Artificial Intelligence (AI) has become a hot industry buzzword. Companies like Facebook, Amazon, and NVIDIA are investing fiercely- poaching researchers, setting up laboratories, and buying startups in an attempt to lead this AI revolution. However, the field is still shrouded in uncertainty. According to a report by BCG and MIT Solan Management Review, only about one in five companies has integrated AI into some processes. The research also reveals significant gaps between leaders—companies that understand and have adopted AI—and laggards in every industry.
The landscape of AI is dense, and it can be a difficult task to select a product that works for your organization from the products that offer empty promises. Before you invest in the technology, ask the following questions to establish competitive advantage and shape value creation from AI:
There is a massive expectation from AI across geographies, industries, companies, and size. While 85% of the executives believe AI will allow their companies to obtain or sustain competitive advantage, only half of the organizations are said to have an AI strategy at the place. There is a huge gap that exists between the ambition and execution level of AI at the corporations. Aadil Bandukwala, Evangelist, Belong.co – an outbound recruiting firm that uses using Artificial Intelligence (AI) and Machine Learning to curate data from social media activity, and from platforms such as GitHub, ResearchGate, etc. shares,” Artificial intelligence is one of the most exciting and transformative opportunities of our time. While it’s important to invest in the right AI and Machine Learning technologies, enterprises must do so by asking themselves what precise business objective will these technologies help them achieve.” He further says, “Beyond the obvious business metrics, enterprises should try and understand how AI and machine learning can help disrupt traditional recruiting practices and potentially unlock newer ways of scaling personalization to increase funnel conversion and reduce time to hire managers to spend on interviewing people by boosting the quality and throughput of the recruiting funnel.”
Understanding of data in AI context
Aadil says, “If firms are ready to deploy AI solutions then they need to understand few data related things. These typically involve mining large streams of data - understanding whether data is available in sequences desired - for instance are your processes and applicant tracking systems ready to integrate with AI solutions, what's the objective of deploying the solution, the immediate impact desired as well as the potential long-term vision of deploying AI to impact specific and measurable people goals.”
Business and Cultural challenges:
The challenges of adopting AI goes beyond data skills and mastery. Before you even strategize your organization roadmap, you first need to embrace the management challenges- mindset change, alignment with business, leadership buy-in, cost-effectiveness, etc. One way of looking solving those challenges would be to understand your business requirements. Aadil says, “Enterprises must ask themselves what precise business objective these technologies will help them achieve. E.g., if we specifically talk about leveraging AI for recruitment, How effectively and efficiently, via predictive analytics or automation of processes, can enterprises unlock business value by creating revenue opportunities, or cost savings at scale? Can AI and ML help companies move away from traditional recruiting metrics like Time to Hire and Cost Per Hire and move towards Time to Value and Revenue Per Employee Contribution?”