By Debjani Chaudhury - April 15, 2020 3 Mins Read
C-suite executives across industries recognize the immediate need to integrate AI capabilities to stay competitive. But, most of them fail to move beyond the concept stage, due to a lack of scalability
One of the biggest reasons for failure is that the C-suite executives are stuck focusing on the less relevant details – building a model to prove a point rather than solving a problem. According to the latest research by Harvard Business Review, three out of every four executives believe that if their company does not scale AI in the next five years, they might even get out of business entirely. The radical solution to this situation is to – kill the proof of concept and focus on scaling.
After surveying 1,500 C-suite executives across 16 industries in 12 different countries – it was found that about 84% realize that they need to scale up AI across their businesses to achieve strategic growth objectives. Also, it was found that only 16% of them have actually gone beyond the experimental stage. And, the companies who have successfully implementing full-scale AI had abandoned proof of concepts in the initial stages itself.
The study revealed that, on average, companies spent $215 million on AI in the last three years – indicating a $115-million gap in terms of missed returns from AI – a 54% difference in RoI. And, the sole focus is not on money as the successful scalers report significant benefits in customer satisfaction and service to workforce productivity.
Replacing the Proof of Concept
The research showed that only one out of five AI applications actually gets to production. These all fall in the low percentage shots, and smart leaders know this. But to skip the POC, successful companies need to follow the below three things:
Pivot to piloting: Every piloted technology includes a fully-baked capability to launch directly into the real world. This allows AI technologists and business leaders to accurately analyze how well the new technology will be received by customers by delivering the promised value. POCs are launched on a comparatively smaller scale, which often makes it difficult to extract the true value from their data.
Commit to action: Businesses are experiencing POC fatigue across different industries. Instead of conducting endless POCs, businesses should consider just limited valuable projects to focus on doing the required research to start production.
Hiring the people with required skills – Having a collaborative team with required skills is crucial. Enterprises’ efforts to scale their AI capabilities are siloed within a single team or department, frequently led by IT. The initiatives that lack the support of a multi-dimensional and a larger team championed by the Data or Analytics Officer, Chief AI, miss out on a crucial connection to business outcomes, and thus they ultimately fail.
To scale value in today’s AI era, enterprises need to think big and start small: prioritizing advanced analytics, ethics, governance, and talent acquisition. Planning plays the most crucial role in keeping the realistic budget and needs into consideration. Enterprises first need to analyze how AI is transforming their industry and the world, and then plan to capitalize on it accordingly.
This is certainly an untrodden path for many, but it is the right time for businesses to lay their grounds to remain competitive going forward. It is crucial not to waste time in proving a concept that already has complete consensus in the market.
Debjani Chaudhury works as an Associate Editor with OnDot Media. In this capacity, she contributes editorial articles for two platforms, focusing on the latest global technology and trends.Debjani is a seasoned Content Developer who comes with 3 years of experience with Fashion, IT, and International Marketing industries. She has represented India in International trade forums like Hannover Messe, Germany.
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