However, this model works when the brand can convert signed properties into revenue-generating assets quickly. The faster a property becomes commercially available and operationally ready, the faster both the owner and the brand begin to realize value.

This is where the traditional approach to hotel onboarding often fails to deliver results.

The hidden friction delaying early revenue capture

In many hotel companies, onboarding still moves through a sequence of functional activities: commercial setup, operations readiness, IT enablement, brand compliance, data collection, distribution configuration and property systems activation. Each team may complete its part, yet the property can still sit between contract and revenue because of handoffs, clarifications, missing data, late infrastructure decisions, vendor dependencies and rework.

The challenge is that onboarding delays are rarely caused by one system or one team. They are usually caused by fragmented ownership, inconsistent data collection, unclear technology expectations, late-stage infrastructure discussions, non-standard platform configurations and multiple teams working with different versions of readiness.

Even today a large majority of the hotels do not have fully integrated core systems. For brands adding properties across multiple ownership models, that fragmentation can turn every new onboarding cycle into a fresh round of data collection, system mapping, template changes and manual validation.

The result is lag: in decision-making, data readiness, system setup, ownership alignment, and ultimately lag in revenue realization. We saw this in our holiday park engagement where onboarding new properties and making them sellable was taking 90+ days at a minimum to some over a year, while service readiness took over 100+ days.

Moving towards #Zerolag: A better scorecard for hotel onboarding

To reduce this lag, hotel onboarding needs to be viewed through the lens of commercial readiness and operational readiness.

Time-to-Sell is the ability to make a property’s inventory commercially available across the right channels as early as possible – ideally well before the physical hotel is ready to welcome guests. This requires commercial, distribution, revenue, brand and technology teams to work from a common playbook, with the right data, standards and platform readiness in place upfront.

Time-to-Serve is the ability to make the physical property ready to operate, serve guests and deliver the brand promise in the shortest possible time. This is where operations, IT, architecture, vendors, property teams and owners must come together around a clear, coordinated onboarding model.

The goal should be to move towards Time-to-Sell at zero and Time-to-Serve at speed.

Designing for velocity, fluidity and scale

However, this is as much an operating model issue as a technology issue. Hotel onboarding slows down when the right information, decisions and dependencies arrive too late. To make onboarding #ZeroLag, hotel companies need to build intelligence into the process upfront.

1. Start readiness earlier

Business growth teams need early guidance on what the owner and property must be ready for. Owners need clarity on infrastructure, systems, data, vendors and approval timelines before the property moves deep into delivery. This helps teams avoid late clarifications that slow down commercial go-live.

2. Create one version of property readiness

Property, room, rate, tax, payment, vendor, channel and operational data should be captured once, validated early and reused across teams. Commercial, distribution, revenue, IT and operations will still play different roles, but they should work from the same version of readiness.

3. Build reusable templates and patterns

Core platforms, configuration models, integration patterns and operating playbooks should be templatized by property type, geography and business model. This helps every new property inherit enterprise standards instead of starting from scratch each time.

4. Coordinate the journey across teams

Onboarding needs one clear view of data readiness, system setup, open decisions, testing status, Time-to-Sell and Time-to-Serve. That visibility helps teams move with Fluidity and gives leadership a clear view of where action is needed.

The path forward

When these pieces come together, onboarding becomes faster and easier to scale.

• Velocity comes from reducing avoidable delay.
• Fluidity comes from helping teams move from the same information.
• Scale comes from making the model repeatable.

It also creates downstream value – better reporting, stronger analytics, cleaner performance insights and a more consistent foundation for AI-led operations. For the holiday parks engagement, this shift could reduce Time-to-Sell from 90+ days to one day and service readiness from 100+ days to four days.
Making this shift requires change management, cross-functional alignment and CXO sponsorship. Hotel companies need to treat onboarding as a strategic growth capability, because every delay between signing and selling affects revenue, owner confidence and the brand’s ability to scale.

To explore how to build #ZeroLag in your hospitality operations to reduce onboarding friction and accelerate faster revenue realization, read our whitepaper: /resource/whitepaper/zero-lag-hotel-enterprise-eliminating-operational-delays-to-boost-revenue-and-guest-experience/.

Author:

Anu Joy,
VP and Industry Group Head for Hospitality

The post From Signed to Sellable: The New Benchmark for Hotel Onboarding appeared first on 91¶¶Ňő.

To the guest, it is one stay. To the hotel, it is often a complex web of platforms, teams, processes and handoffs.

That gap is becoming harder to ignore. Travelers increasingly expect hotels to recognize context and respond with relevance. A 2025 Mews survey found that 68% of travelers say they are more likely to stay loyal to hotels that deliver standout, personalized experiences, showing how closely loyalty is now tied to the quality of the experience, rather than only points or rewards.

This is where Agentic AI can redefine what a frictionless guest journey means. The opportunity is to go beyond chatbots and use AI agents to understand guest intent, interpret context and orchestrate action across the hotel enterprise. In a #ZeroLag enterprise, this is the essence of Fluidity: systems, teams and touchpoints working together without making the guest or associate carry the burden of complexity.

From discovery to booking – understanding intent earlier

The guest journey begins well before arrival and with different expectations. A family planning a resort stay, a business traveler attending a conference and a couple looking for a wellness weekend are not shopping for the same experience, even if they are looking at the same hotel.

At the discovery and inspiration stage, AI agents can understand travel intent, purpose of stay, budget, loyalty status, preferences and contextual signals to help present the most relevant hotel, room type, package or experience.

As AI-led search and travel planning become more common, hotel content also needs to become easier for intelligent systems to understand. Room types, amenities, policies, packages, accessibility features, dining options, family services and local experiences must be structured clearly enough for both humans and AI agents to act on them.

At the shopping and booking stage, agents can connect central reservation systems, revenue management systems, customer data, loyalty profiles and offer engines to recommend a more relevant stay proposition. This could include early check-in, dining credits, spa packages, meeting support, family amenities or loyalty-linked benefits, depending on the guest’s context.

Pre-arrival – converting signals into action

The pre-arrival phase is where many hotels lose the thread. A guest may share arrival time, dietary needs, room preferences or a special occasion. Too often, those signals do not flow cleanly into operations. The guest arrives expecting recognition and meets disappointment.

Agentic AI can help convert pre-arrival intent into operational action. It can prioritize room readiness, alert housekeeping, inform the front office, coordinate amenities, check package inclusions and proactively communicate with the guest.

However, a truly fluid guest journey goes beyond internal operations. External environment such as flight delays, weather disruptions, local events, traffic conditions or conference schedule changes can all affect the stay. If a guest’s flight is cancelled, an agent should be able to detect the disruption, check availability, understand reservation context and initiate an approved communication: would the guest like to extend the stay for another night? Behind that simple message sits orchestration across availability, rate rules, housekeeping, loyalty, payment authorization and guest messaging.

This is where Agentic AI moves hospitality closer to service anticipation.

Arrival and stay – helping associates deliver better service

At arrival and check-in, a guest should not have to repeat preferences or wait while teams manually check status. Agents can bring together PMS, housekeeping, loyalty, payment, identity verification and guest messaging to help associates deliver a more confident and personalized welcome.

During the stay, the journey becomes more dynamic. Dining, housekeeping, engineering, concierge, spa, retail, transportation and service recovery all shape the experience. A request for extra towels, a restaurant booking, a room temperature complaint or an upgrade inquiry should not become a chain of manual calls and follow-ups.

Agents can classify the request, understand priority, trigger the right workflow, update the right system, notify the right team and close the loop with the guest. Associates remain central to the experience, especially in high-empathy moments. Agentic AI gives them better context and fewer manual steps.

This is how #ZeroLag starts to show up in the guest experience: less waiting, less repetition, fewer dropped requests and more confident service.

Closing the loop with checkout and post-stay engagement

At checkout, the opportunity is to reduce payment, billing and loyalty friction. Charges from room, dining, spa, retail and other services should come together accurately. Exceptions should be flagged early. Loyalty points, benefits and invoices should be handled with minimal guest effort.

Post-checkout should also be part of the journey. Agents can help interpret feedback, detect unresolved dissatisfaction, trigger service recovery, personalize future offers and capture insights that improve the next stay. A truly connected journey learns from every interaction.

The architecture beneath the experience

Ultimately, what the guest experiences as seamless service depends on the operations orchestration underneath. The hotel does not need every system to become one system. It needs the journey to behave like one connected experience.

Agentic AI allows hotels to move faster across systems and workflows. Agents can sit across processes, interpret signals, retrieve knowledge, trigger workflows and guide associates.

That requires trusted data, API-led integration across PMS, CRS, POS, RMS, CRM, loyalty and service platforms, event-driven workflows, real-time operational signals, external signals, identity and consent management, security guardrails and clear rules for when AI acts and when humans stay in control.

In many ways, the frictionless guest journey is only as strong as the invisible architecture beneath it. To know more about this architecture, read our whitepaper: /resource/whitepaper/zero-lag-hotel-enterprise-eliminating-operational-delays-to-boost-revenue-and-guest-experience/

Author:

Anu Joy,
VP and Industry Group Head for Hospitality

The post Architecting the Frictionless Guest Journey Through Agentic AI appeared first on 91¶¶Ňő.

AI-Native Applications: From Code to Cognition

Applications are no longer static pieces of software; they are AI-native ecosystems. The latest generation of models, such as Claude Opus 4.6, GPT-5, and Gemini Ultra, exemplify this shift. With massive context windows and multi-modal reasoning, these systems can process entire codebases, legal archives, or research datasets in one pass, while seamlessly integrating text, images, and structured data.

The result is software that designs, tests, and debugs itself, while delivering hyper-personalized user experiences. What once took months of development now unfolds in days, redefining the very meaning of “software.”

Democratization Through Low-Code and No-Code Platforms

Innovation is no longer confined to developers. Low-code and no-code platforms empower business users to build applications without deep technical expertise, while developers evolve into AI orchestrators, responsible for governance, integration, and optimization.

This democratization accelerates cycles dramatically. Enterprises across banking, healthcare, and compliance-heavy industries are delivering solutions in weeks rather than months. The enterprise of 2026 is agile by design, with innovation embedded into every function.

Intelligent Execution Pipelines: Speed, Scale, and Resilience

Execution has become the new competitive frontier. Intelligent pipelines integrate cloud-native orchestration, agentic AI, and continuous intelligence. Features like Claude’s “Agent Teams” demonstrate how specialized AI agents collaborate like human teams, managing billions of micro-decisions in deployment, monitoring, and optimization.

Elite performers now deploy code thousands of times per day, achieving unprecedented speed and resilience. For industries like manufacturing and financial services, these pipelines are not just operational tools, they are strategic differentiators.

Zero-Trust Security: Embedded by Design

Cybersecurity is no longer an afterthought; it is embedded into the DNA of enterprise execution. Zero-trust frameworks ensure identity-first access, continuous monitoring, and integrated compliance at every stage of the lifecycle.

As AI expands the enterprise’s risk surface, zero-trust has become the default safeguard. Financial institutions, healthcare providers, and global manufacturers are embedding these frameworks into AI-native pipelines to protect sensitive data and maintain trust at scale.

Enterprise and Policy Implications

The convergence of AI-native apps, democratized development, intelligent pipelines, and zero-trust execution carries profound implications:

• Workforce transformation: Developers must reskill into AI orchestrators, governance specialists, and integration architects.
• Governance frameworks: Multi-agent systems demand accountability, transparency, and ethical oversight.
• Global leadership: Nations and enterprises are setting benchmarks in AI-native execution, with India, the US, and Europe leading the charge.

This is not just a technological shift, it is a governance and policy challenge. Enterprises and governments must collaborate to ensure innovation remains inclusive, ethical, and globally interoperable.

Risks and Challenges

The promise of AI-native enterprise comes with challenges:

• Infrastructure strain from compute-intensive workloads.
• Talent gaps as organizations struggle to reskill developers.
• Governance gaps in multi-agent execution environments.
• Market consolidation, with smaller players at risk of being absorbed into curated ecosystems.

Addressing these challenges requires coordinated action across industry, academia, and government. Investments in infrastructure, workforce reskilling, and governance frameworks will be critical to sustaining momentum.

Conclusion

Enterprise IT in 2026 is not simply evolving, it is reinventing itself. AI-native apps, intelligent pipelines, and zero-trust frameworks are redefining how enterprises innovate, secure, and scale. With Claude, GPT-5, and Gemini Ultra leading breakthroughs, and global IT firms embedding these paradigms into practice, the future of enterprise innovation is clear: intelligence, speed, and trust will define competitive advantage.

Author:

Kishore Kamarajugadda,
VP-Enterprise Architect

LinkedIn:

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Today, the tells us that as of 2023, surpassing human performance in image classification, visual reasoning, and understanding natural language has been achieved. Together with several notable changes in AI, the report pointed out that until 2014, academia was primarily releasing Machine Learning models. That had changed. In 2023, academia released 15 models while industry released a massive 51.

In May 2024, (Omni), its “new flagship model that could reason across audio, vision, and text in real time.” The model could listen, watch, talk, sing, question, joke, laugh, reprimand, and role-play. OpenAI invited Salman Khan of Khan Academy for one demo to showcase GPT-4o’s capabilities. Salman Khan used the model to problem simply by asking GPT-4o to “visually” examine a screenshot of the problem. GPT-4o combined its ability to listen, see, and read with impressive dexterity to show it could understand the problem and be a great tutor.

What could the next five years bring? The question is important because, as Gartner predicts, “By 2026, (GenAI) application programming interfaces (APIs) or models, and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023.” So, here are our top three predictions (spoiler alert: the third one is a no-brainer):

One, a vast amount of expertise will be free. The Khan Academy demo should be an early indication of what is coming. Children will find AI-driven tutors. Doctors, lawyers, financial advisors, and citizens will have access to affordable AI-based experts.

Two, personal AIs will know everything about us. Crunching mountains of data in real time is no longer a challenge. Apple is already doing this with , which understands personal context to simplify tasks and take action. In a recent TED Talk called , Fei-Fei Li, the AI pioneer, computer science professor at Stanford University, and founding director of the Stanford Institute for HAI said, “The urge to act is innate to all beings with spatial intelligence, which links perception with action. And if we want to advance AI beyond its current capabilities, we want more than AI that can see and talk. We want AI that can do.” She was talking in the context of embodied AI, but her thinking also holds good for digital in-the-cloud GAI. Personal AIs will step in with action because they will have access to our health records, our bank accounts and investments, our educational backgrounds, our journals and emails, and our likes and dislikes about organizations, businesses, news sites, movies, books, food, places, and people. Businesses and other institutions have been using APIs for years. Soon, APIs designed for personal AIs will be available. These APIs will have a public interface, letting us make data about ourselves available selectively for specific types of relationships.

Three, people will become the key differentiators of a service or a brand. Reason: Nothing is more potent than human experience and intuition, nothing more comforting than a promise made by a human, and nothing more reassuring than the human touch. Human connection is a craving firmly etched into our DNA that will take centuries to change. At work, we will always want to speak to the head of HR to sort out our problems, we will always want to talk to a human when the car service or a watch service goes awry, and we will always want to hear the reassuring voice of an actual human airline executive when we reach the airport late, and our flight has taken off.

Surpassing human performance is a legitimate goal. But human connection matters more than productivity, efficiency, and the power of logic. GAI and personal AIs cannot create organizational cultures and social contracts. Humans can. So, organizations will keep AI away from managing several types of processes independently. Instead, far-sighted organizations will work towards creating an environment where humans and AI can work together. Machines will, thankfully, do what we find boring; we will do what we have always excelled at—building and maintaining reputation and trust.

Author:

Sandeep Kumar,
Sr. VP & Head Global Consulting

The post Forecasting Change in the Time of GPT appeared first on 91¶¶Ňő.

Can AI superintelligence really be around the corner? In a 165-page paper called (June 2024), Leopold Aschenbrenner reasons that “GPT-2 to GPT-4 took us from ~preschooler to ~smart high-schooler abilities in 4 years…we should expect another preschooler-to-high-schooler-sized qualitative jump by 2027.” Further, says Leopold Aschenbrenner, “AI progress won’t stop at human-level. Hundreds of millions of AGIs could automate AI research, compressing a decade of algorithmic progress into ≤1 year. We would rapidly go from human-level to vastly superhuman AI systems.” This means Generative AI will play a much more significant role in business faster than we think.

What is Responsible AI (RAI)?

How fast is `fast’? In a few months from now, businesses without a Generative AI strategy will struggle to keep pace with the competition. And if they do not begin to support Responsible AI (RAI) now, their strategy and investments in Generative AI will, sooner rather than later, be wasted.

Broadly, RAI covers the governance, ethics, morals, and legal values that go into designing, developing, and deploying beneficial AI. Everyone agrees that RAI should aim to mitigate the risk of adverse outcomes on society and ensure privacy to build trust in technology.
The idea of RAI has a rich history. In 1950, mathematician, computer scientist, and logician Alan Turing proposed the Turing Test, which set the standards for machines to show human-like intelligence responsibly. The same year, Isaac Asimov’s Three Laws of Robotics appeared in his book I, Robot, establishing the early guardrails for intelligent machines. This historical context highlights the long-standing importance of responsible considerations in AI development and implementation.

However, RAI has not kept pace with the rapid breakthroughs in AI technology. A  showed that a quarter of respondents had experienced AI failure, ranging from lapses in technical performance to outcomes that put individuals and communities at risk. Poor understanding and implementation of RAI are slowing down the ROI on AI investments. Enterprises need—but currently lack—the RAI expertise to create a framework around RAI principles, policies, tools, and implementation processes.

The business case for RAI

It requires specialists to stay on top of RAI. It is a new but complex vocation. At last count, the of Algorithm Watch had 167 listed guidelines. Some guidelines, such as IEEE’s Ethically Aligned Design are 290 pages long. The comments in the consultation for the European Union’s High-Level Expert Group (HLEG) on AI’s Ethics Guidelines run into hundreds of pages.

Meanwhile, the global AI regulatory landscape is gaining momentum with the , the , , and a slew of other such acts across nations. The penalties for regulatory breaches are becoming stiff. The most recent version of the EU AI Act proposes or 7% of worldwide annual turnover for the preceding financial year, whichever is higher. The New York City Law on Automated Employment Decision Tools carries a for non-compliance. It should be evident that enterprises that do not think of RAI are building their technical debt, which could devastate their business.

Becoming a good actor

With enterprises deploying Generative AI solutions for their businesses across the globe, enterprises are becoming more vulnerable to local regulations, increasing the need for RAI.

The ideas that create the foundation for RAI are simple to understand. Most of these naturally focus on data and its use—because data is the basis of large language models (LLMs), which are at the heart of Generative AI.

Among the foundational ideas is informed consent when collecting and consuming data from its owners, using high quality and debiased data to train AI models, maintaining transparent and explainable AI models, keeping data owners informed about the use of the data and the risks/benefits associated with the use, maintaining privacy and disclosing who the data is being shared with, providing the ability to data owners to opt in/out of AI-powered programs, and adherence to local laws and regulatory requirements.

These ideas are simple to understand. However, we would consider a good actor in the RAI context to be one who focuses on fostering a productive relationship with the technology and, second, uses AI for its intended and stated purpose while being aware of the limitations of the tools being used.

Without a doubt, RAI has a long way to go—but it will evolve into one of the most important investments a business can make in the age of exponential change at the hands of Generative AI. The time to begin the investment is now.

Author:

Sandeep Kumar,
Sr. VP & Head Global Consulting

The post How to Be a Good Actor in the Race for AI Superintelligence Adoption? appeared first on 91¶¶Ňő.