In the age of global uncertainty, colossal data and rapidly shifting and customer expectations, companies must simultaneously navigate digital transformation, while identifying and incorporating emerging trends and technologies in parallel. This dual imperative forces a difficult balancing act between adapting strategies and adopting new capabilities, all while proving efficiencies and generating new value across stakeholders.
The co-evolution of artificial intelligence (AI) is compounding the challenge of digital transformation, because it is causing the very definition of digital transformation to evolve. Understanding the relationship between these phenomena is essential for business leaders, not only to guide resource allocations, but to achieve real maturity– a state of business vitality enabled through a culture of innovation.
Based on our going research and client work in both areas, what follows are three reasons AI is transforming the very notion of digital transformation.
1. AI is fundamentally shifting the customer experience, and what it means to businesses. CX has long been a core driver of digital transformation, particularly given customer demands for digital channels, real-time interactions, and brand authenticity. First, AI represents new opportunities to scale personalization far beyond what many imagined even a few years ago. For example, companies are using AI techniques to develop highly targeted content, based on each customer interaction, at the massive scale. Second, AI is extending brand interactions in novel ways, with the use of chatbots, voice-interactive AI “agents,” and automating brand tone across “touchpoints” throughout the customer journey. Third, AI-driven personalization + big data analysis = a catalyst for connecting CX to broader operations efficiencies and revenue generation. Advanced AI deployments will be marked by the ability to infuse both user-facing services and interactions with back-end or enterprise process and supply chain optimization, such as in retail, financial services, energy, and healthcare.
2. AI is expanding what can be digitized, and how. AI techniques such as deep learning and computer vision enable the digitization of assets and processes that, until a few years ago, were simply not possible to render digitally without massive resources. The rapid rise and open-sourcing of training data and frameworks now allows companies to incorporate new capabilities into existing processes. For example, a manufacturer embarking on a digital transformation effort is likely focused on factory process efficiencies by electrifying equipment and migrating certain workflows to the cloud for real-time visibility. In this context, AI could be applied to…
Product assembly (e.g. sorting, packaging, etc.) through object recognition, robotics
- Quality assurance processes, through image recognition
- Machine performance monitoring, through anomaly detection
- Predictive maintenance, through machine learning
- Real-time and personalized reporting, through automated report generation
- Access controls authentication, through biometric or voice authentication
- Hypotheses testing and digital twin analysis, through AI-powered cloud services
These are just a few ways AI can expand core digital transformation objectives (e.g. efficiency improvements) by extending what data are collected and how processes are defined. Use cases of AI are applicable across every sector, business function, and value chain, and it is not just about operational efficiencies. AI also compounds broader opportunities around enterprise data, as deep learning can be used to illuminate hidden patterns and big “dark” unstructured data sets, to simulate scenarios for decision-making, and enable altogether new products.
3. AI is evolving the opportunities and challenges of digital transformation, and introducing novel considerations to even the most digitally advanced companies. Across AI use cases, profound opportunities lie in forecasting, empirical decision-making, operations automation, product optimization, new business models, greater access to services, targeted services, enhanced user experiences, and even improved environmental and public health. Simultaneously, it poses urgent challenges: data integrity, re- skilling workforces, diverse ethical uncertainties, privacy concerns, uncharted legal and regulatory questions or standards, and the explainability and accountability of deep neural networks, among others.
AI applications mark the next evolutionary step in digital transformation
Like digital transformation, AI is alluring to modern businesses, yet often oversold. Both involve significant technological resources and reconfiguration, but require equal or greater investment in people and a culture of innovation. Taken in sum, each of the above dynamics require businesses understand AI in the broader context of digital transformation for what it is– not synonymous, nor separate, but part of an ongoing evolution in how organizations innovate to meet customer needs in the digital age.