In this chapter we trace the shifts that have moved innovation from a seasonal activity to a permanent organizational need: accelerating change, artificial intelligence and what it means for the value of people, and the changing skills that work demands. We then read these shifts in the Saudi context and show that investment in the inputs of innovation pays off only to the extent that an organization is able to turn them into results.
Three years ago, a public agency set up an innovation center. It gave the center a budget and a fully equipped space, launched a platform to collect employees’ ideas, and ran a series of hackathons. Its annual report carried striking figures: hundreds of ideas, dozens of participating teams, prizes for the winners. Then an internal review asked a simple question: how many of these ideas are in actual use today? The answer: very few.
The agency had every input in place: funding, space, a platform and enthusiasm. The fault lay in the distance between inputs and results. This leads us to two linked questions in this chapter: why has innovation become a permanent organizational need under present conditions, and why does investment in its inputs fall short of achieving it on its own?
After studying this chapter, the reader should be able to:
Traditional management had good reason to care about discipline and standardization. In the early twentieth century, the principles of scientific management, with their division of labor, standardized procedures and performance measurement, delivered enormous gains in productivity and quality (Taylor, 1911). This logic remains essential for any work that must be done correctly every time, such as dispensing medicine, paying salaries or processing regulatory transactions.
Yet this logic was designed for problems whose causes and solutions are known, where the challenge lies in skilled execution. Its limits show when it is applied to shifting opportunities and uncertain paths, where the need is not fully understood and the link between intervention and outcome is unclear. In such situations, insisting on a detailed plan drawn up in advance becomes a source of error, because the plan rests on assumptions that have not been tested.
The shift required, therefore, is to hold two kinds of discipline that work together: operational discipline, which protects quality and reliability, and innovation discipline, which guides exploration, experimentation and learning. The management literature calls the combination of the two organizational ambidexterity, which we discuss in detail in Chapter 3.
One of the most common management errors is to apply one discipline where the other belongs. An exploratory initiative is judged by the standards of an operational project and is stifled before it can learn, or sensitive operational work is handled with the logic of experimentation and the service is exposed to needless errors. The first management question in any situation is therefore: do we know the solution and need to implement it, or do we first need to discover it?
The value of any organization rests on a set of assumptions about the world around it: who its beneficiaries are, what they need, what alternatives are open to them, and what technology and regulation allow. The conditions surrounding the organization, and the ways they change, make up its external realities. These include shifts in markets, society, legislation and technology, and the points where they intersect, as when a social shift in young people’s expectations meets a technological shift in the ways services are accessed.
The danger is that external realities usually change gradually and quietly, while the organization carries on working on assumptions that held true in the past. As the changes accumulate, the organization may find that the foundation on which its value was built is no longer there.
The pace of this change is quickening. A study of corporate longevity among firms in the S&P 500 index of large US companies tracked the average tenure of a company in the index and found that it fell from about thirty-three years in 1964 to about twenty-four in 2016; the authors projected that it would approach twelve years by 2027, and linked the decline to shifts in technology and business models and to waves of mergers and acquisitions (Anthony et al., 2018). The phenomenon resembles what Christensen described as disruptive innovation, in which a new solution starts out simple at the margins of the market and then improves until it displaces leaders that served their existing customers well (Christensen, 1997). Government organizations are less exposed to being pushed out of the market, but they face the mirror image of this phenomenon: a widening gap between what they provide and what beneficiaries expect, having grown used to fast digital services in their daily lives.
Set against these are the organization’s internal realities: its current state in terms of leadership, culture, structures, roles, internal relationships and the skills of its people. If external realities determine what the organization should innovate, internal realities determine its capacity to deliver it. The distinction is close to the familiar SWOT analysis of strengths, weaknesses, opportunities and threats: opportunities and threats belong to external realities, strengths and weaknesses to internal realities. The difference is that an innovation perspective treats strengths and weaknesses as things that can be built and developed.
Teece described an organization’s capacity for renewal in a changing environment as dynamic capabilities, and identified three components: sensing, which means detecting opportunities and threats and listening to beneficiaries before changes force themselves on the organization; seizing, which means mobilizing resources and making decisions to capture the opportunity; and reconfiguring, which means continually adapting structures, assets and practices (Teece, 2007). Sensing without seizing produces reports that no one acts on; seizing without sensing produces activity in the wrong direction.
Building these capabilities is a responsibility shared across every level of the organization. Sensing often happens at the organization’s edges, with the employee closest to the beneficiary; seizing requires leadership decisions and resources; and reconfiguring calls for the participation of all levels. The organization therefore needs a vision that makes renewal part of its purpose, and an environment that encourages initiative at every level, so that responding to change becomes an organizational habit. As change accelerates, building this capability becomes a condition for survival and excellence, and a priority that takes precedence over many operational ones.
Ask yourself: when did your organization last learn of a change in its beneficiaries’ needs, and how? If it was through complaints or falling numbers rather than regular dialogue with them, sensing is weak. If trends are noted in reports but lead to no decision in the plan or the budget, seizing is weak.
Innovation management has gained importance because the sources of value and competition, and the very nature of work, are changing. The most influential forces can be summed up in five:
The fourth force is evident in the World Economic Forum’s Future of Jobs Report 2025, in which employers expect 39% of workers’ core skills to change by 2030. Notably, the list of skills expected to grow in importance goes beyond technical skills such as artificial intelligence and cybersecurity to include creative thinking, resilience and flexibility, curiosity and lifelong learning, and leadership and social influence (World Economic Forum, 2025).
This finding bears directly on innovation management: the capacity to innovate depends on people who are ready to question, experiment and learn, and on a work environment that nurtures these abilities, as much as it depends on technology and structures.
Generative artificial intelligence is changing how much a single person can accomplish. In a field experiment with 791 employees at Procter & Gamble working on real product-development challenges, an individual working with AI performed on a par with a team working without it. Those using AI also produced solutions that were better balanced between technical and commercial considerations, whatever their specialty, as though the tool had softened the effect of functional silos (Dell'Acqua et al., 2026). What once required a team and a long stretch of time may now be accomplished by one person, faster and at comparable quality.
The picture has another side, however. In a study of short-story writing, AI-generated ideas raised the creativity of each individual writer, with the largest gains among the less creative, but the stories became more similar to one another, so the diversity of ideas across the group declined (Doshi & Hauser, 2024). When everyone uses the same tools in the same way, their ideas converge.
How the tool is used is what makes the difference. In a study of 123 university students working on entrepreneurial projects, the students followed a structured five-stage collaboration protocol that asked the tool to question their assumptions and suggest alternatives, while leaving evaluation, revision and reflection to them. Of these students, 91.9% reported a marked development in their project ideas, and many showed improvement, to varying degrees, in aspects of their strategic analysis (Alsamani, 2026). These are self-perceptions from an exploratory study, but they suggest that the tool can act as a thinking partner when collaboration with it is deliberately designed.
In this sense, we argue in this book, artificial intelligence raises the value of people in the innovation system. When generating alternatives and building prototypes becomes easy and available to everyone, the sources of distinction shift to what the tool cannot produce on its own: choosing the problem worth solving, understanding the context and the beneficiaries, exercising judgment when weighing alternatives, diversity of perspectives, and the motivation to keep trying. This shift requires innovation management to revisit entrenched assumptions: team size, the division of roles, the cost of experimentation, which has fallen sharply, and the way individuals and their contributions are evaluated.
A more productive question than “How do we use artificial intelligence?” is “What has it made possible, and what remains for people to add?”
Innovation holds a central place in Saudi Vision 2030 as a means of diversifying the economy, raising public-sector efficiency and improving quality of life (Saudi Vision 2030, 2016 [Arabic]). On June 30, 2022, the national aspirations and priorities for the research, development and innovation sector over the next two decades were announced. They set out four priorities: health and wellness, sustainable environment and supply of essential needs, energy and industrial leadership, and economies of the future. They also set targets for annual spending on the sector to reach 2.5% of gross domestic product by 2040 and for the sector to contribute some SAR 60 billion to GDP, with the Research, Development and Innovation Authority (RDIA) serving as the sector’s enabler and regulator (Saudi Press Agency, 2022 [Arabic]).
These efforts are reflected in the Global Innovation Index published by the World Intellectual Property Organization (WIPO): Saudi Arabia rose from 66th place in 2020 and 2021 to 42nd out of 139 economies in 2026 (World Intellectual Property Organization [WIPO], 2026).
A careful reading of the index, however, goes beyond the overall rank. In 2026 Saudi Arabia ranked 30th on innovation inputs and 62nd on innovation outputs, and the report notes that the country produces less innovation output than its level of investment would warrant. The picture was similar in 2025, when it ranked 31st on inputs and 61st on outputs (WIPO, 2025, 2026). Among the notable strengths the report identifies are Saudi Arabia’s first place worldwide in the use of information and communication technology and its strong position in startup financing and e-government services; among the weaknesses is its lower ranking in knowledge and technology outputs and in creative outputs.
This gap matters for the subject of this book. Inputs such as funding, infrastructure, centers and programs are a necessary condition for innovation, but they do not turn into outputs automatically. The conversion happens inside organizations: in the way priorities are set, opportunities captured, ideas tested and decisions made, in how people are involved and motivated, and in how solutions are moved into operation. This is the domain of innovation management. Setting up an innovation center in a university or government agency without linking it to the daily work of departments and units, and to the way their performance is assessed, raises the inputs while the outputs remain contingent on what happens inside those units.
International innovation indices are tools for comparison at the national level, and they are affected by changes in methodology and data sources from year to year. Their general trends and major gaps are what should be read, and they should be used alongside other sources when judging the performance of a particular organization.
The aim of renewal is for the organization to maintain its relevance to its beneficiaries’ needs, now and in the future, which is what reconfiguring means in the dynamic capabilities framework. The difference between an organization that responds and one that reacts is that the first reads change before it is forced upon it, and so is prepared, while the second reads it after the effect has been felt, and so acts under pressure.
Innovation has its risks, because it pursues what has not yet been tried. Risk management in this context is an ally of innovation: it sets the boundaries within which experimentation can proceed without unacceptable harm, and it uses methods such as limited experiments and staged funding to reduce risk. Yet not innovating is not as safe an option as it seems; an organization that stops renewing itself gradually loses its relevance. The right comparison, then, is between risks that can be managed and risks that accumulate unseen.
The need for efficiency persists through all these shifts; skilled operations are what fund renewal. Some problems, moreover, need only adherence to a known standard or the application of a proven practice, and using innovation tools on them slows the solution and undermines the credibility of innovation itself.
| Situation | Indicators | Appropriate approach |
|---|---|---|
| Execution failure | The cause is known, the solution proven, and the gap lies in compliance or resources | Operational management and follow-up |
| Opportunity for improvement | Performance is below standard, the causes can be analyzed, and solutions are mostly known | Continuous improvement and quality tools |
| Technically complex problem | Requires specialized expertise, but can be broken down and planned | Expert analysis and design |
| Unresolved need or opportunity | The solution is unknown, and there are essential assumptions about the user, the implementation or the value | Innovation approach: experimentation and staged decisions |
A distinction drawn by Bennett and Lemoine helps with this diagnosis. They observed that describing the environment as “volatile, uncertain, complex and ambiguous” becomes an excuse for not planning when it is used as a blanket label for every difficulty, and that its value lies in identifying the type of challenge: volatility is met by building reserves and flexibility, uncertainty by gathering information, and complexity by restructuring and building expertise, while ambiguity, where causal relationships are unclear and there are no precedents to go by, calls for experimentation (Bennett & Lemoine, 2014). Experimentation is the core of the innovation approach, which is why innovation is the most fitting response precisely when ambiguity defines the situation, and why other approaches take precedence for the remaining difficulties.
Organizational maturity shows in diagnosing the situation and choosing the approach that creates value with no more complexity than necessary.
Use this matrix to turn the external changes you observe into questions that can be decided on, in strategic planning sessions or periodically every six months. Fill in one row for each significant signal of change.
| Domain | Signal observed | Likely effect on us | Opportunity it opens | Type of challenge | Proposed response |
|---|---|---|---|---|---|
| Beneficiaries and society | What has changed in their expectations and behavior? | Which assumption in our model does it threaten? | What new need is emerging? | Volatility / uncertainty / complexity / ambiguity | Act now / explore / monitor |
| Technology | What new possibilities are available? | ||||
| Regulation and legislation | What is changing in regulations and policies? | ||||
| Economy and resources | What is changing in funding and costs? | ||||
| Competitors and alternatives | Who offers an alternative from outside the sector? | ||||
| Environment and sustainability | What environmental pressures or opportunities are there? |