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Everything Connected

Mon, 05 Aug 2024 14:55:00 -0500

Yet another acronym to remember: What is AIoT?

For 250pts. name that classic, iconic, pop-culture 80s AI movie!

With all those devices logging all that data what are businesses to do? Data is the new gold, but only if you know what to do with it.

The Internet of Things (IoT)

Many of us are already very familiar with IoT, whether we realize it or not. If you can turn on your lights at home, open your garage door, or adjust your water heater settings from your phone while on the road, then you know exactly what IoT is. These everyday conveniences are powered by the Internet of Things, where devices are interconnected through the internet, enabling them to collect and exchange data seamlessly.

From self-driving cars to wearable devices that can improve health outcomes, IoT is becoming ubiquitous in modern-day life. By 2025, IoT devices are expected to outnumber non-IoT devices by 3:1.¹⁰ While IoT provides a skeletal framework of devices interconnected through the internet capable of collecting and exchanging data, AI acts as the brain, analyzing and interpreting this data to make intelligent decisions without human intervention. When AI fuses with IoT – or AIoT – the result is a smarter, self-improving network of devices that not only collect data but also learn from it, optimizing processes and creating more value.

As the number of IoT devices and the data that they process grows, the business opportunities for AIoT also grow. The main challenge for business leaders will be to use this data in a meaningful way. According to Gartner, 87% of organizations have limited business intelligence and analytics maturity.¹¹ This gap in analytics resources means that many businesses underutilize their data and fail to get value from it. Integrating AI-based solutions and enlisting the support of AIoT service providers can help companies maximize their IoT investments while achieving more efficiency and cost savings for their organization.

AI & IoT (AIoT) Use Cases

Computer Vision: Security, Quality Assurance, Safety Monitoring
Route Optimization
Logistics Asset Tracking
Energy Consumption Analysis
Predictive Health Analytics
Sensors: Olfactory, Auditory, Pressure
Smart Agriculture: Precision farming, crop monitoring, soil analysis, and automated irrigation systems.
Smart Cities: Waste management, public transportation optimization, traffic optimization, and infrastructure monitoring.
Smart Manufacturing: Predictive maintenance, process optimization, and automated quality control.
Supply Chain Management: Demand forecasting, inventory optimization, and supplier performance analysis.
Smart Grid Management: Real-time demand response, energy distribution optimization, and grid fault detection.
Environmental Monitoring: Air quality assessment, water quality monitoring, and wildlife tracking.
Wearables: Fitness tracking, health monitoring, and personalized health recommendations.
Autonomous Vehicles: Real-time navigation, obstacle detection, and traffic management.
Smart Buildings: HVAC optimization, lighting control, occupancy detection, and energy management.
Smart Retail: Personalized shopping experiences, inventory management, and automated checkout systems.

Computer Vision – Physical Security

Protecting the physical environment has long been the task of facilities managers and security personnel. Adding AI with IoT cameras and sensors to an organization’s physical security strategy dramatically enhances the fight to protect all people and assets.

Imagine hiring a security team that has eyes everywhere, and that never sleeps. Using AI with site-wide cameras and access controls, businesses can have an always-on security team to protect and monitor their physical environment. Taking the data feeds from IP cameras, the AI system can create alerts for virtually any desired activity, such as weapons detection, fire detection, slip and fall, and people or assets entering or exiting designated areas. This allows designated personnel to respond accordingly and save the day

Predictive AI – Home-Based Healthcare

As the burden on our healthcare system continues to grow, care in the home has become increasingly important. Wearable IoT devices collect data on a patient's vital signs and health metrics, acting as an extra set of eyes for the healthcare provider. Predictive AI algorithms analyze this data to predict potential health issues before they become serious, enabling proactive care and intervention.

Healthcare professionals spend most of their time providing care after an event has happened. Take for example a heart attack: In most cases a doctor will see the patient after the event, limiting their impact. What if we could predict that event BEFORE it happened? With IoT sensors to monitor and provide accurate and timely vitals, AI can analyze trends with a patient and warn healthcare professionals of potentially life-threatening events.

Energy Efficiency – Building Management

In smart homes and buildings, IoT sensors monitor real-time energy usage and environmental conditions, such as temperature, lighting, and water usage. AI algorithms then process this data, learning from patterns and making intelligent decisions to adjust energy consumption without compromising comfort or operational needs.

Imagine a commercial office building equipped with various IoT devices, including temperature sensors, occupancy sensors, light level sensors, water leak detectors, and smart meters. AI algorithms analyze the collected data to understand usage patterns and automatically lowers heating in unoccupied rooms, dims lighting in areas with sufficient natural light, and identifies potentially costly water leaks (the top insurance claim for property damage). This leads to lower utility bills and a reduced carbon footprint, as the building only uses the amount of resources needed based on real-time conditions and predicted needs, all while maintaining comfort for the occupants.

The Future of AIoT

The potential applications of AIoT are vast and varied. In agriculture, AIoT can help farmers monitor soil conditions, predict weather patterns, and optimize irrigation systems, leading to increased crop yields and reduced resource consumption. Heck, you can even have computer vision systems Identify weeds and shoot them lasers! DUDE, that's cool! In manufacturing, AIoT can enhance predictive maintenance, reducing downtime and improving operational efficiency.

As AIoT technology continues to evolve, we can expect to see even more innovative applications that will transform industries and improve our daily lives. However, businesses must address challenges such as data privacy, security, and interoperability to fully harness the power of AIoT.

With three times as many IoT, connected devices and even more data, IoT security must constantly be considered. Organizations must prioritize implementing and continuously evolving advanced security strategies to address threat detection and fraud prevention or face costly consequences.

The fusion of AI and IoT into AIoT represents a significant leap forward in technology, offering a smarter, more efficient way to harness the power of connected devices. By leveraging AI to analyze and interpret the vast amounts of data (and even more yet to be) generated by IoT devices, businesses can unlock new levels of efficiency, innovation, and value. As the landscape of AIoT continues to expand, staying informed and investing in the right solutions will be crucial for organizations looking to thrive in this rapidly evolving field.

By investing in robust AIoT (and security) solutions and working with trusted agents to steer organizations toward the right experienced providers, companies can stay ahead of the curve and unlock new opportunities for growth and innovation.

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About Cloud 9

At Cloud 9, we are deeply passionate about the transformative power of emerging technologies, especially the exciting innovations in Artificial Intelligence and the Internet of Things. Our mission is to help clients harness the immense potential of these technologies to enhance and improve our four key technology segments: Cybersecurity, Communications, Connectivity, and Cloud.

We empower clients by providing unparalleled education, resources, and supplier guidance on cutting-edge AI and IoT technologies and solutions. Our expertise spans customer experience (CX), cloud computing, mobility, IoT, and advanced networking, with a strong focus on integrating AI to enhance both new and existing IoT applications.

Our engineering and architecture teams are at the forefront of AI-driven business transformation and cloud innovation. We are dedicated to guiding you in identifying and seizing new AI opportunities to tackle your most critical business challenges. As your business evolves, we ensure you stay protected and ahead of the curve by providing clear, actionable insights and strategies for an AI-enhanced future.

Peas in a Pod

Tue, 30 Jul 2024 09:55:00 -0500

What goes better together than AI and the Cloud?

AI and Cloud Computing: The Indivisible Partnership Driving Modern Enterprise Growth

The technological evolution that enabled massive, scalable computing is now the only viable platform for advanced Artificial Intelligence solutions. Understanding this convergence is the key to strategic resource allocation.

Updated!

For 100pts. name that movie! (should be an easy one).

It could be said that AI was born in the cloud, although many purest and diehard AI-nerds would disagree. But it's hard to argue that the tech that made cloud possible also enabled the resurgence of AI as we know it today.

The convergence of AI and Cloud is not just a technological coincidence; it is an economic and technical imperative that fundamentally dictates the future IT strategy of every organization seeking a competitive advantage. The scale, flexibility, and cost structures of the cloud are precisely what transform AI from an academic exercise into a practical, business-enabling service.

The Economic and Technical Imperatives of Cloud-Native AI

To understand why AI is inseparable from the cloud, we must look at the immense demands placed on infrastructure by modern AI models, such as the large language models (LLMs) driving generative tasks and sophisticated predictive analytics engines.

1. The Requirement for Vast, Elastic Compute

AI models are trained on huge amounts of data—often petabytes—requiring an immense, sustained amount of compute power, including specialized resources like high-performance GPUs (Graphics Processing Units) and powerful CPUs.

Elasticity vs. Investment: Most businesses do not have the resources or capital to purchase, house, power, and maintain the hyper-scale infrastructure required for this training themselves. Furthermore, the compute needs for AI are bursty; they peak dramatically during model training and then drop off sharply during inference (real-time use). The traditional CapEx model is incapable of handling this fluctuation without massive over-provisioning.
The Cloud Solution: Cloud providers offer elasticity—the ability to dynamically provision tens or hundreds of thousands of GPUs for a training run lasting a few days, and then instantly release those resources. This consumption-based, OpEx model allows businesses to pay only for the compute they actively use, making AI economically viable.

2. Democratization through AI-as-a-Service (AIaaS)

The cloud has allowed AI to move from the domain of a few tech giants to a widely accessible tool.

By accessing AI-as-a-Service (AIaaS) through cloud-native platforms, businesses can leverage real-time data collection and analysis, pre-trained models, and pre-built data pipelines required to enable AI—all delivered via a simple API or subscription. This democratization is vital for small and mid-sized businesses, allowing them to skip years of internal development and infrastructure build-out, accelerating their time-to-value for AI initiatives.

3. The Centrality of Data Management

AI is worthless without organized, accessible data. Cloud environments inherently facilitate the massive Data Warehousing, Data Pipelines, and ETL (Extract, Transform, Load) capabilities that AI models depend on. Cloud services provide a unified and flexible environment to manage structured and unstructured data, ensuring the high availability and consistency necessary for continuous model refinement and retraining. The seamless integration of these data services is what makes the cloud the natural habitat for sophisticated AI operations.

Strategic Use Cases: Cloud-Powered AI in Action

The real value of this convergence is demonstrated in the strategic operational efficiencies it creates. When AI is embedded within the cloud management layer, it transforms how resources are consumed, maintained, and optimized.

Optimized Scaling and Intelligent Resource Allocation

Traditional resource allocation often relies on fixed thresholds or manual forecasting, which inevitably leads to either performance bottlenecks or costly over-provisioning. AI-driven cloud management platforms eliminate this guesswork.

Predictive Analytics for Proactive Allocation/Predictive Scaling: Cloud providers use AI algorithms to analyze historical workload patterns, traffic flows, and operational metrics. This allows the system to dynamically adjust server capacity, storage, and networking resources predictively—scaling resources up before a workload spike occurs and scaling down immediately afterward. This ensures optimal performance during peak demand while drastically improving cost-efficiency.
FinOps Optimization: The cloud introduced FinOps (Cloud Financial Operations) as a discipline. AI takes this further by continuously analyzing consumption against budget. AI tools can identify underutilized resources, flag inefficient configurations, and even suggest cost-saving measures, often identifying savings that human administrators might miss. This AI-driven oversight is essential for managing the sheer scale and complexity of a multi-service cloud deployment.

Operational Resilience and Predictive Maintenance

For organizations running critical infrastructure—whether internal IT systems or large-scale manufacturing machinery—downtime is measured in lost revenue and credibility. AI in the cloud dramatically shifts the maintenance paradigm from reactive to predictive.

Proactive Maintenance: AI analytics are deployed to analyze vast amounts of data collected from the cloud infrastructure, IoT sensors, and application performance monitoring (APM) tools. By processing these streams, AI algorithms can detect subtle patterns, anomalies, and potential failure risks long before they manifest as critical errors.
Real-World Application: Consider a manufacturing company using AI algorithms to analyze sensor data from industrial machinery across dozens of work sites. The AI detects an unusual vibration frequency or a minor temperature fluctuation that, based on historical patterns, indicates an impending malfunction within the next week. By proactively scheduling maintenance tasks based on this predictive warning, the business minimizes disruptions to production and maintains operational efficiency, shifting the business from reactive firefighting to strategic maintenance.

Process Automation and Efficiency (Robotics Process Automation - RPA)

AI-driven, cloud-based Robotics Process Automation (RPA) enables businesses to automate their processes more quickly and easily than traditional, often rigid, on-premises RPA solutions. The cloud provides the necessary infrastructure for rapid deployment, high availability, and easy scaling of automation bots.

Scalable Automation: A finance team, for instance, can leverage RPA technology hosted in the cloud to automate the entire invoice processing lifecycle: receipt, data extraction using advanced Optical Character Recognition (OCR), validation, entry into the ERP system, approval routing, and archiving.
Efficiency and Accuracy: By integrating directly with various cloud applications and accounting software, the RPA system efficiently processes hundreds of invoices in diverse formats with minimal human intervention. Crucially, the cloud environment allows this automation capability to scale instantly to manage peak times (such as end-of-quarter closes) without the need for manual server adjustment. This flexibility results in significant cost savings and improved accuracy over traditional solutions.

The Strategic Mandate: Optimizing the AI Ecosystem

The "natural" integration of AI and cloud computing is more than a technological advancement; it is a strategic imperative for businesses aiming to stay competitive. By embracing these advanced, cloud-native technologies, organizations achieve essential goals:

Resource Optimization: Guaranteeing that compute and storage are aligned precisely with demand.
Disruption Prevention: Anticipating and preventing critical failures.
Automation at Scale: Automating complex processes across the entire enterprise.

This strategic perspective demands that IT leaders view every technology investment—from the data centers of cloud providers to the devices on the network edge—as part of a cohesive AI ecosystem.

At Cloud 9 Advisers, we are deeply passionate about the transformative power of emerging technologies, particularly the opportunities around Artificial Intelligence. We empower clients to leverage our unparalleled education, resources, and vendor-neutral guidance for cutting-edge AI technologies and cloud solutions.

Our expertise spans the critical areas of technology integration, including customer experience (CX), cloud computing, mobility, IoT, and advanced networking, with a strong focus on enhancing security measures (like SASE and CASB) that protect the massive data flows essential for AI.

Our advisory teams are ready to guide you in identifying and seizing new AI opportunities to tackle your most critical business challenges. As your business evolves and embraces these powerful new tools, we ensure you remain protected and ahead of the curve, providing clear, actionable insights and strategies for a secure, AI-enhanced future.

KITS: Keep IT Simple.

AI and Cloud Use Cases

Optimized Scaling and Resource Allocation Based on Real-time Demand
Predictive Analytics for Proactive Allocation/Predictive Scaling
Automation - Resource Provisioning, Software Deployment, Config Mgmt, etc.
Application Performance Monitoring
Application Tuning and Optimization
Load Balancing/Dynamic Workload Scheduling
FinOps - Optimize Spending
Data Management - Warehousing, Pipelines, ETL
Robotics Process Automation (RPA)
Artificial Intelligence solutions - AI has its own needs requiring an intense variety of resources (CPUs, GPUs, Storage)

AI Readiness Assessment

Am I ready for AI?

What AI solutions should I apply in my business?

Where should I start?

Our proprietary and innovative tool will help you get started, streamlined, and focus your AI strategy by asking the right questions, matching your business needs with solutions and suppliers, and eliminate guesswork.

You'll get a custom, tailored report specifically for you and your situation - giving you clear guidance on where to start and a roadmap for the future.

Increase value to your stakeholders and ownership - we'll help you become an AI expert, quickly.

More Cloud Stuff!

See the other articles in our Data Readiness Series:

The Unseen Imperfection

Governing Your AI's Future

The Engine that Drives AI

Security & AI

Mon, 22 Jul 2024 13:36:00 -0500

Better Security with AI?

For 100pts. name that movie! (should be an easy one).

Just like the term Cloud, AI covers a wide and growing range of technologies and solutions: each with its own unique set of capabilities and applications. Cyber (and even physical) security is an area where Artificial Intelligence might be a "no-brainer"

The Bad Guys use it...

As the cyberattack landscape becomes increasingly complex, AI plays a pivotal role in advancing threat mitigation strategies. Not only are cyberattacks more sophisticated than ever, but much like ransomware and malware kits a few years ago, AI hacking tools are starting to become available online. This inevitably puts them into the hands of untold numbers of less sophisticated cybercriminals. Deep Instinct’s 2023 Voice of SecOps Report reveals 75% of security professionals witnessed an increase in attacks over the past 12 months, with 85% attributing this rise to bad actors using generative AI.

The imperative is clear

Organizations must prioritize implementing and continuously evolving advanced security strategies to address threat detection and fraud prevention or face costly consequences. According to IBM’s 2023 Cost of a Data Breach Report, the average cost of a breach is over $4 million in lost business and fines. Yet 35% of US companies don’t have dedicated cybersecurity staff.

AI and Cybersecurity Use Cases

LLMs to Conduct Code Reviews, Preventing Vulnerabilities (AWS)
Penetration Testing
Attack Identification
Rapid Response
Real-Time Network Traffic Analysis
Natural Language Interfaces for Configurable Resources (e.g., Firewalls)
Vulnerability Categorization/Prioritization
Automated Fixes/Patching
Incident Data Analysis
Biometrics Authentication Systems
Social Engineering Detection

Threat Detection

AI-powered cybersecurity systems use machine learning algorithms to detect and respond to security threats in real time. For example, an AI-based intrusion detection system can analyze vast network traffic patterns to identify and mitigate potential cyberattacks before they cause damage. By proactively monitoring for suspicious activity and adapting to evolving threats, businesses can strengthen their security posture and safeguard sensitive data.

Fraud Prevention

AI algorithms analyze transaction data to detect patterns indicative of fraudulent activity. For instance, a financial institution can use AI to identify anomalies in account behavior, such as unusual spending patterns or login attempts from unfamiliar locations. By flagging suspicious transactions for further investigation, businesses can minimize financial losses and protect customers from fraud.

Zero-Trust

Traditionally, IT leaders have relied on perimeter security strategies like firewalls to protect their most valuable resources like user data and intellectual property. However, with digital transformation and the move to off-premises cloud infrastructure, relying on a network perimeter is no longer sufficient. One of the strategies an organization can implement to secure their data and stave off cybercriminals is to implement zero-trust inside and outside their network. Rooted in the principle of "never trust, always verify," the zero-trust model emphasizes continuous verification of every user and device, regardless of their location.

AI Readiness Assessment

Am I ready for AI?

What AI solutions should I apply in my business?

Where should I start?

You'll get a custom, tailored report specifically for you and your situation - giving you clear guidance on where to start and a radmap for the future.

Increase value to your stakeholders and ownership - we'll help you become an AI expert, quickly.

Book now

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About Cloud 9

At Cloud 9, we are deeply passionate about the transformative power of emerging technologies, particularly AI and cybersecurity, and are committed to helping our clients harness their immense potential. We empower clients to leverage our unparalleled education, resources, and supplier guidance for cutting-edge AI technologies and robust cybersecurity solutions. Our expertise spans customer experience (CX), cloud computing, mobility, IoT, and advanced networking, with a strong focus on integrating AI to enhance security measures.

Our engineering and architecture teams are at the forefront of AI-driven business transformation and cybersecurity innovation, ready to guide you in identifying and seizing new AI opportunities to tackle your most critical business challenges. As your business evolves, we ensure you stay protected and ahead of the curve, providing clear, actionable insights and strategies for a secure, AI-enhanced future.

AI+CX=$$$

Mon, 15 Jul 2024 13:36:00 -0500

Transforming CX with AI

AI has quickly taken center stage in many areas of business. Perhaps non more so than the dizzying array of Customer Experience (CX) solutions. Making CX a simple way to start your path to AI.

Free t-shirt and 100pts to the first person who correctly names the popular TV series from the image above!

AI in Customer Experience (CX)

In CX, generative AI is poised to redefine how businesses engage with their customers and streamline operational efficiency. In Metrigy’s AI for Business Success 2024-25 survey, 39% of IT, CX, and business unit leaders said 2024 will be the turning point for their company’s acceptance of using AI for customer interactions. Another 20% said that will happen in 2025.⁴ For companies who are already experimenting with AI in CX, the proven impact is far-reaching, from predictive and responsive services to enhancing customer satisfaction and efficiency.

Many subsets of AI are and have been in the contact center for some time, especially AI tech like Natural Language Processing (NLP).

AI and CX Use Cases

Virtual Agent Bot (Phone, SMS, Web Chat, Messaging)
AI-Driven Quality Management
Real-Time Agent Assist
Knowledge Base Interaction/Management with LLM
Robotic Process Automation (RPA) - Process Tasks Driven by CX Experiences
Data Analytics
Outbound Sales Campaign Management and Automation
Marketing and Messaging Campaign Managementrketing and Messaging Campaign Managementrketing and Messaging Campaign Managementrketing and Messaging Campaign Management

Personalization

Businesses that master personalization at scale, using AI for ultra-tailored experiences, boost customer engagement even further. This level of personalization ranges from individualized product recommendations to tailored communication strategies. According to Adobe, 89% of marketers are now reporting a positive ROI when they use ultra-tailored personalization techniques versus when they don’t.

Imagine a retail clothing store using AI tools to analyze customer data, identifying individual fashion preferences and sizes. This allows them to tailor email marketing campaigns to each customer (not just each segment), recommending items that align with their unique style and fit.

Omnichannel Engagement

The omnichannel customer experience approach as a best practice isn’t new. Customers expect a seamless experience across all touchpoints and media channels with the businesses and brands they use, whether text, web, social media, or email, and more so than they ever have before. The right AI puts this within easy reach

Imagine a healthcare clinic where patients enjoy seamless and efficient interactions. They easily schedule appointments via a chatbot and receive proactive treatment reminders via text. After the visit, they receive an immediate AI-generated visit summary and automated outreach to ensure timely support, enhancing their overall experience and adherence to treatment plans. Not only is the patient thrilled by the personalized care through their preferred communication channels, but the healthcare provider has optimized operational costs and improved productivity.

Emotional AI

Emotional AI is revolutionizing customer service by fostering a deeper emotional connection between customers and brands, a crucial factor as 86% of customers are more likely to repeat business with a company if they feel emotionally connected to a contact center agent.⁶ AI technology enhances customer experience by utilizing sentiment analysis, personalized interactions, real-time feedback, predictive analytics, and natural language processing to better understand and respond to customer emotions.

Imagine a banking app with a chatbot that can analyze a customer's transaction history and recent interactions to predict stress about potential fraud. It proactively reaches out with reassurance and steps for fraud prevention, demonstrating human-like understanding and empathy, increasing the customer’s trust and satisfaction with the brand. This approach not only elevates the emotional intelligence of customer service teams but also solidifies emotionally-driven customer loyalty.

AI Readiness Assessment

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Can it really help me or my business?

Can AI really improve my CSAT?

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You'll get a custom, tailored report specifically for you and your situation - giving you clear guidance on where to start and a radmap for the future.

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About Cloud 9

At Cloud 9, we are deeply passionate about the transformative power of emerging technologies like the numerous possibilities in the world of AI We are committed to helping our clients harness its immense potential. Contact Center as a Service solutions as a small part of Customer Experience overall is particular strength of Cloud 9. Additionally, we help clients leverage our unparalleled education, resources, and supplier guidance for other cutting-edge AI technologies across cybersecurity, cloud, mobility, IoT, and advanced networking. Our engineering and architecture staff are at the forefront of AI-driven business transformation, ready to guide you through identifying and seizing new AI opportunities to meet you most critical business challenges now and as your business evolves.

Proven Value

Mon, 08 Jul 2024 08:41:32 -0500

AI’s Proven Business Value

AI encompasses a range of technologies, each with its unique capabilities and applications. The chart above by Forbes shows a number of important and practical business uses for AI tech.

4 Key Areas to start

AI has quickly taken center stage in enhancing workforce productivity, operational efficiency, and end-customer experience across various aspects of a business. Here are just a few examples of relatively easy AI entry point and how nearly any company can benefit from AI-enabled tools today in practice areas where Cloud 9 can help with proven experience.

1. Customer Experience

Customer Experience (CX) is likely the simplest way to quickly get started and reap the benefits of AI. Contact Center solutions have had AI in one form or another for some time now - with advancements in predictive analytics, natural language processing, machine learning, and large language models any business can transform CX and the bottom line (if done well). Now, Generative AI brings a whole new perspective and world of possibilities.

Virtual Agent Bot (Phone, SMS, Web Chat, Messaging)
AI-Driven Quality Management
Real-Time Agent Assist
Knowledge Base Interaction/Management with LLM
Robotic Process Automation (RPA) - Process Tasks Driven by CX Experiences
Data Analytics
Outbound Sales Campaign Management and Automation
Marketing and Messaging Campaign Management

2. Cybersecurity

Not only are cyberattacks more sophisticated than ever, but much like ransomware and malware kits a few years ago, AI hacking tools are starting to become available online. AI enabled and augmented security solutions are quickly becoming de facto.

AI and Cybersecurity Use Cases

LLMs to Conduct Code Reviews, Preventing Vulnerabilities (AWS)
Penetration Testing
Attack Identification
Rapid Response
Real-Time Network Traffic Analysis
Natural Language Interfaces for Configurable Resources (e.g., Firewalls)
Vulnerability Categorization/Prioritization
Automated Fixes/Patching
Incident Data Analysis
Biometrics Authentication Systems
Social Engineering Detection

3. Cloud Computing

Somewhat ironically it is cloud computing that helped enable and perpetuate this new AI "spring" we are experiencing. Cloud spend is expected to nearly double by 2027. Much of this spend will be driven by the growing demand for new AI-powered platforms and as-a-service offerings.

Clever AI tools and techniques will be needed just to keep up and optimize.

AI and Cloud Use Cases

AI needs a variety of resources (CPUs, GPUs, Storage) in and of itself
Optimized Scaling and Resource Allocation Based on Real-time Demand
Predictive Analytics for Proactive Allocation/Predictive Scaling
Automation - Resource Provisioning, Software Deployment, Config Mgmt, etc.
Application Performance Monitoring
Application Tuning and Optimization
Load Balancing/Dynamic Workload Scheduling
FinOps - Optimize Spending
Data Management - Warehousing, Pipelines, ETL
Robotics Process Automation (RPA)

4. The Internet of Things (IoT)

IoT is becoming ubiquitous in modern-day life. They say that by this time next year IoT devices will outnumber non-IoT devices (computer, tablets, mobile phones, etc.) by 3 to 1. That's a tremendous amount of data that needs to be turned into information. That is where AI comes in.

AIoT Use Cases

Computer Vision (Security, Quality Assurance, Safety Monitoring)
Sensors (Olfactory, Auditory, Pressure)
Predictive Health Analytics
Energy Consumption Analysis
Logistics Asset Tracking/
Route Optimization

Stay tuned as we dive in deeper with each of these four important AI applications and cases for businesses. Learn more about how to apply realistic and practical AI systems and solution in your business.

AI Readiness Assessment

Am I ready for AI?

Do I need AI?

What AI solutions should I apply in my business?

Where should I start?

This innovative tool streamlines and focuses your AI strategy by asking the right questions, matching your business needs with solutions and suppliers, and eliminates guesswork.

You'll get a custom, tailored report specifically for you and your situation - giving you clear guidance on where to start and a radmap for the future.

Increase value to your stakeholders and ownership - we'll help you become an AI expert.

Book now

Get Started Now

About Cloud 9

At Cloud 9, we are deeply passionate about the transformative power of emerging technologies like AI and are committed to helping our clients harness its immense potential. We help clients leverage our unparalleled education, resources, and supplier guidance for cutting-edge AI technologies across CX, cybersecurity, cloud, mobility, IoT, and advanced networking. Our engineering and architecture staff are at the forefront of AI-driven business transformation, ready to guide you through identifying and seizing new AI opportunities to meet you most critical business challenges now and as your business evolves.

Decoding AI

Mon, 01 Jul 2024 11:05:49 -0500

Understanding Key Terms and Technologies Shaping Business Solutions

AI encompasses a range of technologies, each with its unique capabilities and applications. Understanding the different types of AI technologies is foundational to any business AI strategy.

A Universe of Tools

Artificial intelligence (AI) is thrown around a lot these days. Unfortunately its used far too liberally where marketing just slaps an AI label on and old solution to freshen it up a bit. Perhaps because the definition of AI is so broad. But it's out there, it is real, it's not going away, and when applied properly, AI can transform your business. We just need a little better understanding of things to help separate the wheat from the chaff.

Demystifying the AI Landscape: A Universe of Tools Under One Umbrella

Artificial intelligence is an umbrella encompassing a range of powerful and specific technologies. It's not a single, monolithic technology. It's a vast and ever-evolving universe of tools, each with its own strengths and applications. And Artificial intelligence (AI) is not a one-size-fits-all solution. Understanding the diverse landscape of AI is the cornerstone of crafting a winning AI strategy and crucial for navigating the exciting world of AI. By exploring the different types of AI technologies, you can unlock their potential to streamline processes, enhance decision-making, and achieve meaningful business impact.

Key AI terms:

Artificial intelligence (AI) : AI refers to the field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. It involves the development of algorithms and systems that can acquire knowledge, reason, learn, understand natural language, perceive the environment, and make decisions or take actions based on that understanding.

Machine Learning (ML) : A subset of AI, ML enables computers to learn from data and make predictions or decisions without explicit programming. By analyzing incredibly vast amounts of data, ML models can identify patterns, make predictions, and inform decisions. Machine learning models are crucial for a wide range of applications used today, including:

Facial recognition: Unlocking devices securely through facial recognition software.
Medical imaging analysis: Detecting abnormalities in medical scans to aid diagnoses.
Predictive analytics: Forecasting sales trends, estimating stock prices, or optimizing resource allocation.
Fraud detection: Identifying suspicious patterns in financial transactions to prevent fraud.

There are important considerations when applying ML like the learning process (supervised or unsupervised), data quality and quantity, and bias - ML models can inherit biases from the data they are trained on

Large Language Model (LLM) : A broad category of advanced programming in it's own right, LLMs are a subset and specialized application of Machine Learning (which is of course under the even broader subject of AI) focused on processing and generating human language at a large scale. Unlike traditional ML techniques primarily concerned with data analysis, LLMs excel at the complexities of natural human language processing tasks like understanding and responding to written or spoken language. Deep learning, a powerful technique within ML, plays a significant role in LLM development. Examples of LLM you might use today include:

Chatbots that can provide customer service or answer questions conversationally.
Translation tools that can translate languages accurately and fluently.
Text summarization tools that can condense lengthy documents into concise summaries.

Generative Pre-trained Transformer (GPT) : Many have heard of or even used GPT. Drilling down a little further, GPT refers to a specific type of LLM architecture developed by OpenAI. GPT models are trained on massive amounts of text data, allowing them to generate realistic and creative text formats, like poems, code, scripts, musical pieces, email, letters, etc. While GPT is a powerful and well-known LLM, it's important to remember it's just one example within the broader category of Large Language Models. Examples include: BERT, T5, GPT, and other language models developed by different organizations.

An illustration

Here's an analogy to illustrate the levels, layers, and differences:

AI = music
ML = Rock
LLM = Classic Rock
GPT = Led Zeppelin

This is elementary and could certainly be expanded to include the many of the other subsets of AI.

But wait, there's more

Additionally, there are important distinctions between Artificial General Intelligence (AGI, or general AI) and Artificial Narrow Intelligence (ANI, or narrow AI). AGI, is still a distant dream. While science fiction often portrays AGI as a near-future reality, it's still a highly theoretical concept. Current AI research almost exclusively focuses on ANI, and achieving true AGI remains a significant challenge. The AI in many science fiction movies, refers more to AGI, AI systems with human-like cognitive abilities and the capacity to understand and perform any intellectual task that a human can. In contrast, ANI refers to nearly all of AI systems encountered today. ANI is designed for specific tasks or domains, such as playing chess or diagnosing medical conditions. Today when AI is mentioned, it most often refers to ANI and not AGI. AGI might be consider the "holy grail" or pinnacle of AI advancement - others might say it will be the death and destruction of humanity!

How does Generative AI Work?

Generative AI learns patterns and structures from large datasets during a training phase, typically using deep learning capabilities like neural networks. Once trained, the AI model can generate new content, such as text, images, or music, by sampling from the learned patterns. It can take input data or seed information and use its internal knowledge to produce coherent and contextually relevant output. Feedback from users can be used to improve the model's output quality over time, making it a versatile tool for various creative and practical business applications.

If we were to squeeze Generative AI into the music analogy above we might replace AI = music with Generative AI and bump AI up a notch to "performing arts".

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About Cloud 9

At Cloud 9, we are deeply passionate about the transformative power of emerging technologies like AI (or ANI, wink, wink) and are committed to helping our clients harness its immense potential. We help clients leverage our unparalleled education, resources, and supplier guidance for cutting-edge AI technologies across CX, cybersecurity, cloud, mobility, IoT, and advanced networking. Our engineering and architecture staff are at the forefront of AI-driven business transformation, ready to guide you through identifying and seizing new AI opportunities to meet you most critical business challenges now and as your business evolves.

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Wed, 19 Jun 2024 17:03:00 -0500

How AI Solves Today’s Business Challenges

Let's explore the current landscape of AI, practical applications to solve modern business problems, and steps you can take to confidently identify the right AI technologies for your evolving needs.

Artificial intelligence (AI) has graduated from hype to everyday use for most of us humans, whether we realize it or not. In a span of just a few years, generative AI in particular has reached revolutionary status thanks to a perfect storm of more data, computing power, and advanced algorithms. And it could be argued that the debut of one easy-to-use interface that anyone with an internet browser could make use of – ChatGPT – signaled the tipping point for widespread AI adoption in the workplace. 71% of respondents in Microsoft’s 2023 Work Trend Index report said their companies are already using AI, mostly for personal productivity.¹

Beyond communication and content creation, AI-enabled technologies have significant potential to tackle a wide range of key business challenges including workforce automation, knowledge management, security, drug discovery and disease diagnosis, engineering design, and other problems that have been difficult to solve until now.

Yet there are legitimate privacy and security concerns as AI continues to evolve and become more accessible to the ill-intended. In PrivacyHawk’s 2023 personal data survey, 48% of people said they fear AI will impact them negatively, while 80% are concerned about the misuse of their personal data.²

There is no question that businesses have a lot to navigate as they define and evaluate the scope and timing of their AI implementation strategies. But early adoption is worth it: Early adopters of the latest technologies are 100% more likely to see high revenue growth and consistently report higher metrics about key business goals such as brand perception and employee satisfaction.³ As a trusted technology advisor, Cloud 9 can play a pivotal role in introducing proven AI opportunities and connecting you with the resources needed to harness AI’s power effectively and gain a competitive edge.

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AI+CC=CX

Fri, 27 Oct 2023 10:58:45 -0500

AI revolutionizes customer service.

AI is revolutionizing customer experience (CX) and customer service, making it more personalized, efficient, and customer-centric.

TechTarget, experts share their insights on how AI can be used to improve CX and customer service in a variety of ways. https://www.techtarget.com/searchcustomerexperience/post/How-AI-can-benefit-CX-and-customer-service

AI is being used to improve CX and customer service today:

AI-powered chatbots and virtual assistants are providing 24/7 customer support and answering customer questions quickly and accurately.
AI-powered recommendation systems are suggesting relevant products and services to customers based on their past behavior and preferences.
AI-powered analytics tools are helping businesses to understand customer sentiment and identify areas where they can improve their CX.
AI-powered automation tools are automating repetitive tasks, such as order processing and customer service inquiries, freeing up human agents to focus on more complex issues.

As AI continues to develop, we can expect to see even more innovative and transformative ways to use AI to improve CX and customer service, like:

AI-powered predictive analytics could be used to identify potential customer problems before they occur and proactively offer solutions.
AI-powered personalized marketing campaigns could be used to deliver targeted messages and offers to customers at the right time and place.
AI-powered virtual assistants could become so sophisticated that they are indistinguishable from human customer service agents.

AI has the potential to revolutionize CX and customer service, making it more personalized, efficient, and customer-centric. Businesses that embrace AI will be well-positioned to succeed in the competitive marketplace of the future. For better or worse, these 'features" are now expectations of your customers.

Learn more about DX / CX

Cloud 9: Technology Sourcing Experts

Cloud 9 Advisers stands between you and potential vendors, carriers, and service providers to slice through all the fluffy marketing, hype, and gimmicks and translate the industry jargon and alphabet soup into usable actionable information. We provide comprehensive, board-ready due diligence and supporting documentation that helps organizations make smart AI investments quickly and confidently. We work with you to employ sound innovations in artificial intelligence roadmap principles and strategies. The primary role of Cloud 9 Advisers is to provide assisted procurement services by working together to identify, research, evaluate, and compare appropriate companies from our Supplier Portfolio of 400+ vendors, carriers, and service providers that can deliver and solve problems fast.

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Healthcare AI

Wed, 25 Oct 2023 10:00:53 -0500

Google is using AI to help transform healthcare.

From early disease detection to expanding access to care, AI is revolutionizing the way we deliver healthcare.

In a new blog post, Google AI shares how they're using AI to improve healthcare for everyone, everywhere.

https://blog.google/technology/health/how-were-using-ai-to-help-transform-healthcare/

Here are a few of the ways Google AI is using AI to transform healthcare:

Developing AI-powered tools to help doctors diagnose diseases earlier and more accurately.
Creating AI-powered solutions to improve access to care in underserved communities.
Using AI to develop new drugs and treatments.
Empowering people to manage their own health and well-being with AI-powered tools.

Google AI is committed to using AI to make healthcare more accessible, affordable, and equitable.

#AI #Healthcare #Transformation #GoogleAI

Cloud 9 Advisers is a tech advisory firm helping organizations safely and securely adopt AI technologies into critical operations. To learn how artificial intelligence, like Generative Pre-trained Transformers (GPT), Large Language Models (LLM), Machine Learning (ML), and Robotic Process Automation (RPA), might help your healthcare organization improve patient experience and operations reach out to the experts at Cloud 9.

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Supplier Spotlight: Verint

Thu, 27 Feb 2020 21:35:32 -0500

The AI-Powered Customer Journey

Cloud 9 Supplier Spotlight: Verint

Actionable Intelligence

2020 Will Shape Up to Become the Year of the AI-Powered Customer Journey

In the evolving Experience Economy, 2020 is shaping up to become the year enterprises apply the building blocks to enable proactive, personalized experiences at every step of the customer journey. In this dynamic environment, organizations must continuously re-calculate the next-best action for each customer, and at each moment.

This requires a shift from traditional customer journey mapping tools to a more holistic and actionable approach of customer journey management powered by AI and reliant on a foundation of data as an enterprise asset. With this shift, enterprises can track, map, analyze, visualize, and personalize customer journeys in real time.

Journey mapping coupled with natural language processing (NLP), predictive analytics, and machine learning will automate systems that can evaluate the behavioral and transactional factors in customers’ decision-making. These factors contribute to whether people are getting through the process outlined for them, determine the reasons why people falter on some journeys, and orchestrate real-time next-best actions to engage with them at critical moments mapped out in the journey.

Recent Ovum survey data points to continued adoption of journey mapping, AI, and predictive analytics to ensure more actionable customer journeys. According to ICT Enterprise Insights 2019/20 – Global: ICT Drivers and Technology Priorities, 24% of respondents have strategic investments planned for customer journey mapping and 37% have minor investment planned.

Additionally, 26% have strategic investments planned for predictive analytics in the area of customer engagement, and 37% are planning minor investments. In terms of AI adoption, 19% of respondents reported having fully deployed packaged AI for the enterprise, 32% are trialing, and 27% were planning to deploy packaged AI.

As these functions inevitably converge, the utilization of AI and machine learning will give context to customer journeys, helping enterprises understand the significance of the events that shape a customer’s behavior. Proactively responding to such key moments will enable true personalization to take hold throughout the digital customer journey. That requires an awareness of both the customer journey touchpoints and their corresponding customer behaviors.

The power of AI-enabled customer journey orchestration is that it can sift through a larger and more complex data space and thereby uncover many more business opportunities and prioritize the insights. It finds every single relationship in the data and predicts the likelihood of future behaviors with high accuracy, while simultaneously identifying the drivers and inhibitors of customer performance.

It also provides important quantitative data to determine the impact of any obstacles along the customer journeys on business objectives, as well as the effectiveness of any remediations. Armed with that information, enterprises can do more than find the “next best action” or the optimal audience. They can take action on data regarding each portion of the journey, measure its impact to advance prediction models, use the results of that analysis to drive better and more predictive models, and even serve as customers’ personal “concierge” for interactions with their brands.

As the quest for connected, intelligent customer experiences accelerates, AI-powered journeys will naturally take the spotlight. But enterprises must first build upon core data integration use cases and start investing in the systems and integrated technologies that will enable them to create precise audiences for activation.

Making the right investments in AI and embedding it across applications will build a foundation to optimize customer experiences that will set enterprises apart from their competition and drive future growth.

By Mila D'Antonio, Principal Analyst, Customer Engagement, Omdia (formerly Ovum), see the original here

Leading brands rely on the Verint Cloud to engage with customers and power more than 3 billion customer interactions per year. The result? Amazing customer experiences. Drive deeper loyalty, enhance business performance and set your organization head and shoulders above the competition. The future of customer engagement is here today.

Contact Cloud 9 Advisers and talk with our verndor-neutral contact center experts to see if Verint is right for your contact center team and organization.

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