patient drop-offs and much more. This capability is not just restricted to surfacing discrepancies in the key performance indicators but also delivering the recommendations on the next-best action. In addition, it captures real-time updates and sends alerts when pivotal changes occur in the market that the user may not even be aware of.

Lower TCO and Higher ROI

There are clear indications that life science companies benefit from cognitive insights platforms, but business users often wait months for next-gen BI products. This is due to more than long deployment cycles—it is also associated with a huge implementation cost—but arguably even greater opportunity cost as business users wait for the insights infrastructure.

Cognitive insights platforms with pre-trained AI for life sciences work right out of the box, with minimal implementation headaches. These solutions integrate with enterprise applications and put data to work right away. Additionally, a no-code environment reduces dependence on expensive programming resources and increases overall data utilization. The cost associated with training is zero to minimal and the commercial teams can leverage the platform from day one. The extensive dependency on IT is also reduced by 40% which frees these resources to focus on the core imperatives rather than attend to ad hoc requests from commercial teams.

It is also important to recognize that as legacy BI began to mature, data volumes, data complexity and emerging technologies, including AI, ML and NLP, had just begun to evolve. Legacy BI could not accommodate these advancements—and life sciences companies could not continue to justify additional investments into legacy systems with little return. Cognitive insights platforms, on the other hand, leverage advanced technologies and take increasing data volumes and complexity into account, delivering necessary insights instantly, without continual investment.

Due to their history with legacy BI, life sciences company leadership carefully evaluates ROI of any new investment. In addition to scalable solutions, life sciences execs are looking for solutions that deliver insights with ease and speed and that can keep up with market shifts on a daily basis. Cognitive insights platforms meet this requirement, giving users direct access to the information to inform decision making and to capitalize on opportunities when they are ripe.

Additionally, with AI taking the role of a personal analyst, business users can identify threats early and intervene while accounts and the market still allow. This new capability can also deliver return in the form of employees who are more motivated, productive and successful.

In the end, life sciences leadership finds that ROI from a cognitive insights platform is multifold, delivering informed decision making, a reduction in errors, early threat detection, and identification of opportunities to increase market share, as well as an enhanced ability to serve patients with timely, effective treatments.

_{About the Author}

_{Rohit Vashisht is the CEO and Co-Founder of WhizAI​}