Earn AI grows into a team of specialised AI agents that help property managers scale operations, automate renewals and embed financial services while keeping people in control of every decision.

Dubai, United Arab Emirates: Rentify, a cutting-edge fintech & proptech startup, announced the launch of Renewal Command Center, the third specialised AI agent within Earn AI, its AI-native operating system for rental operations.

Today, Earn AI supports property portfolios representing more than AED 22 billion (USD 6 billion) in real estate assets and over AED 1.3 billion in annual rental value, demonstrating how some of the region’s largest property portfolios are already adopting AI-native rental operations.

Unlike traditional software that only records information, Earn AI detects what needs attention, prepares the required work and coordinates execution. Property managers can upload spreadsheets, lease agreements and fragmented portfolio information and see it transformed into a live, structured portfolio view in under 60 seconds.

Each specialised Earn AI agent has a clearly defined responsibility while operating within approval workflows that ensure property managers remain in complete control of every important decision.

Nearly 80% residential tenants renew their tenancy agreements each year, making renewals one of the largest recurring operational responsibilities for property managers. Across the real estate industry in the UAE, experienced property managers are expected to oversee larger portfolios while managing renewals, collections, tenant onboarding, documentation, compliance and financial coordination across multiple disconnected systems. As portfolios grow, operational work grows faster than teams.

Through Rentify’s product Rent Shield in partnership with YallaCompare, landlords and tenants can seamlessly access rental insurance during the renewal process. Integration with Spare, a leading open finance provider, enables Open Finance-powered affordability assessments and Pay-by-Bank payment orchestration that supports Rentify’s existing digital payment infrastructure. The platform will also make rent renewal seamless with embedded financial services.

Rajneel Kumar, Co-founder, Rentify, said, “We built Earn AI around a simple idea that technology should handle the operational heavy lifting so property managers can focus on outcomes for landlords and tenants. For decades, the only way to scale was to hire more people, but AI changes that. Renewal Command Center takes that idea into one of the most repetitive workflows in property management, connecting renewals, tenant intelligence, Open Banking, embedded insurance and payments in a single flow, with every critical decision still made by a person.”

Rashed Hareb, Co-founder & CEO, Rentify, said, “Property managers don’t need more dashboards. They need greater operational capacity. Over the next decade, every major operational function  within the property management business will gain a specialised AI counterpart. Our role is to build an operating system that enables those teams to work together seamlessly.”

Rentify believes the future of property management is not about replacing people with artificial intelligence. It is about giving every property manager a specialised AI workforce that quietly handles repetitive operational work, allowing people to focus on relationships, judgement and portfolio growth.

Alteryx, Inc., an AI-ready data and analytics company, today released its “2026 IT Leader Research: The State of AI Ownership, Agents, and ROI” report, revealing that organizations are entering a new phase of AI maturity where success is no longer defined by AI adoption alone, but by the ability to translate AI investment into measurable business outcomes.

Among 1,400 IT leaders surveyed globally, 80% expect AI spending to increase over the next two years, while 69% report moderate or significant ROI from their AI investments. Yet despite growing investment and early returns, more than half (53%) say their organization struggles to translate business context into AI systems and workflows. At the same time, 77% agree business context is critical to producing accurate and relevant AI outputs, underscoring a widening gap between AI ambition and operational readiness.

Key Findings at a Glance:

• 80% of organizations expect AI spending to increase over the next two years.
• 69% report moderate or significant ROI from their AI investments.
• 93% of IT leaders are confident agentic AI could deliver measurable ROI for their enterprise within the next two years.
• 77% agree business context is critical to accurate, relevant AI outputs.
• 53% say their organization struggles to translate business context into AI systems and workflows.
• Only 18% of organizations have achieved fully self-service access to cloud data for business users.

AI Investment Is Accelerating. Expectations Are Rising Even Faster.

AI investment continues to accelerate as organizations move beyond experimentation toward enterprise-scale deployment. Eighty percent of organizations expect AI spending to increase over the next two years across infrastructure, workflow automation, data platforms, and governance.

With that investment comes greater accountability. Technology leaders are increasingly measuring AI success through productivity improvements (53%), cost reduction (45%), and revenue growth or broader business impact (39%). More than one-third (35%) say the ability to measure AI ROI will be one of the capabilities that most distinguishes technology leaders from their peers.

The findings suggest AI has entered a new phase where organizations are no longer asking whether AI works. They are asking whether it consistently delivers measurable business value.

Business Context Is Emerging as a Barrier to Enterprise AI

As AI becomes embedded in everyday business processes, organizations are discovering that models alone are not enough. According to the research, 77% of IT leaders agree that business context, including the rules, definitions, and operational knowledge that shape how their organizations operate, is essential for producing accurate and relevant AI outputs. Yet more than half (53%) say their organization struggles to translate that business context into the systems and workflows AI depends on.

The challenge isn’t simply giving AI more data. It’s giving AI the business logic that tells it how the business actually works. While AI can analyze information and generate responses, it cannot consistently apply company-specific rules, policies, thresholds, and decision criteria unless that knowledge is built into the workflows it uses to make decisions.

Much of that business logic still lives in spreadsheets, macros, documentation, email threads, and the expertise of the people closest to the work. A financial forecast depends on assumptions. A tax process depends on rules and exceptions. A supply chain decision depends on inventory thresholds and timing. AI should be grounded in the rules and logic the business already trusts.

“Our research highlights a growing gap between AI ambition and enterprise-scale execution,” said Andy MacMillan, CEO of Alteryx. “Organizations have proven they’re willing to invest in AI, and many are already seeing returns. But scaling AI requires more than better models. It requires making the business knowledge people use every day available to the systems making decisions.”

Limited Data Access Continues to Slow AI Adoption

Despite years of investment in data democratization, only 18% of organizations report that business users have fully self-service access to cloud data. Most organizations continue to rely on IT or data teams for routine data access and analytics, with 38% describing a mixed model and 15% saying business users remain largely dependent on technical teams.

The findings suggest this dependency extends beyond productivity. The employees with the deepest understanding of how the business operates are often the same people waiting on IT to access the data needed to build, validate, and improve AI workflows. That disconnect makes it more difficult to embed business context into enterprise AI systems, limiting AI’s ability to generate meaningful business outcomes.

Enterprise AI Works Best When IT and the Business Work Together

The research also points to a growing consensus that AI performs best when technical expertise and business expertise work together. Two-thirds of technology leaders say AI and agent-based systems are most productive when managed within the line of business. Additionally, 71% believe AI initiatives are most successful when IT and business teams collaborate closely.

This sentiment correlates with past Alteryx research. Yet, strategy (37%) and delivery (38%) remain concentrated within IT, while business teams are most often responsible for defining requirements (30%). That disconnect between where business knowledge resides and where AI systems are built continues to slow enterprise AI adoption.

MacMillan concluded, “The organizations creating lasting value from AI will be the ones that operationalize their business logic so it becomes visible, governed, repeatable, and ready for AI.”

To learn more and explore the full findings, download the “2026 IT Leader Research: The State of AI Ownership, Agents, and ROI” report.

By Jürgen Salenbacher, Creative Leadership & Personal Brand Strategist, Founder of CPB-Lab. 

Consider a family-owned retail group in Dubai, third generation, four hundred staff, twenty-two stores. Its marketing director resigns. The instinct built over fifty years is to replace her: post the role, run a six-month search, pay a full package. Instead the board hires a chief marketing officer for nine days a month, who also works with a logistics scale-up in Riyadh and a hospitality brand in Doha. Twenty years ago that would have signalled a business in trouble. Today it signals a business paying attention.

Fractional leadership, meaning chief marketing, financial and technology officers holding part-time mandates across several companies at once, has moved from the start-up margins into the mainstream of the Gulf economy. Interim and fractional C-suite engagements have risen sharply worldwide since 2021. The UAE now counts more than 1.4 million registered companies, a quarter of a million added last year alone, and nine in ten GCC organisations reported a skills gap in 2025. The model is what happens when demand for judgement outruns the supply of executives who have done the job before.

Artificial intelligence is the accelerant. There is an old cartoon about the company of the future: a man, a machine and a dog, where the man feeds the dog and the dog makes sure the man doesn’t touch the machine. That is not what has happened. AI has not deleted the marketing department. It has collapsed the execution layer between a decision and its consequence.

Take that retail group. A full-year media plan across six markets in Arabic and English used to occupy four people for three weeks. A competent strategist now produces a defensible first version in an afternoon, with scenario models at three budget levels attached. The scarce thing is no longer the work. It is knowing that the real question was never the media plan, but whether the group should be defending its hypermarket position at all. That judgement takes twenty years to acquire and about four hours a week to apply. A region that appointed the world’s first minister of state for artificial intelligence in 2017 is feeling this shift faster than most boards have adjusted for.

The case in favour is strong. Cost is the obvious argument: senior expertise without the salary, bonus, visa and gratuity of a full package. Speed is the better one. A mid-market logistics company facing a funding round and a tax filing in the same quarter does not need a permanent CFO. It needs someone who has closed eleven rounds, embedded within three weeks for ninety days, who leaves behind a data room and a finance manager able to maintain it. Breadth matters too, since an executive advising four companies across three sectors carries pattern recognition no single-employer colleague can match. And the mandate is honest. Reid Hoffman described careers as a series of tours of duty, time-bound alliances built on ethics rather than the fiction of permanence. Both sides know the brief, and both know when it ends.

The case against deserves equal weight, and it matters more here than in most markets. Attention is divided by design. When a distribution partner walks away on a Tuesday, or a product recall lands, the fractional leader is on a call with another client. Accountability blurs, since an executive with three other mandates absorbs only a fraction of the consequence when a strategy fails. And knowledge leaves on the last day. The most common failure is not a bad strategy but an excellent one: a brilliant repositioning handed to three people who were never taught to run it, quietly abandoned by the following spring.

Then there is the deeper problem. Culture is the bridge between strategy and implementation, and culture is biological, growing at the pace of a tree rather than a quarter. Entropy is real: an ordered system left without energy drifts towards disorder. Trust cannot be installed part-time and left to hold while the installer is elsewhere. The word “company” comes from the Latin companio, one who eats bread with you. The majlis makes the same point without the etymology. In a family business here, an executive who appears for nine days and never sits at the table will find his recommendations politely received and quietly ignored, whatever his record elsewhere.

So the model works only under conditions. The first is that the fractional leader arrives to facilitate rather than instruct. Consulting is not the way forward, facilitating collective learning is. A CFO who instructs leaves a slide deck and a hole. One who facilitates spends the ninety days turning the finance manager into someone who no longer needs him. Instead of authority, inspiration. Instead of hierarchy, collaboration. Instead of delegation, participation.

The second condition is character, in four parts. Substance: genuine expertise, not a LinkedIn headline. Style: clarity in how a leader communicates and shows up. Conviction: a world view worth being held to. Grace: the elegance to enter someone else’s culture as a guest rather than an occupier.

The reality of tomorrow is not fewer leaders. It is leaders held differently, by invitation rather than org chart, by contribution rather than title. The movement runs from dependency, through independency, into an age of interdependency, and the fractional C-suite is an early expression of it.

Organisations want to work with the machines, not for them. The ones that remember the difference will attract the people worth having.