Article written and supplied by EY, Platinum Sponsors of MOVE America 2024 at the Austin Convention Center, 24-25 September, Austin, TX. Get more exclusive insights from EY at the event and make sure you meet them there.
Artificial intelligence (AI) is much more than a “buzz phrase” and is set to have a deep and wide impact across all industrial sectors. While many mobility companies already are using traditional AI approaches, the rapid adoption of generative AI (GenAI) has raised awareness of AI’s capabilities and potential to transform and disrupt.
Despite uncertainty, we have identified five key steps mobility leaders can take today to confirm that their companies are AI ready. These initiatives address the challenges to AI deployment and differentiate between how companies can “act now” vs. “evolve later.”
Initiative 1. Establish an “AI value realization office” and evolve into a control tower
The value realization office coordinates knowledge sharing and shapes governance, but its primary goal is to realize benefits, conduct project and risk management, and optimize resources.
Act now: Set up an AI value realization office with a focused scope to test and learn
One way to experiment with AI is to create a value realization office via a simple project management office within a single business unit. This should involve stakeholders with different specializations, with C-suite support, focused on high-priority or quick-win projects. Leveraging easier-to-use technologies, such as GenAI and low-code or no-code, can lower technical barriers to experimentation, enabling nontechnical people to participate earlier.
Evolve later: Scale up the value realization office into a control tower
As the office grows in creditability and scope, companies can increase its autonomy and responsibility to scale up AI deployment with increasing C-suite oversight. It should evolve into a control tower, with formal positions, governance and resources.
Initiative 2. Explore future scenarios to align the approach to AI
There is no shortage of potential use cases for AI, but organizations often struggle to align these use cases with overall strategy and vision.
Act now: Use future-back planning linking AI to business value
To develop focused initiatives aligned with an overall AI vision, companies should begin with future-back planning to identify the potential impact of AI. Future scenarios should consider regulatory, macroeconomic, supply chain and resource constraints and link AI activities to business value.
Evolve later: Allocate resources continuously
To transition the value realization office into a control tower, link the top-down scenario planning and capital allocation with bottom-up learnings and activities. Scenarios can also inform which skills are needed for reskilling plans, data requirements for data architecture upgrades and which competency white spaces exist for ecosystem strategies.
Initiative 3. Develop a workforce reskilling plan
AI is widely anticipated to have a sweeping impact on work and talent, as its rapidly advancing capabilities allow it to perform a wider variety of work with increasing ease and sophistication.
Act now: Create a skills assessment to identify reskilling needs
Begin by assessing what tasks AI is likely to take on and what competencies are required for workers.
Evolve later: Develop and implement a reskilling plan
Organizations should strive to create a continuous learning culture to enable them to adapt to constantly changing skill needs. Incentives can play an important role, linking reskilling to career advancement and financial compensation.
Initiative 4. Create a data architecture assessment and upgrade roadmap
Having the right data architecture is critical for the effective deployment of AI across an organization. The challenge is compounded by the different kinds of data used by traditional AI (structured data) and GenAI (which excels at working with unstructured data). To use GenAI across the workforce, large language models (LLMs) should be trained on operating procedures and leading practices — building a “knowledge graph” for the organization.
Act now: Conduct a data architecture assessment
The data architecture should be evaluated to identify process design, dependencies, and data quality and security. Appropriate benchmarks can provide a performance baseline and support future AI business cases. Map the potential system upgrade scenarios by pairing phased upgrades with the potential ROI of corresponding use cases.
Evolve later: Execute a data strategy with phased upgrades
After the infrastructure is mapped, companies need a strategy to collect, store and manage the data needed for AI applications. The first step is to identify and implement processes for boosting data quality. Secondly, they can adopt an ROI-driven, phased approach to capture data and introduce new use cases.
Initiative 5: Develop AI ecosystem partnerships
AI partnerships bring greater complexity and depth of integration because AI solutions need to connect to central systems, be adaptable and be managed over time.
Act now: Map AI ecosystems and complementary capabilities
When choosing partners for AI projects, companies should compare their AI capabilities, maturity and ecosystems against emerging leading practices. Partners and ecosystems with complementary capabilities and experience can augment gaps in skills, technology and implementation. However, new partner and ecosystem relationships also require new governance. By establishing early partnerships with multiple entities, and identifying small pilot project opportunities, companies can build experience ahead of larger projects.
Evolve later: Use key evaluation criteria to support a narrow but strong AI ecosystem
As the AI partner ecosystem evolves, it is essential to establish key criteria to evaluate relationships. This helps to select and develop relationships with priority partners and act decisively to remove unnecessary partners that fail to deliver value.
In summary
Many of the benefits AI can bring to mobility companies come from the foundational work that needs to happen inside companies pre-deployment: It’s not solely a technology upgrade; it’s also an organizational and cultural upgrade.
Some see AI as a buzz phrase, but it’s worth remembering that AI isn’t a switch, it’s a journey. Read the full white paper on AI use cases and implementation here.
To learn more about implementing AI in your mobility business, come hear from EY professionals on the Finance and Business Models stage during day 1 of MOVE America.
The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.
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