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����.

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����.

The post Generative AI, a New Catalyst for D2C Expansion appeared first on 91����.

The post SaaS for Design and Manufacturing Environments: Reduce Running Costs and Increase Collaboration�� appeared first on 91����.

The post Digitisation of Lending Business appeared first on 91����.

The post 10 Critical Learnings to Win in D2C in Mid to Large Sized Organizations appeared first on 91����.

91���� is on a journey towards Sustainability 2.0, an agenda that reinvents sustainability under the compelling challenges of climate change and social inequity. This new agenda is driven by a remarkable combination of thought and action on 91����’s part with meaningful public-private-people partnerships. The Sustainability Report 2.0 is available .

The Need for a Sustainability Data Analytics Platform

Research by states that 86% of business leaders have invested in sustainable practices to protect their organizations from disruptions. However, reporting on sustainability initiatives and finding various data types is difficult for all relevant parties to access. In order to drive sustainability performance management, the data analytics platform is vital.

Why is Sustainability Data Analytics Platform Necessary?

• Unable to detect human errors during data collection – Leveraging AI-driven document processing methods
• Unable to trace and audit data – Ensuring end-to-end traceability by customizing system logs as user-friendly. Approvers can easily interpret and make approval decisions with sustainability data reviews
• Time-consuming while aggregating data – Introducing the power of data aggregating capabilities by customizing industry-standard data management tools
• Inconvenience while accessing sustainability reports – Enabling self-serving capability with ideal role-based access control for different stakeholders
• Difficulty in getting the approval of reported sustainability data – Leveraging integrated workflow for end-to-end automation of processes ranging from data preparation to approval
• Proactive methods needed for meeting sustainability targets – Introducing machine learning models with the help of predictive analytics tools for preventive actions to meet the defined sustainability goals
• No single source of truth for sustainability reports – Introducing a self-service platform for accessing sustainability artifacts

How Will the Sustainability Data Analytics Platform Help?

• Integrated platform with low environment code to address the above-mentioned pain points
• Single source of truth for stakeholders for complete transparency of sustainability reports
• Secured access to data across stakeholders, data providers, reviewers, sustainability officers, and external auditors
• Automated workflow for end-to-end data management and reporting
• Complete traceability of data preparation, reviews, corrections, and approvals
• Complex calculations to arrive the critical sustainability measures across environmental footprints – energy, emission, water, and waste
• Prebuilt data model to reduce the time of implementation
• Various connectors to extract data from scanned documents, PDFs, enterprise resource planning, and excel sheets

How is the 91���� Sustainability Data Analytics Platform different from other platforms?

Here are the key differentiators that make 91���� a strong player among others:

• Single Version of Truth
• Pre-built data model suitable for manufacturing and consumer product companies
• Automation of data consistency, accuracy, and completeness verification in the data collection process
• Inbuilt predictive insights for corrective actions to meet sustainability targets
• Role-Based Access to Data
• Accessible roll-out features across businesses
• Configuration options for third-party audits
• Full Stack Solution ensures data collection till sustainability reporting and analytics
• Easy and convenient to deploy across different business units and facilities
• The functional view of each of the components is depicted below:

The 91���� Sustainability Data Analytics Platform is based on cloud technology, favouring the resource-efficient use of IT resources, and enabling every company’s flexible and expandable growth. With integrated solutions, companies can introduce sustainability goals more confidently and cleanly into their daily activities and move strategically toward building more resilient, sustainable businesses.

The post Sustainability Data Analytics Platform for Implementing Sustainability 2.0 appeared first on 91����.

Sustainability Management imperatives are taking more prominence due to environmental concerns and will continue to be in focus in the coming days with increased awareness on the subject from governments, business & society at large.

Businesses have a significant role to play & sustainable practices are considered a corporate responsibility with leading companies taking sincere initiatives to measure and minimize environmentally unfriendly operations and to address various stakeholders’ (internal & external) priorities:

• Brand reputation – As companies introduce sustainable methods in their manufacturing systems, their reputation among investors, stakeholders and consumers improves
• Operational efficiency – Reduced usage of energy and other resources leads to reduction in costs which eventually leads to improved operational efficiency
• Societal impact – Creating a strong image with sustainable methods sends out a strong message to the society and this in return creates a positive impact on consumers’ minds
• Transparency from Business Partners – Companies are expecting from their partners Sustainable business engagement & moving away from ties with partners at risk
• Shareholders – Beginning to use ESG scores as one of the criteria to make investment decisions
• Regulatory Requirements: These are becoming more stringent
• Customer perspectives – Becoming more conscious about sustainable Products & Practices & do not like Greenwashing

The manufacturing industry is setting ambitious and sustainable targets for improving the planet with meaningful environmental changes. Intricately connected with this are corresponding opportunities for technology companies. According to recent surveys, the sustainability market size for green technologies is expected to grow significantly with a Compound Annual Growth Rate (CAGR) of >25%, with market size expanding to almost $45b+ by 2028. The management of greenhouse gas emissions, energy consumption, waste management, green product development, and water conservation—is seen no longer as a cost but as a critical value differentiator.

Challenges faced by manufacturing companies:

Operational Inefficiencies

• Manual Method of collecting sustainability data & transformations leading into errors
• Auditability/ Traceability issues due to manual methods of operation
• Deficiencies in ability to do basic analysis on the data

Newer Asks:

• Suppliers following the organization’s environmental standards – Whether the suppliers are complying with the organization’s green standards with the components that they are providing
• Lifecycle assessment of products – Assessing the sustainability footprint of the product (e.g., from cradle to grave)
• Climate Risk Analysis (e.g., while setting up new units – Challenges of setting up new factories as per climate standards and how these setups are going to affect the environment)
• Evolving regulatory frameworks & higher reporting frequencies

These challenges need to be intrinsically addressed by a digital solution to improve efficiencies, enable auditability, reduce non-compliances & make the enterprise future-ready.

Conclusion

When it comes to sustainability in manufacturing companies, a remarkable change is afoot, resulting in more significant thinking—especially on the factory floor and value chain partners. Manufacturers prepared to adopt the change will find opportunities for innovation—with sustainability targets inspiring green design, manufacturing, sourcing and novel technology applications. The time has come for data platform solutions for ESG and other newer asks that are emerging.

Author:

Sandip Mitra,
Business Consulting Group, 91����

The post Sustainability Imperatives in Manufacturing Companies appeared first on 91����.

It is a common situation that most shoppers face while buying packaged goods online. Due to high demand many daily used products go out of stock in retail sites. But it is more annoying to experience longer time in any shopping website due to lack of clarity about how to select and pay for a product. Direct-to-customer (D2C) is the new trend shaping the consumer-packaged goods (CPG) industry across the globe. Primary reason being low entry barrier, which is enabling private label brands to appear in the market with frequency higher than ever. However, not all brands could sustain the race of attracting consumers to their brands. It is more due to the marketing strategy they follow than the quality or features in the products. Drawing consumers to the products is the primary major challenge in this business model. Second major challenge is ensuring continuous availability of products, which means ensuring an uninterrupted supply chain process. If we introspect more into the primary challenge, we find three reasons underlying. First one is all about understanding the need of each & every customer and designing product as per their requirements. Second is keeping a tab of competitors’ offerings in the marketplace. Third is using information related to earlier two cases to develop personalised buying experience for every customer. The only solution that could help us resolve all these problems is data. Data is the ammunition in this expanding war of customer acquisition and retention. Collection of data about customers’ preferences and competitors’ new offers, storing those data and using those effectively are of primary importance in building a successful D2C business model.

If we go deeper into this, we find that next level of challenge is identification of data. It is customary for every marketer to understand and find the type of data one needs to understand customers’ requirements. Parallelly, one needs to find the data required on competitors’ offerings to strengthen the grip on the marketplace. Once understood, firms should think about the resources through which all these data can be collected and stored. And finally, how these data can be used to generate insights about customer’s behaviour.

To help firms in this journey, digital technology services are on the rise. The advent of intelligent automation has equipped the software service providers develop digital tools to understand the ongoing trend and find which areas need more focus to make the business run profitably. Robotic process automation (RPA), cloud technology, Artificial Intelligence (AI) based algorithms and machine learning (ML) programs are examples of digital tools which have paved the way for technology focused CPG firms to monitor their businesses closely and engage consumers more effectively. Every consumer has a different and unique way of shopping. Therefore, engaging with each consumer through the right channel, right promotion and right offer is undoubtedly a critical task. In top of that increasing penetration of mobile technology has brought all category of consumers at the same place and at the same time resulting in an extra level of complexity in data collection. Therefore, developing a robust data storage facility is no more a choice but a necessity for managing such increasing diversified consumer base. 91���� through its rich experience of working with multiple CPG consumers can provide both on-premises and cloud-based database solutions to manage terabytes of data on a continuous basis.

Once the data storage facility is set up, suitable data collection resources should be developed to ensure uninterrupted data feeding to the repository. Whenever a consumer engages in any purchasing activity through online channel, millions of data are transferred to the marketing firm. Multiplying this with number of available channels, nearly trillions of data are needed to be handled every day. Hence, the solution is to have resources having the capability to find relevant data in any format from any channel and incessant feeding of these data to the storage facility. 91����’s intelligent automation team can develop any customised intelligent bots using robotic process automation (RPA) for collecting data from any channel at any time with 100% efficiency. Not only from websites, but these bots can also even identify the necessary data from any document in any format. This can help the firms analyse multiple purchasing invoices to figure out average purchase value for any customer.

After collection the data, next important steps are identification of relevant KPIs and development of analytical models for the purpose of understanding the status of ongoing business and generation of insights about customer’s buying pattern. 91���� has a dedicated analytics team who has developed analytical models as per business need and has helped firms understand consumer journey at every touchpoint starting from exploration to procurement of any product.

After data management the next critical area where D2C firms should put highest focus is managing the supply chain of entire processes. If designing products as per consumer’s need is the primary challenge that D2C firms face now-a-days, then the next big challenge is taking these products to the consumer’s doorstep. The solution that addresses this concern is smart supply chain. Although most of the firms engage third parties for delivery of products, making the products available to delivery partner’s warehouses or distribution centres need a thorough monitoring of supply chain. 91����’s Industry 4.0 and Digital team together provide efficient and effective supply chain solutions with the help of Internet of Things (IoT), RPA and AIML. Not only our solutions ensure automated monitoring of entire supply chain process but also can eliminate the redundant processes, reduce the energy consumption at intermediate stages wherever possible and build models for optimising selection of packaging materials.

The problem that Sanaya faced at the beginning of this article could have easily been avoided had the brand manufacturer focussed on these issues and collected data about Sanaya’s buying pattern. Also, they should have ensured availability of the product at retailer’s site by adopting smart supply chain practices. Emerging digital technologies like robotics, artificial intelligence, cloud technology and IoT are a blessing for firms. However, leveraging these technologies to maximise the profit and capture the market share needs a blend of expertise & experience over the years. 91����’s rich experience of working with global CPG manufacturers in multiple channels and multiple regions for diversified products have helped develop suitable digital technology-based offers for D2C firms. In an industry like CPG where competition has been a paramount concern from new product perspective, emergence of D2C practice has made the situation more complex. To compete successfully and lead in the marketplace, firms must embrace the digital tools for navigating through the wave of unlimited new products.

Author:

Debal Chakraborty,
Principal Consultant

The post Brands that Embrace Digital will Stay Ahead in the D2C Model appeared first on 91����.