Adopt AI/ML at scale and lead with insightful analytics
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After many false starts, the AI/ML engines are running on all cylinders and driving unprecedented business growth. One need not look beyond the top five high-tech leaders who use AI/ML at scale. Amazon, Apple, Google, Microsoft and Facebook have nearly doubled their market capitalization to $8 trillion in the past year. Even more impressive is that they’re growing fast while increasing their business profits. For example, Amazon’s profits of the 2021 first quarter were equivalent to the total profits of the past three years.1
The market leaders have two things in common 1) they use AI/ML at scale and 2) capitalize on the rise in digital adoption. Your business can ride the waves of success too.
Feed the digital adoption beast
There is no denying the massive rise in digital adoption. “Over a year into the pandemic, digital transformation curves aren’t slowing down. They’re accelerating,” said Microsoft’s CEO.2
Success in the coming years requires business strategies that revolve around the data-driven digital economy. We are in the midst of transformation but are past the tipping point and the time to put your business on the growth path is now. A recent global survey found that 50% of companies have adopted AI/ML in one or more business functions.3
Isn’t AI dead?
Treating AI as a science experiment is dead, but AI/ML are valuable business growth tools that drive unprecedented business value. About 28% of AI/ML initiatives failed due to a lack of staff with the necessary expertise, lack of production-ready data, and lack of integrated development environments.4
To better understand the potential for AI/ML adoption, one needs to remember that AI and the cloud are two faces of the same coin. They support each other and create demand for one another. The cloud’s massive growth has enabled practical applications of AI/ML and provided the data crunching muscle and the scale needed for efficiency. The drop in the cost of data storage and processing by about 10 million times since 1980 made AI/ML adoption economically viable.5
Succeeding with AI/ML
The organizations that successfully transform their data analytics with AI/ML are disciplined practitioners and follow known and proven operating models. We will look at the main elements of a successful AI/ML adoption: AI/ML at scale, data architecture, integrating humans and machines and rethinking risks.
AI/ML at scale.
The cornerstone of success is developing an AI/ML strategy across the organization followed by a disciplined agile implementation using MLOps similar to DevOps culture and processes. The support of senior management is as critical for success as having teams with the right skills. Having a scale-oriented mindset is a vital part of AI/ML success that maximizes cumulative gains.
Good data architecture.
AI and ML performance and value are dependent on the quality of the analyzed data. Companies need to establish a robust information architecture that supports their business goals. An example of the data value and limitations of ML came to light at the start of the pandemic. High-frequency trading models filed to provide meaningful analysis due to lack of historical data that resembles the situation at hand then. There is no AI without IA.
Human and machine integration.
This aspect is more about ensuring that people are familiar with what AI/ML solutions offer and utilize the new capabilities. For example, a company might improve a new set of manufacturing processes but fails to train employees adequately or secures buy-in from operators. The natural result is that the new procedures go unused, and the efforts go to waste. The teams expected to benefit from AI/ML introduction should be part of the development and deployment processes from the start. Siloed efforts court failure.
Rethinking risks.
Adopting AI/ML at scale could result in magnifying some risks. Successful implementation takes a holistic view of risks and strives to minimize them by design. Awareness of potential risks might be tricky, as in the cases of bias in the AI models. Teams building AI/ML models need to carefully evaluate the potential for accidental outcomes based on the nature of the data. Good data and information architectures lead to more efficient AI/ML models.
Business benefits of AI/ML
The business value of AI/ML adoption touches many aspects of operations, including:
Superior customer experience.
Early adopters realized almost 25% improvement in customer experience.6 AI/ML models are mature enough to learn in real-time and offer contextual solutions
Faster time to market.
With AI/ML, companies can enhance business insight, accelerate detection of potential deficiencies, and shape new products and services targeted at emerging customers’ needs.
Better competitiveness.
AI/ML models help enterprises work more efficiently, with shorter innovation cycles, lower costs and higher profit margins. Understanding customers’ and markets’ sentiment enable companies to get ahead of the competition with new desired offerings and services.
Shift into high gear with AI/ML
AI/ML adoption at scale is driving unprecedented growth and profits in early adopters’ organizations. Join the march to more sustainable business growth by building an agile and competitive enterprise with AI/ML to compete and thrive in the data economy.
- 1,2. WSJ, May 1, 2021. “Five Tech Giants Just Keep Growing.”
- 3. McKinsey Digital November 2020. “Global survey: The state of AI in 2020.”
- 4,6. IDC June 2020. “IDC Survey Finds AI Adoption Being driven by Improved Customer Experience, Greater Employee Efficiency, and Accelerated Innovations.”
- 5. McKinsey and Company 2021. “AI at scale: Propelling your organization into the next normal.”
