What separates a performance management system that drives real results from one that simply produces reports?
According to Ghazi Hael Alanazi, the answer lies in execution, accountability, and disciplined decision-making.
As the Administration Director of Northern Area Armed Forces Hospital in Saudi Arabia, Alanazi shares valuable insights on the future of performance management, the growing role of AI and sustainability, and why organizations must move beyond traditional KPI tracking toward systems that actively guide strategy and operational outcomes.
What key trends in organizational performance management have you observed emerging so far in 2026?
In 2026, performance management is shifting toward real strategy execution. Organizations are using real-time KPIs, clearer decision ownership, and AI-driven insights. There is also a stronger connection between performance, risk, and sustainability, making systems more practical and closely tied to actual business outcomes.
Which existing trends, topics, or aspects within performance management have lost their relevance or importance?
Traditional KPI reporting without action has lost relevance. Static annual plans, disconnected scorecards, and overengineered frameworks that fail to support decision-making are becoming obsolete. Focusing only on measurement without accountability, execution, and real business impact is no longer acceptable in today’s performance environment.
What does the corporate performance management system of the future look like?
The future system is fully integrated with strategy execution. It connects objectives, KPIs, initiatives, and risk within a unified framework. It operates on real-time data, supported by AI-driven insights and clear decision ownership. The focus is less on reporting and more on guiding decisions, enforcing accountability, and continuously improving performance.
What will be the major challenges in managing performance in the future, and how should organizations prepare?
The main challenge is maintaining discipline. Organizations often struggle to enforce accountability, align decisions, and sustain focus. Data overload is another growing issue. To prepare, organizations need strong governance, clear decision rights, simplified KPI structures, and leadership commitment to using performance systems as management tools.
How is technology impacting the way organizations conduct strategic planning and manage performance?
Technology is transforming performance management from periodic reporting into continuous monitoring. AI and analytics provide faster insights, while integrated platforms connect strategy, KPIs, and execution. Tools such as BI dashboards and AI copilots improve visibility, but their real value depends on how effectively organizations embed them into decision-making and governance processes.
How is sustainability impacting the way organizations conduct strategic planning and manage performance?
Organizations are integrating ESG factors into KPIs, risk management, and decision-making. This shift encourages a stronger focus on long-term value rather than short-term results. The challenge is ensuring sustainability becomes measurable and actionable, rather than remaining only a reporting requirement, while linking it directly to performance and accountability.
Practice
What should be improved in the use of strategy and performance management tools to make organizations more resilient to future crises?
Most tools need to become simpler and more connected. Organizations should reduce complexity, link KPIs directly to decisions, and integrate risk into performance systems. Flexibility is also essential, as systems must adapt quickly during disruptions. The focus should move from tracking performance to enabling fast, informed, and aligned decision-making.
While navigating challenging times, what would you consider a best practice in performance management?
The key practice is maintaining focus. Organizations should prioritize a limited number of critical KPIs, align leadership around them, and review performance frequently. Clear decision ownership is essential. During difficult periods, simplifying the system and enforcing accountability has greater impact than adding more metrics or complex frameworks.
How does benchmarking support the improvement of performance management and target-setting systems?
Benchmarking introduces external perspective into the system. It helps validate targets, identify performance gaps, and challenge internal assumptions. When applied effectively, it shifts discussions from opinion to evidence. Its real value emerges when organizations use benchmarking to drive decisions and continuous improvement.
Research
Which organizations would you recommend observing for their approach to performance management, and why?
Organizations such as Amazon, Microsoft, and Saudi Aramco are strong examples. They combine clear strategy, disciplined execution, and data-driven decision-making. What stands out is how leadership uses performance management to drive accountability and results at scale.
What aspects of performance management should be explored further through research?
More research is needed on how performance systems influence decisions and organizational behavior. The relationship between KPIs, incentives, and actual execution outcomes remains weak. In addition, the role of governance and decision rights in making performance systems effective requires deeper practical exploration.
What are the key competencies of a successful business leader or C-level executive?
A successful C-level executive must think systematically. They need strong decision-making skills under uncertainty, clear ownership of outcomes, and the ability to align the organization around priorities. Discipline in execution, governance awareness, and the ability to translate strategy into results are more critical than technical expertise.
What are the key competencies of a strategy and performance manager today?
They must be able to connect strategy to execution. Strong capabilities in KPI architecture, data interpretation, and performance analysis are essential. More importantly, they must enforce accountability, support decision-making, and understand how organizations operate to ensure performance systems function effectively in practice.
What are the recent achievements in generating value from performance management in your organization?
We shifted performance management from reporting to execution control. We redesigned KPIs to align with strategic objectives, introduced clearer ownership, and improved executive dashboards for decision-making. This increased visibility, reduced ambiguity, and helped leadership respond faster. The greatest value came from transforming performance management into an active management tool.
Meta To Roll Out Changes to Performance Review System in 2026
Tech giant Meta is redesigning the way it reviews employee performance in 2026, according to a report by Business Insider.
The revamp will incorporate a review platform dubbed Checkpoint, which will be used to re-examine employee performance biannually to determine if there are any changes. Checkpoint will hone in on identifying both top and bottom performers, rewarding the former with bonuses that could amount to up to 300% of their pay.
“While our employees have always been held to a high-performance, impact-based culture, this new direction allows for more frequent feedback and recognition in a more efficient way,” a Meta spokesperson said.
Meta is set to implement the changes in the middle of 2026.
Amazon Now Requiring Proof of Productivity for Performance Evaluations
Amazon’s annual review process, known internally as Forte, now reportedly requires employees to list three to five primary accomplishments for the year as proof of their performance. This information was gleaned from internal guidelines acquired by Business Insider.
The guidelines define accomplishments as “specific projects, goals, initiatives, or process improvements that show the impact of your work.”
Amazon’s mandate for employees to provide proof of productivity during performance reviews appears to be part of a larger cultural shift in the corporate sector. In September 2025, xAI employees were also asked to list their responsibilities and accomplishments to determine their future in the company.
AI Layoffs Continue to Impact Tech Sector
The technology sector has been hit with another round of layoffs. Quarterly reports indicate that one of India’s prominent IT services firms, TCS, has laid off around 30,000 employees over the span of six months. This massive downsizing was reportedly driven by widespread artificial intelligence (AI) adoption within the tech industry.
These layoffs are not localized phenomena. On the other side of the world, Silicon Valley has faced similar circumstances, as 2025 also saw several AI-driven layoffs.
The layoffs appear indicative of a trend, something many experts expected. In 2025, Goldman Sachs published a report predicting AI-driven layoffs to continue. .
Study Shows Employees Find Narrative-Based Performance Reviews Most Fair
A study conducted by researchers at Cornell University found that narrative-only feedback is considered by employees as the most fair form of feedback in the context of performance reviews. Published in December 2025, the study compared responses from 1,600 employees to performance feedback organized in three formats—numerical-only, narrative-only, or mixed.
The researchers attribute the study’s findings to the employees’ perception and interpretation of data. “We guess that the presence of a numeric component in the combined feedback may have been interpreted as evaluative or accountability focused [sic], rather than developmental. Employees may view feedback with numerical ratings as highlighting their weaknesses,” they wrote in the report.
Despite the findings, the researchers are hesitant to recommend exclusively using narrative-only performance assessments, stating, “…if you don’t have numbers, there can be some other disadvantages when you are trying to do things like administer bonuses or promotions.”
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.
Artificial intelligence (AI) has emerged as a transformative force in corporate strategic management, fundamentally altering the way companies make strategic decisions. AI is crucial in driving innovation even in the face of dynamic business environments and data abundance.
The integration of AI into corporate strategic management offers a myriad of benefits for businesses seeking to navigate the complexities of the modern market, namely:
Data-driven decision-making: AI empowers companies to transform raw data into actionable insights to identify market trends, assess customer preferences, and predict future outcomes more accurately. AI supports data-driven strategy, leading to better resource allocation, risk mitigation, and operational effectiveness. For instance, a company can leverage AI predictive analytics capabilities to forecast future revenue, competitive threats, the likelihood of expansions succeeding, and other core strategic considerations years in advance.
Enhanced strategic planning: AI’s capabilities extend beyond data analysis to encompass strategic planning and scenario modeling. AI-powered tools can simulate tens of thousands of realistic scenarios per minute, allowing companies to evaluate the potential impact of strategic decisions and identify potential risks and opportunities before committing to major investments. Maersk, for example, uses cutting-edge AI algorithms to revolutionize its container shipping operations. These algorithms optimize vessel routes for efficiency, predict equipment maintenance needs to minimize downtime, and provide real-time insights into cargo location and status, ensuring unparalleled transparency and efficiency.
Customer-centric strategies: AI plays a pivotal role in understanding and anticipating customer needs, enabling companies to develop customer-centric strategies that foster long-term customer loyalty and enhance brand reputation. AI-supported tools can analyze customer behavior, preferences, and feedback; thus, AI can provide valuable insights to personalize marketing campaigns, improve product offerings, and optimize customer service experiences. For instance, H&M Group uses AI to create personalized shopping experiences, optimize product offerings, and improve customer satisfaction by analyzing customer data and preferences.
Competitive advantage: AI adoption provides companies with a competitive advantage in a rapidly evolving market. AI-driven strategies enable organizations to adapt quickly to changing market dynamics. By leveraging AI’s capabilities, corporations can outstand competition and establish themselves as leaders in their respective industries. Unilever, for example, leverages a system to analyze sales data, marketing campaigns, economic trends, and weather patterns to predict future demand more accurately. This enables the company to optimize production planning, reduce waste, and improve profitability.
AI challenges within corporate strategic management
While AI presents immense opportunities, it is crucial to address the ethical considerations surrounding its implementation, such as the potential for AI to perpetuate biases and discrimination. AI algorithms can be trained on biased data sets, leading to partial decision-making and deeper inequalities. Transparency and accountability are other ethical concerns in AI decision-making as it is important to foster trust and understanding of how decisions are made. This is to ensure that everyone involved can act on a just and well-informed strategy.
Successful AI integration also requires a cultural shift within organizations. Companies need to develop training and educational programs to equip employees with the needed skills and to work with AI systems and capabilities in harmony. This should improve communication and collaboration, leading to better alignment with corporate strategic objectives.
Companies that embrace and adapt AI strategically will be well-positioned to navigate the complexities of the modern market and achieve sustainable competitive advantage. For effective adoption of AI capabilities, companies need to develop transparent and accountable AI systems and establish clear ethical guidelines for AI use. Additionally, companies need to engage in ongoing dialogues with stakeholders to build trust and ensure that AI is used responsibly and ethically.
