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Huawei Cloud: Accelerating Intelligence in Europe, for Europe

EMEA organisations expect cloud computing to improve productivity and expedite new product development



Huawei Cloud: Accelerating intelligence in Europe, for Europe

The Digital Future in Europe

The future of European organisations and industries is looking increasingly digital and cloud-centric.  According to Foundry’s Digital Business Study 2023, 91% of EMEA organisations have adopted or plan to adopt a digital-first business strategy. At the same time, Foundry’s Cloud Computing Study 2023 found that 71% of EMEA organisations are defaulting to cloud-based services when upgrading or purchasing, while 60% have been accelerating their cloud migrations over the past year.

EMEA organisations expect cloud computing to improve productivity, enable innovation and new product/service development, and upgrade legacy tech with lower total cost of ownership. 54% of IT Decision Makers also expect to use cloud capabilities to leverage Artificial Intelligence (AI)/Machine Learning (ML) over the next year, which many see as a potential game changer for their industries.

However, EMEA organisations are facing challenges in their digital and cloud journeys. 34% reported a lack of the right skill sets, while 29% cited complexity of IT infrastructure as a major hurdle. There were also concerns around data privacy and security, cloud costs, and compliance issues which would need to be addressed before organisations could reap the potential offered by the cloud.

Enabling Industries through Everything as a Service

To help European industries and organisations overcome these challenges and quickly enable cloud initiatives, Huawei Cloud has adopted a strategy of “Everything as a Service.” This comprises multiple tenets:

  • Infrastructure as a Service allows industry partners to rapidly scale their computing, network and storage across Huawei’s global access network, comprising 30 Regions and 84 Availability Zones across more than 170 countries
  • Technology as a Service makes available Huawei’s continuous innovation and leading-edge capabilities in cloud-native, Artificial Intelligence (AI) and data and media, offering organisations more flexibility and choice in their technology partnerships
  • Expertise as a Service taps on Huawei’s three decades of experience in ICT, as well as the best practices of its network of global partners

Leading innovation in cloud-native, data, and AI

Huawei’s answer to the growing demand for cloud-native as the platform-of-choice for digitalisation is KooVerse: Huawei Cloud’s distributed infrastructure of storage, computing, networking, and security resources, designed with a unified architecture on a globally accessible platform to help organisations leverage the latest in cloud-native services and technologies.

To that point, Huawei is the only founding member of the Cloud-Native Computing Foundation (CNCF) from Asia, due to its consistent investment in developing and improving cloud-native services such as Ubiquitous Cloud-Native Services (UCS), Cloud Container Engine (CCE) Turbo, Cloud Container Instance (CCI), and CCE AutoPilot. In the Netherlands, these services helped 433, the world’s biggest football community, improve production efficiency and reduce costs by 25%.

On the data side, GuassDB is the result of 20 years of experience developing databases. Huawei’s next-generation distributed cloud database is designed for high availability and security, performance and flexibility. GaussDB has been widely used in banking, insurance, securities, and energy.

Tailoring AI for Industries

Building on its “AI for Industries” strategy, Huawei Cloud designed Pangu pre-trained AI models, tailored for a range of industries including finance, government, manufacturing, mining, meteorology, and railways.

“Huawei Cloud Pangu models will empower everyone from every industry with an intelligent assistant,” says Zhang Ping’an, Huawei’s Executive Director and CEO of Huawei Cloud, “and help to reshape all industries with AI.”

In the field of meteorology, the Pangu weather model has become the first to achieve better precision than state-of-the-art numerical weather prediction methods, with a prediction speed several orders of magnitude faster. Consider how the Pangu model was able to predict a 10-day typhoon trajectory in 10 seconds against five hours using current methods. These ground-breaking results were published in the July edition of the scientific journal Nature. In pharmaceutical R&D, the Pangu drug molecule model has drastically shortened lead compound discovery cycles from years to a single month, while achieving a 70% reduction of costs.

Building Trust in the Cloud

Addressing a key concern for EMEA IT leaders, Huawei Cloud has more than 120 security and compliance certifications, including C5 certification with zero deviation. Huawei Cloud provides compliance consulting and security services, while Huawei’s Compliance Compass platform helps users ensure security and compliance.

To help build trust in cloud services in the EU, Huawei Cloud works with SCOPE Europe and EU Cloud CoC to contribute to the Third Country Transfers Module and GDPR Compliance Standards. Huawei Cloud has also released white papers to share its expertise on privacy protection, data security and cloud security, and is a Board Member of the EU Cloud Code of Conduct General Assembly.

Huawei Cloud sees stability and reliability as critical lifelines. Deterministic Site Reliability Engineering (SRE) offers service availability of over 99.99%, with recovery four times faster than industry average. In addition, Huawei Cloud offers a three-level support system, deploying five service teams in Europe, and a series of 24/7 support centers worldwide. 

Bringing Local to a Global Ecosystem

As a global cloud service provider, Huawei Cloud offers expertise and experience in digitalisation and globalisation, as well as ecosystem capabilities to help partners expand into global markets. A leading Chinese car brand

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5 Ways Artificial Intelligence is showing Promise as a Decision Maker

CIOs are seeing payoffs from using Artificial Intelligence to automate myriad types of business tasks and workflows



5 ways AI is showing promise as a decision-maker

Artificial Intelligence holds immense promise not just in automating tasks but taking actions that deliver results.

CIOs and others in the C-suite are already seeing payoffs from using AI to automate myriad types of business tasks and workflows. Now they’re eyeing a next-phase opportunity—relying on machine intelligence to handle complex decisions.

“If you look at the advances we have seen in Artificial Intelligence, with the large amounts of data that large language models can process, we can safely hand off various decisions to machines,” says Prasad Ramakrishnan, CIO & SVP of IT at Freshworks.

AI is becoming an integral part of decision-making for many different business functions – from finance to manufacturing to sales. Here’s a look at a few areas where it’s gaining influence.

Chatbot conversations and decisions

By some estimates, intelligent chatbots can already answer 80% of routine customer questions. This reduces costs while improving customer experience. Instead of waiting on hold or navigating through phone menus, customers can instantly get answers from a virtual agent that is far more engaging and knowledgeable than past generations of chatbots.

“Chatbots can come to your rescue with an answer derived from a knowledge base and know what type of tone to use when responding,” says Ramakrishnan.

Companies are now moving toward AI-powered decision-making in customer service—tapping into voice and sentiment analysis to automate complex processes such as recognizing customer intent and taking a recommended action to resolve it.

Sales optimization

In sales, AI can provide account reps with the information they need to close deals. An AI system can gather data from customer relationship management software, social media profiles, email interactions, and purchase histories to identify the candidates most likely to convert.

It can also factor in data specific to a sales prospect, such as whether the person has downloaded a resource or engaged with a particular email message. AI can then guide sales reps to follow up on the most promising prospects.

“It can even feed into the sales narrative, prompting the rep to ask the right questions or use offers that have a higher propensity to appeal to a particular customer,” Ramakrishnan says.

Outcomes are fed back into machine learning models to improve prediction accuracy continually.

Dynamic pricing

Airlines, ride-sharing services, and online retailers have long used dynamic pricing to adjust to changing market conditions. Utilities are an advanced use case: Power companies use sophisticated algorithms to set prices dynamically according to the volume of electricity generated by renewable energy sources and demand at different times of the day.

AI makes this capability available to any business. For example, a retailer could adjust prices on its website based on the visitor’s identity, inventory levels, and competitor prices. Hotels could dynamically adjust room rates based on traffic forecasts, weather conditions, and events in the area.

Supply chain logistics

Optimizing supply chains is a daunting task because of the number of variables involved. AI can help every step of the way. AI-generated “digital twins,” or virtual representations of physical assets or systems, can replicate live scenarios and predict breakdowns.

AI analytics tools can assess supplier performance and capabilities to help companies choose the most reliable sources at the lowest cost; they can further streamline operations by using blockchain technology to execute smart contracts, in which transactions are automatically triggered when certain conditions are met.

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AI & Augmented Decision Intelligence: The Next Paradigm Shift in Hospitality Revenue Management



ai augmented decision making

Artificial Intelligence is everywhere. It’s making inroads into practically every field of human activity. It’s integrated into search engines, word processors, and various apps likely installed on your smartphone in your pocket. And there’s a reason for the tech’s proliferation: it is incredibly adept at analyzing data much faster and at a much larger scale.

In the hospitality realm, most people consider revenue management to be something that only humans can do effectively. Now, with the advent of AI, that’s no longer the case. As technology continues to advance, there is a growing trend toward enhancing, automating, and optimizing decision-making processes, even for complex and collaborative scenarios. This shift reflects the ongoing integration of AI technology to make decisions more efficient, accurate, and streamlined.

Indeed, a growing number of hotels and hotel chains have been looking to reap the rewards of an AI-driven revenue management system (RMS) to optimize their pricing strategies. However, just because a system bears an AI moniker in its marketing doesn’t suddenly turn it into a revenue management silver bullet. The truth is that legacy solutions have algorithms that were created before AI made its appearance, so they can’t be AI-first. These days, that’s a costly mistake.

Hoteliers should recognize the distinctions between a revenue management system just using buzzwords like AI and what a truly modern AI-first system will do when it’s well designed. FLYR’s solution is also designed to be user-friendly and intuitive. It presents complex data in a simple, easy-to-understand format, enabling hoteliers to make informed decisions quickly and efficiently.

We’ll look at why FLYR’s AI-first solution leads the RMS pack with its decision intelligence capabilities in a little more detail, but first, let’s take a deeper look at what decision intelligence is.

What is Decision Intelligence?

Decision intelligence (DI) is a multidisciplinary field that uses advanced techniques from data science, machine learning, and artificial intelligence to guide decision-making processes. In the context of hotel revenue management systems, DI can bring about a transformative change by enabling more accurate, data-driven decisions.

In the realm of dynamic pricing, DI utilizes comprehensive analyses of booking patterns, market trends, and competitor pricing to optimize rates strategically for maximum revenue. Its impact extends to enhancing demand forecasting and providing unparalleled precision for inventory control and staffing decisions.

DI refines guest segmentation by delving into data, allowing personalized services that elevate satisfaction and boost revenue. Furthermore, it guides strategic choices in distribution channel management, optimizing focus for revenue maximization. In addressing the perennial challenge of overbooking, DI’s predictive capabilities empower informed decision-making, minimizing the risks of guest disappointment.

Moving beyond conventional revenue management systems that offer decision support solely based on supply and demand cues, decision intelligence algorithms and models meticulously examine data to grasp price sensitivity. They can autonomously modify rates for specific rooms, nights, and lead times, providing a more dynamic and responsive approach.

Unlike other systems, FLYR algorithms are totally prescriptive, providing insights that would have otherwise flown under the radar – and delivering recommendations for sound decision-making to respond to data-based signals rapidly.

Historical vs. Real-time

A lot of customers of other “AI” RMS providers we talk to describe the challenges around the importance those systems place in historical data. These systems rely on historical data as the basis for their pricing and inventory recommendations, limiting the scope of those recommendations to past patterns and trends. This is less than ideal in dynamic markets prone to rapid shifts – historical data can lose its relevance rather quickly.

The algorithms used in decision intelligence continually analyze your hotel’s stream of data. They can make micro-targeted adjustments on room nights, providing an edge to revenue managers who can quickly respond to shifts in market conditions, which translates to enhanced profitability. That’s no small benefit when demand patterns fluctuate rapidly, such as in the hospitality industry.

In many of the older RMS systems, they typically have to run for a minimum of three months in order to generate useful recommendations. You really don’t get optimized results until you’ve been running the system for a year. In a next-generation system like FLYR, you can start getting optimized pricing and forecasting results as soon as a week after the system is connected. This is what differentiates the FLYR technology and is a true indication that it just works in a new and better way.

Automation & Scalability

With a fully automated pipeline, decision intelligence-capable systems are quite simply more efficient and less error-prone than legacy systems. The latter typically rely on rule-based approaches and manual analysis to spawn their inventory and pricing recommendations. Again, this slows down the process, and the resulting insights may well be out of date. And we all know that human intervention can introduce errors in the data, resulting in errors in the recommendations.

Case in point: a McKinsey study found that an AI-driven RMS could reduce errors by 20% to 50% and reduce lost sales by up to 65% – not a fringe benefit by any measure.

You may have noticed my use of the word “recommendation” when discussing other revenue management systems. That’s because these solutions typically provide recommendations rather than actionable insights. They make suggestions, but the final decision-making will hinge on human judgment, leading to the potential introduction of errors and biases.

They also struggle to handle the sheer volume and complexity of data available today. In contrast, FLYR’s solution automates these processes and uses AI to analyze data more accurately and efficiently. It is also scalable enough to grow your business without skipping a beat. The bigger your business, the more data it generates. So you’re going to want a system that won’t balk under pressure.

Other systems tend to be developed for specific hotel sizes or types and cannot easily accommodate changes in business needs or market conditions. FLYR’s solution is more adaptable and responsive to changes in the market. It continuously learns and adjusts its predictions and recommendations based on new data, ensuring that hotels are always equipped with the most up-to-date and accurate information. This is a significant advantage in the fast-paced and unpredictable hospitality industry.

Integrated vs. Siloed

As mentioned above, decision intelligence operates holistically. That means it integrates, among other metrics, booking patterns, market demand, competitor pricing, and guest preferences. So, it will require access to existing systems for optimal performance and to yield the maximum benefits. FLYR’s decision intelligence-enabled RMS is designed for easy, seamless integration with a plethora of existing systems. This enables hotels to leverage data from various sources in real-time, leading to more informed revenue and guest relationship management decisions.

With limited integration capabilities, legacy systems can’t consider many complex and interrelated factors that significantly impact revenue management. They cannot factor in external data points, like market demand, competitor pricing, and customer segmentation, limiting the insights they can produce.

Leveraging Decision Intelligence

FLYR brings together AI and intuitive workflows to deliver the first-of-its-kind AI-augmented decision intelligence platform. This is a paradigm shift for the hospitality industry, providing massive accuracy, efficiency, and profitability gains over legacy RMS systems.

Offering a trifecta of advantages, decision intelligence enhances predictive insights, optimizes pricing strategies, and refines guest segmentation. Recognizing that the effectiveness of any algorithm hinges on the quality of the input data, as the principle “garbage in, garbage out” aptly underscores, FLYR places a paramount emphasis on obtaining accurate and high-quality data, aiming for a standard of precision that surpasses previous benchmarks.

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Artificial Intelligence Drives Efficiencies in The Supply Chain

AI is transforming supply chain management by automating tasks and improving efficiency



How AI can drive efficiencies in your supply chain

Artificial Intelligence can untangle the increasing complexity of supply chains by automating mundane work.

Companies are leveraging artificial intelligence to drive up supply chain resilience, as issues such as materials shortages and natural disasters threaten business stability.

Enterprises across industries will increasingly use AI for tasks such as answering complex procurement questions, which will in turn improve supply chain efficiency.

“Supply relationship management will enter an entirely new phase when so much more intelligence is available to buyer and supplier both,” says Paul Blake, senior director of product marketing at GEP.

One major benefit of AI in supply chain management is that, in the source-to-pay process, companies can gather immediate intuitive intelligence. AI helps to turn past activities and successes into actionable strategies for future projects at a stroke. For example, AI can quickly answer: what is the best strategy for optimizing savings in a rising market for a particular category?

“When in the thick of running a complex RFP, we might ask what combinations of suppliers give the best savings and lowest risk,” says Blake. “What AI will do is radically reduce the effort required to reach the correct information.”

What’s more, when dealing with vast repositories of documents such as contracts, AI allows users to request specific actions, such as which contracts are affected by a change in law or by new regulations.

By automating repetitive tasks, difficult problems get more attention, too. Automation elevates risk management, opportunity identification and effective relationship management.

The quest for the ‘holy grail’

These are “the holy grail of procurement, but so often are always on the horizon as savings tracking, invoice management, and order handling take up so much more of the bandwidth,” says Blake. “Automation takes care of the drudgery so people can tackle these harder problems.”

All of this enables enterprises to be proactive, rather than reactive.

“It’s huge that we face the possibility that we will be able to ask ‘what if,’ before it happens, and ‘what now,’ when it happens and for the answers to be rapid and actionable,” adds Blake.

One scenario in which AI can help is with a category or sourcing manager at a data center who needs to run a request for proposal with over a dozen suppliers, some they’ve worked with before.

Using AI, the manager easily has at their fingertips information on:

·      How many suppliers or bids there are,
·      Which responded to the last RFP,
·      And how this response compares to their last RFP.

Triggered by data, AI can also generate an email to suppliers who have yet to bid.

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