Section 2:

AI’s Proven Business Value Across CX, Cybersecurity, Cloud, and IoT

Section 2

AI’s Proven Business Value Across CX, Cybersecurity, Cloud, and IoT

AI has quickly taken center stage in enhancing workforce productivity, operational efficiency, and end-customer experience. In this section we'll cover just a few examples of how nearly any company can benefit from AI-enabled tools in practice areas where you are already selling – and why data readiness is a great way to start the conversation.

In this section:

Customer Experience (CX)
Cybersecurity
Cloud Computing
Internet of Things (IoT)
Data Readiness: The Key to AI Success

Section 2: AI’s Proven Business Value - Customer Experience (CX)

Customer Experience (CX)

Generative AI is revolutionizing how businesses engage with their customers across a range of channels— from email support to more intelligent live chat assistants. As Amazon CEO Andy Jassy recently pointed out in his annual letter to shareholders, “Generative AI is going to reinvent virtually every customer experience we know, and enable altogether new ones about which we’ve only fantasized.”⁶

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. AI technology can lead to faster response times, improved agent productivity, better personalization, and consistent brand messaging.

CX and AI Use Cases

Conversational AI (Phone, SMS, Web Chat, Messaging)
Agentic AI
AI-driven quality management
Real-time agent assist
Knowledge base interaction/management with LLMs
Data analytics
Outbound/inbound sales campaign management and automation
Marketing and messaging campaign management

Section 2: AI’s Proven Business Value - Customer Experience (CX)

Agentic AI

Agentic AI detects customer friction (e.g., shipping delays or abandoned carts), evaluates the situation, and initiates personalized outreach via email, SMS, or chatbot—sometimes with a resolution already in place. AI agents are tasked with achieving outcomes, as opposed to just executing commands. In CX, this may include resolving billing issues end-to-end; recovering churn-risk customers; or orchestrating a post-sale onboarding journey.

Agentic AI turns CX from reactive to proactive, from fragmented to orchestrated, and from static workflows to intelligent, outcome-driven experiences.

Use agentic AI for: Customer support (fully automated case resolutions), sales assistance (like following up on warm leads or booking demos), and measuring customer success (managing NPS follow-ups or triggering renewal campaigns).

Conversational AI

Recent advancements in voice AI are making interactions between businesses and customers more natural than ever. Companies across all industries are deploying voice bots and conversational AI to better understand and respond to customer needs and deliver personalized experiences at scale.

Conversational AI can improve customer satisfaction, expedite resolutions, and lower contact center operating costs.

Use conversational AI for: Customer self-service, 24/7 support, routing optimization,and serving diverse customer bases.

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.

Enhanced personalization opens the door to improved loyalty, better reviews, and stronger returns over the customer journey.

Agentic AI is increasingly being used to drive personalized experiences. Agentic AI pulls from CRMs, conversation histories, product data, and real-time inputs to make contextual decisions. It can detect a customer’s preferences, purchases, and pain points—and adapt tone and solutions accordingly.

Section 2: AI’s Proven Business Value - Customer Experience (CX)

CX and AI “Gotchas” – Points Clients Often Overlook

As CX and AI deployments increase, it’s important for decision makers to keep the following points in mind:

Lack of clear business outcomes: Deployments often focus on shiny features instead of measurable goals. Without a well-defined outcome—like improving CSAT, reducing AHT, or increasing conversion rates—AI can quickly become a cost center instead of a value driver.
Cyber and data security: Adding new touchpoints and personalized experiences expands the attack surface and introduces greater risk. As companies enhance AI with CX, embedding cybersecurity into the CX architecture becomes critical.
Data quality and integration: To deliver personalized customer experiences in real time, businesses need large volumes of high-quality, accurate data. Fragmented systems and poor-quality data can lead to flawed decisions.
Customer trust and transparency: Today’s customers are highly privacy-conscious, and demand full transparency around how data is collected and protected. CX teams must be able to communicate their data practices and demonstrate accountability to win over customers and maintain regulatory compliance.
Potential bias in AI models: Training models on incomplete or biased data can lead to inconsistent and inaccurate outputs—damaging brand reputation and impacting customer trust.
Change management and team readiness: Frontline employees must understand exactly how AI supports (not replaces) their roles. Leadership must champion change and upskill workers to leverage AI efficiently and effectively.
Over-automation at the expense of experience: More and more organizations are learning that just because you can automate doesn’t mean you should. Misplaced automation (like chatbots handling complex issues) can increase churn and reduce brand loyalty.
Continuous training and model maintenance: AI isn’t a technology you can “set and forget.” Models degrade over time as data shifts. Ongoing tuning, retraining, and human oversight are critical for long-term accuracy and relevance.

Section 2: AI’s Proven Business Value - Cybersecurity

2. Cybersecurity

As cyber threats evolve in scale, speed, and sophistication, businesses are shifting from reactive security models to proactive, AI-first cyber defense strategies. The rise of AI-generated attacks, deepfake fraud, and machine-speed threats has given way to a new era of cybersecurity—one where intelligent systems continuously monitor, defend, and adapt in real-time.

This creates significant opportunity to guide customers toward future-ready, resilient, and compliant cybersecurity architectures.

Cybersecurity and AI Use Cases

Autonomous threat containment
AI-generated threat detection
Deepfake & synthetic media identification
AI in penetration testing
Risk-adaptive multi factor authentication (MFA)
Post-quantum encryption
AI security copilots
Behavioral biometrics

Section 2: AI’s Proven Business Value - Cybersecurity

AI-Generated Attacks

Just as AI defends, it also powers the next wave of attacks. Tools like WormGPT, FraudGPT, and AI-driven phishing kits have democratized cybercrime, allowing non-technical bad actors to launch sophisticated, multi-vector attacks at scale.

To stay ahead, cyber teams now need to think like attackers—and move faster, with greater visibility and precision.

AI-to-fight-AI use cases: Autonomous threat containment, AI-generated threat detection, behavioral biometrics, AI security copilots, risk-adaptive MFA.

Autonomous SOCs

Traditional security operations centers (SOCs) are evolving, with companies increasingly relying on AI-driven threat detection and response systems that operate 24/7. These new AI-driven SOCs can rapidly identify and neutralize threats with minimal human intervention.

Looking forward, expect AI to analyze logs, triage incidents, and even quarantine devices in real-time—saving security teams time and allowing them to do more with less.

Identity Fabric and Decentralized Access

In a world with no network perimeter, identity becomes the new control plane. Identity fabric solutions unify user, device, and workload identities across clouds, endpoints, and edge networks. This cohesive layer enables secure access no matter where resources live.

Building on that foundation, Zero Trust is evolving into Adaptive Trust, where behavioral AI analyzes real-time signals such as login patterns and location to assess risk and adjust access dynamically. AI in cybersecurity marks a critical shift by enabling context-aware decisions, not static rules.

Section 2: AI’s Proven Business Value - Cybersecurity

Quantum-Resilient Encryption

Quantum computing threatens to break today’s encryption standards. The race is on to deploy post-quantum cryptography, with cloud-native platforms and AI pipelines already starting to adopt hybrid crypto models.

Staying ahead of quantum threats is critical to long-term data integrity and trust.

AI-Powered Insider Threat Detection

AI can now detect subtle insider threats such as abnormal login times, unusual data access, or changes in tone during communications. These tools are reshaping risk management across finance, healthcare, and critical infrastructure—and allowing companies to thwart attacks before they lead to costly breaches.

Behavioral analytics replace manual, time-consuming audits for faster, smarter threat detection.

Section 2: AI’s Proven Business Value - Cybersecurity

Navigating Governance: Key Aspects and Benefits

As organizations integrate AI, they must also implement comprehensive governing frameworks to avoid introducing new risks. However, a survey from Gartner reveals that just 12% of IT and data and analytics leaders have a dedicated AI governance framework in place—leaving them vulnerable to risks like ethical concerns, data privacy violations, and compliance failures.⁷

For technology advisors, this is yet another opportunity to add value and help customers avoid AI pitfalls.

Key Ingredients of an AI Governance Framework

Principles: AI governance is built on core principles like transparency, accountability, fairness, safety, and explainability. 
Policies and procedures: These outline how AI systems should be developed, deployed, and used, including data handling, algorithmic bias mitigation, and security protocols. 
Risk management: Frameworks help identify, assess, and manage risks associated with AI, such as bias, discrimination, and security vulnerabilities. 
Accountability: Clear roles and responsibilities are defined to ensure that individuals or groups are held accountable for the actions and decisions of AI systems. 
Monitoring and Auditing: Regular monitoring and audits are conducted to ensure that AI systems are operating as intended, adhering to policies, and mitigating risks. 
Training and Education: AI literacy programs educate employees and stakeholders on the responsible use of AI and the implications of AI governance.

Section 2: AI’s Proven Business Value - Cybersecurity

Benefits of an AI Governance Framework

Reduced Risks: AI governance minimizes the potential for harm, bias, and security breaches. 
Increased Trust: Transparency and accountability build trust among stakeholders, including employees, customers, and the public. 
Enhanced Innovation: A well-defined framework can foster responsible innovation by providing a clear path for developing and deploying AI solutions. 
Compliance: AI governance helps organizations comply with regulations and ethical guidelines related to AI development and use. 
Improved Decision-Making: Clear guidelines and processes can support better decision-making related to AI investments and deployments.

AI Governance Frameworks: Common Examples
NIST AI Risk Management Framework	Provides a structured approach to managing risks associated with AI systems, including safety, security, and fairness.
European AI Act	A proposed legal framework that aims to provide AI developers, deployers, and users with a clear understanding of requirements for specific AI uses.
ISO/IEC 42001	AI management system standard that provides guidance for organizations to address AI challenges such as ethics, transparency, and continuous learning.

Section 2: AI’s Proven Business Value - Cybersecurity

Cybersecurity “Gotchas” – Points Clients Often Overlook

As cybersecurity and AI deployments increase, it’s important for decision-makers to keep the following points in mind:

AI model manipulation and adversarial attacks: Threat actors are beginning to manipulate input data and AI models to deceive systems into making flawed decisions. These attacks, which can be difficult to detect, can create harmful outputs and compromise system security.
Supply chain security issues: Compromised AI systems can introduce downstream threats that impact all parties within a supply chain. By conducting routine security audits for third-party AI systems, companies can improve visibility and reduce risks from adversarial attacks and reduce the potential for model poisoning.
Lack of secure APIs: Application programming interfaces (APIs) are necessary for sharing data and connecting systems. However, they can provide easy entry for bad actors. Strong encryption, access controls, and authentication are critical.
Poor incident response planning: All organizations will eventually experience cyberattacks and data breaches. Having a clear, tested response strategy in place is key for containing damage and reducing recovery times.

Section 2: AI’s Proven Business Value - Cloud Computing

3. Cloud Computing

As AI becomes the cornerstone of modern digital transformation, cloud computing is evolving beyond a mere platform. It’s becoming the very foundation of AI. By 2027, public cloud spending is forecast to exceed $1 trillion, with a significant portion driven by AI-native workloads, data-intensive applications, and next-generation “as-a-service” innovations.⁸

In the not-so-distant future, the cloud won’t just be where AI resides. It will co-evolve with AI, transforming infrastructure into a smarter, more adaptive entity. This deep integration will seamlessly connect cloud technologies with business functions, enabling organizations to optimize performance, accelerate innovation, and rapidly accommodate shifting demands.

Cloud and AI Use Cases

AI-optimized resource scaling
Predictive maintenance
Cloud RPA
FinOps + GreenOps
Data Lakes + LLM tuning
Intelligent load balancing

Section 2: AI’s Proven Business Value - Cloud Computing

AI-Native Infrastructure

Modern cloud providers are rapidly building AI-optimized environments—complete with GPU orchestration, model-serving platforms, and foundational model APIs—to support complex AI use cases like LLMs, computer vision, and digital twins.

Think of cloud as the “training ground” for enterprise AI models and copilots.

Sustainable Cloud (GreenOps)

With sustainability at the core of environmental, social, and governance (ESG) mandates, cloud providers are now embedding AI-powered energy optimization, carbon accounting, and workload orchestration for energy efficiency.

Customers are increasingly choosing providers that can optimize carbon impact—not just cost.

Sovereign & Industry-Specific Cloud

To address regulatory, privacy, and compliance demands, sovereign clouds (geo-fenced and policy-bound environments) and industry-specific clouds are rising. AI workflows are increasingly being embedded within these tailored environments.

Sovereign clouds are a must for government, healthcare, and global enterprises.

FinOps AI

AI-driven financial operations (FinOps) tools now use ML to predict cloud cost spikes, optimize spend in real-time, and automate rightsizing decisions.

AI is now managing the cloud, not just running in it.

Quantum-Ready Cloud

More and more cloud providers are building quantum simulation tools and early-stage quantum-inspired algorithms into their platforms, unlocking new frontiers in AI model optimization and materials science.

Expect early adoption in pharmaceuticals, logistics, and finance.

Section 2: AI’s Proven Business Value - Cloud Computing

Cloud and AI “Gotchas” – Points Clients Often Overlook

As cloud and AI deployments increase, it’s important for decision makers to keep the following points in mind:

Data privacy and compliance: The cloud can introduce risks like misconfigurations and jurisdictional complexities that can contribute to breaches and compliance failures under regulations like GDPR and CCPA.
AI model and data security: While cloud providers often secure the underlying infrastructure, the user is typically responsible for securing AI models. This may include configuring access controls, managing API keys, and ensuring the security of serverless functions or containerized environments.

Latency and performance concerns: Large, complex AI models can require more computation. This can lead to longer processing times and higher latency. To mitigate these issues, companies must have the right networking and hardware components in place.
Integration with existing systems, applications, and data sources: Data silos, API incompatibilities, and legacy systems can all act as barriers when implementing and scaling AI. Before getting started, running a complete system audit and developing a comprehensive integration strategy is necessary. This will ultimately save time, reduce expenses, and improve outcomes.

Section 2: AI’s Proven Business Value - The Internet of Things

4. The Internet of Things (IoT)

According to IoT Analytics, the number of connected IoT devices will reach 40 billion by 2030.⁹ While IoT provides a skeletal framework of devices interconnected through the internet capable of collecting and exchanging data, AI serves as the brain, analyzing and interpreting this data to autonomously make intelligent decisions. When combined, AI and IoT—or AIoT—create a smarter, self-improving network of devices that not only collect data but also learn from it, optimizing processes and delivering more value.

As the volume of IoT devices and data grows, so do the business opportunities for AIoT. However, a major challenge for leaders is utilizing this data effectively. According to an Accenture study, 75% of executives believe high-quality data is key to enhancing generative AI capabilities. Yet 48% report lacking enough quality data to operationalize their generative AI initiatives.¹⁰

AIoT Use Cases

Computer vision (security, quality assurance, safety monitoring)
AIOps
Sensors (olfactory, auditory, pressure)
Energy consumption analysis
Logistics asset tracking
Route optimization

Section 2: AI’s Proven Business Value - The Internet of Things

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.

Fleet Tracking and Management

AI analyzes real-time data from GPS trackers, vehicle sensors, and driver apps to optimize routes, predict maintenance needs, monitor driver behavior, and improve fuel efficiency. This can help fleets cut costs, reduce downtime, boost safety and deliver goods faster.

Picture a fleet with 200 trucks operating across a major metro area. Each vehicle is equipped with onboard GPS, engine sensors, and a driver behavior app—all streaming data into an AI-powered platform. Managers can use this data to identify key trends and optimize efficiency.

Energy Efficiency – Building Management

IoT sensors in smart homes and buildings 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.

In a commercial office building, AI can analyze data from various IoT sensors. It can automatically raise or lower the temperature, control lighting, and identify costly water leaks—leading to lower utility bills, a reduced carbon footprint, and a safer environment.

AIOps – Identifying and Resolving Network Issues

Companies can use AI to automatically detect, diagnose, and resolve network issues—reducing manual IT intervention and minimizing downtime. For example, Juniper’s Mist AI platform combines AI, ML, and data science to deliver proactive network automation with “self-healing” capabilities, enhancing performance and driving greater operational efficiency.

A multi-location enterprise can deploy AIOps to gain real-time visibility and control across all global sites—enabling continuous monitoring, rapid response troubleshooting and support, and consistent support.

Section 2: AI’s Proven Business Value - The Internet of Things

AIoT “Gotchas” – Points Clients Often Overlook

As AIoT deployments increase, it’s important for decision makers to keep the following points in mind:

Interoperability challenges: The IoT landscape is highly fragmented, with diverse communication protocols and data formats. To alleviate interoperability issues, businesses can leverage cloud-based IoT platforms that offer embedded integration capabilities and implement data transformation tools within cloud environments.
Incomplete data pipelines: AI models need complete and optimized data pipelines to learn and perform optimally. This can lead to flawed predictions, poor decision-making, and missed opportunities. Data readiness is a fundamental prerequisite for success with the IoT.

Lack of real-time feedback loops: With the AIoT, real-time feedback loops are necessary for enabling AI models to learn and adapt. Without a continuous cycle of action and reaction, AI is unable to respond dynamically to changing conditions.
Scalability gaps: Limitations in processing power, network bandwidth, and cloud infrastructure capacity can make scaling beyond localized or small-scale deployments difficult. Overcoming scalability gaps requires careful architectural planning and strategic workload distribution.

Section 2: AI’s Proven Business Value - Data Readiness

Data Readiness: The Key to AI Success

Whether organizations are using non-generative AI to detect cyber threats or generative AI to automate customer service, one thing remains constant: data readiness is essential. It determines how quickly and effectively your AI tools can deliver results.

What is data readiness?

It’s the extent to which an organization’s data is accurate, complete, accessible, and secure—ready for immediate use in the right format and structure. Without it, even the most advanced AI solutions struggle to move beyond pilot stages or deliver long-term business value.

In fact, a recent report by MIT and Snowflake found that 78% of businesses lack a “very ready” data foundation to support generative AI.¹¹ Meanwhile, Capital One reports that 70% of technical teams spend hours each day fixing data issues, and only 35% say their company has a strong data culture due to limited support and education.¹²

To help clients succeed, it’s critical to explain what makes data truly “AI-ready.” Key components include:

Data hygiene – Ensuring data is clean, current, and free of duplicates or errors.
Data accessibility – Making sure authorized people and systems can access data, while keeping it secure from those who shouldn’t.
Data quality – Keeping data accurate, consistent, and complete across all sources.
Context – Organizing and labeling data properly so it’s usable by AI models.

Explaining data readiness to clients and making sure they have the right components in place, will lead to superior results and stronger ROI. It will also open the door to additional sales and revenue streams—setting you up for even greater success.

Download our Data Readiness eBook for more!