Scanning the signals
As machine learning (ML) and artificial intelligence (AI) gain more industry adoption, they are enhancing — rather than replacing — human talent by automating data processing tasks and freeing people up to use their experience, creativity, and intuition. These systems enhance productivity in two main ways: making predictions to assist humans in making decisions, and automating decision-making completely.
The trend towards autonomous, machine-made decisions can have a significant impact on our lives and needs to be considered from an ethical perspective. This is driving research and industry interest in explainable AI (XAI) and stronger AI governance processes.
- Burgeoning investment in AI research and applications. Bloomberg estimates civilian spending on AI in the United States grew 22% in fiscal 2019, while government spending grew almost 70%
- Massive demand for ML, AI, and data specialists in the job market. According to LinkedIn AI specialist was the fastest-growing job category in 2020
- Growth in ML/AI start-ups, specialized products, and acquisitions. As of this writing Angel.co lists 5,711 companies and 2,790 investors in the ‘machine learning startups’ category
- Existing jobs and roles changing. Rote tasks are being automated, human workers pairing with machine counterparts, and people freed to use their experience and intuition to provide value. For example, Amazon announced that it will spend $700 million to help about 100,000 workers in the US move into more highly skilled jobs by 2025
The rapid advance of AI and ML-based tools will benefit businesses on two main fronts. For workforces, automating repetitive and mundane tasks will enhance productivity, leading to gains in efficiency and output. Employees will also be freer to focus on higher-value activities that require more human creativity and ingenuity, such as developing the next product or service innovation. This will have positive consequences for employee morale and overall business performance.
Applying AI and ML to reduce inconsistencies and the probability of human error, and reduce turnaround times in delivering products or services, could significantly enhance customer satisfaction and ultimately retention. Companies can also draw on data-based solutions to learn more about and accurately anticipate customer needs, though caution needs to be exercised to ensure this is done in a way that respects privacy and security.