Tag: AI

Enterprise Networks: How Modern Infrastructure Works
Enterprise networks are the invisible highways that keep business computers and devices running. Whether it’s employees collaborating across offices, customers accessing online services, or devices exchanging real-time data, the enterprise network quietly handles the heavy lifting behind the scenes.

As companies grow larger and more connected, the need for reliable, secure, and scalable enterprise network infrastructure has become more critical than ever. Understanding how enterprise networks work (and how they’re evolving) is key to building a strong foundation for modern business oeprations.

What is an Enterprise Network?

An enterprise computer network is a system that connects everything inside a business. It links computers, servers, devices, and applications so people can share information, run programs, and stay connected. While a home network might connect just a few devices, an enterprise network supports hundreds or even thousands of users.

The main goal of an enterprise network is to keep information moving safely and quickly. It allows employees to work together, whether they are in the office, at home, or travelling. The enterprise network connects not only people, but also machines, sensors, and other smart devices that help the business operate.

Because companies handle large amounts of data, they need an enterprise network infrastructure that can support heavy traffic. A large enterprise network must be able to grow as the business grows, which is why the right enterprise network architecture is so important for modern companies.

The Traditional Enterprise Network Architecture

In the past, enterprise networks were built mostly with physical hardware. Companies set up equipment in their offices and data centers to handle all of their network traffic. This setup connected employees, devices, and applications through cables, routers, and switches.

The enterprise network infrastructure included several key parts.
- Routers directed traffic between networks and allowed access to the internet
- Switches sent data to the right device inside the network
- Gateways helped different types of networks work together
- Firewalls protected the netweork from outside threats
- Load balancers made sure no single server became overwhelmed with too much traffic
- To work remotely, employees often connected through VPN servers, which created a secure tunnel bak to the office network.
A large enterprise network used wide area networks (WANs) to link different offices and buildings. Many companies paid for private connections, such as MPLS (Multiprotocol Label Switching), to create fast and stable links between locations.

While this traditional enterprise network architecture worked well for many years, it also had problems. Buying and maintaining hardware was expensive. Upgrading the system often required installing new equipment. And as more work moved online, these networks sometimes became slow or overloaded. Managing them also took a lot of time and effort for IT teams.

The Evolution of Enterprise Networks: Cloud and Hybrid Models

As businesses grew and technology changed, the traditional enterprise network started to fall behind. Companies no longer kept everything in one place. More data and applications moved to the cloud, where they could be accessed from anywhere.

This shift led tot he rise of hybrid enterprise network architecture. In a hybrid setup, most systems will run in private data centers, while others operate in public cloud services like AWS, Microsoft Azure, or Google Cloud. Employees can now work from the office, from home, or while traveling, and still access the tools they need.

New technologies like Software-Defined Wide Area Networks (SD-WAN) have helped businesses manage these changes. SD-WAN allows companies to use regular internet connections, instead of expensive private lines, to connect different locations. It can automatically choose the best path for data to travel, which improves speed and saves money.

As a result, enterprise network infrastructure has become more flexible, easier to scale, and more cost-effective. Businesses can grow faster, open new offices more easily, and give employees better access to cloud services. But this new model also brings new security challenges, which is why security models have had to evolve alongside the network.

New Security Challenges in Modern Enterprise Networks

As enterprise networks expanded beyond office buildings, protecting them became more difficult. In the past, most employees worked inside the same building, and the enterprise network had a clear “perimeter” that could be guarded with firewalls and security systems. But now, with cloud services, remote work, and mobile devices, that perimeter has disappeared.

Data no longer stays inside one location. Employees connect from different places and devices, and sensitive information moves across the internet every day. This makes it harder for businesses to control who has access to their systems.

To handle these risks, companies need stronger security models that protect data no matter where users are. Modern enterprise network security focuses on constantly checking who is trying to access the network and making sure only the right people and devices get in. This shift has led to new security approaches like Zero Trust and Secure Access Service Edge (SASE).

Read more on the topic from our previous articles:
Enterprise Network-as-a-Service (NaaS)

As enterprise networks became more complex, many companies started looking for easier ways to manage their systems. This led to the rise of Network-as-a-Service (NaaS). With NaaS, businesses no longer need to buy and maintain their own networking hardware. Instead, they rent networking services from a cloud provider.

In a NaaS model, the provider handles tasks like routing traffic, managing firewalls, balancing network loads, and protecting against attacks. The business only needs an internet connection. Employees simply log in and connect to the enterprise network, no matter where they are.

The main benefits of NaaS include:
- Flexibility: Changes can be made quickly through software without replacing equipment
- Scalability: As the business grows, companies can easily increase network capacity
- Lower costs: There is less need for expensive hardware and on-site IT maintenance
- Simpler management: The cloud provider takes care of updates, security patches, and repairs
- Built-in security: Many NaaS providers include security features as part of their service
Some enterprises may find challenges with adopting a NaaS model. Some older systems may not work well with cloud-based networks. Businesses also need to be careful about depending too much on a single vendor, which can make switching providers difficult later.

Even with these challenges, many larger enterprise networks are now moving toward NaaS. It offers a simple way to modernize enterprise network infrastructure while keeping up with the fast pace of business and technology.

Enterprise Networks and Secure Access (SASE, Zero Trust):

As enterprise networks spread across locations and cloud platforms, businesses need stronger ways to control who can access their data. Two important solutions that have emerged are Secure Access Service Edge (SASE) and Zero Trust Network Access (ZTNA).

Zero Trust is built on a simple idea: never automatically trust any user or device, even if they are inside the network. Instead, every time someone tries to connect, the system checks their identity, device, location, and behavior. Only approved users with the right permissions can access company resources. This greatly reduces the risk of unauthorized access and data breaches.

SASE combines networking and security into one cloud-based service. Instead of having separate systems for firewalls, VPNs, and web filtering, SASE brings them together. This makes it easier to secure the enterprise network while also improving performance for users, no matter where they are working.

By using SASE and Zero Trust together, companies can protect their enterprise network infrastructure while still giving employees the flexibility to work from anywhere. This approach also helps simplify security management for large enterprise networks, especially as more businesses rely on cloud-based systems.

Where Enterprise Networks Are Heading Next

Enterprise networks are entering a new phase of businesses adopt advanced technologies like artificial intelligence, real-time analytics, and distributed cloud computing. These systems require large volumes of data to move smoothly between offices, cloud providers, and data centers. All while meeting strict security, performance, and compliance standards. As a result, enterprise network infrastructure must now support far more than just speed or uptime.

This shift is driving the need for modular, fully managed network architectures that offer flexibility without losing control. Telecom-inspired models are becoming more relevant, where service providers deliver an integrated stack combining services like connectivity, VoIP, SD-WAN optimization, cross-border interconnects, and AI-ready infrastructure all managed through a unified platform. This approach helps large multinationals and cloud-reliant organizations handle regulatory data residency, regional performance, and compute-heavy AI demands, while keeping systems fully integrated.

Instead of managing separate solutions for each network function, these models simplify deployment, scaling, and compliance through a single, customizable enterprise network platform. The goal is to deliver the performance, security, and scalability businesses need without the cost and complexity of running everything in-house.

While full-stack enterprise networking is still evolving, this reflects the clear direction of the industry: highly-integrated, partner-delivered networks that let businesses stay agile and secure as technology demands continue to grow.

AI, Data Governance, and the New Demands on Enterprise Networks

As more businesses adopt artificial intelligence, the way enterprise networks handle data is becoming even more complex. AI systems depend on enormous amounts of data to function, but it’s not just about moving data quickly anymore. Companies must now carefully consider where data is stored, where it’s processed, and how it’s used – especially when AI models are trained or hosted by third-party providers.

This creates new challenges for enterprise network architecture, particularly for companies that operate across multiple countries or work in highly regulated industries. Data privacy laws often require that sensitive information stays within certain borders. In some cases, even the AI processing itself must happen in specific regions to comply with national or industry-specific regulations.

Because of this, enterprise networks must go beyond simply connecting offices and cloud providers. They need to support secure data storage, controlled data flows, and AI services that can be deployed flexibly while meeting strict privacy and security standards in every region. This adds another layer of complexity to network design, making data governance and AI governance key components of enterprise network infrastructure.

In the future, companies will also increasingly rely on network service models that not only deliver global connectivity, but also offer full integration with data protection, compliance, and AI performance requirements, all managed through a unified platform.

Why Enterprise Network Infrastructure Matters for Businesses

Enterprise network infrastructure is more than just the technology that keeps businesses online. It directly affects how companies operate, serve customers, and stay competitive. A strong enterprise network allows employees to collaborate easily across offices and time zones, while provider fast, secure access to data and applications from anywhere.

For companies operating internationally or in highly regulated industries, the enterprise network plays a critical role in protecting sensitive information and meeting legal requirements. As more business activities depend on cloud services, AI tools, and real-time data, enterprise networks must ensure that information moves securely while staying compliant with local laws.

Modern enterprise network architecture also helps companies grow more easily. With scalable, fully managed platforms, businesses can open new offices, expand into new markets, and adopt new technologies without the heavy cost and complexity of managing everything on their own. A well-designed enterprise network supports flexibility, speed, and security, all of which are essential for long-term success in today’s global digital economy.

The Future of Enterprise Networks

Enterprise networks are evolving from simple connections into complex, fully integrated platforms that support the growing demands of modern businesses. As companies adopt cloud services, AI technologies, and global operations, enterprise network infrastructure will remain a critical foundation. This is not only for performance and security, but for compliance, flexibility, and long-term growth. Businesses that invest in modern, scalable enterprise network architecture will be better equipped to compete, adapt, and thrive in an increasingly connected world.
06/12/2025
Contact Center AI: What Should You Know?
Contact center AI is reshaping how businesses serve their customers by boosting speed, improving quality and opening the door to better support. From chatbots and virtual agents to sentiment analysis and automation, many companies are exploring AI to create smoother, more intelligent customer experiences.

But with so many tools available, how do you decide which ones are worth investing in? Not every feature fits every business model, and the real value of AI often depends on how it’s integrated into your voice and service infrastructure.

In this article, we break down the most common contact center AI solutions and offer a practical way to evaluate what’s useful, scalable and truly supports your long-term CX goals.

Contact Center AI: When Features Become Frictionless

Contact centers sit at the heart of customer experience. They’re where questions about your business are answered, problems get solved, and brand impressions are formed.

For this, they generate huge amounts of human interaction data including calls, chats, inquiries, follow-up, and everything in between. Each conversation carries details about customer intent, urgency, and what happens next.

As volume grows, especially in multinational companies or large enterprises, managing that complexity becomes harder. To respond quickly, teams need to recognising patterns, understanding context, and taking the right action at scale.

This is where contact center AI is proving its value. It helps agents speed up repetitive tasks, flag key signals, and makes insights easier to analyse and act on. It can turn a messy web of daily conversations in various channels into structured information that can improve service delivery and customer experience over time.

Contact Center AI in Action: Which Tools Are Worth It?

Platform leaders are applying contact center AI in different ways. Each new tool category is designed to solve specific problems or unlock a new kind of value. How do you know which one you need?

1. Virtual Agents

Virtual agents are AI-powered assistants that handle simple, repetitive customer queries through voice or chat. They’re designed to offload tasks that don’t require human judgment, letting agents focus on more complex or emotional conversations.

Agents in most contact centers spend a significant amount of time responding to routine questions. These include things like “Where’s my order?”, “Can I change my appointment”, or “What’s my balance?”. They’re interactions that follow predictable scripts, but still take time to manage.

Here’s what AI can help with:

• Answer routine questions so agents don’t have to

Answer routine question so agents don’t have to. Virtual agents are built to respond instantly to FAQs through natural language processing. They recognise voice or typed input like “When will my package arrive?” and provide accurate, consistent answers without needing a human agent. For contact centers handling thousands of similar queries per day, this dramatically reduces queue times and ensures responsive support, even during off-peak hours or global spikes in volume.

• Handles small tasks behind the scene

Beyond answering questions, virtual agents can complete simple but time-consuming tasks. These include updating customer details, processing cancellations, or resending invoices. These tasks may not need empathy, but must be done accurately. AI connects with backend systems to carry out these actions in real time. This removes the need or reduces time for agents to jump between multiple screens or tools to retrieve and deliver information.

• Passes complex issues to agents with helpful context

When an issue is urgent, emotional or complex for the virtual agent to handle, it hands the case off to a human. The AI passes along key details including who the customer is, why they reached out, and what’s been tried. This lets the agent skip repetitive questions, focus on resolving the core issue, and keep the experience seamless for the customer.

Expected results:
- Fewer repetitive tasks for agents
- More time for complex, meaningful conversations
- Faster service for customers, especially during peak periods
Faster service for customers, especially during peak periodsVirtual agents help teams work smarter by filtering and forwarding what truly needs a human touch

2. Sentiment and Intent Detection

Contact center agents also do more than just respond to questions. They’re constantly interpreting tone, urgency, and frustrations (spoken or unspoken!). But when your team is handling hundreds or thousands of interactions a day, it’s hard for supervisors or systems to catch those emotional signals at scale.

Contact center AI tools for sentiment and intent detection are designed to help. They listen for how customers feel and what they’re trying to achieve, using tone of voice, keywords, pacing, and chat patterns to draw conclusions in real time.

• Detects when customers are frustrated

AI tools can detect emotional cues like rising frustration, hesitation, or urgency. They monitor pitch, repetition, and word choice to flag if a customer seems annoyed, confused, or at risk of dropping off. This makes it easier to identify struggling customers or difficult calls before they escalate, giving agents or supervisors a chance to step in early.

• Figures out what customers want

Customers don’t always explain their needs clearly. AI uses pattern recognition to understand intent, whether it’s a refund, complaint, or service change even when a customer’s words are vague. This helps agents get to the point faster and avoid asking repetitive clarifying questions.

• Finds patterns in real conversations

Over time, sentiment and intent data reveal patterns like which services cause the most frustration or what phrases indicate a risk of escalation. Managers can use such insights to improve training, refine scripts or adjust how issues are routed and resolved.

Expected result:
- Agents can respond more precisely to customers’ tone and needs
- Supervisors gain better visibility into customer pain points
Overall, the contact center becomes more emotionally intelligent and better prepared to act before problems grow.

3. Assistive AI

To answer questions effectively, agents often need to jump between multiple systems and databases. They need to look up product info, customer history, write case notes, and try to keep everything accurate, all while staying polite and fast. This multitasking can be stressful, time-consuming, and prone to errors.

Assistive AI works in the background to support agents during calls and chats. It doesn’t take over the interaction, it simply helps agent stay focused and efficient by automating the small but critical tasks around them.

• Suggests replies in real time

While the agent is speaking with the customer, AI offers response suggestions based on the conversation so far. These aren’t one-size-fits-all scripts, they’re tailored to the issue at hand. The agent can choose to use, adjust, or ignore the suggestions. This speeds up responses and helps maintain a confident, helpful tone, especially for new or overwhelmed agents.

• Writes call summaries automatically

After each interaction, agents usually need to write up what happened. It takes time and often varies in quality. Assistive AI listens to the call and generates a summary with key details: what the issue was, what was resolved, and what steps were taken. Agents can review and edit before saving, which cuts down after-call work significantly.

• Finds helpful information instantly

Instead of manually searching through documents or help centers, agents get relevant links or knowledge articles pushed to them based on the conversation. This keeps them from switching tabs or digging for answers, which improves accuracy and speeds up call resolution.

Expected results:
- Less mental load for agents
- More consistent documentation
- Faster service and fewer erors
Agents can stay focused on the customer instead of navigating systems and information.

4. Generative AI

Contact centers create a huge amount of information every day, from customer problems, resolutions, edge cases, and patterns. But capturing that knowledge is a challenge. Agents don’t always have time to write detailed summaries, and documentation often gets skipped or becomes inconsistent.

Generative AI helps turn conversations into useful, structured content. It writes drafts of notes, summaries, and even internal articles that frees agents from repetitive admin tasks and improves knowledge sharing across the team.

• Summarises cases automatically

After a call or chat, generative AI reviews the interaction and produces a written summary. It includes key issues, actions taken, and outcomes. Agents can quickly check and edit the draft before saving it. This reduces after-call time and ensures every case is documented clearly, without relying on memory or manual effort.

• Drafts knowledge articles from calls

If a call reveals a new type of problem or workaround, generative AI can turn the conversation into a knowledge article. It pulls core the core details, organises them, and offers a first draft that the agent or supervisor can refine. This helps grow the shared knowledge base organically and ensures useful fixes are shared quickly.

• Keeps documentation clear and consistent

When multiple agents write their own notes or guides, the tone and structure can vary a lot. Generative AI helps standardise how information is recorded. It follows a standard format, making it easier for others to search, understand, and reuse especially when onboarding new agents or reviewing past cases.

Expected results:
- More reliable documentation with less effort
- A faster-growing, more consistent knowledge base
- Less after-call work and reduced agent fatigue
- Faster onboarding using AI-assisted prompts
Agents can spend more time helping customers and less time writing, and nothing important gets lost in the shuffle.

Contact Center AI in Real Life

Now that we’ve looked at what each AI tool is designed to do, let’s see how they work in real contact center environments. These examples combine common challenges with realistic AI-driven solutions, showing how contact center AI not only improves customer experience, but makes life easier for agents too.

Use case #1: Virtual Agent Covers First Line

Scenario: A retail customer wants to track their delivery. It’s 9:00PM and the contact center is closed.

How AI Helps: The customer chats with a virtual agent on the company’s website. The virtual agent confirms the delivery status, updates the customer’s address, and offers a digital receipt, all without human intervention.

Later, another customer reaches about a billing issue. The virtual agent tries to assist, but the issue is more complex than its capability to handle. The AI offer the option to hand the case off to a live agent, with all the conversation history, attempted resolutions, and customer details pre-filled.

Outcome: The agent picks up where the virtual agent left off, without needing to ask the customer to repeat themselves. The issue is resolved faster, and the customer feels heard. Meanwhile, the virtual agent handled similar inquiries independently.

Use case #2: Assistive AI Supports Live Calls

Scenario: An insurance agent is helping a customer file a complicated claim. The customer is upset and confused about the process. The agent is juggling multiple screens to find the right policy details, explanations, and next steps while trying to sound calm and confident.

How AI Helps: As the agent talks to the customer, Assistive AI listens and offers reply suggestions in real time based on company policy and context. It also pulls up the exact section of the knowledge base that relates to the customer’s situation.

After the call, the AI generates a complete summary of the conversation, icnluding the issue, actions taken, and any follow-up needed.

Outcome: The agent doesn’t need to search or take detailed notes during the call. They’re more focused, less flustered, and able to resolve the issue smoothly. The agent can review the notes and after-call work is cut in half. The documentation is more consistent across the team.

Use case #3: AI Detects Emotions and Intent

Scenario: A telecom customer contacts support about dropped calls. They sound calm at first but become frustrated when the agent can’t find the problem. The agent juggles at resolving the issue and addressing the customer’s frustration, increasing chances of a negative experience, complaint, or escalation.

How AI helps: With sentiment and intent detection running in the background, the system flags that the customer’s tone is shifting. IT alerts the agent and suggests more empathetic responses. IT also surfaces related cases and technical logs based on keywords in the convesation.

By the time the customer expresses their frustration, the agent already has an alternative solution prepared and offers it.

Outcome: The customer feels heard and doesn’t have to escalate the issue. The agent keeps control of the conversation. Managers get better visibility into interaction quality and can intervene sooner when emotional calls are flagged.

Use case #4: Generative AI Captures Knew Knowledge

Scenario: A customer calls a software company with an uncommon issue after a new update. The agent manages to solve it through trial and error, but it’s a unique case that no one else has documented.

How AI Helps: Generative AI listens to the call, identifies that it contains a new solution path, and drafts a knowledge article based on the conversation. The agent reviews the draft, adds a few notes, and submits it for approval.

Later, another agent helping a different customer with a similar issue finds the published article instantly, saving time and effort.

Outcome: The fix doesn’t get lost. Future customers benefit from the resolution, and agents don’t need to reinvent the wheel. Over time, the knowledge base grows naturally through real interactions, keeping it relevant and reducing repeat investigations.

Contact center AI makes everyday work smoother, faster, and more accurate. Virtual agents free up your team from repetitive tasks. Assistive tools give live agents better focus and less stress. Emotional cues and intent signals help prevent churn. And generative AI keeps knowledge flowing without extra burden.

When these tools are thoughtfully applied, they can strengthen human agents without replacing them. And they help businesses turn everyday customer interactions into smarter, more scalable operations.

How to Decide What’s Worth It

The right approach to contact center AI depends on your customer experience goals, your team’s capacity, and how much flexibility you want over time.

Here’s how to evaluate contact center AI options in a way that actually supports your operations and your software stack:

Start with the workflow, not the tool. Look at where agents spend time, where bottlenecks occur, and what customers repeat over and over. Tools like virtual agents or assistive AI are most effective when they slot into real points of friction, not when they’re deployed just in case.

Don’t chase everything at once. It’s tempting to buy the most feature-rich platform. But value comes from precision. Prioritise features that solve real pain-points: handoff clarity, agent overwhelm, or poor documentation. A smaller AI toolkit used well can outperform a bigger one poorly implemented.

Ask how adaptable the system is. Your CX needs will change. The best contact center AI solutions let you scale, adjust, or swap modules as your processes evolve. Look for platforms or partners that lock you into rigid setups.

Train your AI to standard. AI needs onboarding, support, and training to provide the best benefits to your team. It requires teams to thoughtfully ground it into existing workflows, feed the right data, and adjust as things change. Make sure human agents know when to trust it, and when to override it.

How Contact Center AI Changes Your Security Posture

Adopting contact center AI isn’t just about performance gains, it also reshapes how your customer data moves, where it’s processed, and who has access to it. As these systems grow smarter, so should your governance over its impact on your data.

1. Broader integrations mean broader risk

AI tools connect to CRMs, ticketing systems, voice platforms, analytics dashboards, and sometimes external databases. Each integration crease a new point of exposure that could be exploited if not governed carefully

What to do:
- Request a full integration and API map from your vendor
- Limit access with least-privilege policies across systems
- Use an API gateway to monitor traffic and apply rate limiting or anomaly detection
2. Data flows become harder to trace

Legacy systems kept data local or in-region. But many AI platforms move data across global servers for processing, especially for training or enrichment. This complicates compliance with laws like GDPR, PDPA, or HIPAA.

What to do:
- Design escalation workflows with clear thresholds and override options
- USe monitoring tools that flag unusual or risky actions in real time
- Build internal protocols for human review of high-impact AI decisions
3. Real-time automation needs real-time oversight

AI systems operate at machine speed but without the right oversight, mistakes can escalate just as quickly. Misrouting a support ticket, mislabeling a customer, or auto-escalating an issue can erode trust or violate policy.

What to do:
- Design escalation workflows with clear thresholds and override options
- Use monitoring tools that flag unusual or risky actions in real time
- Build internal protocols for human review of high-impact AI decisions
4. Black-box tools raise accountability issues

When AI decisions can’t be explained, such as sentiment scoring or case classification. It creates a gap in accountability. This becomes especially serious in sectors that demand audit trails or regulatory justifications.

What to do:
- Prioritise platforms with explainability features or detailed action logs
- Document business rules that guide AI behaviour and model boundaries
- Include AI outcomes in internal audits and post-incident reviews
5. Secure-by-design platforms make the difference

AI should be an enhancement, not a control point. When platforms dictate your routing, data paths, or storage, it reduces flexibility and increases vendor lock-in. This is risky for long-term adaptability and compliance.

What to do:
- Choose modular contact center platforms where you control infrastructure
- Ensure voice, routing, and AI components can be configured or replaced
- Build around systems that support open standards, observability, and exportability
Trends in Contact Center AI: Beyond Just Features

AI innovation is happening beyond algorithms; companies are evolving how they host and operate artificial intelligence programs. For enterprises navigating tight compliance boundaries or multi-marketing complexity, the biggest breakthroughs might not be in features but in flexible global deployment.

AI on Private Networks. Running AI over a private or hybrid network offers more control over data routing, latency, and security. This is especially useful for financial institutions, government clients, or enterprises in regions with strict data governance requirements.

It offers tighter governance, better integration with legacy systems, and more predictable performance. Especially in sectors where keeping customer data off the public internet is non-negotiable.

Open-source and custom models. Rather than using generic, opaque AI systems, forward-thinking teams are experimenting with open-source LLMs or domain-trained models that live inside their environment. These tools can be customised, tuned to industry nuances, and easily audited.

They make AI safer to deploy at scale, reduce vendor reliance, and align better with internal governance models where traceability and fine-tuning are essential.

Small-scale, on-prem AI. Self-hosted AI models are becoming more viable, even on modest infrastructure. Contact centers are deploying tools like summarisation engines, speech recognition, or intent tagging locally, running them where the data already lives.

This enables highly secure, cmpliant deployments without sacrificing speed. It also gives organisations more freedom in how and when to scale AI initiatives.

Modular integration, not monoliths. Instead of committing to full-stack AI suites, enterprises can adopt modular AI components that connect easily with their current stack. This allows teams to gradually adopt automation without re-architecturing from scratch.

This keeps IT and compliance teams in the loop, reduces friction in rollout, and supports future flexibility as business needs evolve.

Contact Center AI, With You in Control

Contact center AI tools can dramatically improve how you serve customers. Faster resolutions, better insights, less repetitive work are real, tangible gains. But they don’t have to come at the cost of control of compliance.

It’s important for businesses to own their communications backbone, from routing and voice infrastructure to how and where AI features are layered in. You don’t need to overhaul everything to get value, just tools that plug into your system and security standards.

The most valuable AI fits will fit your system, protect your data, and support your agents without locking you in.

Deploy smarter, stay in control.

We help companies launch UCaaS and CCaaS systems around the globe, securely, compliantly, and without vendor lock-in. From voice infrastructure to private network deployments, our solutions simplify multi-country rollouts, integrate with leading CX platforms, and support AI adoption on your terms.

Gain a centralised architecture that respects data sovereignty, upholds your security posture, and leaves room to evolve.
05/30/2025
Tales of Practical AI in 2024
“Facts are stubborn things.” John Adams, second president of the United States

John Adams’ famous statement of 1770 is still relevant today. Because 250 years later, facts still are quite stubborn – and can lead to costly consequences.

Consider the churn rate in contact centres.

According to a 2022 study conducted by NICE, contact centre attrition rates were 42 percent. And in organizations with more than 5,000 employees, churn was as high as 50 percent. Even more worrisome, 31 percent of agents were actively looking for another job, according to the NICE study.

Add up the numbers. Consider the impact on the bottom line. Year over year, the agent drop-out rate translates to a major cash burn.

Worse, consider the emotional condition of your agents. Those who stay could be coping with burnout. But can a burned-out agent really build brand loyalty? That’s a big ask.

According to a McKinsey study, it costs between $10,000 and $20,000 to train an agent.

So, if a contact centre has 100 agents, and 42 quit annually, that’s a loss of $420,000 to $840,000. Add to that the cost of losing customers because of negative experiences, and these facts are not just stubborn. They’re painful. As agents race to the exits, the cost of doing business gets higher.

Fortunately, there’s a way forward. It starts with increased management awareness of agent burnout and implementing policies to counter agent churn by improving agent well-being. It’s a two-prong approach: better training and providing agents with omnichannel solutions that include AI tools.
Engaged and satisfied call center employees are:
- 8.5x more likely to stay than leave within a year
- 4x more likely to stay than dissatisfied colleagues
- 16x more likely to refer friends to their company
- 3.3x more likely to feel extremely empowered to resolve customer issues”
Source: McKinsey.

AI: Now In Omnichannel Platforms Near You

To move the needle in improved CX and agent performance, CXaaS vendors like NICE offer AI-enhanced capabilities and are already providing help.

Other CXaaS vendors like Simplify360, with its AI-powered chatbots, have added AI capabilities to improve agent performance by providing a unified interface for managing multiple contact channels (voice, email, chat, social media, etc.). Key capabilities often include skills-based routing, automated workload balancing, customer journey analytics, and AI-driven agent assistance.

The Customer Meets AI With NICE

Enlighten Autopilot from NICE offers a specialized AI solution crafted for customers, offering personalized experiences to enhance customer loyalty. It ensures the delivery of seamless interactions through digital journeys or AI-designed virtual agents.

Customers are provided tailored self-service options, strategically positioned on their customer journey. It’s based on training models and LLMs comprised of company knowledge, aligning each response with the goal of serving the client and the brand.

When The Customer Calls, The Agent Is Ready

NICE also addresses the crucial need to provide agents support at every moment. Enlighten Co-pilot is one such tool of tremendous value: a conversational AI tool for agents.

It generates precise, knowledgeable conversational responses. It alleviates agents from repetitive tasks, granting them quicker access to knowledge and answers. Better still, the aim is to significantly reduce agent stress and improve their well-being with more meaningful customer conversations.

Supervisors can facilitate more intelligent guided interactions and implement AI-driven coaching, enhancing agent and consumer experiences.

Give Them The Tools

When in 1940, Winston Churchill said, “Give us the tools, and we will finish the job,” the future of Britain hung in balance with the threat of an imminent invasion by Nazi Germany. Consider a similar clarion call by contact agents today: “Give us the tools – backed by AI – and we’ll finish the job.”

We’re witnessing an exuberant AI hype cycle. History shows that it takes about a decade for a new technology like the first web browser from Netscape, released in 1994, to find a market with measurable, practical, and profitable benefits. It took a while for broadband speeds and easy-to-use payment systems to be developed. Then came audio and video streaming. technology became far better. Today we can’t live without the internet.

While the AI hype continues to burn bright, we have no idea where and when the practical tools will take root. It’s the start of a new technology era, and it will also take a while for things to take shape.

Start-ups are already developing AI applications to meet unmet needs across all industries, especially in health care.

But an innovation cycle is already happening in the CXaaS space. Companies like NICE have seen the enormous potential for AI to transform customer journeys while easing agent strain and improving employee satisfaction.

CXaaS AI tools are in their infancy. In five years, the contact centre might look a lot different, thanks to the practical benefits of CXaaS platforms enhanced by the power of AI.
02/08/2024
Show Me The Money
Financial Impact of Exceptional CX

Investors can be unforgiving. They have high expectations for returns on investment (ROI) and rightly so. They expect – and deserve – a healthy return. When they don’t, it can be curtains for the CEO.

And so, “show me the money,” that iconic line from the movie “Jerry Maguire,” really nails it. Show me great ROI, and I’ll continue to invest in your company. It couldn’t be simpler. Right?

However, calculating ROI isn’t straightforward. Different CEOs and CFOs have varied approaches to measuring it. The road to achieving it is diverse, with no one-size-fits-all strategy.

Consider how businesses like Tesla and Amazon, had initial losses of billions for years before turning a profit. Both companies were granted generous market valuations by the investors, and the parties’ insight into future profitability was evident.

Meanwhile, Macy’s, the storied clothing retailer of the US market, is rethinking its approach to the brick-and-mortar vs. digital landscape, just at the end of last year as consumer spending has drastically changed. With declines in its in-store sales, it’s now catching up with the market’s new preferences, prioritizing leaner architectures and more automation practices.

Whether we choose to invest in potential, or adapt once it becomes clear that we have to, the cost of transition is unavoidable. Customer loyalty, on the other hand, tends to favor the innovators.

Follow The CX Dream: It’s Paved With Gold

Excellent CX is the North Star. Companies that create a loyal customer base have gained more than a toehold in the market. They’ve gone beyond increased revenue, profit, and brand awareness. They have their own momentum and they just don’t follow trends, they’re a part of making trends.

Consider Apple’s success with the iPhone, the iPad, music streaming, the original Macintosh with its graphical user interface, which spawned desktop publishing. Apple promised, and Apple delivered amazing customer experiences. For the investors, it has been a good ride and Apple today has a $2.87 trillion valuation.

So, how do you create and maintain excellent CX? These days, for CEOs and CFOs, following the CX dream presents stark choices.

CX and the Cloud – Not If, But When

The crucial role of CX is hardly in dispute. For modern, agile companies, loyalty can’t be underestimated, and you’ll find it on the bottom line where it’s spun into gold.

As Forbes magazine points out, great CX has a procession of benefits.
- Strengthens brand loyalty, encouraging repeat purchases.
- Boosts and spreads brand reputation, thanks to positive social media reviews.
- Increases customer retention, reducing new customer acquisition costs.
- Encourages customers to buy more and pay a premium for better experiences
- Empowers and fulfills purpose in the organization
According to PWC, 73% of consumers consider their experience with a company a key factor in their purchase decisions, as reported by PwC. Consumers demand not just quality service and fair pricing, but personalized and connected experiences across digital channels.

The Journey to Better CX Is Paved With The Cloud

Key trends in the CX landscape, including the omnichannel experience and Communications Platform as a Service (CPaaS), are shaping the future. As a CX Today article points out,new messaging apps, social media platforms, and even GenAI-powered search have expanded sophisticated touch points throughout the customer journey.

An omnichannel approach, supported by CPaaS solutions, offers convenience and seamless record access, enhancing both agent performance and customer service. And, when it comes to remaining agile, CPaaS provides a foundation for future demands.

Analytics and AI allow for real-time learning from engagements and aid in tailoring training programs, which traditional methods of supervision can’t achieve due to capacity constraints.

Justifying Capital Expenditures on Cloud Technologies

Whether you’re a market leader or only catching up, continual investment in technology is essential. It’s not a small feat, when simultaneously controlling operating costs while making (what you hope are) prudent capital investments.

Calculating the ROI of complex investments like cloud technologies and omnichannel platforms can be nuanced and intricate. In complex business scenarios such as global enterprises, calculating net profit and investment costs on cloud platforms can involve many elements.

For instance:
- Understanding Net Profit in Tech Investments
For cloud technology investments, net profit isn’t just about direct financial gains. It includes savings from process efficiencies, productivity improvements, enhanced customer satisfaction, and potentially increased sales or market share. These factors can be hard to quantify but are crucial for a comprehensive ROI analysis.
- Unpacking Investment Costs
Beyond the initial purchase or cloud subscription cost, investment costs can include implementation expenses, training costs for staff, ongoing maintenance, and even the cost of changing business processes.
- Considering Timeframes in ROI
ROI is often considered over time. An investment might have upfront costs that seem high, but if it leads to significant long-term savings or revenue growth, its ROI improves over time.

For a deep dive into calculating the ROI of customer experience initiatives, we recommend a Gartner report that examines financial services operations.

Let’s Do Omnichannel

An Adobe study on omnichannel strategies indicates that companies with robust omnichannel customer engagement typically produce ten percent year-over-year growth and a 25 percent increase in close rates. Indeed, an omnichannel customer experience enables better data flow, channel shift, and contact handling as agents enjoy more customer context.

An Adobe study shows that companies with robust omnichannel customer engagement report a 10% annual growth and a 25% increase in close rates. Indeed, this strategy enables better data flow, channel shift and contact handling as agents enjoy more customer context.

Small steps lead to big gains, tweak and tune as you grow. It’s a complex blend of technologies, market forces, and keeping a contact center team motivated and inspired. Excellent CX is continuous work in progress that pays off.
02/01/2024
Cloud AI in the Mobile First Markets

In 1959, Esso, now ExxonMobil, launched a consumer advertising campaign in the US dubbed, “Put a tiger in your tank.” The slogan suggested that Esso’s petrol stood out from other brands because it was like adding the strength of a tiger to a car engine. The Esso ‘tiger’ became one of the most well-known ad campaigns of the 1960s.

These days, we’ve gone from tigers to llamas. The hottest topic today is not the petrol in your engine. It’s about the AI in your pocket. Consider Meta and Qualcomm Technologies, Inc., the mobile phone chip company. They’re working together to optimize Meta’s Llama 2 large language models (LLMs) directly on-device with generative AI applications using the AI capabilities of Qualcomm’s Snapdragon platforms.

Starting in 2024, generative AI models (like Llama 2) will be available on smartphones, PCs, VR/AR headsets (and don’t forget cars) to provide users with private, more reliable, and personalized experiences. This will allow intelligent virtual assistants, productivity applications, content creation tools, entertainment, and more.

So, as the roar of the internal combustion engine gradually gives way to the silent surge of instant torque of the electric car, the smartphone will perform (and look) a lot differently, too – thanks to AI. Within a decade, you could have a “llama in your pocket,” so to speak, offering up personalised recommendations in real-time, observing your precise location, writing emails on the fly, and acting as a personal assistant to guide you through food and menu choices based on your health profiles.

Already Samsung has three Galaxy S24 models that are expected to include generative AI capabilities. The company has also registered two trademarks; ‘AI Phone’ and ‘AI Smartphone.’ And companies like NICE, the CXaaS (Customer Experience as a Service) company, have reoriented their corporate focus to AI-inspired initiatives. Clearly the world is on the cusp of an entirely new era. The ‘smart’ of smartphone is poised for a big upgrade. The AI phone is at hand.

AI and Cloud Projects Grow in APAC

Until now, AI in smartphones has primarily played a supportive role (neural processing units embedded in chips have been instrumental in powering features like on-device translation, voice, image recognition (Google Lens), and virtual assistants such as Siri and Google Assistant). But smartphones are evolving into more than just endpoints; they are becoming active participants in the cloud ecosystem, particularly in APAC (read our last article on the growth of mobile within the region).

Consider the collaboration of digital payments between Apple Pay and Hyundai Card in South Korea which was supported by the government of South Korea. South Korea is the tenth Asia-Pacific country where Apple Pay is available. The fin-tech service already exists in Australia, New Zealand, China, Hong Kong, Macao, Japan, Malaysia, Singapore and Taiwan, according to Apple.

As if to cement the East’s growing potential, reported by Nikkei Asia last month was the case of NVIDIA, whose GPUs power ChatGPTs servers, visited nearby Southeast Asia to discuss investments in AI infrastructure; a move that leverages the region’s established experience with technology supply chain. It’s one of the many decisions by global tech companies that are highlighting the emerging market’s path to advanced cloud innovation.

The accelerating demand for mobile payments, e-commerce, and other transactions makes AI in mobile a continuous cycle of benefits.

CX in Emerging Markets Trends Looks Bright

The arrival of AI with mobile-first cloud services will have a transformative effect in emerging markets, creating opportunities for innovation and economic growth.

This growing potential is not lost on even prominent cloud vendors from the established markets of North America, and Europe. 2023 Gartner Magic Quadrant CCaaS Leader, Genesys, reports of CX in APAC: “it’s now critical for organizations to have a connected voice and digital strategy that empowers customers across their journey.”

It’s particularly important when you consider that 63% of APAC consumers will pay more to buy from companies that support their values, and a third of respondents reported that they stopped engaging with a service after a negative interaction.

A challenge consistent to further CX innovation in the region is the issue of connecting technology and data to create end-to-end customer journeys. Without the capabilities of a well-integrated multichannel strategy, a customer that switches between a chatbot interaction and contact center loses their historical context leading to frustratingly longer resolution times of their cases. According to Genesys’ report, only 18% of APAC companies have achieved efficient communication within a multi-channel CX presence.

Languages Make Challenges. But Not Impossible Ones

To delegate tasks from human agents to automation, AI and LLMs need to be more proficient and handle more complex requests. Most popular LLMs have been based on the English language, posing challenges for deploying chatbots in non-English regions like Asia where there are dozens of languages with local complex colloquialisms. The sheer diversity of languages in these regions is no small feat for LLMs to master.

But within a decade, these limitations could disappear.

Singapore, both multilingual and flagship to technological innovation within Southeast Asia, is already initiating its own multimodal and localised LLMs to manage context-switching between languages in the region. In India, a travel service has utilized Microsoft’s Azure OpenAI Service-powered chatbot to accommodate customers in a country with 22 official languages and dozens of dialects. It caters to users who prefer voice interactions over smartphone apps for tasks like booking holidays. The travel service AI chatbots serve as intelligent assistants to human agents, enhancing their productivity and efficiency with more qualified leads.

The goal is to enable voice interactions in multiple languages, making the platform accessible to all Indians. This requires refining natural language models to grasp colloquial speech patterns typical in rural towns and villages. The travel service intends to offer a multimodal chatbot that integrates text, voice, video, and image interactions.

It’s still early in the game, and perfecting such a comprehensive system is a challenge yet to be mastered in the industry. But, considering the astonishing speed of AI progress since 2022 and the widespread adoption of ChatGPT and its competitors, the language barriers in emerging markets will be surmounted within 10 years or less.

Adios Tiger. Let’s Explore With LLama and AI

Generative AI, with its advanced capabilities like natural language processing, image generation, and predictive analytics, will enhance, improve, and deepen the smartphone experience.

The tiger in the tank today is the llama in your pocket. Unlike the tiger, who prefers solitude, the llama is a sure-footed and adaptable companion in your pocket who can take you to new and enhanced digital experiences. With AI in your pocket, you can go almost anywhere your smartphone suggests.

01/25/2024
Clouds of Change: AI’s Role in Revolutionizing Cloud CX in 2024
“AI is the new electricity. Electricity revolutionised all industries and changed our way of life, and AI is doing the same. It’s reaching into every industry and discipline…”

Andrew Ng, Founder of deeplearning.ai, Co-Chairman and Co-Founder of Coursera, and Adjunct Professor at Stanford University.

“AI is the most profound technology humanity is working on. More profound than fire, electricity, or anything that we have done in the past.”

Sudar Pichai, CEO of Alphabet, Google’s parent company.

2024 will be another good year for the cloud. According to a Gartner report, global spending on public cloud services is forecasted to grow by 20.4% to $678.8 billion. That’s up from $563.6 billion in 2023. 

Coincident with this growth, artificial intelligence will continue to escalate in demand, and it will continue to shape the cloud landscape, most especially in customer experience.

It’s more than cost, time, and efficiency. The cloud is now a catalyst for innovation, agility, and success across diverse industries – much of it spurred by AI.

Consider NICE, a CXaaS company specialising in improving customer experience. NICE has shifted its entire focus to AI-based tools.  Why? Because they see the obvious. AI is here. And at an astonishing rate, it will be even more capable and intelligent. NICE and dozens of other cloud solutions firms are augmenting their product portfolios with AI. Those companies that harness it and provide useful and meaningful solutions for customers will attain remarkable market dominance. 

With NICE, AI has augmented its tools for tasks like social listening, digital CRM analytics, intelligent call routing, understanding caller intent, and analytics in quality monitoring. These AI-driven tools help reduce average handle time, increase first-contact resolution and enhance overall customer satisfaction.

2024 will be a robust year for cloud companies. AI’s impact will continue to influence how companies and their customers interact. It all comes down to improving customer experience.

Companies in the Customer Experience as a Service (CXaaS) space are at the forefront of this AI integration.

These companies employ AI to transform contact centers, enhancing customer interactions through advanced AI tools. 

Integrating AI in these platforms is not just about automating tasks; it’s about creating a more intuitive, responsive, and personalised customer service experience.

The surge in AI-powered virtual assistants has provided a view of the future, revolutionising customer interaction and offering automated yet personalised responses. 

CXaaS companies that have already deployed advanced AI capabilities include:
- Zoom: Known for its video conferencing solutions, Zoom has been integrating AI features to enhance the user experience, which can be applied in contact centre scenarios.
- CallCabinet: known for its complete product offering to capture data, track voice calls, and ensure high security and compliance requirements, it utilises AI algorithms to analyse call data. This can include sentiment analysis, speech analytics, and keyword detection, which help understand customer interactions more deeply and improve the overall customer experience. 
-       Simplify360: Specializes in AI-driven customer support and marketing, using chatbots with Natural Language Processing (NLP) and machine learning for personalized, efficient service across multiple channels.
- NICE: Capabilities include full journey orchestration with advanced analytics, automation, machine learning and predictive algorithms for enhancing customer interactions and operational efficiency
- RingCentral: This platform provides AI-driven insights and analytics for contact centres, helping to improve customer interaction and operational efficiency.
Key developments and trends in AI features. Now till 2029.
1. Emotion detection and sentiment analysis will become highly accurate. AI will be able to detect subtle voice inflections and word choices to determine how customers truly feel with over 90 percent accuracy. This will enable companies to deliver ultra-personalized service and support.  
1. Predictive analytics will anticipate customer needs. By crunching massive datasets, AI will foresee problems and predict optimal solutions for customers, sometimes before the customer is even aware of the issue. This proactive care will wow customers.
1. Chatbots and virtual assistants will handle increasingly complex queries. With advanced natural language processing capabilities, virtual agents will resolve up to 70 percent of customer support tickets with no human involvement required. This will slash costs while also providing 24/7 assistance.
1. Full omnichannel integration will anticipate needs. AI will combine data from all channels (voice, chat, email, etc.) to discern customer intents, emotions, and expectations. It will then direct outreach to the ideal channel for each situation. This contextual care will build strong loyalty. 
1. Hyper-personalization will feel like working with a friend. With full customer insights, CX platforms will enable authentic personalized small talk, thoughtful gestures like discounts, and spot-on anticipatory problem-solving, making customers feel genuinely cared for. This human-like treatment will elicit intense customer loyalty.
By 2029, AI may handle nearly all routine inquiries, freeing humans to focus solely on complex issues. 

 

We’re Not in Kansas Anymore

In 2024, the landscape of cloud computing will continue to be reshaped by AI. Not just as a technological trend but as a customer-centric revolution, redefining how businesses interact with and serve their customers. Its ease of access, provided by leading CXaaS providers, will dramatically impact the intelligence of digital interactions.

Unlike the advent of electricity, AI is evolving much faster and becoming more powerful without our intervention. Who knows what the AI-powered customer experience will look like in 10 years? The focus today remains on balancing innovation with security and compliance.

The future looks incredible. AI is here to stay. Stay tuned.
12/26/2023
2024 Outlook in Cloud Communications: AI’s Impact in Three Areas
“It is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself.”

Charles Darwin, “Origin of the Species”.

As 2024 begins, all of us can take inspiration from Darwin’s observation. In this era of hyper-change, if we skilfully adapt, we have a good chance of thriving – and enjoying the process. After all, collectively, we face the most dramatic and consequential changes in human history (economic uncertainty, AI, war, political upheaval, and climate change). Darwin’s “changing environment” is clocking at a pace like no other. Despite everything, some industries and technologies, like cloud-native business communication platforms (or cloud communications platforms) for instance, steadily continue to adapt and evolve.

AI and ChatGPT appeared all of a sudden, or so it seemed. But in fact, AI has been evolving for decades out of the mass consumers’ spotlight. Then it arrived; a powerful technology like no other. Our first seemingly self-aware companion, offering up answers tirelessly in response to our endless queries. And beyond the hype cycles and buzz about AI, businesses worldwide will continue to focus its impact on three crucial areas of their digital communications deployments. Those are bolstering security, ensuring regulatory compliance, and delivering a seamless customer experience (CX).

These three areas are not just individual goals but intertwined elements that will collectively shape the future of cloud-native communications platforms.

Security: The Unwavering Pillar of Cloud Communications

In the era of rampant cyber threats, security remains a top priority for cloud communications providers and adopters. This coincides with the rise of remote and hybrid work, which have increased security threat levels significantly.

According to a report by Cybersecurity Ventures, cybercrime damages are projected to cost the world $9.5 trillion annually by 2025, making security risks an existential threat to businesses and organizations. And, with the widespread use of multi-channel communications and its high volume of video, voice, text, and whiteboard communications, digital cloud systems must address its relative challenges.

Cloud communications solutions will continue to incorporate advanced security tools like end-to-end encryption, multi-factor authentication, and AI-powered threat detection systems. AI – powered by machine learning (ML) and natural language processing – are becoming a critical part of advancing and improving security, and compliance platforms, according to an article in UC Today.

Below are some of the ways innovative platforms are elevating security:
- Data Encryption: Optimizing encryption methods with AI based on the type of data and its usage patterns, and automating the complex process without manual intervention or performance compromise.
- Data Loss Prevention (DLP): Utilize machine learning to predict potential data breach scenarios by analyzing patterns in data usage and access.
- User Access Controls: Advanced systems can use AI to analyze user behaviour to identify deviations from normal patterns, enhancing security by detecting potential threats or compromised accounts.
The UC Today article also mentions quantum cryptography as a valuable emerging technology, as it introduces the most sophisticated encryption to date, safeguarding data transmissions against advanced threats.

Compliance: Steering Through Regulatory Landscapes

Compliance with regulatory standards such as GDPR, HIPAA, and various national privacy laws is a significant challenge for cloud communications providers.

Consider the $1.8 billion (US) fines against Wall Street firms in 2022 for failure to abide by compliance regulations. The trend continued in September 2023 when the U.S. Security Exchange Commission (SEC) fined five broker-dealers $79 million for “failures to maintain and preserve electronic communications in violation of federal laws.”

As these regulations evolve, and they evolve quickly and constantly, cloud communications providers must adapt and even future-proof compliance to avoid penalties and maintain customer trust.

Here’s how cloud communications platforms are enhancing their compliance tools with AI, machine learning and automation:
- Archiving and Record-Keeping: Classify and index records with AI, and using the same system for quick retrieval based on AI assistants.
- Compliance with Regulations: Training AI systems to update to the latest regulatory changes and automatically adjust as needed. Teams can also utilize machine learning for ongoing risk assessments and ensure the compliance standards of data handling.
- Audit Trails: Utilize machine learning to sift through vast amounts of log data to identify patterns, anomalies and other signs of potential issues.
RingCentral, Cisco Webex, Zoom, NICE, Microsoft Teams, CallCabinet and other firms are a few of the many companies that are integrating advanced compliance tools into their platforms.

Seamless CX: The Holy Grail

The quality of customer experience is a measure of how well a company embraces agile processes. One that deftly adapts to unexpected events – such as the pandemic – will thrive. Consider the pandemic. A Deloitte report indicates that with customers in the pandemic lockdown, omnichannel communications suddenly became the de facto method to enhance customer loyalty, even as restrictions have lifted. Customers demanded it. They want brands to deliver a consistent, positive experience at every touchpoint. Technology solutions need to align with customer values and anticipate their needs.

AI tools have already begun to improve CX on cloud communications platforms. For example, with AI, speech analytics assesses emotional intent, leading to more personalized and effective service. Leading technology and analytics vendors that provide AI solutions to help companies improve and transform customer engagement and experience include:
- Salesforce – Salesforce offers Einstein AI capabilities across its platforms.
- Adobe – The Adobe Experience Cloud integrates Adobe Sensei AI engines for capabilities like automated customer journey mapping, and predictive analytics.
- SAP – SAP C/4HANA is SAP’s suite of customer experience solutions leveraging machine learning.
- Microsoft – Microsoft Dynamics 365 AI uncovers customer insights that help sales, marketing and service teams better align with customer needs.
- Verint – Verint offers AI-infused omnichannel customer engagement including areas like journey orchestration, contact centre intelligence, automated quality management and advanced text and speech analytics.
The future looks bright with AI. It can enhance and transform CX by understanding language, analyzing unstructured data like customer feedback, recommending relevant offers or information, and even conducting increasingly natural conversations.

The push for advanced personalization experiences opens up new disciplines in data and lifecycle management. Within a decade, AI bots could be so powerful that they will help guide a customer through even considerably nuanced dilemmas that require unique solutions.

Conclusion: Bit by Bit, Things Get Better. Not in Leaps.

Cloud communications platforms and solutions will continue improving incrementally,  driven by customer demand, technological innovation, and an ever-demanding customer.

As we navigate through 2024, the cloud communications landscape is being reshaped by the collective forces of security, compliance, and seamless CX and enhanced by careful and practical AI innovation.

These are not standalone goals but interdependent objectives that must be pursued together for businesses to thrive in the dynamic cloud communications market. As these technologies evolve, they hold the promise of transforming not just communication strategies but the very fabric of customer and business interactions.
01/11/2024
Generative AI Can Accelerate Learning Curves

The integration of artificial intelligence (AI) tools stands as a transformative catalyst, revolutionizing how employees engage with their work and facilitating accelerated learning curves. But the introduction of AI into the workforce requires a thoughtful and staggered approach to ensure a seamless transition and optimal results. How do these tools actually impact work processes?

A study from National Bureau of Economic Research, USA examined the effects of generative AI-based conversational assistant using data from 5,179 customer support agents. In their findings were a 13.8% increase in issues resolved per hour by customer support agents, including a 35% improvement for low-skilled and novice workers. The same metrics also showed minimal impact on experienced and highly-skilled workers.

Contact Centres Already See Benefits of AI

According to a recent study by the National Bureau of Economic Research, conversational AI tools are providing a crucial boost for contact centre agents, especially less experienced ones.

The study analysed data from a group of 5,179 contact centre agents.

The results are impressive: a 35% increase in issues resolved per hour for novice agents and a nearly 14% jump overall. More experienced agents saw a less pronounced, albeit positive, effect. Meanwhile, customer satisfaction is rising thanks to more positive interactions. It’s a classic win-win.

Accelerating Learning Curves

What’s more intriguing is the journey of the least experienced agents, who, with AI, witnessed the most significant uptick in their resolution rates.

Generative AI catalysed this by emulating the best practices of highly-skilled members, effectively democratising knowledge and expertise when it provides further recommendations to the rest of the team. This evolution wasn’t just beneficial for productivity; it also started a positive feedback loop that could propel continuous improvement.

Improving Overall Sentiments

Yet, an increase in productivity metrics isn’t the sole barometer of success. The true gauge is the human experience, both for the customer and the employee.

Agents on the frontline face a lot of customers express their frustrations. Thanks to AI-fortified recommendations, contact agents are much more likely to double down on more empathetic responses, significantly improving their social awareness. This, in turn, led to happier customers and a reduction in the frequency of escalated calls.

Tread Carefully, But Don’t Wait

The advent of generative AI tools is a significant milestone in workforce management and the professional growth. These tools could eventually offer substantial benefits – they improve productivity, improve the customer experience, and foster a culture of relentless learning and development in the organisation.

Organizations can fully leverage the potential of AI, unlocking new dimensions of employee performance and setting the stage for sustained success in the ever-evolving business landscape.

But AI tools not only lead to quick gains in productivity. Managers must provide ongoing training and support just to keep up with the speed of improvement of the AI systems themselves, to avoid locking themselves out of AI’s potential.

About us

ULAP Networks has assisted Fortune 500 companies with our coverage in over 113 countries, specializing in cloud solutions in complex jurisdictions from APAC, MENA to LATAM.

We’re partnered with leading collaboration vendors, including Zoom, NICE, Simplify360, MS Teams and CallCabinet. Work with their leading AI capabilities to bring the best of global innovation to hard to reach markets.

Reach out to us to see how we can help you with your global requirements at info@ulap.net

11/06/2023
AI: Appearing In An Emerging Market Near You

In Mary Shelley’s 1818 literary masterpiece, “Frankenstein, A Modern Prometheus,” the protagonist, Dr. Victor Frankenstein, creates a sapient creature in his laboratory that crudely resembles a human being. When Frankenstein first sees his living creature, he is horrified. Unable to accept his attempt at creating a misshapen human being, he flees, leaving the intelligent creature to fend on its own.

But unlike Victor Frankenstein, the creators of artificial intelligence and ChatGPT are racing headlong toward more powerful, capable, and useful forms of AI. Rather than a misbegotten creation, they see it as a bona fide, if not logical consequence of evolutionary technology.

According to them, AGI offers the potential to remake nearly every aspect of human endeavour.

Sam Altman, the CEO of OpenAI, the company that created ChatGPT, believes that we all need time to reconcile the notion “that we may soon share Earth with a powerful new intelligence, before it remakes everything from work to human relationships. ChatGPT was a way of serving notice.” (Atlantic magazine article, September 2023.)

But this doomsday sentiment isn’t necessarily shared across the board, especially outside the Western hemisphere.

Global Attitudes About AI Vary Considerably

A 2019 study by The Oxford Commission On AI & Good Governance revealed that the perception of AI varies widely from region to region. The survey used data from a sample of 154,195 respondents in 142 countries that analysed basic indicators of public perceptions about the potential harms and opportunities of involving AI in personal affairs and public life.

Internationally, sentiments about technology are ambivalent at best. North Americans and people from Western Europe see the development of AI and robotics as more likely harmful than beneficial, whereas South and East Asia are much more likely to see these developments as beneficial.

View image and read the full study here.

Technology-Enabled Disruptions (TEDs) in Southeast Asia

With their exploding populations, widespread reliance on smartphone apps to fuel business transactions and a burgeoning young demographic of tech-savvy users, emerging markets offer nearly limitless untapped opportunities for growth.

A Bain & Company research report indicates that technology-enabled disruptions (TEDs) like AI are the greatest force of progress in most developing countries. Businesses in these markets are motivated to find ways to “leapfrog” developed economies by jumping straight to the latest innovations.

To Deliver State-of-the-Art AI, You Need State-of-The-Art Connections – And Partnerships

The study suggests that global enterprises that want to integrate their AI solutions will find more acceptance in APAC, the Middle East and Africa compared to the attitudes of people in the Western Hemisphere.

But the map isn’t the terrain. Setting up operations in the complex emerging markets is not as simple as the Western hemisphere. Cultural nuances, language barriers, government regulations and compliance rules create unforeseen hurdles for enterprises accustomed to the familiar landscape of North America and the EU.

When it comes to deploying AI in an emerging market, you need experienced partners who know the terrain.

About us

ULAP Networks has assisted Fortune 500 companies with our coverage in over 113 countries, specializing in cloud solutions in complex jurisdictions from APAC, MENA to LATAM.

We’re partnered with leading collaboration vendors, including Zoom, NICE, Simplify360, MS Teams and CallCabinet. Work with their leading AI capabilities to bring the best of global innovation to hard to reach markets.

Reach out to us to see how we can help you with your global requirements at info@ulap.net

10/30/2023