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Issue 01 · 2025
Recent years have seen a qualitative leap in artificial intelligence tools, enabling project managers to make more accurate decisions and complete tasks faster and with higher efficiency. Despite this progress, many organizations still treat AI as a marginal technical tool, ignoring its growing impact on reshaping the roles of project management itself.
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Recent years have seen a qualitative leap in artificial intelligence tools, enabling project managers to make more accurate decisions and complete tasks faster and with higher efficiency.
Despite this progress, many organizations still treat AI as a marginal technical tool, ignoring its growing impact on reshaping the roles of project management itself.
This report starts from the need for a practical understanding of the AI tools already used in project environments, through a functional review of the support they give project managers in planning, review, analysis, and follow-up. It also shows how to put those tools to work in daily tasks in direct, practical ways, supported by selected Saudi and international cases, and by an executable roadmap that helps organizations move toward more mature and efficient projects.
This report is not written for technical specialists. It is written for project managers and executive leaders who want to grasp what these tools can do now, and to use them to improve institutional performance and produce a tangible effect on projects.
37%
of organizations use AI to improve decision-making — PMI reports
33%
of organizations use AI to accelerate tasks — PMI reports
70%
of large organizations had begun integrating AI tools into the project lifecycle by 2023 — Gartner
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AI tools are no longer confined to laboratories or data-analysis teams. They are now within reach of every project manager, and they sit inside the core of the project lifecycle. They are changing how work is planned, how tasks are tracked, and how performance is reported, by turning traditional activities into algorithm-supported processes. That includes, for example:
Reading historical patterns and deviation signals to anticipate risk before it lands, and ranking priorities by the size of the likely impact.
Turning progress, resource, and spend data into stage and executive reports that are ready for review, instead of spending hours collecting and drafting.
Proposing a better allocation of tasks and capacity from operating load and past performance, and reordering when priorities change.
Drafting charters, scope, plans, and minutes in the institution’s language, from short briefs and lessons from earlier projects.
Those who understand change lead better
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From a technical tool to an intelligent decision system
Digital transformation is a wide umbrella that includes several technical pillars, most notably cloud computing, automation, the Internet of Things, and artificial intelligence. While these technologies work together to raise organizational performance, AI stands out as the inferential element that turns data and facts into intelligent decisions and recommendations. It does not stop at making information available. It produces alternative scenarios from that information, explains hidden patterns, and proposes the better course of action. For that reason, AI is not merely one component inside digital transformation. It is an interactive engine that reshapes the idea of management itself in business contexts.
Artificial intelligence is defined as the ability of digital systems to simulate human mental capabilities, such as understanding, inference, learning, and decision-making. The levels of AI differ by scope and by the capacity to learn and interact.
Generative AI is a branch of artificial intelligence that uses machine learning and neural networks to create original content such as text, images, and video. It can produce outputs that match or differ from the type of input, for example turning text into an image or a video.
Dedicated to a single task, such as prediction or classification. It is the most common type today in project-management tools: scheduling, summarizing, risk prediction, and report generation.
Simulates human intelligence in the ability to learn across many domains. It is still under research and development, and has not yet entered the project environment as a stable operating product.
A theoretical advanced level that surpasses humans in every field, and has not been achieved. It is mentioned here to set the boundary of expectation: what we use in projects today is narrow AI directed at specific tasks.
A field of computer science focused on building systems that can perform tasks that normally require human intelligence. Example: learning, reasoning, and self-improvement.
A subfield of AI concerned with systems that learn from available data so they can act automatically or decide from new data without explicit programming.
A subfield of machine learning that uses several deep layers in neural networks to solve complex problems by identifying the specialized patterns that matter in the input data.
A subfield of deep learning that uses deep neural-network techniques to simulate the human ability to create new data or original, inventive content.
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The effect of AI in project management is not measured by the number of tools purchased, but by how far it enters the cycle of decision and delivery. That effect can be gathered into five linked dimensions: prediction, automation, decision support, stakeholder experience, and continuous learning. Each dimension frees the project manager from a different layer of operating burden, and returns the manager to the seat of leadership.
AI reads performance history, load indicators, and spend patterns to show deviation before it becomes a crisis. Instead of discovering delay after the fact, the manager receives an early signal: a resource shortfall in two weeks, a threatened critical path, or a budget that will be breached if the current pace continues. The value is not the number alone. It is the time it gives to intervene before the damage lands.
Manual drafting and follow-up consume hours that could have been spent on analysis and leadership. Intelligent automation drafts documents, summarizes meetings, reschedules the day, writes stage reports, and extracts tasks from minutes. Automation does not cancel human judgment. It moves repeated effort to the machine so judgment stays where it belongs: with the manager.
AI offers alternative scenarios, explains the causes of deviation, and proposes correction paths built on project data rather than impression. The manager can ask: what happens if we reduce suppliers? Or if we reorder the task sequence? and receive a comparison that can be shown in the executive meeting, not a guess.
Stakeholders are not waiting for raw data. They are waiting for a clear picture at the right time. AI speeds the preparation of executive decks, adapts the language of the message to the audience, and shows progress and risk on one board. The result is faster communication, better decisions, and more trust because the picture is shared, not fragmented.
Every project that closes without intelligent archiving is a lost lesson. AI organizes documents, links content to earlier projects, and extracts patterns: where do delays repeat? which estimates were inflated? which teams delivered at higher quality? Knowledge then moves from the memory of individuals to an institutional asset used in the next plan.
Artificial intelligence does not cancel the role of the project manager. It frees the manager from the details and returns the manager to the core of leadership.
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From organizing tools to strategic partners in delivery
This chapter reviews the leading smart tools now used in project environments, through a functional description of what each tool offers, its direct benefits for the project manager, and a practical example of its effect on daily work. AI tools have become an essential part of project management. They are not limited to organizing tasks or analyzing data. They support decisions and forecast challenges. As dependence on data grows and change accelerates, these tools have become an important factor in enabling the project manager.
The tools reviewed in this chapter show that AI is no longer confined to analysis or documentation. It has become an operating backbone for modern project management. These tools enable the project manager to build a more connected and flexible work environment, where decisions become faster, information becomes smarter, and delivery becomes more efficient.
A smart tool that helps draft documents, reports, and execution plans, generate ideas, and summarize meetings, by understanding context and producing professional text.
Applied exampleThe project manager enters a six-line brief, or asks for a project charter, and within minutes receives a complete draft ready for submission.
An AI tool built into Microsoft 365, Microsoft Project, and Teams. It helps plan projects, analyze gaps, summarize meetings, and extract a task list.
Applied exampleAfter a Teams meeting, Copilot summarizes the discussion and produces a task list. When a new project idea is entered, it builds a plan with tasks and timings from similar past projects.
AI built into the ClickUp platform. It offers live analysis and smart recommendations from team performance and in-system content, and supports plans, schedules, and risk reading.
Applied exampleIn the weekly meeting the project manager asks for late tasks and risks. ClickUp AI analyzes the data and returns a summary with actionable recommendations. It can also generate a two-week execution plan with responsibilities assigned.
A smart tool inside Notion that turns scattered notes and points into integrated reports and content, so the workspace becomes a project knowledge base rather than loose pages.
Applied exampleThe manager enters quick notes after a meeting. Notion AI turns them into an official report sent to the team, and links the points to the related tasks.
An AI tool that automatically reschedules tasks every day, according to priorities, live changes, and available team energy.
Applied exampleWhen an urgent meeting or task is added, Motion automatically reorders the day while keeping the important appointments.
An AI platform that helps plan resources, analyze performance, and forecast spend with precision, and alerts to resource shortfalls and risks before they occur.
Applied exampleDuring delivery, Forecast predicts that the workload will breach the budget, or warns of a resource shortfall two weeks ahead, so the manager can reallocate tasks and avoid the deviation.
An AI tool that records and summarizes meetings on Zoom and Google Meet in real time.
Applied exampleDuring a virtual meeting, Tactiq produces a summary with the key points and tasks and distributes it to the team as soon as the meeting ends.
A smart assistant inside the Hive platform that creates project plans and reports automatically from the team’s workflow.
Applied exampleThe manager enters the project goals, and HiveMind produces a comprehensive plan with tasks, dates, and responsibilities.
An AI tool that creates professional visual presentations automatically from project inputs.
Applied exampleAfter a stage performance analysis, the manager enters the key points. Beautiful.ai builds a slide deck with professional graphics and design, ready for management, without a designer.
An AI tool that turns ideas into professional presentations without manual design.
Applied exampleThe manager writes the deck structure as bullets. Gamma turns it into slides ready for an executive discussion, without hours of formatting.
AI inside the Monday platform that watches team load, issues alerts before work piles up, and proposes redistributing tasks to reduce stress and delay.
Applied exampleWhen tasks pile up on one employee, Monday AI proposes a redistribution to avoid delay and burnout.
An AI tool inside Asana that analyzes workload, offers progress insights, and alerts when balance breaks or a bottleneck appears on the critical path.
Applied exampleThe system sees that one member is overloaded, or that a critical path has slowed, and proposes adjusting the schedule or spreading tasks across the team to avoid delay.
An AI tool that rearranges tasks automatically from your priorities and daily preferences.
Applied exampleWhen tasks from several projects pile up, SkedPal reschedules the day automatically for a better distribution of effort.
A visual Kanban tool that can now turn notes and discussions into smart task cards through AI.
Applied exampleAfter a client meeting, notes go into Trello and AI produces task cards directed to team members.
A smart analysis tool that tracks project progress and alerts to deviations early, by reading team performance and delivery indicators.
Applied exampleDuring a marketing campaign, Wrike spots a slowdown in the design stage and proposes adding resources to avoid a late delivery.
An AI analysis platform that turns cyber risk in projects into clear financial figures, and helps rank priorities by the size of the potential impact.
Applied exampleOn an electronic-portal project, RiskLens shows that delaying the link to the identity system will cost 200,000 dollars within six months, which requires immediate intervention.
Smartsheet supports AI through advanced analytics on AWS. It is used to track performance, forecast risk, and recommend actions on schedule, budget, and resources.
Applied exampleOn a technical project, Smartsheet AI shows a gap in estimates between teams and proposes one unified plan so the project can proceed without delay.
It analyzes engineering-team performance on technical projects by tracking code, delivery cycles, and production quality. It provides a live board that supports project managers on complex software work.
Applied exampleOn a software project, Athenian shows that code review is taking excessive time, so management redistributes roles to speed delivery.
A tool that connects more than 5,000 applications and runs tasks automatically through Zaps based on set conditions.
Applied exampleWhen a task is completed in Trello, Zapier automatically sends a Slack notification and updates the Google Sheet log.
A smart workspace that combines documentation, planning, and live assistance in project management.
Applied exampleWhen the idea “improve customer experience” is entered, Taskade creates a five-stage execution plan with a customizable task board.
A visual platform for building automated workflows between different tools without programming.
Applied exampleThe manager creates a staged budget-approval path, where a notification is sent to the next stage only after the previous one is complete.
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From daily tasks to intelligent executive direction
In fast-moving project environments, the challenge is no longer a shortage of tools. It is how to harness them to support the project manager’s core tasks without draining mental and time capacity. It is no longer acceptable for a project manager to spend hours drafting a report or tracking progress by hand, while AI can now carry that burden, so the manager is free for leadership, analysis, and decision-making. This chapter offers a functional, practical view of how AI can take a direct role in supporting every main task in the project lifecycle.
The following table shows how AI contributes to the project manager’s main tasks, and which tools fit each task. The tasks are then set out across the project stages: initiation, planning, execution, monitoring, and closing.
Generates first drafts from short descriptions, and proposes clear splits that use earlier data and lessons learned.
Analyzes goals and turns them into time stages and a logical task sequence that fits the organization the manager works with.
Analyzes performance history and proposes a better allocation of tasks based on capability.
Generates risk scenarios and forecasts deviations from similar precedents.
Analyzes project criteria and proposes measures that fit the goal.
Analyzes results automatically and finds deviations from quality standards.
Records and summarizes meetings, highlighting decisions and follow-up tasks.
Issues smart alerts when early risk signals appear in delivery.
Merges actual progress with the schedule to generate a full report and recommendations.
Offers causal analysis of late tasks, with alternatives for solving the problem.
Analyzes interactions and meeting data to generate useful satisfaction indicators.
Automatically summarizes every project stage and links results to goals and budget.
Turns data into professional slides that include automatic visual analysis.
Organizes, archives, and describes text content automatically and links it to earlier projects.
Tracks spend and analyzes variances to offer a precise forecast built on financial behavior.
| Main task | What AI does | Suitable tools |
|---|---|---|
| Document preparation | Drafts charters, scope, and reports from short briefs, and proposes splits that draw on earlier lessons. | ChatGPT, Copilot, Notion AI |
| Schedule planning | Turns goals into stages and a logical sequence, and reschedules automatically from priority and resources. | Motion, ClickUp AI, Forecast |
| Meeting management | Records and summarizes the meeting, highlights decisions, and extracts follow-up tasks as soon as it ends. | Tactiq, Copilot, Fireflies |
| Performance analysis and reporting | Merges actual progress with the plan, explains delay, and generates a full report with recommendations. | Power BI + Copilot, HiveMind, Tableau Pulse |
| Risk management | Builds risk scenarios, forecasts deviation from similar precedents, and alerts before the impact lands. | Forecast, ChatGPT, ClickUp AI |
| Knowledge management | Organizes and archives content, links it to earlier projects, and turns minutes and notes into a reusable asset. | Notion AI, Copilot, Guru |
| Stakeholder communication | Turns data into executive slides, adapts the message to the audience, and raises the quality of the presentation to senior management. | Beautiful.ai, Gamma, Copilot |
Artificial intelligence does not only change how tasks are done. It redefines who does them.
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From organizational units to systems of institutional management intelligence
At a time when billions of riyals are managed through diverse projects across many sectors, it is no longer enough to rely on traditional offices that act as a mere timekeeper or report writer. AI is now leading a qualitative shift in the structure of the project management office, so that it becomes the executive mind of the institution and an operating-leadership platform where data is integrated, tools interact, and the relationship between planning and delivery is rewritten.
The intelligent office is not merely a technical upgrade. It is a new operating model. It is the office that does not stop at telling you where you have arrived. It asks an intelligent question: are you on the right path to realizing value? This chapter reviews the deep shift in intelligent project offices (IPMO) in their characteristics, how they are built, and how they operate, and it clarifies the complementary relationship between them and the higher management system.
The aim of the intelligent office (IPMO) is no longer only to control schedules. It is to ensure that projects contribute to the institution’s goals, at the lowest cost and the highest efficiency. The intelligent office does not work alone. It speaks with the rest of the systems.
The realistic challenge: owning smart tools without actual integration leaves the intelligent office without a real institutional context. What is required is a connected system that knows the effect of each project on the budget, human capital, and strategic plans: linking AI tools to ERP, CRM, and human-resources platforms; turning operating indicators into strategic indicators that support senior decisions; and making the intelligent office’s outputs accepted inside the official reports of finance and legal departments.
The intelligent office integrates with the institution’s systems at three levels: strategic, functional, and technical. The value is not in an isolated tool, but in a system that knows the effect of each project on budget, resources, and plan. Integration does not mean replacing the existing systems. It means speaking with them.
Direct access to data from operating systems enables leadership to decide immediately. With predictive analysis, deviations are known before they occur, so action becomes proactive instead of reactive. Liveness here is not a colored board. It is the ability to change course at the right time.
The intelligent office links new projects to performance history, lessons learned, and strategy maps. Estimates are reviewed against what actually happened, risks are read from similar precedents, and knowledge does not remain locked with one manager after the project closes.
The intelligent office raises reporting to the level of institutional vision. It does not stop at telling leadership where the projects have arrived. It asks whether the path is realizing value. The office then becomes an operating-leadership platform, not a time-monitoring unit.
Integrates with it to extract budget, commitments, and resource consumption, and to link every project to its actual financial effect.
It does not replace the financial-planning or accounting system.
Integrates with it to turn operating indicators into strategic boards that give senior management the cause, not the number alone.
It does not replace the data warehouse or the approved analytics tool.
Integrates with it to pull repeated data between systems, reduce manual entry, and speed the reporting cycle.
It does not replace process engineering or the system owner’s responsibility.
Integrates with them to link quality-standard deviation to the project path, and to raise an alert before a defect becomes rework.
It does not replace the quality policy or approved audit.
| Dimension | Meaning | Effect |
|---|---|---|
| Real time | Direct access to data from operating systems. | Enables leadership to take immediate decisions. |
| Predictive analysis | The ability to know deviation before it occurs. | Proactive action instead of reaction. |
| Management agility | The ability to change course from live data. | Stronger adaptation in changing environments. |
| Knowledge leadership | Linking projects to performance indicators and strategy maps. | Raising report quality to the level of institutional vision. |
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How do you run an intelligent dashboard that links planning to results?
The intelligent dashboard is not merely a dashboard that displays colored data. It is a strategic-thinking tool that turns data into decisions that can be executed, and links the plan to reality moment by moment. The transformational path is simple on the surface: from information, to knowledge, to decision.
A practical example of integration: in a service organization, the intelligent office was linked to the human-resources platform. When a delay was recorded on a training project, an automatic signal appeared on the HR board to activate an early-support initiative before the full effect appeared.
Merging data from performance, resources, and spend into one picture, so projects are not run from separate sheets that cannot see one another.
Analyzing the cause of deviations, not only showing a red number. The manager asks why the path was late, and receives an explanation that can be discussed, not a silent indicator.
Sending immediate notifications when critical thresholds are crossed: cost, time, quality, or team load. Early alert is what turns the board from an archive into a leadership tool.
Automatically proposing alternative plans: reducing suppliers, changing the task sequence, or redistributing load, and attaching an updated schedule that can be shown in the executive meeting.
While following an operations-digitization project, repeated alerts appear on the board on the “development cost” axis. The tool proposes reducing the number of suppliers or changing the task sequence to cut waste, and automatically attaches an updated schedule to be shown in the next executive meeting.
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From vision to the ground: how do you move the institution toward management intelligence?
Responsibility sits between seeing the opportunity and acting on it. The earlier chapters have shown that AI in project management is no longer a knowledge luxury or a deferred option. It has become an urgent executive tool that requires a clear vision, courage in adoption, and discipline in application. This chapter places practical recommendations in the reader’s hands to make the move from interest to transformation easier, through three practical pillars: a technical adoption roadmap, building digital leadership teams, and steps that start tomorrow.
Why is a roadmap needed? Because using AI tools at random wastes resources, confuses the team, and fails to create real value. The path is: from experiment... to intelligent integration.
Inventory the current digital tools, assess the institution’s digital maturity, expose the gaps, and measure data readiness. Without an honest diagnosis, the experiment becomes a purchase of tools that find no data to stand on.
Choose a pilot project and one tool for a simple, non-critical daily task, then measure time, accuracy, and output quality. Start with non-critical work to reduce resistance to change, and learn before you scale.
After value is proven: usage policies, integration with the institution’s systems, training that understands outputs rather than buttons alone, then an intelligent office that links planning to results. Scaling without governance only repeats randomness at a larger size.
| Stage | Proposed action | Practical example | Professional notes |
|---|---|---|---|
| Institutional readiness | Inventory current digital tools and assess data readiness. | Analyze current follow-up tools and how they connect to reports. | A digital-readiness assessment tool is preferred. |
| First experiment | Choose one tool for a simple daily task. | Use AI to draft meeting minutes. | Start with non-critical work to reduce resistance to change. |
| Technical integration | Connect the tool to the institution’s systems (ERP, HRMS). | Integrate ClickUp AI with institutional systems. | An internal or external integration specialist can be used. |
| Practical training | Prepare a training plan that covers the technical and the analytical. | A half-day workshop on risk analysis with AI. | Training does not mean “usage.” It means “understanding the outputs.” |
| Measure and adjust | Develop success indicators that measure the effectiveness of AI. | Time saved, output quality, and forecast accuracy. | Do not stop at the team’s impression. Rely on numbers and outputs. |
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The team is the first tool... what follows is detail
Artificial intelligence improves decisions, but it does not create leadership. For that reason, no tool has value without a team that manages it, interprets its outputs, and activates them in the practical context of the project.
The components of the ideal team for intelligent project management gather four complementary roles: the person who binds the tool to the goal, the person who reads the data, the person who connects the systems, and the person who keeps the knowledge. The absence of one weakens the whole chain.
Inside your current project, using one AI tool. Choose a specific stage: drafting documents, distributing tasks, or summarizing reports. Make the goal measurable: did it save time? were the results more accurate?
Identify who has analytical knowledge, and who is proficient with scheduling tools. This matrix will determine where training starts, and who will lead the change.
Include: why do we want AI? and where will we start? Share it with the project team, so it becomes the start of institutional commitment.
| Role | Primary tasks | Required skills |
|---|---|---|
| Digital project manager | Linking AI to the project’s goals. | Technical intelligence and strategic thinking. |
| Data specialist | Building predictive models and reading trends. | Power BI, Python, and an understanding of indicators. |
| Technical integration expert | Connecting systems and easing the flow of data. | API, RPA, and workflow management. |
| Knowledge manager | Archiving and organizing project content. | Documentation, classification, and knowledge of content platforms. |
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From building infrastructure to influencing the global landscape
When artificial intelligence is mentioned, what usually comes to mind are the applications and operating tools that change the shape of daily work. The real power does not lie in use alone, but in enablement: how are systems designed? how is infrastructure built? and how are policies set that turn AI from a technical option into a sovereign engine and a strategic lever?
The Kingdom of Saudi Arabia took this path with early awareness. It did not see AI as a supporting tool, but as a central component of its ambitious Vision 2030. For that reason it launched the National Strategy for Data and AI (NSDAI) to establish a historic shift in the legislative, technical, and institutional enablement structure.
By contrast, global experiences differ in how they treat AI: some excel in research without application, and some expand in use without governance. The Kingdom remains one of the few countries that have joined legislation, application, and direction under a single institutional umbrella.
How did the Kingdom build a comprehensive enabling environment for AI? Through regulations, policies, and institutional enablers that reshape policy, direct investment, and prepare the infrastructure for large-scale adoption — not through isolated tools.
What initiatives and enablers moved it from planning to application? From the ALLaM model to SAMAI, NPSAI, and the energy and language centers, the strategy becomes programs that can be measured on the ground.
Where does the Saudi experience stand beside leading international models? Between research leadership in the United States, wide application in China, and an ethical focus in the United Kingdom, the Kingdom is building a model that joins legislation, application, and direction.
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Regulations, policies, and institutional enablers
In the world of AI, owning data or developing algorithms is not enough. True enablement begins with building a regulatory and legislative environment that reshapes policy, directs investment, and prepares the institutional infrastructure for large-scale adoption. The Kingdom has taken this path through a comprehensive strategic roadmap.
The National Strategy for Data and AI (NSDAI) was launched in October 2020 under the supervision of the Saudi Data and AI Authority (SDAIA). It is the supreme framework for developing the data and AI sector in the Kingdom.
The authority that supervises the national strategy, and the institutional umbrella for directing data and AI in the Kingdom. It sets the frame, binds legislation to application, and leads national enablers from governance to adoption.
Place the Kingdom among the world’s leading countries in AI by 2030.
Raise the efficiency of government performance and strengthen data-driven decision-making.
Support innovation, localize technology, and develop national talent.
Data governance, quality, and sharing are the base of any reliable adoption of AI. Without usable data, models remain without context.
Adoption and application in vital sectors, from government to energy, education, and health, so the strategy moves from a document to an effect.
Building national capability, research, and development, so enablement is not entirely imported, and a path is built for Saudi talent.
Governance, investment, and infrastructure: six pillars that work together — governance, capabilities, research and development, adoption, investment, and infrastructure.
Issued by royal decree in 2021. It forms the legal base for protecting individuals’ data, and obliges entities to follow strict standards in collection and processing.
Issued by the National Cybersecurity Authority to set the minimum data-protection requirements across the data lifecycle, which secures AI applications in government institutions.
Opens access to public data to foster innovation, which serves the development of AI applications.
Governs the processing of sensitive data and protects privacy in AI environments.
Regulates data exchange between government and private entities, and strengthens transparency and data quality.
Responsible for data governance and for setting the frames and policies that support a reliable adoption of AI.
SDAIA’s executive arm. It designs and develops intelligent models and advanced applications.
Secures AI applications through legislation and controls that protect data and infrastructure.
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As part of the Kingdom’s effort to strengthen AI as a strategic component of government and economic work, a group of pioneering initiatives has been launched that represent the shift from policy to application. Among the most prominent of these initiatives are:
The first open-source generative Arabic language model, developed by SDAIA. It is among the largest models in the region, and supports intelligent text processing and Arabic linguistic interaction.
A national initiative to motivate innovators and entrepreneurs to develop applicable AI solutions in vital sectors such as health, education, and energy.
Launched by the National e-Learning Center to provide a safe, open environment for testing AI technologies in digital education and developing adaptive solutions.
A technical project to develop a national Arabic voice-recognition system, used in government and security applications, and delivered by SDAIA in partnership with SCAI.
Launched by the Ministry of Communications and Information Technology to qualify one million Saudi citizens with the knowledge and skills needed in AI, through specialized training programs in cooperation with leading technology companies.
Launched by the Kingdom under the supervision of the International Center for AI Research and Ethics (ICAIRE) to support generative-AI research and development and to raise awareness of its ethics. Public qualification programs such as the Gen AI Academy sit under this umbrella.
Established as an arm of the Public Investment Fund in AI and emerging technologies. It works on innovative solutions in several fields, including smart cities, energy, and healthcare.
The King Salman Global Academy for the Arabic Language launched this initiative to establish the Arabic Intelligence Center, which houses specialized AI labs that aim to develop smart technical tools that support Arabic, through text analysis, speech processing, and machine-learning applications.
Established by the Ministry of Energy in cooperation with SDAIA as the first center specialized in AI for the energy sector, to improve operating efficiency and support technical innovation in renewable energy and smart consumption.
A program to enable the nonprofit sector with AI, launched by the National Center for Nonprofit Sector Development to raise the readiness of sector organizations to use AI tools, through specialized workshops, advisory hours, and directed learning platforms.
The national intellectual-property exchange platform “Attam” uses AI to digitize and activate trading procedures for IP assets, which helps stimulate innovation and ease access to knowledge rights.
Alongside these initiatives, the Kingdom has launched many other programs and policies that strengthen the readiness of the digital infrastructure, and enable government and private entities to adopt AI inside an integrated system.
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An analytical comparison between the Saudi path and other countries
While the Kingdom of Saudi Arabia is making notable progress in AI through its national strategies and diverse initiatives, other countries are taking different paths in this field. This analytical comparison shows how Saudi Arabia can benefit from international experiences, and at the same time how the Kingdom forms a model to be followed in some aspects.
It is among the leading countries in AI research and development, investing heavily through its academic institutions and major technology companies. AI techniques are used in a wide range of applications, from healthcare to transport and energy. Despite the progress, the United States faces challenges in regulating the use of AI, especially around privacy and ethics.
China focuses on building a strong infrastructure to support AI applications, including data centers and fifth-generation networks. The technologies are used in a range of government applications, from traffic monitoring to public services. China faces challenges related to privacy and human rights in some AI applications.
The United Kingdom focuses on developing regulatory and ethical frameworks for the use of AI, with an emphasis on transparency and accountability. It seeks to cooperate with other countries to develop international standards for AI use. It faces challenges in applying these regulatory frameworks in a rapidly changing technical environment.
The Saudi model of enabling artificial intelligence is the result of a careful reading of global experiences, and a smart adaptation of technology in a national sovereign context. The Kingdom is not content to be a user of AI. It seeks to lead its development in regulatory and executive environments, in a manner that befits its political and economic weight.
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Disclaimer: This report was prepared on the basis of the information available at the time of publication, with care to achieve the highest degrees of accuracy and credibility. Nevertheless, NIRROV LIMITED accepts no responsibility for any errors, omissions, or gaps in the data, or for any results that follow from using the content of this report.
NIRROV works with organizations that want strategy, operations, and digital delivery to move as one system.