Learn how double materiality & generative AI transform performance management by shifting focus from individual tasks to organizational decision-making.
Materiality is the line in the sand between what is essential and what can wait. If performance is the outcome we care about, then understanding the material risks, systems and process is the path that guides organizations there. Today, leaders must command performance not only by optimizing results but by clarifying what truly matters across the system; what is essential versus what can wait.
The biggest mistake organizations can make is viewing materiality as purely financial. In the production economy, measuring performance generally focuses on throughput, production, cost, and schedule. However, in the complexity of today’s transition economy, performance is no longer linear or local. Applying to the concept of materiality must include the processes that enable systems to help organizations achieve objectives – i.e., getting down to the task level and focusing on those that drive value.
In the era of generative AI, understanding materiality also means understanding the difference between collective industrial tasks and individual administrative tasks. Many off the shelf AI products are designed to support individual administrative tasks – writing emails, summarizing documents, or managing personal workflows. These tools optimize productivity at the individual level but do little to address how organizations collectively make decisions, align objectives or command performance across complex systems. The contrast is to look at those tasks that are collective and enterprise-focused (i.e. achieving objectives). The tools that InterKnowlogy offers are built for collective industrial tasks – where decisions depend on many roles, layers of context, and the ability to understand how actions in one area impact performance elsewhere in the organization.
Generative AI can either enhance or undermine that trust, depending on how it is applied. When AI is used only for individual administrative tasks, it risks creating fragmented decision-making and a false sense of productivity. However, when applied to collective organizational tasks, such as system performance measurement, resource allocation, or strategic alignment, AI becomes a tool for systematic understanding. InterKnowlogy’s approach to generative AI is centered on this collective function, helping organizations gather information, connect decisions across teams and uncover what is material to performance.
A performance model that suits today's economy requires the consideration of a new kind of materiality called double materiality (Responsible Investment Association, 2019) (Figure 1). Traditional reporting looks inward, primarily considering metrics that impact the organization. To command performance, leaders must look both inward and outward to understand how operations impact people, systems, and trust. Double materiality is a framework that evaluates both how performance is affected and how performance affects a variety of external components.
To successfully implement double materiality, organizations must have access to reliable data, a robust process to evaluate performance, as well as a framework to determine issues that have the greatest impact on the business, environment, and society at large over the near, medium, and long term. In the age of no rules, organizations need to be equally focused on what’s happening outside the fence as well as inside the fence.
This is where the difference between collective and individual tasks becomes material. Double materiality relies on shared understanding across an entire organization, not just individual inputs. It demands that performance data, stakeholder feedback, and strategic intent be integrated into a common framework that everyone can trust. InterKnowlogy enables this by using AI to facilitate collective decision-making environments, helping organizations translate complex data into actionable insights that drive performance and trust system wide.
The classic Double-Materiality matrix plots issues along two axes that measures the effect on the business as well as the effect on society (Responsible Investment Association, 2019) (Figure 2). What moves this static expression of risk exposure to a more informed dynamic tool is the quality and velocity of information – for both internal and external factors.
Large complex organizations using this model need to be able to implement the objectives they identify. This moves the materiality discussion out of the boardroom and to wherever work happens. The aim of double materiality is not perfection, but rather continuous learning. The right process to determine what is material versus what is not material involves constant engagement with stakeholders, analysis of context, and adjustments to business activities to ensure delivery on commitments. It requires thinking about what decision-making environments organizations are making accessible and how stakeholders can make judgements based on the organization's ability to command performance.
Generative AI can serve as the connective tissue for these conversations, when built for collective contexts. InterKnowlogy’s systems are designed to help organizations see across silos, understand cross-functional dependencies, and recognize how individual actions contribute to organizational outcomes. This is a fundamental difference from off-the-shelf AI tools, which operate at the individual level and lack of contextual understanding required for organizational performance management.
Collective tasks such as decision alignment, strategic prioritization, and performance management require AI that can understand relationships between people, processes, and outcomes. InterKnowlogy’s purpose is built for these tasks, helping organizations command performance through systemic understanding rather than simply optimizing individual productivity. InterKnowlogy supports leaders in identifying what is material, why it matters, and how to act on it at scale.
There are four questions for organizations to consider as they analyse issues through the lens of double materiality:
Double materiality is complex and requires a dynamic approach as issues can move quickly across the materiality spectrum. It can be overwhelming for organizations to determine which issues to focus on. Having a framework in place to evaluate performance provides a systematic way to monitor risks and assess their impact on the business, environment and society. Organizations that will be trusted and successful moving forward will be the ones that can demonstrate that double materiality is at the core of how they command performance.
Collective industrial tasks will define the next frontier of performance. InterKnowlogy’s focus on collective industrial decision making is what sets it apart – turning AI intro a trusted partner in commanding performance, rather than a personal assistant for administrative efficiency.
Commanding performance is about surfacing real-time insights where decisions happen. Learn how IK-CADDI equips mining and energy leaders to do exactly that.
