3 Essential Steps to Exploit the Full Power of AI

Last year was difficult for individuals, companies, and governments — the pandemic and the resulting economic downturn have impacted all of us. If there is one silver lining in this gloomy review of 2020, it is the rise in the use of digital, analytics, and AI. PwC’s survey of over 1,000 US executives, conducted during November last year, revealed that a quarter of the companies reported widespread adoption of AI, an increase of over 7% in a difficult year from 2019. This was also reflected in a record breaking $71.9 billion global VC investment in AI in Q3 2020.

Although 52% of the companies had accelerated their adoption of AI due to the pandemic, nearly 25% of them had fully embraced AI and were reaping better ROI than their peers. These AI leaders had adopted three key practices that set them apart from their competitors:

1. Focus on strategic AI initiatives

Before the pandemic, the primary AI use cases for companies were focused on increasing productivity and reducing costs. While these were still important, the survey results show that all companies – especially the AI leaders – reaped more value The sharp increases or decreases in demand during the early days of the pandemic and the subsequent uncertainty around the COVID-19 virus and economic consequences meant that companies needed better ways of estimating customer demand, improving the experience for online purchases, managing the suppliers and production of goods. AI leaders resorted to scenario planning, simulation and the strategic use of AI to address these challenges. Workforce planning (58% had made significant investments in this area), simulation planning (48%), supply chain resilience (48%), scenario planning (43%) and demand projection (42%) were the key strategic areas for investments in AI.

Companies that have been using advanced analytics and/or automation should expand their portfolio to include the strategic use of AI, including increased investments to drive more effective decision-making. Others that are experimenting with analytics and AI need to accelerate adoption — first focusing on productivity improvements to build momentum and then quickly migrating to strategic AI initiatives.

2. Deploy AI/ML models in production

Companies that have exploited AI have also moved from a standalone experimental use of analytics/AI models to a model factory approach in which the AI models have been integrated into the broader application and technology infrastructure of the company. In addition to continuous integration and continuous delivery (CI/CD) of software modules, these companies also have continuous learning (CL) AI/ML modules embedded into the software. CI/CD Machine Learning (ML) engineering, ML operations, data and security operations DataSecOps have become sought after skills in enterprise technology. Nearly 36% of company respondents are moving their AI/ML models from an experimental to an operational delivery platform. The integrated delivery model is critical here as the skills for this area must be sourced across multiple disciplines: IT, automation, data science, and operations.

During the pandemic, companies must react rapidly to changing and uncertain customer demand, workforce availability and supply chain disruptions that required companies to accelerate the model deployment lifecycle and develop AI/ML models that performed continuous learning. The shift to operational or production models also meant that companies had to mitigate the risks. Nearly 72% of the companies already have company-wide governance and oversight of AI or have taken steps to achieve this oversight. In addition, 70% of the companies have addressed all ML governance issues or have taken steps to address these issues. A similar number addressed data governance (67%).

Companies that are primarily experimenting with AI models within small groups need to scale their models for production. They need to acquire or grow the right talent — ML engineers and ML operations — and collaborate with the IT organization to have AI tools and techniques embedded within the IT stack.

3. Adopt an integrated AI delivery model

A key to generating a good ROI is in executing data, automation, analytics and AI initiatives. Close to 23% of respondents have already set up or are in the process of setting up an AI Center of Excellence that shares and coordinates resources across different areas of the company. This number has risen from 18% just a year back. Also, nearly 19% of companies have a company-wide AI leader who oversees AI strategy and governance. The reason why such an integrated delivery model makes sense is the convergence of the cloud infrastructure that provides the storage and compute, the data that is the raw material for the analysis, the automation that operates on the technology infrastructure, the analytics that operates on the data to generate better insights, and the AI that enhances both the automation and the analytics resulted in decreased costs and better revenues. In large (greater than $1 billion revenues) companies the existing data and analytics group have expanded their remit to include AI.

Companies that currently have separate centers of excellence (COE) for analytics and/or automation and/or AI must integrate, or the very least, coordinate their initiatives. Doing so would provide more seamless integration and yield better ROI. Companies that are just starting their journey in analytics and AI can start with an analytics or automation COE that expands to include AI capabilities.

The pandemic has accelerated the adoption of AI requiring companies to focus their AI initiatives, deploy them into production, and adopt an integrated operating model. Companies that invested in AI before the pandemic have been able to do this well and are further investing in AI to reap greater ROI — creating a virtuous cycle of value creation through AI.

As PwC’s Global US Artificial Intelligence and US Data Analytics Leader, Anand Rao helps senior executives structure, solve and manage critical issues facing their organizations. With more than 30 years of AI industry and research experience, Anand has worked extensively on business, technology, and analytics issues across sectors globally.