Imagine a manufacturing plant aiming to maintain operational excellence while facing potential safety hazards every day. In such a scenario, tracking key performance indicators (KPIs) such as production efficiency and output is needed for assessing performance. However, without considering key risk indicators (KRIs) like workplace incidents or equipment failure rates, the plant may overlook critical safety concerns until they become costly disruptions or accidents.
Integrating KPIs and KRIs enables the plant to proactively manage both performance and risk and ensure smooth operations while prioritizing employee safety. Overall, this integration is essential for promoting ongoing improvement and awareness of risks within the organization.
The KPI Institute defines KPI as a measurable expression for the achievement of a desired level of results in an area relevant to the evaluated entity’s activity. KRI is a measure used to evaluate the likelihood of an event’s probability and consequences that could exceed the organization’s risk appetite and significantly harm the success of the organization.
While most organizations rely heavily on KPIs, rooted in historical data, these may offer limited insight into future threats. KRIs modify the narrative by beginning with a proactive framework for risk management and developing measurements around prospective pitfalls in the future.
Improving risk management
Utilizing both KPIs and KRIs would provide a more systematic approach to risk management compared to relying solely on KPIs. For instance, within the supply chain context, KRIs may cover aspects, such as supplier performance, reporting accuracy, and emerging industry trends. This gives the organization a clear picture of all possible hazards and enables it to foresee and handle issues before they have an adverse effect on operations. Here are the overarching benefits of using KRIs in risk management:
Proactive identification: With KRIs, organizations can proactively detect potential risks before they occur. For example, by monitoring supplier performance to anticipate supply chain disruptions or analyzing industry trends to predict market shifts, organizations can minimize possible harm. This proactive approach enables early intervention and allows the organization to implement preventive measures.
Root cause analysis: KRIs encourage delving deeper than immediate events to identify the underlying root causes behind potential risks. For example, rather than simply reacting to a decrease in supplier performance, KRIs can signal organizations to uncover the reasons behind it, whether due to internal issues, external market forces, or other factors. By addressing root causes, organizations can develop more effective risk management strategies and prevent similar issues from recurring in the future.
Decisions based on data: Integrating risk assessment into current data streams allows organizations to make informed decisions in real-time. By leveraging KRIs and building alerts or other KRI-based solutions, organizations can access timely and pertinent information to guide decision-making processes. For instance, by monitoring relevant data points, such as financial indicators, organizations can quickly identify emerging risks and take appropriate actions to manage them. This allows organizations to be resilient and agile in the face of uncertainty.
Implementing KRIs
Organizations must understand the relationship between risk and performance to improve cross-functional collaboration and incorporate risk concerns into business decisions. For the integration to be successful, KRIs should be reported and communicated effectively. To create KRIs and corresponding mitigation plans, the individual who oversees the Enterprise Risk Management (ERM) process should work with the risk owners. The “risk owners,” who can effectively oversee their business units in line with their individual units’ risk goals, are the main benefactors of KRIs.
Risk owners must evaluate KRI data pertaining to risks that impact their units on a frequent basis. It is important to acknowledge that the different methods for reviewing KRI data also depend on an organization’s functions. In addition, successful identification and implementation of KRIs also requires a structured approach with the following key steps: identifying key metrics, assessing gaps, improving metrics, validating and setting trigger levels, and establishing a risk control plan.
Harnessing the power of KRIs alongside KPIs emphasizes the link between successful risk management and successful organization outcomes. This encourages a proactive attitude to risk, in which mitigating risk is viewed as an investment in accomplishing corporate objectives rather than as a cost.
Nawaf Al Omari boasts over a decade of experience in optimizing teams and driving project management success. He excels at forecasting staffing needs, resource management, and fostering collaborations, with a 40% increase in stakeholder satisfaction. Prioritizing data-driven decision-making, he is adept at mitigating risks, tracking KPIs, and achieving cost reductions. Nawaf is strongly committed to delivering results and operational excellence.
The popularity of sustainability has surged in recent years, causing organizations to grapple with balancing short-term profits with long-term sustainable practices. This has led to concepts like shared value and corporate social responsibility, with companies aiming to create economic and social value while reducing their environmental impact. The movement has sparked active efforts, with social innovators, policymakers, investors, and academics all striving to measure sustainability.
In today’s world, companies must move beyond outdated economic metrics and adopt KPIs that consider the triple bottom line, including social, economic, and environmental aspects of their operations, all while promoting sustainable human well-being.
However, sustainability is a constantly evolving concept that adapts to context and cannot be measured with a single yardstick. The balance between social, economic, and environmental considerations is crucial to achieving sustainability. It is like walking on a tightrope, requiring constant adjustments to maintain equilibrium in a changing world. Each context requires a unique approach, with varying weights and measures for different factors. Customized solutions are needed that address stakeholder needs while maintaining long-term balance, as a one-size-fits-all formula won’t work.
About the Expert
• As a Managing Director, Teodora leads development initiatives to support and enhance the organization’s strategic plan and manages the development and growth of the MENA branch of The KPI Institute.
• An expert researcher, consultant and practitioner with six years of experience in the deployment and implementation of KPI Management Frameworks.
• Pursuing a PhD. in Management on the topic: Rethinking the Performance Management Systems to ensure organizational sustainability, Lucian Blaga University, Romania
• Postgraduate Program in Entrepreneurship and Venture Creation, ISCTE Business School Lisbon, Portugal
• Master’s Degree in Project Management, Romanian-German University, Romania
Key performance indicators (KPIs) have been the north star guiding business strategy for decades. These criteria measure not only sales and revenue but also customer satisfaction as well as employee engagement.However, as the business landscape continues to evolve at an unprecedented pace, the need for deeper insights and more agile measurement arises. This is where the potential of generative artificial intelligence (GenAI) shines, opening doors to a new era of KPI innovation.
GenAI goes beyond automation to produce entirely novel content. It is a creative catalyst, opening up unprecedented possibilities for KPI innovation. Forget rigid, one-dimensional metrics. Powered by GenAI, KPIs become fluent, adaptive, and poetic, capturing not only the whats but also the whys and what-ifs.
Reimagining KPIs for exponential growth
From static to dynamic: GenAI is capable of integrating dynamic KPIs, meaning they can evolve alongside the company that uses them. KPIs also fit seamlessly into a changing market, with trends and strategies naturally shifting along the way.
Unveiling the unseen: Traditional KPIs often fail to hit the nail on the head by overlooking key, intangible factors that could affect performance. GenAI, however, can delve much deeper. With the help of GenAI, it is possible to determine brand sentiment before a particular campaign is launched, anticipate employee engagement within remote teams, or even predict customer turnover before it happens.
Personalized insights, enhanced action: Data mountains no longer need to be intimidating.GenAI transforms data into personalized narratives, crafting stories tailored to individual stakeholders. Sales teams can access actionable insights, marketing managers can monitor real-time customer sentiment, and CEOs can explore what-if scenarios for strategic foresight. This data-driven storytelling fosters informed decision-making and ignites action across the organization.
A practical guide to unlocking GenAI’s potential for KPI innovation
To effectively utilize GenAI tools like Gemini and ChatGPT for KPI innovation, follow these guidelines:
Define goals and challenges: Clearly articulate objectives, whether uncovering customer sentiment or anticipating market shifts.
Frame specific prompts: Use concise prompts such as “generate potential KPIs for measuring brand sentiment on social media.”
Provide relevant context: Enhance responses by furnishing background information about your industry, business model, and existing KPIs.
Experiment and refine: Iterate prompts, rephrase questions, and provide feedback to improve AI understanding.
Collaborate with experts: Involve human expertise in evaluating and implementing AI-generated insights.
While GenAI’s potential for KPI innovation is undeniable, it thrives on synergy, not substitution. The point is this: human guidance is essential. Act now, invest in your future, and become a master of the new KPI era by enrolling in The KPI Institute’sCertified KPI Professional course.
In May 2023, Samsung Electronics prohibited its employees from using generative artificial intelligence (AI) tools like ChatGPT. The ban was issued in an official memo, after discovering that staff had uploaded sensitive code to the platform, which prompted security and privacy concerns for stakeholders, fearing sensitive data leakage. Apple and several Wall Street Banks have also enforced similar bans.
While generative AI contributes to increased efficiency and productivity in businesses, what makes it susceptible to security risks is also its core function: taking the user’s input (prompt) to generate content (response), such as text, codes, images, videos, and audio in different formats. The multiple sources of data, the involvement of third-party systems, and human factors influencing the adoption of generative AI add to the complexity. Failing to properly prepare for and manage security and privacy issues that come with using generative AI may expose businesses to potential legal repercussions.
Safety depends on where data is stored
So, the question becomes, how can businesses use generative AI safely? The answer resides in where the user’s data (prompts and responses) gets stored. The data storage location in turn depends on how the business is using generative AI, of which there are two main methods.
Off-shelf tools: The first method is to use ready-made tools, like OpenAI’s ChatGPT, Microsoft’s Bing Copilot, and Google’s Bard. These are, in fact, nothing but applications with user interfaces that allow them to interact with the base technology that is underneath, namely large language models (LLMs). LLMs are pieces of code that tell machines how to respond to the prompt, enabled by their training on huge amounts of data.
In the case of off-the-shelf tools, data resides in the service provider’s servers—OpenAI’s in the instance of ChatGPT. As a part of the provider’s databases, users have no control over the data they provide to the tool, which can cause great dangers, like sensitive data leakage.
How the service provider treats user data depends on each platform’s end-user license agreement (EULA). Different platforms have different EULAs, and the same platform typically has different ones for its free and premium services. Even the same service may change its terms and conditions as the tool develops. Many platforms have already changed their legal bindings over their short existence.
In-house tools: The second way is to build a private in-house tool, usually by directly deploying one of the LLMs on private servers or less commonly by building an LLM from scratch.
Within this structure, data resides in the organization’s private servers, whether they are on-premises or on the cloud. This means that the business can have far more control over the data processed by its generative AI tool.
Ensuring the security of off-the-shelf tools
Ready-made tools exempt users from the high cost of technology and talent needed to develop their own or outsource the task to a third party. That is why many organizations have no alternative but to use what is on the market, like ChatGPT. The risks of using off-the-shelf generative AI tools can be mitigated by doing the following:
Review the EULAs. In this case, it is crucial to not engage with these tools haphazardly. First, organizations should survey the available options and consider the EULAs of the ones of interest, in addition to their cost and use cases. This includes keeping an eye on the EULAs even after adoption as they are subject to change.
Establish internal policies. When a tool is picked for adoption, businesses need to formulate their own policies on how and when their employees may use it. This includes what sort of tasks can be entrusted to AI and what information or data can be fed into the service provider’s algorithms.
As a rule of thumb, it is advisable not to throw sensitive data and information into others’ servers. Still, it is up to each organization to settle on what constitutes “sensitive data” and what level of risk it is willing to tolerate that can be weighed out by the benefits of the tool adoption.
Ensuring the security of in-house tools
The big corporations that banned the use of third-party services ended up developing their internal generative AI tools instead and incorporated them into their operations. In addition to the significant security advantages, developing in-house tools allows for their fine-tuning and orienting to be domain and task-specific, not to mention gaining full control over their interface user experience.
Check the technical specifications. Developing in-house tools, however, does not absolve organizations from security obligations. Typically, internal tools are built on top of an LLM that is developed by a tech corporation, like Meta AI’s LLaMa, Google’s BERT, or Hugging Face’s BLOOM. Such major models, especially open-source ones, are developed with high-level security and privacy measures, but each has its limitations and strengths.
Therefore, it would still be crucial to first review the adopted model’s technical guide and understand how it works, which would not only lead to better security but also a more accurate estimation of technical requirements.
Initiate a trial period. Even in the case of building the LLM from scratch, and in all cases of AI tool development, it is imperative to test the tool and enhance it both during and after development to ensure safe operation before being rolled out. This includes fortifying the tool against prompt injections, which can be used to manipulate the tool to perform damaging cyber-attacks that include leaking sensitive data even if they reside in internal servers.
Parting words: be wary of hype
While on the surface, the hype surrounding generative AI offers vast possibilities, lurking in the depths of its promise are significant security risks that must not be overlooked. In the case of using ready-made tools, rigorous policies should be formulated to ensure safe usage. And in the case of in-house tool deployment, safety measures must be incorporated into the process to prevent manipulation and misuse. In both cases, the promises of technology must not blind companies to the very real threat to their sensitive and private information.
Remote work and the implications of continuing the process, including its potential impact on employee performance, are widely discussed. However, there is no right answer, and it is not one-size-fits-all.
The future of work includes flexibility, employee experience, agility, and the responsible use of artificial intelligence (AI)—these significant shifts impact where and how employees work. With an increase in remote work options, we have seen positive trends in work-life balance, employee empowerment, inclusivity, and an increase in diverse talent. These factors are also known to increase employee productivity and retention. According to BCG, a considerable population of employees are ready to leave their jobs if they find their flexible work arrangements unsatisfactory. Based on their survey, approximately 90% of women, caregivers, individuals identifying as LGBTQ+, and those with disabilities, deem flexible work options as crucial in determining whether they will continue or resign from their current employment.
Remote work productivity is subject to debate due to various factors that must be considered. Some suggest remote work can increase productivity due to a flexible schedule, no commute, and fewer interruptions. While many employees thrive in a remote work environment, some find it challenging due to the discipline it demands.
Remote work was on the rise even before the COVID-19 pandemic. A July 2023 report from Stanford University found that working remotely has doubled every 15 years. Then, when the pandemic occurred, although devastating, it provided a new perspective for those previously constrained, forced to relocate, or live in less favorable locations to work for a specific company and advance their career. Worldwide ERC states that around 56 million Americans moved to new residences between December 2021 to February 2023 due to COVID-19-related shutdowns and the surge in remote work and online education. With such a huge increase in their number over the past few years, this begs the question: do employees working remotely demonstrate productivity?
Taking a deeper look into the study by Standord University, researchers shared that remote work employees’ productivity differs depending on perceptions—the nature of the research and the conditions under which it was conducted. The report revealed that workers believed productivity was higher at home (approximately 7% higher), while managers perceived it lower (around 3.5% lower). Another example, according to a poll by the video presentation applications mmhmm, 43% prefer office work and 42% favor working from home for peak productivity. Moreover, 51% of employees stated that working asynchronously or having the flexibility to set their schedules contributed positively to their productivity. Perceptions aside, the Stanford analysis found a 10% to 20% reduction in productivity across various studies.
The bottom line is today’s company culture is crucial. Ensuring work-life balance and putting the employees in the driver’s seat are the best ways to retain and increase productivity because they will feel valued and empowered. In a 2022 Microsoft employee engagement survey, 92% of employees say they believe the company values flexibility and allows them to work in a way that works best for them. An even higher percentage (93%) are confident in their ability to work together as a team, regardless of location. People have different preferences—some individuals opt for a hybrid approach, while others choose either remote or in-person work exclusively.
Regardless of the work setup, company leaders and human resources (HR) or human capital management (HRM) executives should ensure that they can still make a lasting impact on employee performance. One measure involves establishing key performance indicators (KPIs) that assess innovation, program, project, and product success—the output, not the physical location. Another crucial step is developing a strategy that includes all future work options, such as in-person, hybrid, and remote choices. Employees tend to be more productive if there is a level of empowerment that allows them to decide where to do their best work.
Planning in person events makes a difference. Leaders who bring new hires and internal transfers, new to the team, on-site for several days should see an uptick in productivity post-gathering. In-person team or company-wide gatherings 1-4 times per year provide employees an opportunity to reset and socialize. Moreover, managers should bring teams together for major program and project kick-offs. When onsite in person, people being present makes a difference. Discourage using Teams or Zoom when employees are in the general vicinity. I have seen companies spew the importance of in-person just to fly employees into a specific location and have people take meetings from their desks or in a different on-site building-conference room, defeating the purpose of in-person interaction.
Having organizations foster all work options is critical and foregoes having to decide which is best. There is no right or wrong answer to this challenge; it should be considered a new way of working and requires future-forward ways of thinking, just as we do with emerging technologies.
About the guest author:
Dr. Malika Viltz-Emerson is a Senior Global Human Resource Leader at Microsoft. She has over 20 years of experience in human capital management. Her mission is to identify and address the real-world challenges and opportunities for employees and the company, and design and implement optimal solutions that leverage the latest tools, technologies, and processes.
