This browser is not actively supported anymore. For the best passle experience, we strongly recommend you upgrade your browser.
| 4 minutes read

Six ways generative AI can transform corporate operating models

Amidst all the buzz about generative AI – from apocalyptic warnings about its threat to humanity, to predictions it could render work obsolete – there has been less discussion about what it really means for corporate operating models.

As we consider here, AI could have – and in some cases is already having – a profound impact across the six key dimensions of corporate operating models: Governance Structure, Organizational Setup, People and Capabilities, Organizational and System Interfaces, Core Processes, and Tools and Technology.

1. Governance Structure: Ensuring corporate policies align with evolving AI regulations

AI has long presented both opportunities and challenges in the pursuit of good governance. It can be a valuable boardroom tool, for example, providing data-driven insights into why quarterly results might be better or worse than expected. It can also create ethical issues, such as concerns around bias and accountability in decision-making (e.g. recruitment and retention). 

However, with the advent of generative AI and the increasing range of use cases to which it is being applied, there is now a clear need to reconsider if traditional oversight structures are fit for purpose. In the TMT sector, for example, the use of generative AI to personalize content, such as marketing materials, increases ethical risks. These involve not only data privacy, but also concerns about how personal data is used to generate content (e.g. racial or gender stereotyping). This necessitates a new, focused governance layer concentrating on AI ethics and legal compliance, to ensure that corporate policies align seamlessly with evolving AI regulations.

In a recent Forbes article, Eilon Reshef, co-founder of Gong, an AI-powered platform that helps businesses draw revenue-generating insights from customer interactions, puts forward the case for establishing a cross-functional AI governance team, to ensure the challenges associated with AI are not left solely in the hands of product and engineering teams.

2. Organizational Set-up: Streamlining for Agility

Many industry sectors have traditionally been characterized by rigid, hierarchical structures, which can make it hard for businesses to respond nimbly to unexpected market developments. Generative AI is accelerating a transition away from such rigidity to more agile, role-specific frameworks.  

This transition is about more than just a redesign of organizational charts; it involves enhancing data-driven, decision-making capabilities, to create more streamlined, adaptive organizational models that are optimized for rapid responses to market changes.

In the automotive industry, for instance, generative AI can help optimize the innovation pipeline, and ultimately transform the way manufacturers approach vehicle design. The adoption of agile frameworks, expedited by generative AI, can accelerate the journey from design concept to production-ready model, through rapid prototyping and simulation.

The capacity of generative AI to transform organizational set-ups must be considered on an industry-by-industry basis. As we outlined in a previous post, Unlocking real EBITDA value with generative AI, businesses must first be clear about what economic challenges or opportunities exist that generative AI might address. 

3. People and Capabilities: Empowering Human-Machine collaboration

While it remains to be seen whether AI will ever fulfil Elon Musk’s prophecy of a future where “no job is needed”, it is clear that businesses will need to shift their human capital strategy from mere headcount to a more nuanced focus on skills and capabilities. This will require a renewed focus on diversifying and developing employee skillsets to enable greater human-machine collaboration.

In the industrial manufacturing sector, for example, rather than simply adding more workers to the shop floor, the focus needs to shift towards equipping employees with the skills to collaborate effectively with AI-driven machinery to fully reap the benefits of advances in areas such as predictive maintenance. For instance, machine operators may need training in data analytics to interpret real-time equipment health metrics, while maintenance crews could benefit from learning how to manage automated diagnostic tools.

Generative AI could be deployed to support this transition, for example, by analyzing common employee queries about predictive maintenance and creating more relevant training materials.

4. Core Processes: Disruptive Process Reengineering

The advent of generative AI is triggering radical shifts across core business processes, from procurement to customer engagement, displacing existing workflows with more efficient operational processes. We can see examples of this today in industry sectors such as financial services. Traditional methods of credit risk evaluation, which are time-consuming and prone to human errors, are being replaced using AI-powered algorithms that can process and analyze vast datasets in real-time, reducing the time required for risk assessments but also increasing their accuracy. 

These kinds of disruptions in core processes using AI can have an impact on other corporate operating dimensions. For example, they could impact human capital strategy – allowing businesses to redeploy people to functions that drive greater value. They also carry consequences for governance, as they can potentially expose organizations to greater scrutiny over how decisions are made using AI (such as whether or not to approve someone for credit).

5. Organizational and System Interfaces: Data-Driven Relational Channels

The impact of generative AI extends to the interaction layers between customers, vendors and internal teams. Real-time analytics can be used to transform interfaces into agile, data-driven platforms, allowing for seamless, dynamic interactions. Generative AI can take this a step further, by automatically generating content for these interfaces, and tailoring content for users.

As we considered in a previous article, Generative AI: How to deliver business results with a new disruptive force, generative AI can deliver the best results in applications where many people use content, or where a few people ask questions with highly valuable answers.

The use of generative AI to enhance user interfaces is part of a broader shift across various industry sectors towards more streamlined customer interactions and internal coordination. In retail, for example, generative AI-backed analytics can augment customer relationship management systems, through real-time inventory tracking and personalized marketing, as well as streamlining internal processes such as automated restocking and dynamic pricing adjustments.

6. Tools and Technology: The New Bedrock of Operational Efficacy

Generative AI is anchoring a rejuvenated tech infrastructure, offering more than just integration. It deploys an array of specialized tools like predictive maintenance and real-time analytics, each critical for modernizing business operations and addressing digital challenges. In the TMT sector, real-time analytics dashboards can significantly improve network management and content delivery, optimizing user experience. Predictive maintenance can prevent downtime in data centers, ensuring uninterrupted service and minimizing operational costs. 

Businesses must, however, understand that this technology should be used to complement human expertise in a hybrid model, rather than supplanting it.

As another previous post – Top GenAI use cases and ‘no regret’ moves – outlines, decisions on where and when to deploy generative AI must also be built on firm foundations – not least through an understanding of the risk-reward ratio for different deployments. 

 

Tags

article, artificial intelligence, disruption, digital workforce, technology, dig, dig ai, germany, united kingdom, emea, english us