Category: What’s New in Technology

Businesses are moving beyond basic automation into a new era of intelligent, self-directed systems. While automation helps with streamlining repetitive tasks, agentic AI workflows enable systems to make decisions, take action, and continuously improve with minimal human oversight.

Most businesses adopting agentic AI have no structured way to prove it is working. Although they can feel the difference, they can’t measure it. Without measurement, return on investment (ROI) conversations stall, budgets get cut, and genuinely transformative tools get shelved.

What Makes Agentic AI Workflows Different

Agentic AI workflows are designed to operate with a degree of independence. Unlike traditional automation, which follows predefined rules, agentic systems are goal-oriented.

Once given an objective, they plan, execute, adjust, and complete tasks across multiple steps, tools, and decisions without requiring human intervention. For example, an agentic workflow may pull data from multiple systems, analyze it, draft a report, flag anomalies, and email a summary.

Another example is a supply chain AI agent that not only highlights anomalies but can also reorder stock, renegotiate pricing thresholds, and even reroute logistics as these actions fall within predefined objectives.

Agentic AI can also improve efficiency and productivity by identifying inefficiencies in workflows and adjusting them in real time.

For businesses facing rising labor costs and increasing demand for speed and personalization, this evolution is more than a technological advancement. It offers a strategic advantage.

Why ROI Measurement Is Different for Agentic AI

Traditional ROI models are rather straightforward as they compare the cost of a system to the output generated. ROI on projects using traditional models is measured based on cost savings, headcount reduction and cycle-time compression. However, agentic AI is more dynamic because the systems improve over time. This means the output isn’t static – rather, it compounds. These systems also reduce the need for ongoing supervision, operate continuously, and often uncover efficiencies that were not initially anticipated.

As a result, the ROI of agentic AI is not just immediate cost savings but also includes long-term gains. These gains include improved decision-making, faster execution, higher productivity, strategic agility and the ability to scale operations without a proportional increase in cost. Measuring this kind of value requires a broader, more forward-looking approach.

Key ROI Drivers of Agentic AI workflows

1. Operational efficiency – unlike conventional automation that is vulnerable to dynamic environments due to fixed rules, agentic AI responds to changes automatically. These systems continuously learn and optimize, delivering ongoing improvements without additional manual effort.
2. Real-time responsiveness – customers expect real-time interaction. Agentic workflows enable this through systems that are always on and context-aware.
3. Scalability – businesses can handle increased demand without a corresponding increase in operational costs or headcount, allowing more efficient growth.
4. Cross-departmental reach – Agentic AI agents can seamlessly connect workflows across different departments like HR, IT, and finance. This reduces operational friction between teams and enhances overall efficiency.
5. Productivity gains – Agentic AI can operate 24/7, completing tasks faster and with greater consistency than human teams. This allows employees to focus on higher-value work, increasing overall organizational productivity.
6. Cost reduction – by automating complex workflows, businesses can reduce reliance on manual labor, minimize errors, and eliminate inefficiencies. This can translate into significant savings.
7. Revenue growth – Agentic AI enables faster go-to-market strategies and more personalized customer experiences. This can directly impact conversion rates and revenue.
8. Improved decision quality – With access to real-time data and advanced analytics, agentic AI systems can make quick, informed decisions. This reduces human bias and enhances accuracy in areas like forecasting, inventory management, and customer engagement.

Strategies for Evaluating Agentic AI ROI

To measure agentic AI ROI, businesses need a structured approach that connects AI deployment to business outcomes.

1. Identify high-impact workflows – repetitive, resource-heavy processes like IT support, sales operations, or compliance.
2. Establish baseline measurements by documenting current costs, completion times, error rates, and headcount before deployment.
3. Compare pre- and post-implementation performance by checking utilization rates, tasks completed, and infrastructure costs to confirm operational sustainability.
4. Estimate agentic impact by projecting improvements in speed, cost, throughput, and quality.
5. If implementing agentic AI in phases, use control groups to isolate its impact from other organizational changes.
6. Measure real business outcomes, including cost reductions, revenue growth, and productivity gains.

Conclusion

Traditional automation delivered value by reducing manual effort. Agentic AI, on the other hand, reduces decision latency, operational friction, and coordination costs. Therefore, AI agents’ ROI is not defined by savings alone. Its real value lies in the ability to generate compounding returns across multiple dimensions of a business. By adopting a broader view of ROI, organizations can better assess impact, build stronger adoption cases, and identify new opportunities for optimization.

Artificial intelligence (AI) is no longer a competitive advantage; it has become a necessary infrastructure. Businesses now heavily rely on AI-powered systems, from automated customer service to predictive analytics and decision-making tools. These platforms are cloud-based, and their reliance comes with growing concern of AI lock-in. This dependence on major cloud providers and the convenience of Big Tech ecosystems can turn into long-term dependency. In response, cloud sovereignty is gaining momentum.

What Is Cloud Sovereignty?

Cloud sovereignty refers to the ability of an organization to maintain full control over its data, infrastructure, and digital assets. This includes where data is stored, how it is processed, and which legal jurisdiction governs it.

Unlike traditional cloud hosting, where companies rely on a single global provider, cloud sovereignty emphasizes:

• Data ownership and portability
• Compliance with local laws and regulations
• Reduced dependence on foreign-controlled infrastructure
• Strategic control over AI models and workflows

The Rise of Big Tech and the AI Lock-in Problem

Over the past decade, companies like AWS, Google Cloud, and Microsoft Azure have built highly integrated AI ecosystems, especially since the surge of generative AI. These platforms offer powerful tools, including proprietary machine learning services, exclusive Application Programming Interfaces (APIs), pre-trained AI models, and seamless infrastructure scaling.

However, when businesses build their AI systems entirely on one provider’s proprietary tools, switching becomes difficult. Platform dependency can also create serious risks when a vendor fails. A good example is the collapse of Builder.ai, an AI app builder backed by giants like Microsoft and the Qatar Investment Authority. Its collapse was an indicator that companies do not have complete control over the software and data on which their operations depend. This is what is known as AI Lock-in, where:

• AI models rely on proprietary APIs
• Data pipelines are optimized for a specific cloud architecture
• Workflows depend on unique vendor tools
• Migration costs become prohibitively high

As a result, businesses suffer:

• Escalating operational costs
• Limited negotiating power
• Reduced flexibility
• Strategic vulnerability

In 2026, with AI deeply embedded into operations, being locked-in can threaten long-term agility and innovation.

Regulatory Pressure is Accelerating the Shift

Governments worldwide are tightening digital sovereignty and data protection rules. From stricter data residency laws to AI governance frameworks, compliance is no longer optional. Industries such as finance, healthcare, and telecommunications face heightened scrutiny. They must prove where data is stored, who can access it, and how AI models are trained and governed. Additionally, businesses can’t afford regulatory risks. Regulations such as the CLOUD Act demand data access transparency, while different states are pushing for data localization policies.

Relying entirely on a foreign-controlled AI ecosystem can raise compliance risks. In some regions, businesses are now required to use local or sovereign cloud providers for sensitive workloads. Gartner predicts 35 percent of countries will adopt region-specific AI platforms by 2027 as countries increase investment in domestic AI stacks to meet sovereignty goals.

Regulation, once seen as a burden, is now a strategic driver pushing companies toward sovereign-first strategies.

How Businesses Are Avoiding AI Lock-in Trap

Businesses are not abandoning cloud AI. Instead, they are becoming more strategic about how they implement it.

1. Embracing open-source and interoperable AI
   Many businesses are adopting open-source AI frameworks and models to reduce dependency on proprietary systems. By building on interoperable standards, they maintain flexibility to deploy workloads across different environments. This approach allows businesses to experiment freely without being tied to a single vendor’s ecosystem.
2. Adopting multi-cloud and hybrid strategies
   Rather than relying on one provider, a business can distribute workloads across multiple clouds. This reduces operational risk, strengthens negotiation leverage, enhances flexibility and improves resilience. Hybrid models, where on-premise infrastructure is combined with cloud services, are also growing in popularity. They ensure sensitive data remains locally controlled while still leveraging AI scalability.
3. Partnering with sovereign or regional cloud providers
   Regional cloud providers are gaining traction as they offer local data hosting, compliance with national regulations, and greater transparency.
4. Strengthening contract and governance frameworks
   Procurement and legal teams are now playing a more active role in cloud decisions. They negotiate stronger data portability clauses, clear exit strategies, transparent pricing structures, and model ownership rights.

Final Thoughts

In 2026, the real risk is not using AI, but losing control over it.

Cloud sovereignty represents a strategic shift while not rejecting Big Tech. It must be viewed as the ability to act strategically, as no business can dominate every layer of the AI stack due to constraints like the high cost of training advanced AI models.

Businesses that prioritize sovereignty today are building resilient, flexible, and future-ready AI ecosystems. Those who ignore it may find themselves powerful – but trapped.

Every modern business is paying rent. Not for office space or equipment, but for the digital infrastructure that runs the company. This might include the cost of CRMs, email platforms, project management tools, automation tools, analytical dashboards, and countless other tools designed to solve a specific business need. Individually, these tools seem affordable; collectively, they form a permanent tax on business growth.

For several years now, software-as-a-service (SaaS) has been sold as a form of freedom. Businesses were promised low upfront cost, instant deployment, and minimal complexity. For a long time, SaaS delivered on this promise. It helped companies move faster, scale quickl,y and compete globally regardless of size.

But this is shifting. Now, business leaders are beginning to question whether renting critical systems is still a worthy strategy.

The SaaS Era

The rise of SaaS was a necessary evolution. It lowered the entry barrier for tools that once required large IT teams and a huge capital investment.

However, this convenience turned into dependency. Businesses not only adapted SaaS tools, but they also built operations around them. Third-party platforms now hold business workflows, customer data, analytics, automations, and even institutional knowledge. This means that a business has dozens of subscriptions they don’t fully control, can’t meaningfully customize, and must keep paying for to keep operating.

What Sovereign Ownership Means

Sovereign ownership doesn’t mean abandoning the cloud or rejecting modern technology; it means owning the core logic of your business systems. The sovereign models emphasize self-management, control and long-term resilience.

When a business practices sovereign ownership, it controls:

• Where data resides (e.g., virtual private clouds or sovereign clouds)
• Access permissions and encryption keys
• Workflows and automations
• Internal knowledge systems
• AI models and training data
• The ability to move, adapt, or rebuild without needing vendor permission

Self-sovereign identity has been a great support for this shift. SSI protocols allow businesses, employees, and customers to control their digital identities and credentials without relying on centralized identity providers. This means that identity is not locked inside the SaaS platform, as it is portable, verifiable, and owned by the entity itself.

The Real Cost of SaaS Goes Beyond the Invoice

SaaS costs more than renting the service. Aside from monthly or annual subscriptions that compound into a huge expense over time, vendor lock-in makes switching platforms painful and risky. The pricing models also keep changing. Features may be removed or placed under higher payment tiers. Other issues include broken integrations and limited or messy data exports.

More critically, companies adapt their workflows to match the SaaS tools, rather than the tool serving the business. Therefore, innovation is constrained by what the platform allows and not what the business needs.

The biggest risk is when a SaaS provider is acquired, suffers downtime, or shuts down entirely. When this happens, your business absorbs the impact without control or leverage.

Why 2026 Is the Turning Point

Why now? Because the alternatives have finally matured. Decentralized physical infrastructure (DePIN), the maturity of enterprise-grade, open-source software, and modular cloud architecture have made system ownership accessible without deep technical teams. AI has transformed how businesses build, automate, and maintain internal tools. Modular infrastructure allows companies to own their core while selectively renting specialized services.

At the same time, external pressure is increasing as data privacy regulations tighten. Regulatory frameworks like the U.S. Cloud Act, the GDRP and the EU’s Digital Operational Resilience Act (DORA) demand operational independence that SaaS cannot fully deliver. Gartner predicts that by 2030, 75 percent of enterprises outside of the United States will implement data sovereignty strategies due to regulatory scrutiny and geopolitical tensions.

Major players are already responding. IBM is one example of the shift, as they already announced IBM Sovereign Core, software that helps businesses take back control of their data and systems.

Customers are also more aware. They want to know how their data is stored, processed, and protected. AI models trained on proprietary information raise new questions of ownership and risk. In an uncertain global economy, businesses want cost predictability and not endless variable subscriptions.

The mindset is shifting from speed at any cost to resilience by design.

From Renters to Owners

SaaS helped businesses grow. But growth built on dependency has limits.

2026 represents a strategic window where ownership is finally accessible, affordable, and necessary. The shift toward sovereign systems is not about rebellion against technology that has previously helped businesses. It’s about leverage, resilience, and long-term value.

The future belongs to businesses that stop renting their foundations and start owning their future.

The way people interact with the web is changing fast. Attention spans are shorter, app fatigue is real, and users no longer want to download, sign up, or navigate complex interfaces just to engage with content. New technologies like frictionless web-based augmented reality (WebAR) are emerging as powerful solutions.

This shift opens great opportunities for creators, brands, and small businesses.

What is Frictionless WebAR?

Every extra step between a user and an experience reduces engagement. Downloading apps, dealing with permissions, updates, and onboarding screens all create friction. However, frictionless WebAR is delivered directly through a web browser. It uses web standards like WebXR and WebGL to deliver digital content without downloads or installations. With a shift in how value is created, communicated, and converted, it is possible to have interactive storytelling, experiential funnels, immersive education, and hyper-local marketing. All this is without the costs and complexity involved in traditional AR.

Transitioning from the attention economy to the experience economy has been driven by content overload from content, ads, and interfaces competing for clicks. As a result:

• Users avoid downloading new apps
• Click-through rates are declining
• Trust is harder to build through a flat screen alone
• Static content struggles to hold attention

Frictionless WebAR addresses these barriers.

Users can easily scan a QR code or tap a link and instantly see a product, explore a story in 3D form, or interact with information visually.

From a business perspective, the value lies in zero-friction entry, instant immersion, and seamless connection between physical and digital worlds. This is because WebAR does not require large development teams or app store approvals. It is lightweight, fast, and accessible. This makes it viable not only for big brands but also for solo creators and small businesses.

From Passive Content to Active Experiences

With most digital content, users scroll, read, watch, and move on. Frictionless WebAR is built to turn audiences into participants. Instead of reading about a product, users can see it in a 3D model. Instead of watching a story, they can step inside it. When audiences interact with something in their own environment:

• Engagement time increases
• Emotional connections deepen
• Information is remembered longer
• Purchase confidence improves

Practical Opportunities for Creators

For filmmakers, artists, game developers, and content creators, frictionless WebAR transforms static content into dynamic, interactive narratives. For instance, scanning a QR code in a physical comic book brings a character to life. This deepens immersion and extends the narrative beyond the printed book. Other examples include AR-enhanced portfolios that showcase work in 3D, behind-the-scenes experiences tied to a QR code, and interactive course previews.

Creators can also monetize WebAR by offering premium AR experiences, bundling AR with digital products, launching interactive experiences for sponsors, and enhancing membership or community access. This makes WebAR part of a creator’s intellectual property and not just a marketing tool.

Practical Opportunities for Brands

Brands leverage WebAR for immersive marketing. Experiential funnels leverage WebAR, allowing brands to engage customers in ways traditional advertising cannot. A good example is a brand launching a new shoe, and customers can scan a QR code on a poster and “try on” the virtual sneakers to see how they look in real time. Luxury brands can offer “virtual showroom” experiences with interactions that deepen the emotional connection.

The low-barrier interaction means higher engagement rates as potential customers are more likely to participate in an experience that doesn’t demand an app download or login.

Practical Opportunities for Small Businesses

Small businesses often struggle to compete with larger brands online. However, now they can access cost-effective WebAR without native app development. This equalizer offers sophisticated marketing and customer engagement tools without the need for a massive budget or IT team. This saves on resources and enables quick campaigns like seasonal promotions.

Since WebAR works through web browsers, a business can gain detailed analytics, such as user behavior. For instance, getting detailed data on dwell time or how long people engage in the experience can indicate how compelling the content is. Spatial analytics, on the other hand, measure how much time users spend on specific scenes, helping make necessary tweaks to optimize user experience. The data collected helps better understand customers and how they engage with content.

Conclusion

Frictionless WebAR represents a fundamental change in how value is delivered online. For creators, brands, and small businesses, it offers a way to stand out by inviting people into meaningful experiences.

In a crowded digital space, ease of access is a competitive advantage.