Four ways companies can lead the charge in the AI future

The automotive sector exemplifies this disruption due to a confluence of technologies and tech-enabled platforms, such as AI, electric vehicles (EVs), autonomous vehicles (AVs), ride-sharing platforms and the IoT. Traditionally, the value drivers of the automotive sector include R&D, vehicle design and manufacturing. However, the application of GenAI across these functions diminishes their roles as value drivers.

Similarly, the mechanical simplicity of EVs may reduce the significance of manufacturing. In a world dominated by AVs and ride-sharing, vehicle design and branding may become less critical as passengers prioritize different features compared with car owners. Sales channels are also evolving, with some EV companies opting to bypass traditional dealership networks in favor of direct-to-consumer sales.

As traditional value propositions diminish, automotive companies must identify new value propositions and develop innovative business models, often together with an ecosystem of external partners. For instance, a consortium of seven automotive manufacturers established charging stations across the US, creating new value pools by generating fees and potentially monetizing user data. With access to user data and increasing vehicle autonomy, in-vehicle experiences could emerge as another value pool, driven by the combination of GenAI and IoT to deliver highly personalized and context-specific experiences.

Thus, companies need to identify unexpected sources of disruption and adopt methodologies that foster agility and adaptability. An effective approach is future-back planning, which begins by envisioning the future state of the sector and developing a plan to build the competencies required for success. Business leaders can also take the future paths approach by reviewing the existing business and identifying white spaces for expansion, leveraging core business capabilities and assets to pursue new opportunities.

2. Strengthen data infrastructure

Data infrastructure serves as the backbone for organizations aiming to effectively harness data. It encompasses the systems, technologies and processes that facilitate the collection, storage and management of data. As leaders increasingly invest in AI, establishing a robust and well-structured data infrastructure is vital.

As data volumes grow, the infrastructure must be able to scale efficiently. This includes managing increased storage needs and processing power without compromising performance. Combining data from various sources, including legacy systems, third-party applications and cloud services, can be complex. A robust data infrastructure enables seamless integration of diverse data sources — both structured and unstructured — from internal and external environments, thereby supporting the training of AI models. A well-designed infrastructure promotes interoperability among data systems and AI tools, facilitating smoother workflows and enhancing collaboration across teams.

Stronger data infrastructure accelerates AI adoption, particularly for large and complex organizations operating across multiple platforms and legacy systems. AI-ready data not only reduces the cost of training but also improves response accuracy and enables models to be adaptable for broader use cases. Organizations must also expand the definition of data to include knowledge assets, a higher-value form of data used for AI decisions and actions.

Creating an AI-ready environment requires a robust data infrastructure that allows organizations to leverage data as a strategic asset and drive innovation. Leaders must develop an enterprise-wide, fit-for-purpose data strategy that guides effective investment aligned to the organization’s highest priorities and supported by stringent data governance. Leaders must strike a balance between seeking to achieve a perfect data infrastructure — which is a significant challenge — and mobilizing efforts to make continuous progress.

3. Address talent and technology gaps

The EY AI Anxiety in Business Survey found that while 80% of employees in the US felt they would be more comfortable with AI if they received more AI training or upskilling opportunities, 73% were concerned that such opportunities would not be sufficient. The survey indicates that employees are already utilizing AI tools and they want a deeper understanding of responsible AI practices.

The scarcity of AI skills in the job market is a clarion call for businesses to invest in employee upskilling and re-skilling. By cultivating AI skills within their existing workforce, companies can not only expedite AI adoption but also help secure a vital competitive advantage. Moreover, by placing a premium on attracting and nurturing AI-savvy employees, organizations can enable operations to be driven by professionals who can unlock the full spectrum of AI capabilities, positioning the business at the forefront of technological advancement.

On the technology front, many organizations often grapple with whether to build, buy or adopt a hybrid approach, such as augmenting existing software or building on existing platforms with proprietary intellectual property. Thoughtful debate and planning are essential to address this issue. Some organizations may choose to delay investments in GenAI applications, with the assumption that their current enterprise resource planning systems will eventually incorporate those functionalities. However, pursuing customized GenAI tools tailored to specific needs across the product development lifecycle may prove worthwhile.

In the EY AI Pulse Survey conducted last year in the US, 95% of senior leaders said their organization was currently investing in AI. The survey noted the importance of a diversified AI investment strategy that balances the acquisition of ready-made AI products with custom AI development.² This strategy allows for tailored solutions where necessary while leveraging the speed and cost-efficiency of pre-built AI technologies.

4. Adopt agentic AI for the next step change

Agentic AI is uniquely characterized by its ability to autonomously perform tasks, make decisions and engage with users and systems. This technology leverages large language models to adaptively plan and create a path to achieve a desired outcome. Unlike traditional AI, which primarily functions as a tool for executing predefined tasks, agentic AI can analyze situations, make choices and take actions based on its understanding of the environment and objectives.

Today, agentic AI is experiencing a maturation phase, trending toward greater autonomy, enhanced reasoning abilities and increased complexity in interactions. AI agents are evolving from mere tools into strategic business partners with autonomy to execute tasks and interact. They are not just intelligent but also informed.

As organizations integrate such intelligent agents into operations, they are discovering their transformative impact on customer engagement and operational processes. For instance, by leveraging historical sales data, seasonal trends and market conditions in the consumer products and retail sector, an inventory optimization agent can continuously adjust its strategies based on real-time data and changing market conditions. This way, products would be readily available when customers need them, which can help enhance their satisfaction and loyalty while achieving key goals, such as lowering cost, greatly reducing stockouts and improving inventory turnover rates.

The agent will operate with a defined level of autonomy. This means it can make independent decisions on inventory replenishment and allocation without the need for constant human intervention while still allowing for necessary human oversight to align critical decisions with broader business objectives.

In the near future, agents and humans could work collaboratively, with humans making the final decisions. By 2028, a third of interactions with GenAI services are expected to use action models and autonomous agents for task completion.³  Over time, agents are expected to gain greater autonomy and ultimately, a swarm of physical and virtual agents may even be able to run the business.⁴ At that point, a “digital flip” would be seen, in which the winners adopt entirely different ways of operating, with AI doing much of the heavy lifting.⁵ The real tipping point would be seen with multiple functions moving to an agent-led model, unlocking entirely different avenues for value creation.⁶

AI is reshaping every aspect of the enterprise. From building a scalable data infrastructure to fostering a workforce fluent in emerging technologies, a holistic approach to AI adoption is essential. Cultivating an innovative work culture that encourages experimentation and provides the necessary resources to drive change will empower teams to embrace new technologies and methodologies.

By achieving strategic AI maturity, organizations can transition into superfluid entities characterized by seamless decision-making processes and a relentless drive for innovation. Such organizations are highly agile and adaptable, leveraging digital innovation to swiftly respond to market shifts, improve processes and drive continuous growth for a sustained competitive advantage. This could propel them toward their goals with unprecedented efficiency and insight.