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In the fifth episode of Prompt & Response, the Inetum podcast about AI in practice, host Piotr Mechliński (Data & GenAI Manager EEMEA, Inetum Polska) talks with Przemysław Saniak (Business Development Director) and Andrzej Gumieniak (IoT Practice Leader) about what changed, what still blocks scale, and where AIoT, Artificial Intelligence of Things, is heading next.

TL;DR. IoT is no longer the buzzword, it is the quiet hero running under the floor of modern enterprises. AI is the brain that finally makes that nervous system pay back, as our expert sums up. The winners in 2026 are the ones who combine edge AI, a Unified Namespace for their device data, and serious data governance, and who start from a business case instead of a shopping list of platforms.

Key takeaways from the episode

• IoT is mature: over 20 billion devices were connected by the end of 2025, a number expected to double by 2030.
• Predictive maintenance can cut unplanned stops by up to 70%. Smart buildings can save 20 to 30% on energy and water (U.S. Department of Energy).
• The next step is AIoT: intelligence moves to the edge, the nervous system gets a brain.
• The closed loop is the real shift: not only reading data, but sending decisions back to devices in real time.
• The biggest blockers are legal (Cyber Resilience Act, NIS2), organisational (who owns IoT inside the company) and technical (20 to 30 year old PLCs, vendor lock-in).
• Unified Namespace is becoming the default architecture to unify device data and feed AI agents with context.
• Data governance is not optional anymore. Without a catalogued context, LLMs and agents cannot act reliably on operational data.

From IoT to AIoT: why the name changed

The term AIoT, Artificial Intelligence of Things, is not marketing polish. It captures a real shift: intelligence no longer lives only in the cloud.

Three forces push in the same direction:

1. Smaller devices, bigger compute. Miniaturisation lets companies deploy far more sensors and actuators without expanding footprint.
2. Edge inference. Predictive models are now small and efficient enough to run directly on the device. Less round-tripping to the cloud, faster action, lower cost per decision.
3. Enterprise context for AI. Real-time sensor streams combine with vector databases, retrieval augmented systems and company documentation so that AI agents can reason over operations, not just transactions.

If IoT is the nervous system of the enterprise, AI is the brain. You just combine them to get value out of the whole body.

Andrzej Gumieniak, IoT Practice Leader, Inetum Polska

Where AIoT actually creates value

There is no shortage of sensors. There is a shortage of good answers to one question: what changes in cost, uptime, revenue or speed because of this deployment?

Przemysław groups the real payback into three streams.

Cost optimisation

• Predictive maintenance. Reduction of unplanned stops by up to 70%, with the matching cut in downtime cost.
• Smart buildings. According to the U.S. Department of Energy, smart buildings can save up to 30% on energy and 20 to 30% on water consumption.
• Smart cities. Harder to quantify, but quality of living and operational efficiency improve.

New business models

Connected products make “as a service” models practical: equipment as a service, usage based contracts, proactive SLA management. The trade-off is real: vendors take on more operational responsibility, but they gain deeper, longer customer relationships.

The closed loop

Andrzej adds the stream that separates mature players from beginners. The data flow is no longer one way, bottom-up. Once you trust the models, you let the system send commands back to the devices and steer production in near real time. That is the step where IoT stops being a dashboard and starts being an operating system.

The value is not in knowing faster. It is in reacting faster, and that is where AI earns its keep.

Przemysław Saniak, Business Development Director, Inetum Polska

Edge AI and custom models: why manufacturing is different

Manufacturing rarely fits a generic AI template. Every plant is different, every process has its quirks, every shop floor has its own mix of old and new equipment. That is why AIoT in factories leans toward custom models and edge AI.

Two practical consequences:

• Not all data is gold. Edge devices filter and summarise at the source, so only relevant signals reach the central systems.
• End-to-end streams. Decisions happen where they belong: at the edge for safety and latency, at the core for cross-plant optimisation and learning.

The integration challenges nobody warns you about

Scaling AIoT is worth it, but the path is rarely clean. Inetum IoT experts name three categories of friction.

Legal and regulatory

The EU keeps raising the bar: Cyber Resilience Act, NIS2, sectoral rules on top. If connected devices touch critical infrastructure, compliance is not a backlog item, it is a design constraint.

Organisational

IoT often lands between departments. Is it IT? Is it production engineering? Who is the product owner? Scaling without a clear owner guarantees pilot limbo. A defined operating model is as important as any platform choice.

Technical and legacy

Factories run on equipment that is 20 or even 30 years old. Old PLCs are already wired into SCADA systems, so connectivity exists, but it is brittle and locked to specific vendors. Inetum has seen sites where assets were linked only by SMS, because that was the only usable channel. That is enough to start, but it is not an architecture.

Unified Namespace: the foundation for agentic IoT

The emerging answer on the shop floor is the Unified Namespace (UNS): a single, standardised place where every device publishes its state in real time, in a format everyone in the organisation can understand.

Two ideas underpin it:

Pub-sub over polling

Instead of legacy systems constantly querying PLC registers, devices publish events to a central message bus. Any consumer, SCADA, MES, analytics, AI agents, picks up what it needs.

Shared naming and semantics

Every signal is named and described consistently, so meaning does not get lost as data propagates.

UNS is not only a networking pattern. It is a data governance decision applied to operational technology. That is exactly what agentic AI needs in order to act safely on the factory floor.

Data governance is the new battleground

LLMs and agents cannot reason well on unlabelled streams. For AIoT to deliver, companies need more than telemetry. They need context:

• catalogued data with clear definitions,
• historical records linked to outcomes,
• documentation, runbooks and maintenance history,
• the tribal knowledge that usually lives in senior technicians’ heads.

Feed that into the “brain” of the enterprise and the agent can make informed decisions in seconds, not after a postmortem. Skip it, and you get confident-sounding suggestions that miss the point.

Data governance, in other words, is now a prerequisite for operational AI, not a sideshow for the data team.

What comes next: agentic IoT and Industry 5.0

Looking a step ahead, the guests see two overlapping horizons.

Fully agentic IoT

Once UNS and governance are in place, AI agents can plan maintenance routes, reschedule production, adjust set points and trigger safety interventions, all while keeping a human in the loop where it matters. Inetum is already seeing early versions of this pattern in manufacturing and utilities.

Industry 5.0 and human safety

The collaboration between IoT, robotics, AI and people is getting tighter. Edge AI is a critical enabler for worker safety in harsh environments: detecting risks and stopping machines before a person can be harmed.

Przemysław references Stanley Kubrick: the HAL 9000-style, AI-enhanced operational control centre is no longer science fiction. With mature data foundations and strong cybersecurity, these centres already exist in advanced industrial sites, taking routine load off operators and letting them focus on exceptions.

Where to start if you are beginning today

If you are just starting your IoT (or AIoT) journey, both guests converge on the same playbook.

1. Start with the business case, not the platform. Name the outcome: less downtime, lower energy cost, new service revenue, safer workers. Then work backwards to technology.
2. Make the value measurable from day one. Run short iterations, define what “worth keeping” looks like, and be ready to kill a use case that does not deliver.
3. Think governance in parallel, not afterwards. Data governance, AI governance and a clear operating model need to grow alongside the first MVPs, not after the third one.
4. Do not try to climb Everest in one jump. Connect devices first. Learn to react on their data. Then add AI on top. Big picture in mind, small moves on the ground.
5. Build vs buy is not the first question. With 600+ IoT platforms by 2021 (a number that has since consolidated, including vendors like Google stepping out of the space), and with GenAI now accelerating custom builds, the choice is easier once you have proven value. Not before.

Start with the business case. Otherwise you will build a compressed solution that connects your freezer to your TV, and not much else.

Przemysław Saniak, Business Development Director, Inetum Polska

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About the speakers

• Piotr Mechliński, Data & GenAI Manager EEMEA, Inetum Polska, host of Prompt & Response.
• Przemysław Saniak, Business Development Director, Inetum Polska.
• Andrzej Gumieniak, IoT Practice Leader, Inetum Polska.

Previous episodes of Prompt & Response

• All episodes
• AI compliance marketing agent
• AI strategy and use case discovery
• Voice of the customer in the age of GenAI
• AI in manufacturing

That was the starting point for Inetum’s webinar SAP Business Data Cloud & Databricks: Unlock AI & Data potential, which brought together practitioners from Inetum and special guests from SAP and Rolls-Royce. The session featured Jan Tretina (Ecosystem Development Manager, SAP), Sebastian Stefanowski (Databricks Practice Leader, Inetum), Raul Muñoz-Gutierrez (SAP Analytics Business Director, Inetum), and Andrew Lager (Program Manager and Digital Delivery Manager, Civil Digital and IT, Rolls-Royce), hosted by Oleh Hudym (SAP Growth Manager Manager, Inetum).

Less than 40% of leaders trust their own data – and that gap stalls AI

“Less than 40% of leaders report high confidence in their data,” Jan Tretina said at the opening of the session. “That gap directly impacts both decision speed and innovation.”

Three issues sit behind this figure. First, data quality: over half of organizations struggle to keep data accurate and consistent across systems. Second, misalignment between IT and business – finance teams need insights quickly, but IT landscapes can’t always deliver at that pace. Third, fragmentation: data spread across multiple systems, bringing it together in real time remains a major pain point.

The business consequence is concrete. Data-oriented organizations – those that have solved the trust problem – are, according to SAP’s analysis, four times more likely to succeed.

What SAP Business Data Cloud is – and how it closes the trust gap

Jan Tretina described SAP Business Data Cloud through a flywheel: AI is only as strong as the data behind it, data is only valuable when it is trusted and accessible, and both require a resilient platform underneath.

“SAP Business Data Cloud is our unified data foundation,” he said. “It brings clean, connected, and trusted business data together – unifying data across SAP and non-SAP systems and making that data immediately usable for AI, analytics, and planning.”

BDC consolidates SAP data services – SAP BW, SAP Datasphere, SAP Analytics Cloud, and extension partners including Databricks and Snowflake – under a single platform. The operational impact Tretina highlighted: instead of maintaining thousands of pipelines, custom integrations, and shadow data, BDC provides one consistent data foundation across all use cases. It embraces non-SAP data, open standards, and a broad partner ecosystem, letting organizations build multi-vendor landscapes without losing control of their data. This architecture makes BDC relevant across sectors – from retailers to manufacturing to financial services and banking.

Migration: BDC works with what organizations already have

A recurring concern when evaluating any new platform is the cost of transition – years of investment in SAP BW, Datasphere, or Analytics Cloud environments that can’t simply be discarded.

Raul Muñoz-Gutierrez addressed this directly. BDC includes all SAP data services within a single platform and is designed to absorb existing environments, not replace them. For SAP BW, BDC offers a “lift and shift” migration path – the existing environment moves in with data and connections preserved.

For SAP Datasphere or SAP Analytics Cloud, a “rewiring” process brings those solutions under the BDC umbrella without rebuilding. “All these tasks don’t require any work from the customers,” Muñoz-Gutierrez confirmed. “They can be carried out directly by SAP.”

Data products eliminate the 80% of a project that adds no value

“Do you know the effort and time required in a data project to perform data extraction, loading and transformation, as well as reconciling all the information?” Raul Muñoz-Gutierrez asked during the session. “All that work, that normally is the 80% of the project, is partially eliminated through a SAP data product.”

SAP data products contain the main data from key functional areas – financial, HR, supply chain – and they inherit all the data semantics from SAP. This makes them usable not just for reporting, but for AI and machine learning development from day one.

For CFOs specifically, the Financial Intelligence Package activates intelligent applications on SAP Analytics Cloud while simultaneously triggering data loading from SAP S/4HANA into BDC data products. “Regularly every 30 minutes, the data is going to travel from our SAP S/4 system to our BDC,” Muñoz-Gutierrez explained. Treasury management, financial planning, and forecasting become available and AI-ready from the moment of activation – with the option to build custom AI and machine learning on top.

SAP Databricks and standalone Databricks: the same engine, different purpose

If Databricks is already in the organization’s stack – or under evaluation – a natural question arises: how does SAP Databricks within BDC relate to the standard Databricks platform?

Sebastian Stefanowski, whose team works with both, explained the distinction. SAP Databricks is a modified release of the Databricks platform, tightly integrated with BDC – authentication, authorization, and billing are all managed through SAP, using SAP compute units. The most important aspect of the integration, in Stefanowski’s words, is a dedicated connector enabling zero-copy data sharing between SAP data products and the Databricks catalog: SAP financial data becomes directly queryable in Databricks without replication or transformation overhead.

“If SAP data is at the heart of your business, and financial data is really central to what you do, then SAP Databricks is definitely worth serious consideration,” Stefanowski said. “It would enable most of the useful features of Databricks with, generally speaking, no technological barrier.”

The trade-off is feature breadth. Organizations with IoT streaming requirements, multi-stage declarative pipeline orchestration (DLT), or a need to manage their own compute clusters will find standalone Databricks richer – with dedicated connectors for external cloud services and platforms like Salesforce. In SAP Databricks, those broader integrations run through SAP. Stefanowski’s summary was clear: for general data integration scenarios, standalone Databricks; for organizations where SAP financial data is the core, SAP Databricks removes the barriers.

“What seemed to be impossible a few years back in terms of AI capabilities is now possible with this amazing partnership between SAP and Databricks,” Oleh Hudym noted during the session.

Six years with Databricks at Rolls-Royce: 50% cost reduction and AI for every analyst

Andrew Lager has been on the Databricks journey at Rolls-Royce for over six years, including as an early adopter of many of its latest technologies – and he came to the webinar with numbers.

“We’ve reduced costs upwards of 50% compared to our previous solution,” he said, referring to the migration from a legacy warehouse to Databricks Lakehouse for engine health monitoring data. Three factors drove that reduction: cheaper compute via Spark, schema evolution that prevents jobs from breaking when table structures change during migrations, and Unity Catalog – which centralizes data access across sources so Lager can produce management information for internal stakeholders without involving additional teams.

On the AI side, his example was equally direct. “Those activities that would take me days can take me five minutes now,” he said, describing Databricks AI Genie – a feature that lets non-technical users query datasets in natural language instead of SQL.

“The biggest benefit, not just for me but for Rolls-Royce in general, has been opening up technical solutions to less technical people.”

What AI on SAP data actually looks like

Sebastian Stefanowski described Databricks as a complete environment for AI and machine learning, where SAP BDC data products are exposed as tables that plug directly into data pipelines. He grouped the platform’s AI and ML capabilities into several practical areas:

• AI Playground – run any off-the-shelf or externally sourced model on a serverless runtime; create custom prompts and test hypotheses before committing to a build
• Model serving endpoints – host and serve custom models at scale
• AI agent framework – build agentic solutions quickly, with a built-in vector search database for RAG implementations
• MLflow – manage and monitor the full training lifecycle; compare experiments and select the best-performing model
• AutoML – Databricks runs parallel experiments across multiple model architectures on your data automatically, then selects the model that best fits your test results
• AI functions in SQL – call AI models directly from a SQL SELECT statement and store predictions as part of the query output
• AI Gateway – govern model usage centrally: block unauthorized access, filter sensitive queries, and maintain full oversight of what models run and on what data

He then connected those capabilities to the most common use cases for SAP data: cash flow forecasting, prices forecasting, and stock level optimization – blending SAP financial and operational data with seasonality data, interest rate history, and interest rate predictions to anticipate future costs, prices, and inventory needs. Databricks AutoML fits naturally here, running statistical algorithms in parallel and surfacing the model that best fits historical test data.

Beyond forecasting, LLM integration opens a second category: automatically generating product descriptions from structured SAP product data; running sentiment analysis on customer feedback to understand the reasoning behind customer behavior; and identifying the most promising customers to approach based on that analysis.

“All these features can be really, really nicely integrated with SAP and blended with SAP data,” Stefanowski said. “I think that’s something which will bring SAP customers to another level.”

Flexible consumption: licensing that moves with the organization

Raul Muñoz-Gutierrez described the BDC commercial model as one of its differentiators. “SAP Business Data Cloud offers a unit subscription model that provides customers with flexible subscription pricing,” he said. “This allows them to subscribe to the services they currently need and easily modify them in the future.”

The example he gave: an organization starts with SAP BW integrated into BDC. Once that environment has been migrated to SAP Datasphere, the BW capacity is removed and reallocated – to SAP Databricks or SAP Snowflake, for instance. A separate data services licence layer complements the platform subscriptions, enabling activation of analytical applications with near real-time data, development of custom data products, and external data sharing via BDC Connect – the service that exposes SAP data to Databricks, Snowflake, and Microsoft Fabric through Delta Sharing.

What is coming in SAP Business Data Cloud in 2026

Jan Tretina outlined SAP’s roadmap across three areas.

First, deeper openness and connectivity: BDC Connect is expanding to Databricks, Snowflake, Google Cloud, Microsoft Fabric, and AWS. Bi-directional data sharing with SAP HANA Cloud is also in development – data flowing both ways, removing silos at the source rather than managing them downstream.

Second, data products: SAP is expanding coverage across more lines of business and releasing Data Product Studio, which will make modeling SAP and non-SAP data into governed, shareable data products “much easier than ever before” – turning data into “a true asset others can consume and trust,” in Tretina’s words.

Third, AI-native capabilities built directly into the data layer: a new AI Hub with new models, enhancements to the SAP HANA Cloud Knowledge Graph, and a fully agentic multi-modal database – including memory for agents. “This is the database AI was really looking for,” Tretina said.

“We are building the data foundation every enterprise will need to power next-generation AI. 2026 will be a breakthrough year for BDC.”

How CFOs should measure ROI on SAP Business Data Cloud

In the Q&A, Jan Tretina identified four areas where BDC generates measurable returns for finance leaders.

Efficiency and proactivity. Fewer manual reconciliations, reduced data preparation effort, faster close-to-forecast cycles.

Data trust. Higher data quality and fewer errors – directly addressing the confidence gap he described at the start of the session.

Agility. Faster time to insight and quicker scenario iterations.

Business impact. Improved forecast accuracy and tangible cost savings from AI-driven recommendations.

“CFOs should look at gains in proactivity, trust, and measurable financial outcomes,” Tretina said. “With SAP BDC, the ROI is visible both in operational efficiency and in the quality of decisions which can power the business.”

How to start – without committing to a licence first

For organizations that want to evaluate BDC before committing to a full rollout, Raul Muñoz-Gutierrez described a concrete entry point. Inetum has three years of experience with SAP Datasphere and related environments, and operates its own SAP Business Data Cloud environment – available to run proof-of-concept scenarios with customers in a live setting, testing whether a target architecture fits their real needs before any licence investment.

“You don’t have to invest in a licence right now,” Muñoz-Gutierrez said. “You have to know what you want, and we are the best company to accompany you to this final scenario – because we have SAP Data Specialists and also global data expertise from non-SAP solutions.”

On the Databricks side, Sebastian Stefanowski was unambiguous: “Databricks is for everyone.” The platform runs on a pay-per-use model – if workloads run once a day for an hour, the cost reflects that hour. If the platform is idle, the cost goes to zero. “The openness and scalability of this platform – which actually scales down to zero – that is what should encourage any company who wants to start cloud data processing,” he said.

The message across the webinar was consistent: before AI delivers value at scale, organizations need a data foundation that is clean, connected, and trusted. SAP Business Data Cloud is built to be that foundation. Databricks extends what can be done on top of it. And when the two are combined with the right approach, the gap between fragmented enterprise data and working AI closes faster than most organizations expect.

The full session is available on demand.

SAP, Databricks, Rolls-Royce, and Inetum practitioners covered migration strategy, data product architecture, and AI implementation – including the unedited live Q&A. Watch the webinar on demand →

To run a proof of concept in Inetum’s own SAP Business Data Cloud environment – without upfront licence commitment – talk to our team →

The key difference does not start with algorithms or platforms. It starts with the nature of data itself.

This episode is especially relevant for leaders and architects who want to move beyond Industry 4.0 dashboards and understand how modern data platforms and generative AI can support real manufacturing processes, not just proofs of concept.

Watch the full episode below

Prompt & Response Webcast #4 – Transcript & Key Insights

Data does not start digital in manufacturing industry

In digital-native industries, data is born digital. Transactions, clicks, and interactions are structured from the very beginning. Manufacturing works in the opposite direction.

Here, data originates in the physical world. Pressure, vibration, temperature, sound, movement – all of these signals must first be measured and translated into digital form. Only then can analytics or AI be applied.

This is why working with manufacturing data is never just about dashboards. It requires engineering understanding and domain knowledge. You need to know how machines behave, how materials age, and how small physical deviations can signal much larger problems.

The challenge is amplified by legacy environments. Many factories still rely on machines and PLC controllers designed decades ago. AI systems must integrate with technology that was never meant to be connected, scalable, or data-driven.

In manufacturing, data doesn’t come from clicks or transactions. It comes from physics. From pressure, vibration, temperature.
If you don’t understand the physical process behind the data, no AI model will save you.

Marek Czachorowski
(AI & Data Practice Leader, Inetum)

Repeatable processes create real analytical potential for manufacturing companies

Despite these constraints, manufacturing has a powerful advantage: repeatability. Production lines, assembly steps, quality checks, and logistics flows run every day, often thousands of times.

Where repetition exists, patterns emerge. Deviations become visible. This is why manufacturing, when approached correctly, is such a strong candidate for analytics and AI.

The value goes beyond the production line itself. Supporting processes like supply chain management or logistics generate equally important signals. When analyzed together, they reveal how a factory truly operates – not just how it was designed to operate.

Products continue to generate data after leaving the factory

Modern manufacturing no longer ends at production. Products themselves become continuous sources of data once they are in use.

Predictive maintenance is often called the holy grail of manufacturing AI. Not because it’s technically impressive, but because even small improvements can save millions when downtime is critical.

Sebastian Stefanowski
(Chief Architect)

Sensors embedded in machines, vehicles, or equipment allow manufacturers to understand how products behave in real conditions. This enables a shift from reactive maintenance to predictive maintenance – identifying issues before failures occur.

In highly complex environments, such as aviation, even small improvements have a massive impact. Unplanned maintenance is expensive, downtime is critical, and better prediction translates directly into safety improvements and measurable cost savings.

What distinguishes successful cases is rarely the sophistication of the model. It is the maturity of the data foundations behind it.

Quality control moves from inspection to prevention

AI also changes how quality is managed inside the factory. Instead of discovering defects at the end of production, machine learning models can identify early signals that something is going wrong.

Subtle anomalies appear long before a defect becomes visible. This allows production to stop earlier, avoid unnecessary cost, and focus resources where they actually matter.

The result is not just better quality, but higher throughput and lower waste at the same time – a combination that traditional approaches struggle to deliver.

Use AI to your advantage: from collecting data to understanding it

For many organizations, Industry 4.0 initiatives focused on connectivity and dashboards. Sensors were installed, data was collected, charts were built. What is changing now is the role of generative AI.

The focus shifts from collecting data to understanding it. GenAI acts as a bridge between humans and complex technical environments, helping engineers navigate documentation, maintenance history, sensor logs, and operational data more efficiently.

This mirrors patterns discussed earlier in Prompt & Response, including voice of the customer in the age of GenAI, where GenAI serves as a translator between raw data and human decision-making.

The impact is especially visible in knowledge retention. Experienced engineers leave, taking years of tacit knowledge with them. AI does not replace expertise, but it makes existing knowledge accessible and usable at scale.

Start with business reality, not with ai

Most AI initiatives in manufacturing don’t fail because of algorithms. They fail because data foundations and business context were never fixed first

Piotr Mechliński
(Host, Head of AI & Data, EEMEA)

One message remains consistent throughout this episode and the entire series. Successful AI in manufacturing does not start with AI.

It starts with business reality. Clear goals, well-understood processes, and reliable data foundations come first. Only then does it make sense to decide whether machine learning, predictive models, or Generative AI are the right tools — and where they will actually deliver value.

This is the natural continuation of the journey discussed in From AI strategy to execution, where strategy only matters if it can survive contact with reality.

Manufacturing is not harder than other industries when it comes to AI. It is simply different. Organizations that respect that difference are the ones that turn AI from hype into measurable results.

What is customer analytics, and why does it matter today?

At its core, customer analytics is the process of turning raw signals from your customers into business decisions. Put simply, analytics is the process of using data to generate a meaningful view of the customer so that you can make better choices about products, service, and communication. Modern customer analytics spans digital channels, support, sales, and even offline interactions – giving a much richer picture than a simple monthly report.

In practice, analytics involves collecting, cleaning, and analysing customer interactions, then feeding the results back into operations. When done well, customer analytics helps you turn scattered events into structured knowledge: which customers are at risk, what drives customer satisfaction, when to act to protect customer retention, and which offers perform best. This is what makes effective customer analytics a strategic capability, not a “nice to have” dashboard.

Imagine a retail bank reviewing its quarterly dashboard.Churn is up by 2%. Conversion is flat. NPS dropped slightly. On paper, nothing looks dramatic – but something feels off. Only when the team digs into customer analytics beyond surface-level metrics do they notice a pattern: customers who recently contacted the call center about mobile app issues are quietly moving their savings elsewhere. No formal complaints. No angry emails. Just silence – and then churn. This is where customer analytics stops being a reporting exercise and starts becoming an early warning system.

The benefits of customer analytics have grown as customers moved online. Switching banks, telcos or retailers is now a few clicks away; your customer base is more fluid than ever. Companies that rely purely on gut feeling struggle to keep up with evolving customer behaviours and preferences. In contrast, data-driven organizations use customer analytics to understand why people stay, why they leave, and how to improve customer outcomes across the entire customer journey.

What types of customer data should you collect?

To do serious customer analytics, you first need the right raw material. Customer data is an essential asset – but only if it actually reflects how people use your services. The most valuable types of customer data typically include:

• Profile & demographic data – age, location, segment, account type.
• Transactional data – purchases, payments, product usage.
• Behavioural data – clicks, sessions, app flows, customer behaviour data in digital channels.
• Unstructured signals – emails, chat logs, call transcripts, customer feedback surveys, social comments.

These categories form the foundation for customer data analytics. Classic analytics tools focused mostly on web traffic, but modern setups enrich that with deeper examples of customer data – such as tone of voice in a support call or topics recurring in complaints. This kind of customer data from various sources allows you to better understand customer expectations instead of guessing.

The process of collecting this data has to be deliberate. Randomly trying to collect data “just in case” quickly becomes a burden. Instead, you should collect data that maps to clear questions: how people use your app, when they drop off, which features correlate with loyalty. Over time, companies can use customer data to better match offers to customer needs and identify new opportunities in their products and services.

Collecting customer data is one thing – making sense of it is another

If you want to understand how to move from raw feedback to actionable customer insights, don’t miss the latest episode of Prompt & Response webcast. It’s a practical deep dive into customer analytics that actually delivers.

 
Watch now

Types of customer analytics: from descriptive to prescriptive

Not all analytics is the same. Understanding the main types of customer analytics helps you choose the right methods for each problem. In a typical maturity model you’ll find four layers:

#1 Descriptive analytics answers “what happened?”. It aggregates and summarizes historical customer data: revenue by segment, tickets by topic, NPS by region. This is where tools like Google Analytics started, and where most organizations still spend the majority of their time.

#2 Diagnostic analytics asks “why did this happen?”. It explores relationships in the data – for example, which changes in onboarding flow led to lower customer satisfaction, or which touchpoints in the customer journey correlate with complaints. This helps you understand customer reactions instead of just observing metrics.

#3 Predictive analytics estimates “what is likely to happen next?”. Predictive customer analytics can forecast customer churn, customer lifetime value or conversion probabilities in specific customer segments.

#4 Prescriptive analytics suggests “what should we do?”. It connects patterns to actions: who to contact, with what offers, through which channels.

Together, these types of customer analytics provide visibility and guidance across the entire customer journey.

How do you actually analyze customer data?

The mechanics of customer data analysis vary by industry, but the principles are similar. First, you collect and analyze customer data that is directly tied to a business question. Then you enrich it with additional context (segments, channels, lifecycle stage) and start analyzing customer patterns.

Modern teams analyze customer data across structured and unstructured sources. On the structured side, they track funnels, flows and key events. On the unstructured side, they use language models to analyze customer reviews, complaints and chats. This analyzing customer interactions produces qualitative insights that numbers alone can’t show – for example, specific phrases that signal frustration or delight.

In our Prompt & Response webcast, we discussed how GenAI accelerates the process of collecting and analyzing customer feedback at scale. Instead of manually reading thousands of comments, AI can summarize themes, detect customer sentiment shifts, and highlight risks or opportunities. That’s where analytics provides not just charts, but concrete next steps.

The key benefits of customer analytics for your business

When done well, customer analytics delivers value far beyond reporting. It directly supports growth, profitability and resilience. Here are some of the most important outcomes that customer analytics helps businesses achieve:

Stronger retention and loyalty.

With the right models, you can detect early signs of customer churn, anticipate issues and act before people leave. This protects customer lifetime value and builds customer loyalty by showing that you react to problems quickly. Over time, this raises overall customer experience instead of treating churn as an unavoidable cost.

More efficient acquisition and marketing efforts.

By understanding which behaviours lead to purchase, you can design smarter marketing campaigns and align product analytics with the channels that drive results. Customer analytics helps organizations focus their marketing efforts on segments with real intent, instead of broadcasting to everyone. This also supports targeted customer acquisition strategies.

Better decisions at every level.

From the customer service team prioritizing tickets to executives planning roadmap investments, customer analytics to understand patterns becomes a shared language. It helps decision makers better understand customer behaviour, improve customer outcomes and align around facts.

How customer analytics improves customer experience and the customer journey

One of the most tangible payoffs of customer analytics is its impact on customer experience. By connecting signals across channels, you can create a customer journey that reflects how people actually behave, not how you imagine they behave in slide decks. You can literally create a customer journey map based on real data instead of assumptions.

For instance, by tracking customer interaction paths, support tickets and customer feedback, companies discover friction points they didn’t know existed. They might find that a specific payment step generates confusion, or that a new login flow harms customer engagement. When you design improvements, you can then measure whether they genuinely improve customer experience across the entire customer journey.

Analytics can also help operational teams. A customer service team can use customer analytics to understand which topics are rising, while customer relationship management professionals can use customer data to better personalize outreach. When you then monitor customer sentiment in reviews, chat and social channels, you get a continuous feedback loop that lets you enhance customer trust and experience over time.

Customer analytics tools, data platforms and tech stack

To deliver all this at scale, you need more than spreadsheets. A modern stack combines customer analytics tools and a robust analytics platform or customer data platform (CDP). Classic analytics tool choices such as tools like Google Analytics remain useful for web metrics, but they don’t cover the full breadth of customer data you need today.

A more complete architecture usually includes:

• Data pipelines that collect data from apps, websites, CRM and support.
• An analytics platform or CDP to unify profiles and events.
• ML and AI components for predictive analytics and prescriptive analytics.
• Dashboards and exploratory tools for teams to use customer analytics in daily decisions.

When evaluating customer analytics tools, focus less on buzzwords and more on whether teams can realistically use customer data every day. In the webcast, we stressed that even the best engine fails if people still export everything to Excel. The right stack lets you plug insights directly into marketing campaigns, product experiments and service workflows.

Collecting and storing customer data securely

All of this only works if you store customer data and use it responsibly. Security, privacy and governance are not side topics; they are central to implementing customer analytics at scale. Customers share information on the assumption that you will both protect it and use it to deliver better services.

That’s why collecting and storing customer data must follow clear policies and legal frameworks. You need to store customer information with encryption, access control and transparent consent. You also need to think carefully about which customer data collection practices are truly necessary. It’s often better to collect customer signals that you know how to use than to hoard data you never analyze.

Done correctly, you both store customer data securely and make it available for analytics. The goal is to collect customer information that helps you enhance customer experiences without compromising trust. Many organizations partner with specialists to ensure their customer data securely supports innovation instead of limiting it.

Customer analytics in action: where insights turn into decisions

Customer analytics proves its value not in dashboards, but at the moment a team changes a decision it would otherwise make on instinct.
Several of the situations described below were discussed during the Prompt & Response webcast on voice of the customer, while others build on the same patterns observed across real projects.

Together, they show how customer analytics turns insight into action.

Pharma: improving patient experience in a regulated process

One pharmaceutical company analyzed unstructured customer data from patient forums, social media, and healthcare professional feedback while redesigning a reimbursement application. The initial goal was straightforward: digitize an existing, paper-based process.

Traditional analytics suggested acceptable completion rates. Customer sentiment told a different story.

By listening to real customer feedback, the team identified two critical gaps: a lack of educational content explaining the disease and unclear communication between patients and healthcare professionals.

These insights didn’t come from standard dashboards. They emerged only after unstructured data was included in customer analytics. By addressing these gaps, the company improved the perceived usability of the process and made the solution easier to adopt — without changing regulatory requirements.

This example, discussed during the Prompt & Response webcast, shows how customer analytics can help product teams refine flows and content even in highly regulated environments.

Banking: predicting churn before customers leave

In banking, customer analytics is less about reacting to problems and more about anticipating them.

Banks combine transactional customer data with behavioral signals from mobile and web channels to build predictive models that identify early signs of customer churn. These signals are often subtle: reduced logins, abandoned flows, or changes in transaction patterns — long before a customer formally closes an account.

By applying predictive analytics, banks can score customer accounts based on risk and propensity, allowing teams to prioritize outreach. Instead of running generic campaigns, they focus on the customer segments that need attention most, improving customer retention and protecting customer lifetime value.

As highlighted in the Prompt & Response webcast, this approach shifts customer experience from reactive problem-solving to prevention.

E-commerce: when growth hides friction in the customer journey

The team was running a fast-growing online store. Sales were increasing and marketing performance looked healthy. On the surface, there was no reason to worry.
Then customer churn started creeping up – quietly, without a clear trigger.

Instead of reacting with discounts or assumptions, the team examined customer interaction paths across checkout, login, and post-purchase flows. Behavioural data was reviewed alongside support tickets and open-text feedback.

Two issues emerged.

A single payment step caused hesitation and repeated retries. At the same time, a redesigned login flow — introduced to improve security — interrupted engagement.

Neither problem looked critical in isolation. Together, they explained the churn pattern.

With a clearer view of the entire customer journey, both flows were simplified and measured again. Checkout completion improved. Engagement stabilized. Churn slowed.

Customer analytics didn’t just explain what was happening — it showed precisely where experience design needed to change.

Customer support: when feedback signals a problem — but not the one you expect

Monday morning, the customer service inbox was full. Chat requests kept coming in. Social mentions were rising.
At first glance, it looked like a typical post-release spike.

A closer look at customer interactions revealed a different picture.

By analyzing tickets, chat logs, and reviews together, the team noticed a surge in questions related to a new feature. There were no bug reports — only uncertainty.

Sentiment analysis confirmed the pattern. Customers weren’t frustrated with the functionality itself, but with how it was explained. Without combining structured metrics and unstructured feedback, the issue would likely have been dismissed as noise.

The response was operational rather than technical. In-app guidance was updated. Onboarding messages were refined. Short, contextual tips were proactively shared.

Within days, ticket volume dropped and sentiment stabilized. Feature adoption improved — without increasing pressure on the support team.

From industry use cases to broader insight

Although these examples come from different industries, the underlying pattern is the same. Customer analytics works best when it combines multiple data sources — including unstructured feedback — and when insights are connected directly to operational decisions.

Rather than relying on one-size-fits-all messaging, organizations use customer data to personalize flows, target the right people at the right moment, and adapt faster as customer expectations change.

Key challenges of customer analytics

Despite the clear benefits, the challenges of customer analytics are real. Common blockers include fragmented data, legacy systems, unclear ownership and the habit of treating analytics as an afterthought. Many teams feel that customer analytics is “something BI does” instead of a shared capability across the organization.

Another frequent issue is misalignment between ambition and practice. Leaders talk about AI and predictive analytics, but day-to-day workflows still run on manual exports and gut feeling. In the webcast, we described cases where customer analytics models were technically excellent but operationally useless because decisions happened weeks after the data was produced.

To overcome these challenges, organizations need to connect data, tools and people. That means defining ownership, aligning on a roadmap, and making sure that every insight has a clear “so what?”. When customer analytics helps organizations act within hours instead of months, you start seeing compounding gains in performance and overall customer outcomes.

Getting started with customer analytics

Many teams feel overwhelmed thinking about how to get started with customer analytics. You don’t need a perfect, enterprise-wide setup from day one. A more pragmatic path is to pick one or two concrete use cases where analytics can also help – for example, reducing complaints in one channel, or improving a specific onboarding flow.

From there, you use customer data already available, fill in a few gaps, and run experiments. This is the essence of implementing customer analytics in an agile way: start small, prove value, then scale. Over time, you can expand from one area of the entire customer journey to others, connecting dots and reusing building blocks such as a unified customer data platform.

As your capabilities mature, you’ll move from simple reports to more advanced techniques such as customer segmentation, predictive analytics and prescriptive analytics. The point is not to chase buzzwords, but to build an engine where customer analytics continuously informs product, service and communication decisions.

The future of customer analytics: AI, GenAI and real-time decisions

Looking ahead, the future of customer analytics is clearly shaped by AI and GenAI. Language models make it much easier to process unstructured customer data, generate summaries, and surface insights into customer intent and frustration. They also democratize analytics, letting non-technical teams ask questions, explore customer behaviour, and design experiments faster.

This opens the door for more real-time, predictive analytics scenarios. Instead of monthly reporting, organizations can run predictive customer analytics and active monitoring on a daily basis, adjust flows within hours, and build playbooks around typical patterns. AI-assisted customer analytics turns your data into a living, continuously updated map of insights into customer behaviour across channels.

Ultimately, the winners will be organizations that combine strong governance, secure customer data practices, and a clear focus on using analytics to improve customer outcomes. When you pair that with modern tooling and a culture that values evidence over opinions, customer analytics helps businesses make decisions that are better for both the company and the people it serves.

Summary: what to remember about customer analytics

To wrap up, here are the key points that matter when you think about customer analytics for your organization:

• Customer analytics is the process of turning raw events into decisions across your customer base.
• Customer data is an essential asset – but only if you use it to better understand customer needs and identify the changes that matter.
• The right combination of descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics gives you a full picture across the entire customer journey.
• Secure customer data collection, governance and the ability to store customer data safely are non-negotiable foundations.
• With AI and GenAI, you can collect and analyze customer data from unstructured feedback, monitor customer sentiment, and act faster on what you learn.

In short, when you bring together the right customer data, tools and mindset, customer analytics helps organizations move from guessing to knowing – and from reacting to shaping the future of their overall customer experience.

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Watch the full episode below, then explore the cleaned and structured transcript.

Prompt & Response Webcast #3 – Transcript & Key Insights

Why customer analytics is more important today than ever

Even if the concept feels old, the context is new. Customers share huge amounts of data with banks, telcos, retailers, streaming platforms – so they naturally expect companies to use it. The frustration of being asked for income details by a bank that already sees your monthly transactions is a perfect example of this gap.

Modern expectations are shaped by:

• constant exposure to personalized digital services,
• dynamic customer behaviour,
• and a market where switching providers takes seconds.

That’s why treating customers the same way they were treated three years ago simply doesn’t make sense any more.

What traditional analytics misses

The classic model focuses on CRM, demographics and historical events. Useful – but not enough. It answers what happened, not why.

Voice of the customer fills this gap by analysing unstructured, emotionally rich feedback coming from:

• social media,
• forums,
• call centre transcripts,
• and even audio signals like tone or sentiment.

This enables companies to understand not only behaviour, but motivation.

“Voice of the customer helps you distil emotions and understand the reasoning behind customer actions – something traditional analytics doesn’t provide.”

This blend of quantitative and qualitative signals becomes especially powerful when used continuously, not as a quarterly exercise.

Real business outcomes: retention, acquisition, experience and cost

Modern customer analytics delivers value across the entire lifecycle:

Retention
Predict who is likely to churn — and act early.

Acquisition
Target the customers most likely to buy, instead of “everyone.”

Experience
Move toward the long-discussed segment of one. Younger generations expect personalized communication, offers and support.

Operational efficiency
Communicating to the wrong customers is costly — not because an email is expensive, but because irrelevant outreach damages loyalty.

Meanwhile, VoC adds something unique: real-time feedback about product perception, service quality and process friction, making it the “cheapest operational audit” a company can have.

Read also: Use data to make informed decisions. Understanding the Importance of Data-Driven Decision Making

Use cases from the field

Pharma: designing what customers actually need

A large pharma company digitized a previously analogue reimbursement process. By analysing comments from patients and healthcare professionals on social platforms, two additional modules emerged as essential:

• direct communication between patient and HCP,
• personalized education on the disease.

These insights weren’t identified internally – they came directly from unstructured market feedback. Including them made the solution more valuable and easier to implement with stakeholders like the Ministry of Health.

Read also: Big Data in healthcare: management, analysis and future prospects for healthcare organizations

Banking: from churn and propensity to behavioural insights

Banks actively use predictions such as churn risk, propensity-to-buy or segmentation models. But the real change comes from combining them with behavioural signals: app usage, transaction patterns, and voice-of-customer input.

Machine Learning finds customer segments automatically, but explaining them used to be difficult. GenAI now helps describe segment behaviour in plain language – making analytics accessible to business teams.

What counts as “Voice of the Customer”?

Practically, it’s the continuous analysis of unstructured data at scale, refreshed daily or even hourly.

Sources include:

• comments, reviews, posts,
• call centre transcripts,
• audio-based sentiment,
• any form of “free speech” not influenced by structured surveys or questionnaires.

What matters most is actionability. Reports without operational follow-up add no value. In a mature setup, organizations use real-time dashboards to guide decisions the same day – not weeks later.

How GenAI transforms VoC and customer analytics

1. Summaries at scale

10,000 comments? GenAI condenses them during your morning coffee.

2. Explaining ML-driven segments

Machine Learning segments are powerful, but often challenging to interpret.
GenAI generates human-readable descriptions that product and marketing teams can use immediately.

3. Personalized communication

With customer attributes in place, GenAI can tailor emails, notifications, offers or scripts – enabling “segment of one” communication in practice.

4. Accessibility for non-technical teams

Sales, CX and marketing teams can finally use advanced analytics without writing code.
This shift accelerates adoption and encourages experimentation.

Low-/no-code tools support this trend, allowing small teams to build prototypes quickly – though enterprise deployment still requires proper governance.

Implementation: where organizations really struggle

The greatest obstacle is rarely the model itself – but the operational process around it.

One bank had excellent analytics, yet results were ineffective. Why?

Analysts exported fresh data to Excel, reworked it for days, prepared presentations, waited for meetings… By the time campaigns launched, customer behaviour had already changed.

The lesson is clear: real-time analytics is useless if wrapped in a slow decision cycle.

Other challenges include:

• unclear ownership of the data and process,
• scattered responsibilities,
• insufficient platform readiness,
• PoCs that never evolve into production.

VoC and advanced analytics require both business and technology ownership – not one or the other.

How to start (and avoid getting stuck in POC mode)

Start small

Choose one product, one market or one business line. Prove value quickly, capture results and use them internally to gain traction.

Design for scale from day one

Even a small PoC should:

• have a clear owner,
• include reporting for decision-makers,
• align with a future architecture that supports 10× more traffic.

Build end-to-end

A PoC isn’t just a model. It should include:

• data ingestion,
• analytics,
• insights,
• dashboards,
• decision outputs.

Only then can leadership understand its value and support further investment.

Why a partner can accelerate results

Customer analytics, VoC and GenAI require a combination of strategy, business understanding, data engineering, modelling and governance.

Not every organization can build all these competencies internally — at least not quickly.

Sometimes the right move is to apply experience gathered from dozens of similar projects, avoid common pitfalls, and accelerate adoption with a partner.

As competition intensifies and fintech-like experiences set new standards, companies that fail to modernize customer analytics risk falling behind.

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This article explains why an AI business strategy is essential, what pitfalls organizations face, and how to design an approach that drives growth, trust, and innovation. Drawing on insights from our Prompt & Response webcast, you will find practical steps, real-world analogies, and lessons that turn hype into impact.

Why AI strategies fail without a clear vision

Most organizations start with enthusiasm. They launch AI projects to explore chatbots, analytics, or generative AI pilots. But enthusiasm alone does not guarantee success. Multiple studies report that roughly eight to nine out of ten pilots never reach production. IDC (with Lenovo) found 88% of AI POCs fail to scale, RAND estimates more than 80% fail overall, and Capgemini reports similar numbers.

The issue is often a missing AI vision. Leaders may explore AI because “others are doing it,” not because they’ve defined what AI can address in their own business process. A well-crafted AI strategy requires more than experiments. It must align with business priorities, resources, and customer expectations so that AI initiatives are aligned with overall business objectives.

Imagine hiring a team of brilliant architects without telling them what kind of house you need. You’ll get beautiful sketches, but no home you can live in. That’s what happens when companies adopt AI without defining overall business outcomes.

What makes a successful AI strategy in 2025?

A successful AI strategy connects four dimensions:

• Business value: AI must deliver measurable business value, such as efficiency gains or revenue uplift.
• Data strategy: Without quality data for AI initiatives, the best ai model will fail.
• Culture: AI adoption depends on people trusting AI and seeing it as support, not a threat.
• Technology and governance: The right AI platforms, combined with AI governance, ensure reliable responsible AI principles and readiness for ai regulations.

Companies that master these elements unlock value from AI. They move beyond demos and into production AI that generates sustainable business outcomes.

How to develop AI business strategy that works

To develop an AI strategy, start with the same discipline you apply to other business strategies. Define your AI vision, objectives, and guardrails. Then ask: what business problems AI could solve better than humans alone?

An effective AI strategy does not chase shiny tools. It asks where AI will create impact. For instance:

• In customer service, AI assistants can handle repetitive queries, freeing humans for empathy-driven tasks.
• In finance, AI systems can flag fraud faster than traditional monitoring.
• In HR, AI tools can recommend training paths based on skills and career goals.

The lesson is simple. A robust AI strategy begins by mapping opportunities to business objectives and designing an effective AI strategy that can scale across the overall business strategy.

Common challenges in AI adoption

AI adoption is rarely only a technical issue. Employees often resist, worried about job loss. Leaders fear compliance risks. Data teams struggle with inconsistent systems. IDC highlights unclear ROI, lack of AI talent, and data quality gaps as top blockers.

Consider a retail bank testing AI applications for churn reduction. Technically, the AI solution works. But adoption fails because staff do not trust the predictions. Without training and cultural support, the AI initiative remains unused.

This is why responsible use of AI matters. Communicate openly, involve employees, and show how AI supports rather than replaces them. AI adoption succeeds when people feel empowered, not threatened. 

The role of governance and responsible AI

No comprehensive AI strategy works without AI governance. Rules on privacy, fairness, and explainability are not optional. With evolving AI regulations such as the EU AI Act, companies must ensure transparency and monitor AI in production. Deloitte’s 2024 research confirms governance and compliance are now the main barriers to scaling AI.

“New regulations, such as the EU AI Act, clearly state that user protection, algorithm transparency, and the ability to demonstrate compliance with ethical principles are no longer just good practice — they are mandatory. AI Governance will therefore become not an option, but a necessary standard for any organization that wants to use AI in a scalable and trusted way. And that’s a good thing – because it means that Artificial Intelligence is truly maturing as a business tool” – summarized Marek Czachorowski, Head of Modern Data Practice at Inetum, in a recent “Business Insider” interview.

A responsible AI framework protects both brand trust and customers. This includes systems to ensure your AI systems remain compliant, auditable, and aligned with responsible AI principles.

Think of governance as the traffic lights of AI. Without them, everyone drives fast, but accidents are inevitable. With them, AI flows smoothly and safely, and it integrates into the overall business strategy.

Choosing the right AI use cases

Organizations often ask: Where should we start? The answer lies in selecting AI use cases that balance feasibility with impact. Leaders succeed when they concentrate on fewer, high-value areas instead of scattering resources across dozens of AI projects.

Examples:

• Automating compliance checks for marketing campaigns with AI marketing agents.
• Using AI agents to classify support tickets in real time.
• Letting AI services suggest optimal inventory levels.

These are specific AI applications that can be piloted quickly, scaled easily, and deliver early business value. Remember: AI initiatives shouldn’t just be experiments. They must connect to business models and KPIs.

“The era of experimentation is coming to an end – the era of responsibility is starting. Over the past few years, many companies have been testing the capabilities of AI in smaller, controlled projects. Today, we are seeing a clear shift: security, regulatory compliance, and full transparency of operations are becoming priorities”. – Marek Czachorowski, Head of Modern Data Practice.

Steps to building an AI roadmap

A practical AI roadmap can be built in five steps:

1. Explore AI opportunities: brainstorm cases for AI with business stakeholders.
2. Prioritize: score use cases by impact and feasibility.
3. Pilot and learn: implementing an AI project on a small scale to test assumptions.
4. Scale and integrate AI: move from prototype to AI deployment and integration of AI into workflows.
5. Monitor AI: continuously check outcomes, compliance, and adoption.

These steps to building an AI roadmap ensure that AI initiatives are aligned with overall business priorities. Accenture reports that scaling even one strategic AI innovation makes companies nearly 3 × more likely to exceed ROI.

The importance of data in AI development

Every AI initiative lives or dies by data. Poor data means poor outcomes. A data strategy is the backbone of developing an AI strategy.

To succeed, organizations must:

• Integrate siloed sources for a single view of the customer.
• Improve data quality to ensure that AI learns from accurate inputs.
• Build governance to track data for AI initiatives responsibly.

Imagine training a chef with spoiled ingredients. The result will not impress anyone. The same is true for AI: investing in AI without clean data wastes resources.

Enabling business outcomes through AI implementation

AI implementation is not about deploying AI tools for their own sake. It is about leveraging AI to reach business outcomes.

When companies implement AI initiatives, they should track both technical and financial impact. Does predictive maintenance cut downtime? Do customers feel more trust?

This is where AI strategy implementation must include KPIs that deliver measurable business value. Otherwise, AI remains a cost, not a growth driver.

Building trust with responsible AI principles

Trust is the foundation of AI adoption. Customers will not accept “black box” AI systems. Leaders will not risk AI investments if AI outcomes are unpredictable.

Embedding ethical AI, fairness, and responsible AI principles reassures stakeholders. Think of it as a contract: AI delivers results within agreed limits. This creates winning AI approaches that scale sustainably and enable AI as part of normal operations.

Practical accelerators from the Prompt & Response webcast

In Prompt & Response #2, we presented a five-hour AI discovery workshop to accelerate building an AI strategy. The format includes four stages:

• Inspiration,
• Demystification,
• Ideation,
• Strategizing.

It is a lightweight way to support AI initiatives, explore AI capabilities, and align AI thinking within the organization.

An AI strategy is a comprehensive blueprint for people, process, and technology. Strategy is a comprehensive plan that leaders must own. AI strategy requires courage and foresight.

Why AI strategy is vital for future business

A strategy is a comprehensive plan for success. In 2025, an AI strategy is vital. Organizations that hesitate risk being left behind as competitors use AI to personalize services, cut costs, and innovate faster. Accenture notes that leaders expect major gains, including 11% cost reduction and 13% productivity improvement, within 18 months.

The impact of AI is comparable to the internet revolution. Just as firms that ignored the web lost relevance, those without an AI plan will fall behind. AI is essential to modernize traditional business models and unlock growth.

Summary: what to remember when developing AI strategy

• Above all, AI strategy requires courage, imagination, and leadership to rethink business models.
• AI strategies fail when they lack vision or connection to business objectives.
• A comprehensive AI strategy balances business value, data strategy, culture, technology, and AI governance.
• Successful AI adoption requires trust, training, and communication.
• Governance and compliance are non-negotiable, design for responsible AI and evolving AI regulations early.
• Focus on AI use cases that are feasible, scalable, and impactful.
• Build an AI roadmap step by step: explore AI, prioritize, pilot, scale, monitor AI.
• Data quality is the foundation of every AI model and AI development process.
• AI initiatives are aligned when they create measurable business outcomes.

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Frequently asked questions about AI business strategy

Introduction

In the second episode of Prompt & Response, host Piotr Mechliński altogether with Wiktor Zdzienicki explore a challenge every organization faces in 2025: how to move from the hype around Artificial Intelligence to a truly business-led AI strategy that scales.

The discussion covers:

• why so many AI pilots fail to grow,
• the essential pillars of a sustainable strategy,
• the risks organizations must anticipate,
• how to select use cases that actually create value,
• introduction of a practical five-hour AI discovery workshop format that companies can apply right away.

Why so many pilots never go beyond proof of concept

Eight out of ten AI pilots never progress to full deployment. This high failure rate often comes down to poor selection of proof-of-concepts, projects chosen at random with no real link to a business problem. Many organizations run pilots simply because the competition does, or because management expects it, not because there is a genuine need.

Another recurring issue is the lack of internal ownership. Without a leader who champions the initiative and aligns stakeholders, even technically successful outcomes remain unused. Teams may deliver working prototypes, but they are treated as isolated experiments rather than embedded business solutions.

Companies also tend to opt for small, risk-free pilots. These projects are easy to deliver but bring no measurable business impact. Instead of targeting use cases that could move KPIs or affect financial performance, organizations pick “safe” ideas and then struggle to prove their value when the pilot ends.

The pillars of an AI strategy

A robust AI strategy is not only about tools and technology. It should connect Artificial Intelligence to the company’s mission and ensure adoption across the organization. Five pillars are especially important:

• Business value: Every AI project must align with corporate goals and deliver tangible outcomes such as revenue growth, customer satisfaction, or efficiency.
• Data: High-quality, accessible, and timely data is the foundation of AI success.
• Culture and organization: People matter most. There must be leaders, champions, and adoption mechanisms to make solutions stick.
• Technology: Models, platforms, and MLOps practices are important, but they should come only after business needs are clear.
• Governance and ethics: In Europe especially, compliance, monitoring, and ethical frameworks cannot be ignored.

Many companies discover that culture and processes, rather than technology, are the main blockers. Tools do not transform businesses, people do.

Where to start: choosing the right use cases

Organizations often wonder how to begin. The answer is not in massive transformation programs or trivial pilots. The best place to start is with quick wins that bring real value.

A practical method is the impact–feasibility matrix, which helps teams identify cases that are both realistic and meaningful. Workshops with business areas such as sales, finance, or operations often generate ideas ranging from small process improvements to ambitious AI-driven innovations.

At the same time, leaders should avoid blindly following hype. Just because chatbots are popular does not mean your company should build one. Sometimes, the real priority is more fundamental, such as fixing reporting or data warehousing. In many projects, “AI” initiatives eventually turn into analytics or BI improvements, because that is where the true value lies.

Also read:

• The rise of AI agents – your new digital coworkers
• AI marketing agents are reshaping how campaigns are built, approved, and delivered
• Revolutionizing coding with the AI coding agent

Common risks to be aware of

Several risks regularly derail AI initiatives:

• Data quality and access: If inputs are incomplete or outdated, the solution quickly becomes irrelevant and may be abandoned within weeks.
• Adoption by employees: Staff who have done their work for years without AI often fear change. Without careful communication and a clear demonstration of benefits, adoption will stall.
• Analysis paralysis: In large or highly regulated companies, months or even years are sometimes spent creating 300-page strategy decks. By the time they are complete, they are already outdated, because technology changes in quarters, not decades.

What AI strategies will look like in the near future

In the next two years, AI strategies will likely become shorter, more iterative, and closer to the business. They will have to account for stricter regulations while constantly refreshing focus, since what is advanced today may be obsolete tomorrow.

Instead of lengthy documents, organizations will move toward lean approaches: iterative roadmaps, actionable guidelines, and strategies that evolve with each new technology wave.

Imagination, leadership, and courage

AI is not just about tools. It is a chance to rethink business models. This requires time for reflection, honest discussion, and leaders who have the courage to break routines. The most dangerous sentence in business remains: “We’ve always done it this way.”

Organizations need leaders who combine imagination with humility. They must be open to testing new paths and willing to accept that ego should never block innovation.

A practical shortcut: the AI discovery workshop

For companies that want to move quickly, a five-hour AI discovery workshop can be the perfect first step. It covers four stages:

1. Inspiration: Industry-specific case studies to show how others apply AI and data.
2. Demystification: A quick overview that clarifies what AI really is, moving beyond the “AI = GPT” simplification.
3. Ideation: Hands-on exploration of a chosen business area, identifying processes and roles where AI can create measurable value.
4. Strategizing: Consolidating insights into the foundation of an AI strategy.

The aim is not to create a final strategy in one session but to ensure organizations leave with more than just a list of use cases. A list is not a strategy. Participants gain a clear sense of what needs attention in the future and practical guidance on how to continue the journey.

Conclusion

The five-hour workshop can be seen as a miniature version of a full AI strategy exercise, or as a demo of how to build one in the long run. It is the first step toward a comprehensive, business-led AI roadmap.

Ultimately, Chat GPT, Google Gemini, Claude Code and other AI tools are only part of the story. The real value comes from combining the capabilities of AI with human creativity, business knowledge, and leadership courage.

If you are considering building an AI strategy, even starting with something as simple as a discovery workshop, reach out to us. We will be glad to explore opportunities and work with you.

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What is an AI agent in marketing, and why does it matter in 2025?

An AI agent is more than a chatbot. It’s an intelligent system designed to act, decide, and iterate autonomously – not just used for content creation. In Prompt & Response, we explored how an AI marketing agent can:

• select the right audience,
• draft personalized notes,
• check every line against compliance rules before sending anything out.

Why does this matter in 2025? Because customers expect precision. Spray-and-pray outreach no longer works. Every marketer knows the frustration of sending broad messages that erode trust. AI agents in marketing address this head-on: they enable “segment of one” campaigns, tailoring offers based on real-time data and keeping every message safe from compliance mistakes.

How AI marketing tools change the way campaigns are built

Think of your last email campaign. You probably spent weeks preparing content, segmenting audiences, and waiting for compliance approval. Now imagine the same campaign being drafted, vetted, and approved in minutes with help of AI marketing agents.

The workflow looks like this:

• A targeting agent selects customers based on data-driven signals.
• A marketing agent tailors the copy, using natural language that reflects your brand’s voice.
• A compliance agent reviews tone, claims, and legal constraints, automating 80% of manual checks.
• A human reviews the messages and gives a final “go”.

The result? Faster campaign execution, higher personalization, and reduced risk. For marketing teams, this feels like getting an extra colleague — an AI assistant who never gets tired of reviewing copy.

From traditional marketing to AI marketing automation

Traditional marketing relied on intuition, manual data pulls, and creative brainstorming. AI marketing automation introduces precision: instead of blasting generic newsletters, AI tools tailor each outreach based on customer activity.

Marketers can now:

• Automate repetitive marketing tasks without losing control.
• Integrate AI features directly into their existing marketing stack.
• Optimize campaign timing, wording, and targeting using signals based on real-time data.

Just as spreadsheets once transformed finance, AI-powered agents are now reshaping marketing operations.

What are the benefits of AI agents for marketing teams?

Let’s break it down:

1. Time savings – campaign assets in minutes, not days.
2. Hyper-personalization – offers tailored to individual behaviors, not just segments.
3. Compliance by design – every campaign includes an audit trail, reducing sleepless nights for legal teams.
4. Scalable creativity – generative AI combined with marketer insights to tailor messages across multiple channels.

According to McKinsey, personalized campaigns can drive 10–15% revenue uplift. The benefits extend beyond marketing teams. Customers receive fewer irrelevant offers and gain more trust in the brand.

How does AI outreach work in practice (and why does it feel human)

One highlight from our webcast demo was seeing AI outreach in action. Imagine entering a simple product brief and letting an AI agent tailor hundreds of one-to-one messages, each checked for compliance before being released.

It’s like hiring a team of AI-powered agents who perform sales and marketing activities tirelessly. They:

• Follow the rules.
• Integrate with your data.
• Optimize the message structure instantly. 

Still, agents cannot fully replace human oversight. Marketers remain the editors who ensure authenticity.

Common frustrations: why marketing AI agents don’t replace human creativity

As Beata, our head of marketing, put it: “Sometimes AI-generated content feels generic. I can spot it instantly.” 

AI systems generate at scale, but agents don’t always capture brand nuance. That is why human input remains vital. Marketers fine-tune tone, context, and cultural references. The lesson is simple: marketing AI agents are intelligent, but they’re not artists.

How do AI agents handle data, compliance, and trust?

Every marketer dreams of full automation, but compliance is the non-negotiable backbone. GDPR fines, the EU AI Act uncertainties, and ethical risks make caution essential.

As Wiktor Zdzienicki, Inetum’s AI & Data Practice Leader, reminded us: “Garbage in, garbage out.” Poor data quality means poor results. AI systems must work with clean, unified customer data to succeed.

That is why AI agents integrate compliance at every step. They timestamp each decision, route sensitive cases to human review, and keep regulators off your back.

What are real use cases for AI agents in marketing campaigns?

From banks to telecoms, use cases for AI are expanding. Imagine:

• A retail bank using an AI marketing agent to target customers eligible for loan offers without violating GDPR.
• A telecom firm reducing churn by letting an agent tailor offers during live customer service calls.
• A B2B company automating email marketing at scale while ensuring every claim is accurate.
• An e-commerce retailer using AI agents to analyze data from 50,000 products in Google Ads, segment items by performance, allocate budgets dynamically, and optimize ROAS across campaigns.

These examples show how AI agents for marketing improve both marketing performance and compliance. They allow teams to perform marketing functions across multiple channels, while staying aligned with marketing objectives.

The future of AI: marketing operations via agentic AI and multimodal campaigns

Looking ahead, the future of AI lies in agentic AI: systems that not only generate content but act independently across business processes. Imagine a generation agent that drafts text, a knowledge base agent that sources references, and a compliance agent that approves, all running in the background.

In marketing in 2025, multimodality will matter most. Agents can even generate images, videos, or personalized graphics, tailoring assets for each customer. This is where agents operate like true colleagues, enhancing marketing effectiveness while marketers focus on strategy.

Top 7 benefits of AI marketing agents in 2025

1. Time savings through automation of campaign workflows
2. Hyper-personalization at scale (“segment of one”)
3. Compliance by design with GDPR and EU AI Act
4. Built-in predictive analytics to anticipate customer needs
5. Audit trails for defensible compliance
6. Integration with CDP and marketing platforms without rip-and-replace
7. Improved customer trust and engagement

The rise of AI marketing agents

AI marketing agents stand out for three reasons:

1. Speed: they process massive data sets in seconds.
2. Predictive analytics: they forecast customer needs and market trends.
3. Compliance: they automate rule-checks and create audit trails.

Instead of relying on fragmented spreadsheets or manual reviews, marketers get clear insights that improve campaign targeting. By combining efficiency, personalization, and analytics, AI agents are transforming marketing strategies.

For companies willing to embrace them, the payoff is clear. Smarter campaigns, faster launches, and a stronger competitive edge in a crowded digital landscape.

Key takeaways for marketers planning their next steps in AI technologies

• AI marketing tools are no longer experimental – they’re core to enterprise marketing.
• An AI marketing agent can help automate workflows, integrate with your stack, and deliver compliance-ready campaigns.
• AI agents are transforming marketing, but agents cannot fully replace human creativity – marketers remain the final editors.
• Data quality, trust, and compliance are as important as personalization.
• The best AI approach combines generative AI, analytics, and human oversight.
• By 2025, AI agents for digital marketing will be standard, not optional.

The big question for every marketer is no longer if you should use AI, but how fast you can implement it responsibly to grow your business.

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Frequently asked questions about AI marketing agents

Prompt & Response – episode 1 – AI compliance marketing agent

How can AI help marketing teams speed up campaigns while staying compliant with brand rules and regulations?
In the first episode of Prompt & Response Inetum experts discuss the benefits, frustrations, and future of AI agents in marketing.

The reality check

The discussion opens with a striking statistic: according to McKinsey, 78% of companies already use AI in at least one business function. At the same time, MIT notes that 78% do not have AI-ready data. The paradox? Everyone wants AI agents in marketing and customer service, yet many organizations lack the foundations to use them effectively.

Leaders like Unilever are already reaping rewards – reinventing product shoots with AI, accelerating campaigns, and halving costs. But speed introduces new risks. That’s why the webcast explores how to make AI both fast and compliant.

How marketers use AI today

Beata recalls her early years in marketing, when almost every asset, texts, graphics, campaign materials, was created manually. Now, more than 40% of marketers use AI for content creation (HubSpot), and her own team relies on it daily.

AI tools are used to spark campaign ideas, generate quick drafts, and personalize messages. The biggest benefit so far? Time-saving. With tools like Contadu, Beata can create SEO-compliant texts in seconds, spending more time on reviewing and polishing rather than starting from scratch.

The promise of personalization

Asked about the future potential of AI, Beata points to personalization at scale. Marketing teams constantly struggle to reach the right audience with the right content while staying compliant with GDPR. AI marketing agents could enable true “segment of one” campaigns – messages so precise that they feel crafted for each individual.

She also highlights onboarding as a less obvious but valuable use case. New employees often need months to learn brand tone and guidelines. AI assistants could speed this up by helping them create compliant, on-brand assets from day one.

AI in marketing: the frustrations

Despite the promise, Beata admits that AI isn’t perfect. Generated content often feels repetitive or generic. As an experienced marketer, she can instantly spot “AI-written” text. That creates a challenge: ensuring quality and adding the “human touch” to prevent campaigns from sounding artificial.

Wiktor, who is seeing this aspect from technical perspective, agrees, adding that the flood of AI-generated content risks lowering the overall quality of the web. Future models trained on today’s AI outputs may end up amplifying biases or losing natural human voice. “If you use it wisely, it’s powerful,” he says, “but blind automation could backfire.”

What the ideal AI assistant should look like

Asked about her dream AI assistant, Beata imagines one tool combining several key features:

• content creation that doesn’t sound robotic
• personalization based on customer data
• brand alignment across tone and messaging

All merged in one AI system that supports marketers instead of overwhelming them.

Why adoption is accelerating

Why AI is being adopted so rapidly in marketing? First, as Wiktor explains, companies are sitting on mountains of customer data, and expectations for personalization are higher than ever. Second, the rise of large language models after 2022 democratized access to AI. For the first time, non-technical staff could use AI tools directly.

Early outputs were generic, but as marketers learned the importance of context and better prompting, results improved. Multimodality: text, image, audio, video, further expanded possibilities. Creating a full ad campaign with just a few prompts is now a reality.

Finally, both executives and customers are driving adoption. CEOs demand that marketing teams “use AI,” and customers expect smarter, more relevant offers.

Data and compliance: the hard truths

“Garbage in, garbage out,” Wiktor warns. Without clean, unified data, AI models will deliver poor results. Many organizations struggle because customer data is scattered across systems, duplicated, or protected under GDPR.

Compliance is the second key risk. Marketers must align with brand rules, internal guidelines, and external regulations like the EU AI Act. Bias in AI models is another concern – sometimes producing offensive or misleading outputs. For now, human-in-the-loop review remains essential before campaigns go live.

Use cases beyond text generation

AI in marketing is evolving beyond simple copywriting. Here are some examples shared by Wiktor:

• product descriptions at scale – transforming technical specs into customer-friendly narratives
• voice of the customer – analyzing reviews, sentiment, and employee feedback at scale using natural language
• campaign optimization – future agents could select channels, monitor budgets, bid on ads, and even generate performance reports

The live demo: Marketing Compliance Agent

The centerpiece of the episode was a demo of Inetum’s Marketing Compliance Agent.

Here’s how it works:

1. targeting agent filters the right customers from databases
2. marketing agent generates personalized notes from a single product brief
3. compliance agent reviews every message against brand and legal rules, automatically making small fixes
4. messages are approved instantly or routed for human review if needed

The system reduces irrelevant outreach, saves manual review time, and minimizes regulatory risks. Every message carries an audit trail for legal and compliance teams.

Why it matters for marketers

Beata as a marketer believes such tools will become essential. “Marketing is about creativity and speed, but also about compliance and trust. These agents cover all those needs.” Faster execution, better targeting, and built-in safeguards make them a natural fit for future campaigns.

Wiktor adds that the roadmap includes more autonomy (fully automated campaign execution), multimodality (text + images), and adaptability to evolving compliance rules.

Wrap-up

AI marketing agents aren’t just a trend, they’re the future. They won’t replace human marketers but will act as intelligent collaborators, helping teams run campaigns that are faster, more relevant, and always compliant.

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AI Applications Reshaping Business in 2025 

Generative AI sparked a wave of revolution that shows no signs of slowing down. Traditional AI already had an estimated global value potential of  $11–18 trillion, according to McKinsey. 2025 is undoubtedly the year of another LLM-based technology: agentic AI.  

By combining autonomous decision-making and system integration, AI agents transform generative AI into digital collaborators, gaining outstanding results.  In call centers alone, according to McKinsey, autonomous proactive agents can reduce resolution time by 60–90%, with 80% of cases being resolved automatically. While agents are commonly associated with chatbots, their applications extend far beyond that across many industries. I’ll aim to explain what agents are and explore typical use cases in selected sectors.  

What is an AI agent? 

Welcome to a world where AI isn’t just helping – you might say it’s working. AI agents are intelligent systems that go beyond scripted interactions. Powered often by large language models, they connect natural language comprehension, data access, and task logic to act proactively and independently. Companies are increasingly willing to deploy AI agents which are advanced AI systems to get competitive advantage.  

Also read:

• AI in project management
• Revolutionizing coding with AI coding agent

How can companies use AI agents? 

Imagine a system that not only understands your request – “prepare the sales forecast for Q3” – but fetches the data, runs the analysis, emails the report, and schedules a review meeting. These agents operate under specified goals, can learn over time, and may run visibly – through chat – or silently in the background. Their defining feature is goal-driven autonomy, transforming artificial assistants into digital coworkers. 

How AI agents work? 

Think of agents as the “turnkey automation engines.” They follow a precise perception–decision–action loop: they observe signals (like system alerts or emails), decide using reasoning engines or LLMs, and then act – updating records, triggering workflows, or escalating tasks. Today’s agents also incorporate memory components, tool orchestration, and task coordination, enabling them to manage complex multi-step processes without intervention. 

Implementing AI agents 

Modern implementations often use a hybrid architecture, featuring an orchestrator that directs specialized sub-agents. Picture a travel-booking agent working alongside calendar and ticketing agents – coordinating to book flights, reserve hotels, and notify participants. This multi-agent pattern is seen across enterprise automation platforms with growing frequency.  

Key types of AI agent system 

There are at least four powerful archetypes shaping agentic AI excellence: 

• Reactive agents are like digital reflexes: they respond instantly to input, without memory or planning. Think about transaction alert monitors or system uptime watchdogs.  
• Deliberative agents act like digital strategists: they reason and plan across multiple steps. Picture a forecasting assistant that builds internal models, evaluates scenarios, and adapts recommendations dynamically.  
• Conversational agents serve as user-facing colleagues: they hold fluid, multi-turn dialogue, pull in context, and even take action – like scheduling meetings or generating analytics. Many organizations are now layering LLMs on top of enterprise chat platforms.  
• Tool-using agents are digital operators: they connect to APIs or apps, generate charts, files, or record – bridging insight and outcome.  

In practice, these archetypes often collaborate – for example, a deliberative agent may trigger multiple tool-using agents to fulfill its plan, overseen by a reactive watchdog. 

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Capabilities and risks of using AI 

Benefits of AI agents 

• AI agents are game-changers because they operate with autonomy, scale dynamically, learn from interaction, and integrate deeply with enterprise systems. AI agents offer a wide range of benefits. 
• Once launched, they relieve teams from repetitive work – running reporting, monitoring systems, or responding to emails – while maintaining 24/7 availability. 
• They reduce time spent, cut errors, and free professionals to focus on strategic tasks. In many early deployments, businesses have reported up to 50% gains in efficiency, especially in HR, finance, and customer service.  

Responsible AI – risks of AI assistants 

• With this power comes responsibility. Not without a reason, there are many discussions on responsible AI. AI agents – especially those relying on LLMs – raise explainability concerns. Decision logs must be traceable, with chain-of-thought audits that shed light on agent reasoning. 
• Accountability needs to be clear: which human is overseeing the agent’s actions? Guardrails, access controls, and regular testing are essential to prevent data leaks or manipulation. 
• Lastly, transparency is key – users must know they’re interacting with AI, in line with regulations like GDPR and the EU AI Act.  

Use cases across the enterprise 

Let’s bring these capabilities to life with practical examples: 

• Customer Service – a telecom provider deployed conversational agents that resolve routine support requests, freeing human agents to focus on high-touch cases and increasing customer satisfaction.  
• Finance & Risk – a financial institution uses a combination of reactive agents for fraud alerts and deliberative agents for compliance task automation – later validated through real-time internal audits.  
• Sales & Marketing – startup sales teams use lead-scoring agents that automatically rank prospects, generate outreach templates, and initiate cadences – removing manual steps from campaign execution.  
• HR – a global services firm adopted tool-using agents to manage onboarding paperwork, schedule training, and answer benefit inquiries – dramatically reducing admin time and increasing satisfaction.  
• R&D – life sciences company built agents that review clinical literature, extract findings, and surface key insights ahead of research meetings, cutting analyst prep time by 60%.  

Stories like these show how agents – working in context and across tools – are delivering efficiency, accuracy, and business agility. 

How to get started with AI agents? Start smart!  

Launching agents is as much about strategic discipline as it is about technology. Here’s our seven-step roadmap: 

1. Identify a focused use case – one that is measurable and meaningful. 

2. Run a short pilot (4–8 weeks) to surface technical and UX insights. 

3. Ensure clear, accessible data to power reliable agent reasoning. 

4. Build in human oversight, especially in critical areas like compliance or finance. 

5. Measure rigorously, tracking effectiveness and user satisfaction. 

6. Educate your team on agents’ roles, responses, and limits. 

7. Scale gradually, expanding only after pilots prove reliable and beneficial.  

Understanding AI agents – summary  

• AI agents are autonomous software entities designed to perform tasks and make decisions within various environments. These agents can be categorized into types such as reactive, deliberative, conversational, and tool-using agents.  

• AI systems often deploy multiple AI agents that can work together, leveraging their unique strengths. For instance, an AI assistant can act as a specialized agent to help users with specific tasks, while other, may focus on data processing or decision-making. 

• AI agents are designed to interact with their environment and can adapt based on feedback, showcasing the difference between traditional AI and more complex agentic AI systems. By utilizing AI agents, organizations can leverage AI capabilities to address real-world problems more effectively. 

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