Tag Archives: Business

Beyond the Cloud: How Artificial Intelligence Is Reshaping the Economics of SaaS

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Artificial Intelligence is no longer an enhancement layered onto Software as a Service. It is rapidly becoming the force that is reshaping the SaaS model itself. What began as cloud-hosted software delivered by subscription is evolving into something closer to “intelligence as a service,” where the primary value lies not in the application interface but in the system’s ability to reason, predict, generate, and act.

From Software Delivery to Decision Delivery
Traditional SaaS focused on providing tools. AI-driven SaaS increasingly provides outcomes. Instead of merely storing data or enabling workflows, modern platforms analyze patterns, surface insights, and automate decisions in real time. Customer relationship systems forecast churn before it happens. Financial platforms detect anomalies and recommend actions. Marketing tools generate campaigns, segment audiences, and optimize performance continuously.

This shift changes the perceived role of software from passive infrastructure to active collaborator. Users are no longer just operators of systems. They are supervisors of autonomous processes. The interface becomes conversational, often powered by natural-language AI agents that allow users to request results rather than configure procedures.

The Rise of AI-Native SaaS
A new category of AI-native SaaS is emerging. These products are not traditional applications with AI features added later. They are built around large language models, machine learning pipelines, and continuous data feedback loops from the outset. In many cases, the application layer is thin, while the intelligence layer carries most of the value.

AI-native platforms can improve automatically as they process more data, creating compounding advantages for early leaders. This dynamic introduces a “winner-takes-most” tendency in some markets, where superior models attract more users, generating more data, which further improves performance.

Vertical SaaS is also being transformed by AI. Industry-specific systems now embed domain-trained models capable of interpreting specialized terminology, regulations, and workflows. A healthcare platform might summarize clinical notes and flag risks. A construction platform may analyze project schedules and predict delays. The result is software that behaves less like a toolset and more like an expert assistant tailored to a particular field.

Automation Becomes Autonomy
Automation has long been part of SaaS, but AI pushes it toward autonomy. Routine tasks such as data entry, scheduling, reporting, and customer support are increasingly handled end-to-end by intelligent agents. Multi-step workflows can now be executed with minimal human intervention, with systems monitoring outcomes and adjusting strategies dynamically.

This reduces labor costs and increases speed, but it also shifts responsibility. Organizations must now manage oversight, accountability, and risk associated with automated decisions. Human roles evolve toward exception handling, strategic direction, and ethical governance rather than routine execution.

Low-code and no-code tools are likewise changing under AI influence. Instead of building applications manually through visual interfaces, users can increasingly describe what they want in natural language and allow the system to generate workflows, integrations, or even full applications. Software creation itself becomes a conversational process.

New Economics and Pricing Models
AI significantly alters the economics of SaaS. Traditional subscription pricing assumed relatively stable marginal costs per user. AI workloads, especially those involving large models, introduce variable computational expenses tied to usage intensity. As a result, many providers are shifting toward consumption-based pricing, charging per query, per generated output, or per processing unit.

This model aligns revenue with cost but can introduce unpredictability for customers. Organizations must monitor usage carefully to avoid runaway expenses, while vendors must balance transparency with profitability. Some providers are experimenting with hybrid pricing structures that combine base subscriptions with metered AI usage.

At the same time, AI can dramatically increase perceived value. A tool that replaces hours of skilled labor may justify higher pricing than traditional software. The focus shifts from cost per seat to cost per outcome.

Data as the Strategic Asset
In AI-driven SaaS, data becomes the core competitive advantage. Proprietary datasets enable model training, fine-tuning, and continuous improvement. Vendors that control high-quality, domain-specific data can produce more accurate and reliable outputs than generic systems.

This dynamic strengthens customer lock-in. As organizations feed operational data into a platform, switching providers becomes more difficult because the accumulated context and model tuning may not transfer easily. Consequently, concerns about data ownership, portability, and privacy are intensifying.

Security requirements are also expanding. Protecting not only stored data but also model behavior, training pipelines, and generated outputs is now essential. Risks include data leakage through prompts, model manipulation, and exposure of sensitive information in generated content.

Human Trust, Transparency, and Governance
AI introduces new forms of risk that traditional SaaS did not face. Incorrect recommendations, biased outputs, or opaque decision processes can have significant real-world consequences. Providers must therefore invest in explainability, auditability, and safeguards that allow users to understand how conclusions are reached.

Regulatory scrutiny is increasing globally, particularly in sectors such as finance, healthcare, and public administration. Compliance frameworks will likely shape product design, requiring clear accountability for automated decisions and mechanisms for human override.

User trust will become a decisive factor in adoption. Organizations need confidence that AI systems are reliable, secure, and aligned with their objectives before delegating critical functions.

The Emergence of AI Platforms and Ecosystems
Many SaaS companies are evolving into AI platforms that host agents, plugins, and third-party models. Instead of a single application, customers access an ecosystem of specialized capabilities that can be orchestrated together. This mirrors the earlier transition from standalone software to cloud platforms, but with intelligence as the connective tissue.

Interoperability becomes crucial. Businesses increasingly expect AI systems to operate across tools, accessing data from multiple sources and executing actions across different platforms. The ability to integrate seamlessly may matter more than the strength of any individual feature.

Challenges and Competitive Pressures
The AI transformation of SaaS also lowers barriers to entry in some respects. New competitors can build viable products quickly by leveraging foundation models rather than developing complex software stacks from scratch. This accelerates innovation but intensifies competition.

At the same time, dependence on external AI infrastructure providers introduces strategic vulnerability. Changes in pricing, access, or model capabilities can ripple through entire product lines. Some companies are responding by developing proprietary models or hybrid architectures to maintain control.

Economic uncertainty adds another layer of complexity. While AI can reduce costs and boost productivity, organizations may hesitate to invest heavily without clear evidence of return. Vendors must demonstrate tangible business outcomes rather than technological novelty.

Toward Intelligence as a Utility
The trajectory of AI-driven SaaS suggests a future in which software behaves less like a static product and more like an adaptive service. Systems will continuously learn, personalize themselves to each organization, and coordinate actions across digital environments. Users will interact primarily through natural language, delegating complex tasks to intelligent agents.

In this emerging model, the value proposition shifts from access to software toward access to capability. Businesses will subscribe not just to tools, but to operational intelligence on demand.

The SaaS model is therefore not disappearing. It is mutating. As AI becomes embedded at every layer, the distinction between software, service, and expertise begins to blur. Providers that successfully combine technical innovation with trust, transparency, and measurable outcomes will define the next era of cloud computing.

When the Disruptors Become the Establishment

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Not that long ago, ride-share companies blew up the taxi business. Taxis were expensive, hard to find, and controlled by licensing systems that made competition almost impossible. Then along came apps that let you press a button and a car appeared. It felt modern, fair, even a little revolutionary. Companies like Uber and Lyft sold the idea that drivers would be their own bosses and riders would finally get decent service at a reasonable price. For a while, that story mostly held up. But success changes things. Once these companies became dominant, they started to look less like rebels and more like the system they replaced. They set the prices, they control which driver gets which trip, and they take a substantial cut of every ride. Drivers supply the car, the fuel, the insurance, and the risk, yet they have very little say in how the business actually runs. Over time, many drivers have realized they are not really independent operators. They are dependent on an app they do not control.

A Different Kind of Challenge
A newer company called Empower is challenging that arrangement in a way that makes the big platforms uncomfortable. Instead of taking a percentage from every trip, it charges drivers a flat monthly fee to use the software. Drivers keep the full fare and can set their own prices. In plain language, the app becomes a tool rather than a boss. That one change flips the economics. If a driver keeps all the money from each ride, even lower fares can still produce higher income. Riders may pay less, drivers may earn more, and the company makes its money from subscriptions instead of commissions. More importantly, drivers start thinking like small business owners again. They can build repeat customers, choose when and where they work, and decide what their time is worth. That shift in mindset may be more disruptive than the pricing model itself.

Why This Actually Threatens the Giants
The real power of the big ride-share companies is control. They control access to passengers, they control pricing, and they control the flow of work through opaque algorithms. Take away that control and they become much less special. A competitor does not need to replace them everywhere. It only needs enough drivers and riders in one city to make the service reliable. Once people can get rides without using the dominant app, loyalty disappears quickly. Most riders already keep multiple apps on their phones. They tap whichever one is cheapest or fastest. Drivers do the same. If a new platform lets them earn more per trip, they will use it alongside the old ones. Over time, that weakens the incumbents without any dramatic collapse.

The Driver Problem Nobody Fixed
There is also a deeper issue. Many drivers feel squeezed. Ride prices have gone up for passengers, but driver pay has often not kept pace. At the same time, drivers absorb rising costs for fuel, maintenance, insurance, and vehicle replacement. Add in sudden policy changes, confusing pay formulas, and the risk of being removed from the platform without much explanation, and frustration builds. When a workforce becomes resentful, it does not revolt all at once. It quietly looks for exits. A company that promises independence rather than dependence taps into that frustration. It does not need to convince every driver, only enough to create a viable alternative.

Regulation Will Decide the Outcome
Whether this new model spreads widely may depend less on business strategy and more on government rules. Cities require ride-share services to meet safety standards, carry commercial insurance, and follow licensing systems. Large corporations can absorb these costs easily. Smaller challengers often cannot, especially if they argue they are only software providers rather than transportation companies. Regulators say these rules protect passengers. Critics say they also protect incumbents from competition. Both things can be true at the same time.

From Revolutionary to Utility
Ride-sharing is no longer exciting. It is infrastructure, like electricity or broadband. People expect it to work and get annoyed when it does not. When a service becomes ordinary, price matters more than brand. That is dangerous for companies whose business model depends on taking a significant percentage of each transaction. If a cheaper option appears that is “good enough,” many users will drift toward it without much thought.

The Real Risk: Losing the Middleman Role
The biggest threat to the current giants is not a single rival taking over the market. It is losing their position as the gatekeeper between drivers and passengers. If drivers build direct relationships with customers or spread their work across several low-cost platforms, the dominant apps become just one channel among many. At that point, they cannot dictate terms as easily. Other industries have seen this pattern before. Once technology allows buyers and sellers to connect more directly, middlemen either adapt or shrink.

About Time Too
There is a certain irony here. Ride-share companies rose to power by arguing that the old taxi system was inefficient, overpriced, and overly controlled. Now they face challengers making very similar arguments about them. Whether companies like Empower ultimately succeed is almost secondary. Their existence proves the market is not as locked down as it once appeared. Uber and Lyft still have enormous advantages: brand recognition, scale, and regulatory approval. But they are no longer the only game in town, and the assumption that they would dominate forever is starting to look shaky.

In the end, this is not just a fight between companies. It is a test of who holds power in the gig economy. Is it the platform that owns the app, or the people who actually do the work? Uber and Lyft once showed that owning fleets of cars was not necessary to control transportation. Their new challengers are trying to show that owning the platform may not be enough either. History suggests that once a business model becomes comfortable and profitable, someone will eventually come along to make it uncomfortable again.

The Fragile Independence of NGOs: Funding, Mission, and the Cost of Survival

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After more than 25 years advising organizations across sectors, I’ve come to appreciate the vital role NGOs play in filling the gaps governments can’t, or won’t, address. From frontline social services to environmental stewardship to global health and education, their work is often visionary, community-led, and deeply human. But I’ve also seen behind the curtain. And one uncomfortable truth emerges time and again: far too many NGOs are built on a financial foundation so narrow that one funding shift, often from a single government department, can bring the entire structure down.

This doesn’t mean these organizations lack heart or competence. Quite the opposite, but when 60 to 80 percent of their time and energy is spent chasing the next tranche of funding just to pay rent or keep skeleton staff employed, something is clearly out of balance. I’ve worked with executive directors who are more skilled in crafting grant proposals than in delivering the programs they were trained to lead. I’ve seen staff burn out, not from the intensity of service delivery, but from the treadmill of fundraising cycles that reward persistence over purpose.

The tension is most pronounced when a single government agency becomes the main or only funder. In those cases, the NGO may retain its legal independence, but it quickly becomes functionally dependent, unable to challenge policy, adapt freely, or pivot when the community’s needs shift. I’ve often told boards in strategic planning sessions: “If your NGO would cease to exist tomorrow without that one government grant, then you don’t have a sustainable organization, you have an outsourced program.”

This is not a call for cynicism. It’s a call for structural realism. NGOs need funding. Governments have a legitimate role in supporting social initiatives. But the risk lies in overconcentration. With no diversified base of support, whether from individual donors, private philanthropy, earned income, or even modest membership models, NGOs are vulnerable not only to budget cuts, but to shifts in political ideology. A change in government should not spell the end of essential community services. And yet, it too often does.

What’s the solution? It starts with transparency and strategy. Boards must get serious about income diversity, even if that means reimagining their business model. Funders, including governments, should fund core operations, not just shiny new projects, and do so on multi-year terms to allow for proper planning. And NGO leaders need to communicate their value clearly, not just to funders, but to the communities they serve and the public at large. You can’t build resilience without buy-in.

Supporting NGOs doesn’t mean ignoring their structural weaknesses. In fact, the best way to support them is to help them confront those weaknesses head-on. Mission matters. But so does the means of sustaining it. And in today’s volatile funding landscape, the most mission-driven thing an NGO can do might just be to get smart about its money.

Results Over Bureaucracy: Transforming Federal Management and Workforce Planning

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Canada’s federal government employs hundreds of thousands of people, yet far too often, success is measured by inputs rather than results. Hours worked, meetings attended, or forms completed dominate performance metrics, while citizens experience delays, inconsistent service, and bureaucratic frustration. Prime Minister Mark Carney has an opportunity to change this by embracing outcomes-based management and coupling it with a planned reduction of the federal workforce—a strategy that improves efficiency without undermining service delivery.

The case for outcomes-based management
Currently, federal management emphasizes process compliance over actual impact. Staff are assessed on whether they followed procedures, logged sufficient hours, or completed internal forms. While accountability is important, focusing on inputs rather than outputs fosters risk aversion, discourages initiative, and prioritizes process over public value.

Outcomes-based management flips this paradigm. Departments and employees are held accountable for tangible results: timeliness, accuracy, citizen satisfaction, and measurable program goals. Performance evaluation becomes tied to impact rather than paperwork. Managers are empowered to allocate resources strategically, encourage innovation, and remove obstacles that slow delivery. Employees gain clarity on expectations, flexibility in execution, and motivation to improve services.

This approach is widely recognized internationally as best practice in public administration. Governments that adopt outcomes-focused management report faster service delivery, higher citizen satisfaction, and better use of limited resources. It is a tool for effectiveness as much as efficiency.

Planned workforce reduction: 5% annually
Outcomes-based management alone does not shrink government, but it creates the environment to do so responsibly. With clearer accountability for results, the government can reduce headcount without impairing services. A planned 5% annual reduction over five years, achieved through retirements, attrition, and more selective hiring, offers a predictable, sustainable path to a smaller, more focused public service.

No mass layoffs are necessary. Instead, positions are left unfilled where feasible, and recruitment is limited to essential roles. Over five years, the workforce contracts by approximately 23%, freeing funds for high-priority programs while maintaining core services. At the end of the cycle, a full review assesses outcomes: delivery quality, service metrics, and costs. Adjustments can be made if reductions have inadvertently affected citizens’ experience.

Synergy with the other reforms
This plan works hand-in-hand with the other two reforms proposed: eliminating internal cost recovery and adopting a single pay scale with one bargaining agent. With fewer staff and a streamlined compensation system, management gains greater clarity and control. Removing internal billing and administrative overhead frees staff to focus on outcomes, while a unified pay scale ensures fair and consistent compensation as the workforce shrinks. Together, these reforms create a coherent, accountable, and modern public service.

Benefits for Canadians
Outcomes-based management and planned workforce reduction offer multiple benefits:
1. Efficiency gains: Staff focus on work that delivers measurable results rather than administrative juggling.
2. Cost savings: Attrition-based reductions lower salary and benefits expenditures without disruptive layoffs.
3. Transparency: Clear metrics demonstrate value to taxpayers, building public trust.
4. Resilience and innovation: Departments adapt faster, encouraging problem-solving and continuous improvement.

Political and administrative feasibility
Canada has successfully experimented with elements of outcomes-based management in programs such as the Treasury Board’s Results-Based Management Framework and departmental performance agreements. These initiatives demonstrate that the federal bureaucracy can shift focus from inputs to results if given clear mandates and strong leadership. Coupled with a predictable downsizing plan, the government can modernize staffing while maintaining accountability and service quality.

A smarter, results-driven public service
Prime Minister Carney has the opportunity to reshape Ottawa’s culture. Moving from input-focused bureaucracy to outcomes-based management, and pairing it with a responsible workforce reduction, creates a public service that delivers more for less. Citizens experience faster, more reliable services; employees understand expectations and have clarity in their roles; and the government maximizes value from every dollar spent.

Together with eliminating internal cost recovery and adopting a single pay scale, this reform completes a trio of policies that make the federal government smaller, smarter, and more accountable. Canadians deserve a public service focused not on paperwork, but on results that matter. This is the path to a modern, efficient, and effective Ottawa.

Food Security Requires a Canadian Grocery Fairness Act to Break the Supermarket Cartel

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Food prices in Canada are now so high that a growing share of households are skipping meals or relying on food banks, yet the country’s dominant grocery chains continue to post record profits. It’s an economic contradiction that Canadians are no longer willing to ignore. After years of voluntary codes, polite meetings with industry leaders, and vague promises of self-regulation, the time has come for Parliament to act. Canada needs a Grocery Fairness and Anti-Cartel Act to restore competition, transparency, and trust in the food supply.

The data are damning. Between 2019 and 2024, grocery prices rose by more than 25 percent, outpacing both wages and overall inflation. Meanwhile, profit margins at the country’s three dominant players, Loblaw, Sobeys’ parent company Empire, and Metro, reached their highest levels in decades. These three corporations control nearly 60 percent of the national grocery market and, in some provinces, more than 75 percent. Despite the removal of gas taxes and a slowdown in supply chain costs, prices have not come down. The explanation is simple: the grocery sector operates as a de facto cartel.

Canadians have seen evidence of this before. In 2018, a major bread price-fixing scandal revealed collusion among suppliers and retailers that spanned more than a decade. The Competition Bureau’s investigation led to fines and admissions of wrongdoing, but no lasting structural change. The same corporate families and alliances continue to dominate shelf space, dictate supplier terms, and shape consumer prices. Voluntary codes have done little to curb their power. When a handful of companies can quietly move in lockstep on pricing, even without explicit collusion, the outcome is the same: higher costs for everyone else.

A Grocery Fairness Act would not be radical. It would simply align Canada with the kind of market safeguards that already exist in other developed economies. The United Kingdom established a Groceries Code Adjudicator in 2013 to oversee fair dealing between supermarkets and suppliers. The European Union enforces strict competition rules that prevent excessive market dominance and punish “tacit collusion.” Canada, by contrast, still relies on a Competition Act designed for a different era, one that assumes the threat to markets comes from explicit conspiracies rather than structural concentration.

The model law proposed by several economists and policy experts would impose a national market-share limit of 15 percent per grocery chain, and 25 percent in any province. Companies that exceed those thresholds would be required to divest stores or brands until the market is more balanced. It would also make the existing Grocery Code of Conduct legally binding rather than voluntary, ensuring that farmers and small suppliers are protected from arbitrary fees, delisting threats, and other coercive practices.

Most importantly, the law would require large grocers to publish detailed pricing and profit data by category, showing whether retail increases are justified by rising costs. If a chain’s margins expand while input costs stay flat, the public deserves to know. Transparency alone would discourage the kind of quiet, parallel pricing behaviour that has become the norm.

Critics will call this “interference in the market,” but the truth is that Canada no longer has a functioning grocery market in the classical sense. When three firms dominate distribution, logistics, and supply contracts, the market’s self-correcting mechanisms are broken. Economists call it “oligopolistic coordination”; ordinary Canadians call it being gouged at the checkout.

Breaking up concentration would also open the door to regional cooperatives, independent grocers, and Indigenous food enterprises that have been squeezed out of distribution networks. Local ownership builds resilience, especially in rural and northern communities where dependence on a single chain often leads to higher costs and poorer food access.

There is also a broader principle at stake: when corporations profit from a basic human necessity, government has a duty to ensure that profit is earned through efficiency, not exploitation. If the banking sector can be regulated for systemic risk and telecommunications companies for fair access, surely food, the most essential of goods, deserves the same scrutiny.

Canada’s political establishment has been slow to move. The federal government has encouraged the large chains to sign a voluntary code, but participation remains partial and unenforced. Provinces have little power to act independently. The result is a cycle of press releases, hearings, and photo opportunities, while the price of a loaf of bread continues to climb.

A Grocery Fairness and Anti-Cartel Act would mark a decisive shift. It would give the Competition Bureau real structural tools rather than case-by-case investigations. It would make transparency mandatory and collusion punishable by substantial fines or even criminal liability for executives. Most importantly, it would restore the principle that essential markets exist to serve citizens, not to enrich monopolies.

Canada prides itself on fairness. Yet fairness in the grocery aisle has become an illusion. If Parliament wants to restore public confidence and make life affordable again, it should begin not with subsidies or rebates, but with the courage to challenge the corporate concentration that underlies the problem. The country needs a real grocery market, competitive, transparent, and accountable. Anything less is a betrayal of every Canadian who still believes that food should be priced by cost, not by cartel.

Sources:
Statistics Canada, Consumer Price Index data 2019–2024;
Competition Bureau of Canada, Bread Price-Fixing Investigation Report (2018);
Office for National Statistics (UK), Groceries Code Adjudicator Review 2023;
European Commission, Competition Regulation 1/2003.

Hosting Your Own AI: Why Everyday Users Should Consider Bringing AI Home

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The rise of high-speed fibre internet has done more than just make Netflix faster and video calls clearer, it has opened the door for ordinary people to run powerful technologies from the comfort of their own homes. One of the most exciting of these possibilities is self-hosted artificial intelligence. While most people are used to accessing AI through big tech companies’ cloud platforms, the time has come to consider what it means to bring this capability in-house. For everyday users, the advantages come down to three things: security, personalization, and independence.

The first advantage is data security. Every time someone uses a cloud-based AI service, their words, files, or images travel across the internet to a company’s servers. That data may be stored, analyzed, or even used to improve the company’s products. For personal matters like health information, financial records, or private conversations, that can feel intrusive. Hosting an AI at home flips the equation. The data never leaves your own device, which means you, not a tech giant, are the one in control. It’s like the difference between storing your photos on your own hard drive versus uploading them to a social media site.

The second benefit is customization. The AI services offered online are built for the masses: general-purpose, standardized, and often limited in what they can do. By hosting your own AI, you can shape it around your life. A student could set it up to summarize their textbooks. A small business owner might feed it product information to answer customer questions quickly. A parent might even build a personal assistant trained on family recipes, schedules, or local activities. The point is that self-hosted AI can be tuned to match individual needs, rather than forcing everyone into a one-size-fits-all mold.

The third reason is independence. Relying on external services means depending on their availability, pricing, and rules. We’ve all experienced the frustration of an app changing overnight or a service suddenly charging for features that used to be free. A self-hosted AI is yours. It continues to run regardless of internet outages, company decisions, or international disputes. Just as personal computers gave households independence from corporate mainframes in the 1980s, self-hosted AI promises a similar shift today.

The good news is that ordinary users don’t need to be programmers or engineers to start experimenting. Open-source projects are making AI more accessible than ever. GPT4All offers a desktop app that works much like any other piece of software: you download it, run it, and interact with the AI through a simple interface. Ollama provides an easy way to install and switch between different AI models on your computer. Communities around these tools offer clear guides, friendly forums, and video tutorials that make the learning curve far less intimidating. For most people, running a basic AI system today is no harder than setting up a home printer or Wi-Fi router.

Of course, there are still limits. Running the largest and most advanced models may require high-end hardware, but for many day-to-day uses: writing, brainstorming, answering questions, or summarizing text, lighter models already perform impressively on standard laptops or desktop PCs. And just like every other piece of technology, the tools are becoming easier and more user-friendly every year. What feels like a hobbyist’s project in 2025 could be as common as antivirus software or cloud storage by 2027.

Self-hosted AI isn’t just for tech enthusiasts. Thanks to fibre internet and the growth of user-friendly tools, it is becoming a real option for everyday households. By bringing AI home, users can protect their privacy, shape the technology around their own lives, and free themselves from the whims of big tech companies. Just as personal computing once shifted power from corporations to individuals, the same shift is now within reach for artificial intelligence.

Sharing as the Core of Influence in Knowledge-Driven Organizations

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In contemporary organizational theory, the capacity to share knowledge efficiently is increasingly recognized not merely as a good practice, but as one of the central levers of influence, innovation, and competitive advantage. Influence in the workplace is no longer determined solely by formal authority or proximity to decision-makers; it hinges instead on who opens up their ideas, disseminates outcomes, and builds collective awareness. Knowledge sharing, properly conceived, is a social process that undergirds learning, creativity, and organizational agility.

Why Sharing Still Matters
Even with advances in digital collaboration tools, hybrid work environments, and more explicit knowledge management policies, many organizations continue to wrestle with information silos, “knowledge hoarding,” and weak visibility of what colleagues are doing. These behaviors impose hidden costs: duplication of work, failure to capitalize on existing insights, slow adoption of innovations, and organizational inertia.

Empirical studies confirm that when organizational climate is supportive, when centralization and formalization are lower, knowledge sharing behavior (KSB) tends to increase. For example, a recent study of IT firms in Vietnam (n = 529) found that a positive organizational climate had a direct positive effect on KSB, while high degrees of centralization and formalization decreased knowledge‐sharing intentions.  

Moreover, knowledge sharing is strongly associated with improved performance outcomes. In technological companies in China, for instance, research shows that AI-augmented knowledge sharing, along with organizational learning and dynamic capabilities, positively affect job performance.  

Theoretical Foundations & Diffusion of Influence
A number of established frameworks help us understand both how knowledge spreads and why sharing can shift influence within organizations.
• Diffusion of Innovations (Everett Rogers et al.): This theory explains how new ideas are adopted across a social system over time via innovators, early adopters, early majority etc. Key variables include communication channels, time, social systems, and the characteristics of the innovation itself.
• Threshold Models & Critical Mass: Recent experiments suggest that when a certain proportion of individuals (often around 20-30%) behave in a particular way (e.g. adopting or sharing an innovation), that can tip the whole system into broader adoption. For example, one study found that social diffusion leading to change in norms becomes much more probable once a committed minority exceeds roughly 25% of the population.
• Organizational Climate & Intention/Behavior Models: Behavior intentions (e.g. willingness to share) are shaped by trust, perceived support, alignment of individual and organizational values, and perceived risk/benefit. These mediate whether knowledge is actually shared or hidden.  

Barriers & Enablers
Understanding why people don’t share is as important as understanding why they do.

Barriers include:
• Structural impediments like overly centralized decision frameworks, rigid hierarchy, heavy formalization. These reduce the avenues for informal sharing and flatten the perceived payoff for going outside established channels.
• Cognitive or psychological obstacles, such as fear of criticism, loss of advantage (“knowledge as power”), lack of trust, or simply not knowing who might benefit from what one knows.
• Technological and process deficiencies: poor documentation practices, weak knowledge management systems, lack of standard archiving, difficult to locate material, etc. These make sharing costly in terms of effort, risk of misunderstanding, or duplication.  

Enablers include:
• Cultivating a learning culture: where mistakes are not punished, where experimentation is supported, and where informal learning is valued. Studies in team climate show that the presence of an “organizational learning culture” correlates strongly with innovative work behavior.
• Leadership that is supportive of sharing: transformational, inclusive leadership, openness to new ideas even when they challenge orthodoxy. Leaders who make visible their support for sharing set norms.
• Recognition, incentive alignment, and reward systems that explicitly value sharing. When sharing contributes to promotions, performance evaluations, or peer recognition, people are more likely to invest effort in it.  

Influence through Sharing: A Refined Model
Putting this together, here is a refined model of how sharing translates into influence:
1. Visibility: Sharing makes one’s work visible across formal and informal networks. Visibility breeds recognition.
2. Peer Adoption & Critical Mass: Innovation often needs a threshold of peer adoption. Once enough people (often around 20-30%) accept or discuss an idea, it tends to propagate more broadly. Early informal sharing helps reach that threshold.
3. Legitimization & Institutionalization: When enough peers accept an idea, it begins to be noticed by formal leadership, which may then adopt it as part of official strategy or practice. What was once “radical” becomes “official.”
4. Influence & Reward: As an individual or team’s ideas get absorbed into the organizational narrative, their influence increases. They may be entrusted with leadership, provided more resources, or seen as agents of change.

Recent Considerations: Hybrid Work, Digital Tools, AI
Over the past few years, changes in how and where people work, plus the integration of AI into knowledge-sharing tools, add new dimensions:
• Remote and hybrid setups tend to magnify the problems of invisibility and isolation; informal corridor conversations or impromptu check-ins become less likely. Organizations must work harder to construct virtual equivalents (e.g. asynchronous documentation, digital forums, internal social networks).
• AI and knowledge-management platforms can help accelerate sharing, reduce friction (e.g. discovery of existing reports, automatic tagging, summarisation), but they also risk over-trust in automation or leaving behind tacit knowledge that is hard to codify.
• Given the increasing volume of information, selective sharing and curating become skills. Not every detail needs to be shared widely, but knowing what, when, and how to share is part of influence.

Implications for Practice
For individuals aiming to increase their influence via sharing:
• Embed documentation and archival processes into every project (e.g. phase reports, lessons learned).
• Use both formal and informal channels: internal blogs or newsletters, but also coffee chats, virtual social spaces.
• Be willing to experiment, share preliminary findings; feedback improves ideas and increases visibility.

For organizations:
• Build a culture that rewards sharing explicitly through performance systems.
• Reduce structural barriers like overly centralized control or onerous formalization.
• Provide tools and training to lower the effort of sharing; make knowledge easier to find and use.
• Encourage cross-team interactions, peer networks, communities of practice.

Final Word
Sharing is not just a morally good or nice thing to do, it is one of the most potent forms of influence in knowledge-based work. It transforms static assets into living processes, elevates visibility, enables innovation, and shapes organization culture. As the world of work continues to evolve, those who master the art and science of sharing will increasingly become the architects of change.

References:
Here are key sources that discuss the concepts above. You can draw on these for citations or further reading.
1. Xu, J., et al. (2023). A theoretical review on the role of knowledge sharing and … [PMC]
2. Peters, L.D.K., et al. (2024). “‘The more we share, the more we have’? Analyses of identification with the company positively influencing knowledge-sharing behaviour…”
3. Greenhalgh, T., et al. (2004). “Diffusion of Innovations in Service Organizations.” Milbank Quarterly – literature review on spreading and sustaining innovations.
4. Ye, M., et al. (2021). “Collective patterns of social diffusion are shaped by committed minorities …” Nature Communications
5. Bui, T. T., Nguyen, L. P., Tran, A. P., Nguyen, H. H., & Tran, T. T. (2023). “Organizational Factors and Knowledge Sharing Behavior: Mediating Model of Knowledge Sharing Intention.”
6. Abbasi, S. G., et al. (2021). “Impact of Organizational and Individual Factors on Knowledge Sharing Behavior.”
7. He, M., et al. (2024). “Sharing or Hiding? Exploring the Influence of Social … Knowledge sharing & knowledge hiding mechanisms.”
8. Sudibjo, N., et al. (2021). “The effects of knowledge sharing and person–organization fit on teachers’ innovative work …”
9. Academia preprint: Cui, J., et al. (2025). “The Explore of Knowledge Management Dynamic Capabilities, AI-Driven Knowledge Sharing, Knowledge-Based Organizational Support, and Organizational Learning on Job Performance: Evidence from Chinese Technological Companies.”
10. Koivisto, K., & Taipalus, T. (2023). “Pitfalls in Effective Knowledge Management: Insights from an International Information Technology Organization.”  

The Independent Knowledge Worker and the Question of Marketability

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Recently, I read a post from a well-known contributor on a community platform. This writer, an accomplished author with years of experience, lamented the decline of opportunities in her field. She spoke of a shrinking market, a lack of viable contracts, and the challenges of her geographical location in trying to generate meaningful revenue. Out of habit, I rarely respond to such posts, but this time I did. My response drew a public reply, and while I tend not to engage in prolonged debates on public forums, too often they dissolve into vitriol, I chose to bring the discussion here, to my own space, where ideas can be unpacked more thoughtfully.

Artificial Intelligence was seen as the main villain in this public debate, but I believe that’s a red herring. Yes, we are all adjusting to the challenge of AI, but the only constant in life is change, so what is the real issue here. 

The heart of the matter is this: the defining advantage of being an independent knowledge worker is precisely the ability to work from anywhere. The office is no longer a cubicle on the twentieth floor of a glass tower, but the laptop on your kitchen table, although I prefer my dedicated home office. The clients may live continents away, but the work flows seamlessly across time zones. In this economy, location is not the limitation it once was. The real limitation is mindset.

Even as I write this post, I am exchanging messages with an Argentine colleague who is currently based in Canada. She is orchestrating a major PR announcement for a company headquartered in the Netherlands. Just last week, I was on a call with a professional in Paraguay to discuss a project in Chile. Another colleague, specializing in agricultural and agri-food writing, maintains an active client list that stretches from Australia to Japan to Portugal. None of us share an office, or a city, but all of us share the same reality: we are independent professionals with global client bases, connected by skill, adaptability, and digital tools.

This is why I push back when I hear colleagues insist that their difficulties are rooted in market decline. It is not the shrinking of opportunity, but the narrowing of their willingness to market themselves that becomes the stumbling block. The truth is uncomfortable: talent alone does not guarantee survival.

The writer whose post sparked this reflection has produced over a hundred articles, essays, and commentaries that I have personally read. Her body of work is substantial, and her craft is evident. Yet the refrain of “just give me work, so I can do my job” misses the larger truth of freelancing. Writing is the service, but self-promotion is the business model. Without branding, without a visible signal to clients about why they should choose you over the hundreds of other qualified voices, the work will not come.

Whenever I submit a proposal for a project, I begin by ensuring I have the necessary expertise and experience; but the more important question quickly follows: “why me?” Why would this client entrust me with their project rather than the next bidder? If I cannot answer that persuasively, I do not waste time chasing the opportunity. The answer to “why me?” is not entitlement, nor is it a résumé; it is positioning, visibility, and the willingness to show that your work has unique value.

In the end, the challenge of independent knowledge work is not scarcity of markets, but the discipline of visibility. The professionals who thrive are those who accept that marketing is not a distraction from their craft, but a core part of it.

Being an Independent Knowledge Worker has a New Trendy Name

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For over 25 years, working as a business consultant has meant managing multiple projects for different clients, each demanding unique skills and contributions. Whether leading a project, analyzing business processes, or facilitating strategic discussions, this multi-faceted approach to work offers both challenges and rewards. One of the most appealing aspects of this style is the built-in networking opportunities. Engaging with diverse clients allows for the development of meaningful professional relationships while creating dynamic ways to generate income. By choosing to work independently and focusing on outcomes-based projects from my own space, rather than embedding within a client’s office, I have embraced a flexible, autonomous way of working that aligns with modern career trends.

This approach aligns with what is now popularly referred to as “polyworking,” a concept that has gained traction in recent years. Polyworking involves taking on multiple professional roles simultaneously, often across different industries or fields, rather than adhering to the traditional single-job model. Its rise can be attributed to advancements in technology, the normalization of remote work, and shifting attitudes toward traditional career paths. It enables workers to diversify income sources, build a broad skill set, and gain greater autonomy over their work schedules.

Polyworking is not without its challenges, however. Successfully managing several roles requires careful time management, as balancing multiple commitments can be overwhelming. The risk of burnout is real, with the potential for fatigue and reduced productivity if boundaries between roles are not clearly defined. Additionally, polyworking often lacks the financial and employment stability associated with traditional full-time jobs, as benefits and protections like health insurance or retirement plans may be absent.

Despite these challenges, polyworking offers notable advantages. By maintaining diversified income streams, individuals can reduce financial vulnerability during economic downturns or unexpected job losses. Exposure to various industries not only broadens professional networks but also fosters personal and professional fulfillment by allowing individuals to pursue their passions alongside their careers. Digital tools and platforms, such as project management software and freelance marketplaces, have played a pivotal role in making polyworking feasible, enabling effective collaboration and organization.

As the gig economy and remote work continue to evolve, polyworking is increasingly seen as an alternative to traditional career paths. For some, it represents freedom and flexibility; for others, it is a necessary adaptation to modern economic realities. While it may not suit everyone, polyworking is shaping the future of work, offering opportunities for greater financial independence, professional growth, and a more tailored work-life balance. Understanding how to navigate its challenges is key to thriving in this emerging landscape.

Maplewashing: The Hidden Deception in Canadian Grocery Aisles

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Maple leaves on packaging, “Product of Canada” claims, and patriotic hues of red and white, these symbols of national pride are meant to instill trust and confidence in Canadian consumers. Yet behind some of these labels lies a troubling trend: the misrepresentation of imported food as domestically produced. Known colloquially as “maplewashing,” this practice is drawing increased scrutiny as Canadians seek greater transparency, and authenticity in their grocery choices.

At its core, maplewashing is a form of food fraud. Products sourced from the United States or other countries are being marketed with suggestive imagery or ambiguous labeling that implies Canadian origin. In some cases, food items imported in bulk are processed or repackaged in Canada, allowing companies to legally label them as “Made in Canada” or “Product of Canada” under current regulatory loopholes. This manipulation undermines consumer confidence and disadvantages local producers who adhere strictly to Canadian sourcing standards.

The Canadian Food Inspection Agency (CFIA) defines food fraud as any deliberate misrepresentation of food products, including their origin, ingredients, or processing methods. While the CFIA has made progress in addressing such issues, the agency still faces challenges in policing the retail landscape. Consumers have reported examples of apples from Washington state sold under Canadian branding, and frozen vegetables with packaging that evokes Canadian farms but are sourced entirely from overseas. These practices erode the integrity of the food system and compromise informed consumer choice.

In response to growing concern, some major retailers have attempted corrective measures. Loblaw Companies Ltd., for instance, has piloted initiatives to label tariff-affected American products with a “T” to signal their origin. Other grocers have begun offering clearer signage or dedicated sections for verified Canadian goods. Despite these efforts, enforcement remains patchy, and misleading labels continue to circulate freely on supermarket shelves.

Digital tools have emerged as allies in the fight against maplewashing. Smartphone apps now allow consumers to scan barcodes and trace the country of origin of a product, giving them the ability to verify claims independently. These apps, combined with mounting consumer pressure, are gradually raising the bar for accountability in food labeling.

Still, the systemic nature of the problem requires more than consumer vigilance. Regulatory reform is essential. Advocacy groups have called on the federal government to tighten definitions for what qualifies as “Product of Canada.” Under current guidelines, a product can be labeled as such if 98% of its total direct costs of production are incurred in Canada. Critics argue that this threshold allows too much flexibility for products with foreign origins to slip through.

Maplewashing is not merely a matter of misplaced labels. It is a breach of trust between food producers, retailers, and the Canadian public. As more shoppers demand transparency and local accountability, there is an opportunity to rebuild confidence through clearer standards, stronger enforcement, and a renewed commitment to honest labeling. Food should tell the truth about where it comes from, and no amount of patriotic packaging should be allowed to obscure that.

Sources:
• Canadian Food Inspection Agency – Food Fraud
• New York Post – Canadian shoppers frustrated at confusing US food labels
• Business Insider – Canadian stores labeling American imports to warn consumers
• Barron’s – Canadian boycott of American goods