The gap between what customers expect and what many brands deliver has never been wider. Businesses can no longer rely on generic, one‑way communication, as rising customer expectations mean consumers brands to anticipate their needs and engage in ways that feel timely, relevant, and personal.

For CX, marketing and product teams, this has pushed personalisation from a ‘nice to have’ to the core of competitive strategy.

This shift toward “intelligent engagement” is being driven by advances in Artificial Intelligence (AI), particularly agentic AI, which enables systems to not only analyse data but also autonomously decide, act, and optimise interactions in real-time across channels such as WhatsApp, SMS, and email.

Agentic AI goes beyond simple rules‑based automation by making its own real‑time decisions based on customer context, intent and behaviour. It constantly interprets signals such as browsing activity and sentiment to choose the most effective message, timing, channel and tone.

Unlike linear chatbot scripts, these systems are built for end‑to‑end resolution, cutting unnecessary handovers, lowering cost‑to‑serve and delivering more consistent experiences. Instead of blasting a generic retention SMS, an agentic system can predict churn risk, pick the best channel for that individual and trigger a tailored offer at the moment it is most likely to change their decision.

Complex engagement landscape

South Africa presents a uniquely complex engagement landscape that is digitally active yet highly fragmented across language, device capabilities, income levels, and connectivity. For example, a retailer may need to support a rural feature‑phone user on SMS and a Johannesburg professional on rich WhatsApp journeys. Customers benchmark interactions against the best digital experiences they have had, whether from a global platform or a local fintech app.

As a result, generic, batch-style communication increasingly feels irrelevant and intrusive, with consumers responding far better to appropriate, contextual digital interactions.

For businesses, that translates directly into higher churn, higher service costs and weaker campaign ROI. Customers expect real‑time updates, channel flexibility, minimal friction and human support when needed.

This is where agentic AI becomes essential, enabling businesses to navigate diversity at scale, delivering rich, personalised WhatsApp engagement to digitally fluent urban customers, while offering simple, concise SMS communication to those with limited bandwidth. In this environment, relevance is no longer a differentiator; it has become the baseline expectation.

Early intervention

Historically, customer communication has been reactive, responding to complaints and missed payments, which increases service costs.

Agentic AI changes this by identifying intent patterns before customers take action. It monitors micro-behaviours to predict outcomes.

Delayed payments can signal an increased risk of default, giving collections teams a chance to intervene before it shows up in impairment metrics or write‑offs.

Reduced product usage often indicates rising churn risk, allowing teams to intervene early to protect revenue and lifetime value rather than losing customers silently. Repeated FAQ browsing is a strong signal of confusion that, if left unresolved, can drive up avoidable call volumes and lengthen average handling time in the contact centre.

Instead of waiting for a cancellation request or service complaint, the system can intervene early with context‑appropriate support. Proactive engagement boosts loyalty, reduces avoidable inbound demand, and frees up operational capacity for higher‑value work.

However, the strength of agentic AI is entirely dependent on the quality of the data environment that powers it. Poor data leads to poor decisions, and in customer engagement, that quickly becomes a reputational risk.

In South Africa, compliance with the Protection of Personal Information Act (POPIA) makes responsible, transparent data practices the foundation of any AI‑driven personalisation strategy.

Organisations need integrated customer profiles, real‑time data streams, a single view of interactions across all channels, and governance frameworks that enforce strict privacy and access controls so that every AI‑driven decision remains accurate, compliant and worthy of customer trust.

A common misconception is that AI reduces humanity. In reality, agentic AI improves empathy by handling repetitive tasks, allowing humans to focus on what truly matters. For frustrated customers, AI provides context-aware solutions and can escalate issues to a human agent with complete history, eliminating the need for customers to repeat themselves.

Sentiment analysis and tone

Well-designed systems use sentiment analysis, flexible tone and consistent messaging across channels like WhatsApp, SMS, and email. Key safeguards include detecting frustration to trigger escalation, disclosing AI use, ensuring seamless transitions to human agents, and natural-language design. Empathy is achieved through smart automation, not through its absence.

This is where AI‑native orchestration platforms bring personalisation to life at scale by coordinating AI agents, customer data and communication channels in real-time. Rather than relying on disconnected tools, they introduce a unified orchestration layer that connects these elements into a single, intelligent decision‑making environment to deliver truly contextual engagement.

For example, in a retail scenario, a customer browsing products without completing a purchase could trigger multiple coordinated actions. One AI agent identifies high purchase intent, another determines the optimal channel based on past behaviour, while a third generates a personalised offer or reminder.

The orchestration layer ensures this interaction happens at the right moment, on the right channel, with messaging tailored to the individual, resulting in a highly relevant, joined-up experience.

This coordinated, real-time decision-making is what elevates personalisation from simple segmentation to true one-to-one engagement.

With platforms like Infobip’s AgentOS, businesses can deploy autonomous AI agents capable of managing entire customer interactions end‑to‑end across channels, without requiring human intervention at every step.

Importantly, these platforms operate as a network of specialised agents, each with a defined role, orchestrated to work together behind the scenes. While the customer experiences a single, cohesive interaction, multiple agents collaborate in real-time, sharing context and continuously optimising the journey.

By unifying previously siloed systems, the orchestration approach enables businesses to move from reactive, campaign-based communication to proactive, goal-driven engagement that adapts dynamically to each customer’s behaviour and intent.

Over the next few years, the best South African brands will make personalisation feel almost invisible: customers will simply get the right message, in the right tone, on the right channel, at the right time.

To get there, CX and marketing leaders need to focus less on sending more campaigns and more on building the data, orchestration and governance foundations for agentic AI. Those who treat AI as a strategic decision layer for customer engagement, rather than just another tool, will set the benchmark for loyalty and growth in their industries.

Yet many businesses are still struggling to meet these expectations. After two decades of rapid communication evolution, from SMS to omnichannel and conversational AI, customer expectations have surged far beyond what traditional automation can deliver.

Consumers now expect interactions that are instant, personal, and deeply contextual, yet most legacy systems are still stuck in a slower, simpler era.

Agentic Artificial Intelligence (AI) is the breakthrough that closes this gap, enabling technology to reason, decide, and act with autonomy while preserving brand intent, regulatory compliance, and human‑level empathy at scale.

At Infobip, we see this shift first‑hand as South African brands start using AI‑driven orchestration to connect channels, data and decision‑making in real time.

In simple terms, agentic AI does not just automate tasks; it understands context, determines the best next step, and then acts on it. These agents operate on a central orchestration layer, essentially an AI ‘control centre’, that connects channels, data, and business rules, giving them a full view of the customer before deciding what to do next.

Instead of each bot or system acting alone, the orchestration layer routes tasks to the right AI agent or human, coordinates their actions, and ensures every step aligns with brand, compliance, and business goals.

Traditional chatbots and rule‑based automation can only follow predefined flows; for instance, “if X happens, do Y”.

Agentic AI moves beyond that limitation by making informed decisions rather than merely executing instructions.

Delivering a better customer experience

In daily life, most people will not even think of this as “AI” but simply feel that the Customer Experience (CX) is getting better. For example, banks will flag suspicious activity faster and more helpfully, mobile providers will warn about outages before complaints arise, and online stores will stop sending irrelevant offers and start anticipating real needs.

The result is less frustration, fewer repeated steps and customer communication that finally feels timely, relevant, and human.

Concerns that this kind of AI will take over human jobs are largely unfounded. In reality, agentic AI is not about replacing people but about automating repetitive, low‑value tasks so humans can focus on conversations that require empathy, judgement, and real problem‑solving.

In the realm of CX, AI manages routine tasks and workflows, allowing people to focus on escalations and building trust.

Organisations are already investing equally in both AI and human roles. In South Africa, an effective CX model combines AI for speed and scale with human empathy and accountability. AI should enhance, not replace, human capability.

Beyond generic messaging

The real opportunity that agentic AI presents is moving beyond generic, poorly timed communication. Many organisations still send messages without considering customer context, leading to noise rather than value. Agentic AI changes this by assessing context first, who the customer is, what has just happened, and what outcome you’re aiming for, and then deciding whether a message should be sent at all.

For example, instead of blasting the same data‑bundle promotion to every prepaid customer, an AI agent can hold back offers for those who have just recharged and instead prioritise a helpful usage alert or roaming reminder when it matters most.

This is where forward-thinking, AI-driven CX strategies play a defining role. Rather than layering AI onto fragmented systems, these businesses introduce a unified orchestration layer that brings together AI agents, customer data, communication channels, and real-time intent into a single, intelligent decision-making environment.

For example, in a banking scenario, a customer making an unusual transaction could trigger multiple coordinated actions through the orchestration layer.

One AI agent detects the anomaly, another verifies the customer’s identity via their preferred channel, while a third prepares a contextual alert or temporarily pauses the transaction.

Instead of disjointed alerts or delays, the customer experiences one real-time interaction that resolves the issue quickly and securely.

Behind the scenes, an orchestration platform, like the AI‑native layers emerging from providers such as Infobip, sequences these agents, shares context between them and keeps the entire interaction feeling like one seamless conversation for the customer.

This ability to coordinate specialised agents in real time is what differentiates agentic AI from traditional automation.

Agentic AI enables businesses to move away from disconnected campaigns and reactive workflows toward autonomous, goal‑driven ‘next best action’ decisions that adapt continuously to customer behaviour and intent.

The latest AI platforms enable businesses to deploy autonomous AI agents capable of managing entire customer interactions end-to-end, across every channel, without requiring human intervention at each step.

Importantly, they do not run on a single AI model, but on a network of specialised agents, each with a defined role, orchestrated to work together seamlessly.

While the customer experiences a single conversation, multiple agents operate behind the scenes, sharing context and progressing the interaction in real time.

In the next 18 to 24 months, South Africans are likely to experience more proactive and conversational customer service across the brands they interact with most.

Expect smarter fraud alerts, clearer notifications, better delivery updates, and stronger self-service options. Organisations will move from blanket campaigns to intelligent next best action engagement using AI-first CX platforms that integrate data, channels, and AI agents.

For customers, the shift will be simple: service will feel less like navigating a system and more like dealing with a brand that already understands the situation. As AI matures, the organisations that stand out will be those focused on real outcomes and trust, not hype.

In a mobile‑first market like South Africa, these improvements will become visible quickly. For South African business leaders, the real differentiator will be how quickly they can connect fragmented data, channels, and AI projects into one coherent, orchestrated customer experience strategy.

This transformation in marketing strategy will shift traditional channel-based approaches and usher in a new era of personalized, autonomous engagement. These AI agents will act as persistent digital concierges, seamlessly spanning marketing, sales and support to create hyperpersonalized experiences.

“This marks the end of channel-based marketing as we know it,” said Emily Weiss, Senior Principal Researcher in the Gartner Marketing practice. “Marketers must prepare by putting strong data governance in place, tracking customer journey changes weekly, and integrating agentic systems into martech stacks to enable secure, ethical personalization at scale.”

“This marks the end of channel-based marketing as we know it. ” — Emily Weiss, Senior Principal Researcher

By 2027, Brands Will Allocate 50% of Influencer Marketing Budgets to Content and Creator Authenticity Initiatives

Initiatives will include identity verification, content provenance checks, and anti-deepfake measures, with the goal of optimizing engagement and monetization in AI search environments.

This shift is driven by the surge in AI-generated content and the growing visibility of social-originated content in search results.

A Gartner Consumer Community survey of 335 U.S. consumers, conducted in October and November 2025, revealed 78% of consumers say explicit labeling of AI-generated content is “very important” or “the most important factor” in maintaining trust.

“Trust is now the most valuable asset in influencer marketing,” said Weiss. “Brands should adopt clear labeling conventions, invest in third-party verification tools, and monitor creator engagement quality to ensure transparency and compliance as AI-generated content becomes mainstream.”

“These predictions signal a fundamental redefinition of marketing, from channel-based strategies to AI-driven personalization, and from engagement metrics to trust metrics,” added Weiss. “The brands that act now will lead the next era of marketing. Those who delay risk falling behind as technology and consumer expectations evolve at unprecedented speed.”

This is largely driven by rapid advancements in Artificial Intelligence (AI) and cloud-based platforms, resulting in the communication landscape undergoing a profound transformation.

When considering the strategic priorities for businesses in South Africa, it is essential to focus on addressing core pain points that hinder growth and sustainability.

These typically fall into several key categories, namely compliance, competition, security, reputation management and operational efficiency – revenue increase or cost decrease.

AI plays a pivotal role in tackling these challenges. Take reputation management, for example. Today’s customers expect their issues to be resolved promptly, regardless of the time of day.

AI-powered solutions such as virtual assistants and chatbots enable businesses to deliver 24/7 customer support, ensuring fast, effective and consistent responses.

Beyond customer service, AI automates routine tasks, freeing human agents to focus on more complex, high-value issues. This not only enhances productivity but also improves overall operational efficiency.

Moreover, AI’s ability to analyse customer data in real time allows businesses to personalise interactions at scale.

Tailored communication fosters stronger customer relationships, boosts satisfaction and loyalty, and can even drive customer acquisition.

For example, a leading South African courier aggregator, Bob Group recently evolved from using simple SMS notifications to adopting a full omnichannel communication strategy, integrating Rich Communication Services (RCS), WhatsApp and email.

This shift has improved delivery transparency, strengthened trust with customers, and cut communication costs by as much as 25%, demonstrating how advanced messaging can directly enhance operational performance.

Role of predictive analytics

Predictive analytics has become another cornerstone of modern communication platforms, ultimately benefiting both the customers and businesses.

These tools help organisations understand who their customers are and what they will need next. By anticipating behaviour, companies can engage more proactively and respond faster.

This approach also sharpens customer segmentation, enabling more relevant content and highly personalised experiences.

As a result, conversions improve, trust deepens, and customers feel seen and aligned with the brand’s values. The outcome is stronger loyalty and increased revenue through deeper, data-driven relationships.

As the communications landscape continues to evolve, cloud-based platforms are increasingly helping businesses reduce infrastructure costs, accelerate time to market and simplify compliance, as providers handle much of the regulatory burden.

Many organisations already have access to these tools through platforms like Microsoft Azure, Adobe Commerce, Oracle, Zoho, and Salesforce, where communication features are often natively integrated.

These platforms offer global reach, scalability, and multi-channel flexibility, all through a single interface.

This enables seamless customer engagement across diverse regions and channels, including emerging ones like TikTok.

As digital transformation accelerates, South African enterprises are rapidly adopting these solutions to stay competitive, agile and connected in a global market.

Eagerness to overcome challenges

However, despite companies’ willingness to invest in this type of technology, key obstacles remain. At least, we are seeing a strong eagerness among businesses to overcome these digital challenges.

Infrastructure limitations persist, with many businesses needing upgrades to reach comfortable operational levels.

There is also a clear skills gap, with many teams struggling to fully leverage available technologies for maximum efficiency and speed.

Data privacy concerns and a mild resistance to change still exist, though most companies recognise the need to invest in transformation.

To navigate these hurdles, businesses must partner with cloud communication providers that offer flexibility, deep expertise and hands-on guidance.

Smooth onboarding and strategic support can make all the difference, helping companies scale communication, engage customers effectively and accelerate their digital journey.

It is fascinating to witness the pace of transformation across Africa. In many ways, the continent has leapfrogged traditional development stages, moving from print media straight into the internet and smartphone era, often bypassing the TV-dominated phase seen elsewhere.

This rapid shift is reshaping how Africa engages with technology and the world.

One of the most striking trends is the evolution of chat apps into super apps, increasingly powered by agentic AI.

These intelligent systems can now handle tasks autonomously and soon will require no coding skills and even make purchases on behalf of users. We are heading towards AI that is intuitive, embedded and action oriented.

Africa’s unique trajectory, marked by adaptability and digital openness, positions it to embrace this next wave of innovation faster than many expect. We are not just witnessing change; we are watching the future unfold in real time.

These new capabilities are designed to help businesses enhance productivity, automate routine tasks, and focus on higher-value work. The features are available immediately at no additional cost across Zoho’s suite of business applications.

The announcement builds on Zoho’s broader AI strategy, which includes Zia Hubs, a system that enables AI access to unstructured company data, and Zia LLM, Zoho’s proprietary large language model developed specifically for business use.

Alongside Zia, Zoho’s long-standing AI assistant, these innovations form the foundation of the company’s agentic AI approach, helping businesses gain deeper insights and efficiency from their data across Zoho’s platform of more than 55 integrated applications.

“Businesses are increasingly eager to leverage AI but often face obstacles such as high implementation costs, data readiness challenges, and fragmented systems,” said Kehinde Ogundare, Country Head, Zoho Nigeria. “Zoho’s unified, homegrown technology stack eliminates these barriers by allowing advanced AI features to be deployed automatically and at no extra cost. Our customers don’t need to invest in third-party integrations or additional tools, the technology simply arrives and works. This approach makes AI adoption practical, affordable, and impactful for businesses across the continent.”

The new agentic features build on Zoho’s existing AI ecosystem, which already includes Zia Agents, Zia Agent Studio, and Agent Marketplace.

These intelligent tools allow users to perform complex, cross-functional actions such as creating contracts from multiple files, checking team availability before scheduling meetings, or automatically converting sales queries from Zoho Mail into CRM leads without manual effort.

Within Zoho Workplace, the company’s collaboration suite, AI upgrades have been introduced across Mail, Cliq, Sheet, and Tables.

The enhanced Ask Zia assistant can now process multi-step commands spanning multiple applications, while Zoho Tables introduces AI Base Creation to help users instantly generate complete data structures from a short prompt. Additional AI-powered features such as Keyword Extraction, Sentiment Analysis, and Language Detection simplify data management and analysis.

In the customer experience category, Zoho Desk, used by over 100,000 businesses globally, now includes pre-built Zia Agents like the Resolution Expert, which records and learns from support ticket resolutions to improve future service.

Meanwhile, Zoho Sign, a secure digital signature platform, now offers Agreement Intelligence, enabling users to draft and query contracts using Ask Zia without relying on third-party tools.

In human resources, Zoho Recruit introduces new agentic AI features that improve candidate matching and assessment creation.

Zia’s Candidate and Job Match features analyse CVs and job descriptions to ensure better hiring outcomes, while AI-Assisted Assessment Generation automatically creates complete tests tailored to each role, saving time and ensuring fair, efficient evaluations.

The data is compelling – according to the World Bank’s recent Global Economic Prospects report, rising global trade and policy uncertainty could weaken growth by 2.3% in 2025, impacting both major economies and emerging markets.

In the face of these headwinds, many businesses are ramping up plans to implement AI, driving new levels of productivity and cost savings. In a March 2025 survey by McKinsey, 78% of respondents reported their organisation already uses AI in at least one business function, up from 72% in early 2024 and 55% a year earlier.

Put simply, AI is the most transformative technology of our lifetime. Its latest iteration, agentic AI—always-on intelligent agents that can learn, reason, and execute tasks independently – will reshape how companies operate, compete, and perform work.

While this new era holds great promise, the journey to becoming an agentic enterprise can feel daunting.

Aligning your business with a tangible agentic AI strategy, readying your technology infrastructure, and understanding the metrics that define success are essential pillars to unlocking the agentic AI opportunity and realising the return on investment (ROI) this moment demands.

Realise trapped value with automation and augmentation

Nearly half of desk workers report spending time on repetitive, low-value, or unrelated tasks to the jobs they were hired to do. Across many industries, operational inefficiencies and unrecognised opportunities are buried deep within data, processes, and human workflows, affecting everything from employee engagement to the bottom line.

The first step to reigniting productivity growth is identifying what’s holding organisations back and finding solutions to these challenges, known as trapped value analysis.

Start by identifying where high-value, underperforming workflows exist. For example, are there manual tasks slowing teams down? Can they be automated? Which customer interactions can be more personalised?

Augmenting human capacity with a digital workforce is key to realising trapped value, enabling smarter, faster decision-making grounded in trusted data and improving personalised customer interactions at scale.

Imagine a marketer at a young company suddenly supported by a team of AI agents to strategise, plan, and deliver best-in-class campaigns. Or a sales rep with instant access to real-time customer data analysis and predictions of which leads are most likely to convert.

Deploying AI agents isn’t about replacing people; it’s about empowering employees to focus on what they do best – strategic thinking, innovating, and building relationships with customers.

Overcoming productivity pain points with a dynamic IT infrastructure powered by digital labour allows businesses to reprioritise their newly available human capacity to where it can have the greatest impact. This is key to realising new revenue streams or business models and is fundamental to unlocking ROI.

Set critical metrics to measure ROI

The advantages of digital labour for optimising operations are clear. However, according to Gartner, over 40% of agentic AI projects will be cancelled by the end of 2027 due to reasons such as unclear business value.

To demonstrate real ROI, organisations need to focus on enterprise productivity, driving business value through quality, cost, speed, and scale.

For customer service teams, measuring ROI can involve assessing customer satisfaction, such as improving experiences with 24/7 support across channels where AI agents triage requests and provide step-by-step instructions using natural responses.

When Salesforce added Agentforce to its Help site in October 2024, the goal was to set a world-class standard for agentic service.

With an AI agent answering customer service questions in natural language, using unified data to provide fast, 24/7 support, our human engineers can focus on complex issues.

To date, Agentforce has handled over 1 million support requests and is projected to save $50 million annually by the end of this fiscal year.

For sales teams, ROI can be seen in faster response times and increased lead qualification. Agentic AI enables scaling with autonomous outreach to answer product questions, handle objections, and book meetings, interacting with customers across relevant channels with personalised responses.

All of these efficiency gains free up time for humans to focus on improving and driving growth in other areas of the business.

Using metrics – from deflection rates to customer satisfaction scores and operational savings — enables organisations to assess the performance of their AI agent initiatives and calculate ROI effectively.

Access long-term value

By focusing on these metrics, African businesses can effectively assess the performance of their AI initiatives and calculate ROI, moving from a position of uncertainty to one of strategic growth.

In essence, the agentic AI era is not about a single, abrupt change but a fundamental shift in how people and technology work together.

By embracing a phased, strategic approach, African businesses can navigate this transition successfully, accelerate productivity, and ultimately unlock significant long-term value. This is the new partnership that will enable businesses to thrive amid today’s challenges.

The company announced the research preview on Tuesday, saying that 1,000 subscribers on its Max plan, priced between $100 and $200 per month, will be the first to gain access. A waitlist is also open for others.

Through a Chrome extension, selected users can summon Claude in a sidebar window that stays in sync with their browsing activity. The agent can summarise pages, interact with content, and, when granted permission, even perform tasks inside the browser.

The browser is quickly becoming a focal point in the competition among AI developers. Perplexity recently released its own AI-powered browser, Comet, while Google has integrated Gemini into Chrome, and OpenAI is reportedly working on its own AI-driven browser. 

These reveal browsing has gone beyond search to handing over routine actions to automated systems.

For Anthropic, the launch is also about safety. The company admitted that browser-based agents carry real risks, including prompt injection attacks, where hidden instructions in a web page could trick the AI into carrying out harmful commands. 

Internal testing showed such attacks succeeded 23.6% of the time before mitigations. Anthropic now claims it has cut that rate to 11.2% by introducing several layers of defence.

Among them are site-level restrictions, default blocks on financial services, adult, and pirated content, and mandatory confirmations for sensitive actions such as publishing, payments, or sharing personal data. “Claude will always ask for explicit permission before taking high-risk actions,” Anthropic said in its blog post.

Google’s Chrome browser, which tops global market share, is at the centre of an antitrust case that could force the company to sell the product. If that happens, ownership of Chrome could reshape the competitive landscape. 

Perplexity has already placed an unsolicited $34.5 billion bid for Chrome, while OpenAI’s Sam Altman has said his company would also be willing to buy it.

This isn’t Anthropic’s first attempt to give its models control over a user’s screen. Last year, the firm tested a desktop-based agent that could operate a PC, but the early version was criticised for being sluggish and inconsistent. 

Since then, the “agentic AI” has advanced considerably, with systems like Comet and ChatGPT’s Agent showing more reliability in handling everyday digital tasks, even if they continue to struggle with more complex scenarios.

Analysts see Anthropic’s decision to limit Claude for Chrome to premium subscribers as a sign of where the industry is heading: towards new business models built around productivity tools, enterprise automation, and personalised web experiences. 

Gartner has projected that the AI security market could reach $15 billion by 2027, driven largely by demand for safe, agentic systems.

According to The Inner Circle Guide to Agentic AI by ContactBabel, fewer than half of contact centres see headcount reduction as a primary objective.

That suggests a deliberate shift in narrative: the focus isn’t overtly on removing people, but on removing friction.

Why Agentic AI Is Different – And Why It Matters

Agentic AI isn’t just another virtual assistant. It doesn’t wait for instructions. It interprets, reasons, takes action, and learns from outcomes. And most importantly, it operates with autonomy to complete end-to-end tasks – not just offer next-best suggestions.

This is a step beyond human augmentation. It’s about replicating the behaviour of highly capable agents in a scalable, intelligent way (you’ll be able to hear more about this at our upcoming AI Community Days in London and Manchester). While the industry often defaults to language about “agent assistance”, Agentic AI systems are increasingly capable of completing tasks without human oversight, a key differentiator from traditional bot or assistive models.

In the report, ContactBabel highlights the challenge that human agents face when switching between several systems to serve a single customer.

This creates complexity, delays, and inconsistency. For an AI agent, those same tasks can be streamlined and orchestrated instantly. There’s no fatigue, no deviation, and no lag in processing.

The Real Drivers of Agentic AI Adoption

The top reasons cited by organisations for deploying Agentic AI were:

• Greater accuracy in responses
• Improved understanding of customer needs
• Faster resolution times
• More effective self-service journeys

These aren’t abstract benefits. They directly correlate to measurable CX and operational improvements: higher customer satisfaction, better first contact resolution, reduced handling time, and improved digital containment rates.

And while only 19% of organisations stated that reducing headcount was of “major importance”, we should not ignore the implicit efficiency gains that flow from automation at scale.

Redefining Metrics for the AI-Enabled Contact Centre

This raises a critical point. If your only success metric for AI is headcount reduction, you’re thinking too small. But equally, if your business case ignores the indirect impact on human workload and resourcing, it’s incomplete.

The future metrics will be broader and more balanced:

• Task completion rates (human and agentic)
• Agent effort vs. AI resolution
• End-to-end containment
• Cost-to-serve per interaction
• Time to value of AI investments
• Your Agentic Agent FTE requirements

These are the measures that matter. Because they reflect real transformation, not just tactical optimisation.

What It Means for Leaders in CX

For contact centre and CX leaders, Agentic AI is not about a binary choice between human or machine. It’s about designing an operating model where AI can handle what it’s best at, and people are deployed where they add the most value: empathy, judgement, and complex exception handling.

At Sabio, we see Agentic AI as an opportunity to move beyond the legacy debate of ‘AI vs people’. The conversation now must shift to:

• Where should automation be fully owned by Agentic AI?
• What work should be left to humans?
• How do we transition intelligently from today’s hybrid models?

This is not just about improving performance. It’s about reimagining service design for an AI-first era.

Final Thought: Agentic AI Is the Catalyst, Not the Threat

Agentic AI won’t replace the contact centre. But it will redefine how it works – who it employs, what skills are needed, and what customers come to expect. That’s not something to fear. It’s something to lead.

The organisations who embrace Agentic AI today are not just modernising their tech stack. They’re preparing for a future where customer service is faster, smarter, and fundamentally different.

Using Zoho Zia Hubs, organisations can now present any type of business content to Zoho’s powerful capabilities and services—including agentic AI, comprehensive analysis, and accurate, unified search—regardless of file format or structure.

“According to IDC, 80 percent of business data is unstructured,” said Kehinde Ogundare, Country Head, Zoho Nigeria. “Most unstructured data is text-based, meaning pertinent information lives within email conversations, social media posts, word processor documents, or audio and video transcripts. With Zia Hubs built into the full product suite, Zoho can provide customers with a deeper integration than any comparable software platform and nearly limitless potential uses for their data.”

Deeper Intelligence Accessing More Customer Data

Launching as part of Zoho WorkDrive, Zia Hubs brings content intelligence to the company’s unified content management and collaboration platform.

Designed with a high level of user control over what content AI is allowed to access, Zia Hubs enables users to organise project or task-specific content into dedicated hubs within WorkDrive.

Each hub serves as a focused space where Zia, Zoho’s flagship AI, can understand and act on the content stored within. This includes a wide range of formats such as PDFs, documents, videos, and audio files.

Zia Hubs automatically organises uploaded content by grouping related information—such as section headings, supporting text, and visuals—to preserve context. For video and audio files, Zia generates transcripts and links key moments to relevant topics, making it easier to pinpoint exactly where something was said.

With Zia Hubs, users can surface the most relevant answers when asked a question, even across different content formats. Each response includes clear citations that link back to the original content, whether it is a document, spreadsheet, image, or a specific moment in an audio or video file.

Organisations can also create custom workflows with Zoho Flow, automating document storage processes for particular projects or specific teams. This ensures that Zia always has access to the latest necessary documents automatically.

Furthermore, content from third-party software—such as Docusign PDFs, RingCentral call logs, Zoom video files—are all readable by Zia, and can be automatically placed into a hub by building a workflow with Zoho Flow.

From Intelligent Content to Intelligent Action with Zia

Zia Hubs is a foundational element of Zoho’s long-term AI strategy. It lays the groundwork for a future where intelligent agents can act contextually on content across the company’s entire product suite. With full ownership of its technology stack spanning more than 55 products, Zoho is uniquely positioned to help organisations unlock deeper value from their business content compared to competitors.

Potential use cases for Zia Hubs include: a legal team’s content hub hosting a case’s relevant files: court files, correspondence, and research, allowing a lawyer to have Zia provide summaries with citations without having to manually scan lengthy files to gain insights; a company’s support center placing call logs into a hub, then querying Zia to identify trends, like, “Have we received an increase in calls regarding specific issues?”

Future updates to Zia Hubs will enable it to identify structured information within unstructured files and trigger specialised agents tailored to specific business needs. This will further establish Zia Hubs as the central content intelligence layer that activates AI-native workflows across the full Zoho ecosystem.

Why it matters:

• 80% of business data is unstructured—Zia Hubs makes it usable.
• Works across documents, audio, and video with smart transcription and contextual analysis.
• Automates workflows via Zoho Flow, ensuring up-to-date content access.

Availability and Pricing

Zia Hubs is scheduled for general release at the end of Q3 2025.

[Disclaimer: All trademarks, product names, and company names cited herein are the property of their respective owners]

Salesforce’s latest State of IT data reveals unanimous optimism about AI agents, with 100% of security leaders identifying at least one security concern that could be improved by agents.

However, despite this hope, the global survey of over 2,000 enterprise IT security leaders highlights significant implementation challenges ahead‌.

48% worry their data foundation isn’t set up to get the most out of agentic AI, and over half (55%) aren’t fully confident they have appropriate guardrails to deploy AI agents.

Why it matters: Both the professionals charged with protecting a company’s data and systems and the bad actors looking to exploit vulnerabilities  are increasingly adding AI to their toolkits.

Autonomous AI agents, which help security teams cut down on manual work, can free up humans’ time for more complex problem solving. However, agentic AI deployments require robust data infrastructure and governance to be successful.

Salesforce perspective: 

“Trusted AI agents are built on trusted data.  IT security teams that prioritize data governance will be able to augment their security capabilities with agents while protecting data and staying compliant.” – Alice Steinglass, EVP & GM, Salesforce Platform, Integration, and Automation.

By the numbers: 

Security budgets ramp up as threats evolve

In addition to a familiar slate of risks like cloud security threats, malware, and phishing attacks, IT leaders now cite data poisoning — in which malicious actors compromise AI training data sets — among their top concerns. Resources are rising in response: 75% of organizations expect to increase security budgets over the coming year.

Complex regulatory environments add a wrinkle to AI implementation 

While four-fifths of IT security leaders believe AI agents offer compliance opportunities, such as by improving adherence to global privacy laws, nearly as many (79%) say they also present compliance challenges. This may stem in part from an increasingly complex and evolving regulatory environment across geographies and industries, and is hampered by compliance processes that remain largely unautomated and prone to error.

• Just 47% are fully confident they can deploy AI agents in compliance with regulations and standards.
• 83% of organizations say they haven’t fully automated their compliance processes.

Trust is a cornerstone of successful AI, yet confidence is nascent

A recent consumer study found that trust in companies is on a precipitous decline, and three-fifths (60%) agree that advances in AI make a business’s trustworthiness more critical. Furthermore, only 42% of consumers trust companies to use AI ethically, a decrease from 58% in 2023. IT security leaders see work to be done in earning this critical trust.

• 57% aren’t fully confident in the accuracy or explainability of their AI outputs.
• 60% don’t provide full transparency into how customer data is used in AI.
• 59% haven’t perfected their ethical guidelines for AI use.

Data governance is a linchpin in enterprises’ agentic evolution

Nearly half of IT security leaders aren’t sure they have the quality data to underpin agents, or that they could deploy the technology with the right permissions, policies, and guardrails, but progress is being made.

A recent survey of CIOs found that four times as much budget was allocated to data infrastructure and management than AI, a signal that organizations were smartly laying the right groundwork for broader implementation.

AI agents offer a salve as adoption ramps up

Over 40% of IT security teams already use agents in their day-to-day operations — a figure that’s anticipated to nearly double over the next two years. IT security leaders expect a range of benefits as their use of agents ramps up, ranging from threat detection to sophisticated auditing of AI model performance. 75% expect to use AI agents within two years — up from 41% today

Tactical overhauls are on tap

In addition to the steps these teams must take to shore up their data foundations for the agentic era, over half admit they have work to do to bring their overall security and compliance practices up to par.

Forty-seven percent believe their security and compliance practices are fully prepared for AI agent development and implementation.

The customer view: Arizona State University (ASU) is among the first universities to leverage Agentforce, Salesforce’s digital labor platform for augmenting teams with trusted autonomous AI agents in the flow of work.

ASU stresses the need for data relevancy, especially as ASU advances its AI initiatives. ASU implemented Salesforce-acquired Own backup, recovery, and archiving solutions, providing ASU with a comprehensive approach to data management, addressing their needs for backup, recovery, compliance, and innovation support.