Across the enterprise, departments are placing increased demands on their organization’s data to enable multi-AI agents. It’s the IT operations (IT ops) department’s challenge to deliver the optimal environment for agentic AI to eventually bring business value.

Enterprises are grappling with a volatile, uncertain business climate – and to address this, they are increasingly turning to their data to draw actionable insights that enable competitive advantages through agents.

As networks grow more complex and the demands on them increase, IT ops departments need to develop better tools, including multi-AI agent systems to enhance the decision-making process by making recommendations aligned to set business goals.

Properly designed and implemented agentic AI solutions are game-changers – but IT ops must be prepared to take advantage of these powerful tools, which requires a well-crafted plan and a partner that can deliver more than just the technology.

The IT ops imperative

In conversations with IT professionals, my ��ѻ��ý colleagues and I have identified a number of common challenges for IT operations at enterprises across all sectors. Simply stated, IT professionals are under pressure to boost service performance while reining in costs – including operating expenses and costs for infrastructure and cloud services. They’re also under pressure to better identify, provision, and deploy the solutions required to allow other departments to take advantage of emerging technologies such as agentic AI.

The organization’s own data is an important source of the information required to help IT professionals achieve these goals. Unfortunately, legacy business intelligence systems often fail to satisfy their needs. There are several reasons for this:

• Analytics systems rarely support strategic foresight and transformative innovation – instead providing business users with yet another dashboard.
• The results are often, at best, a topic for discussion at the next team meeting – not sufficient for a decision-maker to act upon immediately and with confidence.
• Systems typically fail to personalize their output to provide insights contextualized for the person viewing them – instead offering a generic, unsatisfying result.
• Systems often aggregate data within silos, which means their output still requires additional interpretation to be valuable.

In short, many legacy systems miss the big picture, miss actionable meaning, miss the persona – and miss the point.

Based on my experience, I recommend an organization address this through multi-AI agent systems.

With the introduction of Gen AI Strategic Intelligence System by ��ѻ��ý, this could be the very system that bridges the gap between the old way, and a value-driven future. This system converts the vast amounts of data generated by each client, across their enterprise, into actionable insights. It is agentic: it operates continuously and is capable of independent decision-making, planning, and execution without human supervision. This agentic AI solution examines its own work to identify ways to improve it rather than simply responding to prompts. It’s also able to collaborate with multiple AI agents with specialized roles, to engage in more complex problem-solving and deliver better results.

How would organizations potentially go about doing this?   

Define the technology and business KPIs

First, organizations must establish well-defined KPIs and associated roadmaps to take full advantage of agentic AI recommendations – KPIs that align technology with business objectives.

This starts by identifying the end goals – the core business objectives and associated KPIs relevant to IT operations. These represent the IT operation’s key activities that support other departments as they contribute to the organization’s value, and strengthening them is always a smart exercise. The good news is that even small improvements to any of these KPIs can deliver enormous benefits.

The roadmap should leverage pre-existing AI models to generate predictive insights. It should also ensure scalability, reliability, and manageability of all AI agents – not just within the realm of IT operations, but throughout the organization. And it should be designed to leverage domain-centric data products from disparate enterprise resource planning and IT systems.

Finally, the roadmap must identify initiatives to ensure the quality and reliability of the organization’s data by pursuing best-in-class data strategies. These include:

• Deploying the right platform to build secure, reliable, and scalable solutions
• Implementing an enterprise-wide governance framework
• Establishing the guardrails that protect data privacy, define how generative AI can be used, and shield brand reputation

Choose a partner that delivers more than tech

Second, the organization must engage the right strategic partner. While innovative agentic AI systems are essential, that partner must also be able to support the IT team with business transformation expertise and industry-specific knowledge.

��ѻ��ý leverages its technology expertise, its partnerships with all major platform providers, and its experience across multiple industrial sectors to design, deliver, and support agentic AI strategies and solutions that are secure, reliable, and tailored to the unique needs of its clients.

��ѻ��ý’s solution draws upon the client’s data ecosystem to perform root cause analysis of KPI changes, and then generates prescriptive recommendations and next-best actions – tailored to each persona within the IT department. The result is goal-oriented insights aligned with business objectives, ready to help IT empower the organization through actionable roadmaps for sustainable growth and competitive advantage.

*Meaningful, measurable benefits

��ѻ��ý estimates that with the right implementation and support, the potential benefits include augmenting the IT workforce through autonomous processing, touchless data crunching, improved data and systems integrations, continuous monitoring of controls and compliance, and real-time access to reports and insights.

The potential for IT operations to translate these internal gains into meaningful advantages for other departments across the enterprise means that leveraging agentic AI for its own strategic insights cannot be ignored.

^*Results based on industry benchmarks and observed outcomes from similar initiatives with clients. Individual results will vary.

The Gen AI Strategic Intelligence System by ��ѻ��ý works across all industrial sectors, and integrates seamlessly with various corporate domains.��Download our PoV��here��to learn more or contact our below expert if you would like to discuss this further.

Google Cloud and ��ѻ��ý brought together more than 1,800 innovators from around the globe for the Google Cloud Agentic AI Hackathon 

With 93 percent of business leaders  believing that scaling AI agents in the next 12 months will provide a competitive edge, according to the ��ѻ��ý Research Institute, it was paramount for ��ѻ��ý and Google Cloud to come together to help customers harness the promise of multi-agents systems and provide a pragmatic way to deploy them at scale  

In this context, by conducting our proven Google Cloud Hackathon for three years in a row, our motivation was higher than ever to develop a suite of repeatable agents capable of solving real-world business challenges.   

The event used Google Cloud’s latest agentic AI tools available in Vertex AI to deploy multi-agent systems with AgentSpace, Agent Development Kit (ADK), and Agent Engine to build solutions for 23 real-life use cases submitted by ��ѻ��ý clients, following the newly released Agent-to-Agent (A2A) standard. 

Over three weeks, hackers explored how intelligent agents can reason, act autonomously, and adapt to complex environments and how they can be applied to real-world business challenges. 

This was a proving ground for how companies can move from experimentation to execution with agentic AI, while fostering a culture of learning and human-AI collaboration.   

A strategic collaboration for innovation 

The hackathon represented our partnership in action. It leveraged Google Cloud’s advanced AI technologies and ��ѻ��ý’s deep industry expertise to co-create solutions that are not only technically robust but also business ready. 

Our original goal was to develop more than 200 agents on Google’s latest agentic AI platforms. The response exceeded expectations:  256 teams from 39 countries, created more than 320 unique agents during the event. Forty-nine mentors and judges were mobilized to select standout, customer-ready innovations.  

Breakthrough solutions with real-world potential  

The use cases included advanced solutions from a wide range of industries and were judged on innovation, feasibility, and alignment with business needs. 

Here are the nine standout projects from the final round. 

• Aerospace: An agentic AI-powered multi-agent system orchestrates the end-to-end requirement validation process by extracting key data from PDFs and IBM DOORS. It then refines and curates requirement statements, analyzing gaps and inconsistencies, and generates comprehensive, actionable validation reports.
• Consumer products and retail: AI agents optimize procurement, reduce waste, and manage inventory to meet sustainability goals. 
• Automotive and manufacturing: An AI system that automates supply chain and manufacturing to cut delays and costs with proactive decision-making. 
• Consumer products and retail: A retail analytics system examines sales data, advising on new promotion strategies and putting together new marketing material. 
• Banking and insurance: A contact center tool that fetches customers data, suggests live actions, and recommends next steps. 
• Public service: An assistant that simplifies public service access with easy sign-up and step-by-step help. 
• Telecommunications: The AI system detects service issues, recommends fixes, and sends alerts for faster support.
• Banking and insurance: An IT assistant that automates ServiceNow tasks like password resets and triaging to reduce help desk load. 
• Public service: This health tool personalizes check-ins and surveys to monitor patient well-being and trigger alerts. 

Several projects are now advancing to MVP piloting and client co-innovation tracks. The list of agents will also be made available to ��ѻ��ý clients through our Group AI Agents gallery as part of our RAISE (Reliable AI Solution Engineering) agent accelerator. 

Learning through doing 

More than 96 percent of participants completed the Google Agentic AI learning path. The hackathon became a live learning lab, combining structured enablement with hands-on experimentation. 

Bootcamps, workshops, and mentorship from ��ѻ��ý and Google Cloud SMEs ensured that every team had the support they needed, setting ��ѻ��ý as one of the largest Google Agentic enabled partners ready to meet the pressing market demand. 

Unlocking AI at scale  

AI can reimagine business but organizations need to scale to really unlock the full potential and uncover real business benefits. Hackathons are one pathway to AI readiness, by providing training and the opportunity to experiment with the technology as well as build working agents. Implementing agentic AI requires a high level of AI readiness, while creating the right human-AI chemistry to ensure lasting adoption.  

The Resonance AI Framework by ��ѻ��ý helps leaders envision AI’s potential, embed it into the foundation of their operations, and enable human-AI chemistry. It is designed to allow effective interaction between people and intelligent systems, and creates the trust, understanding, and collaboration needed for human and AI agents to build reliability over time, ensuring hybrid teams thrive. This democratization of AI empowers businesses to embed AI into the fabric of everyday operations.����

A culture of collaboration and intrapreneurship 

The hackathon fostered experimentation and cross-functional collaboration. Teams were assembled from different business lines, geographies, and technical backgrounds. This diversity of thought was a key driver of success. 

The hackathon was structured in four phases: 

1. Onboarding, use case definition, and account selection 
2. Client onboarding and business and technical scoping 
3. Training and hackathon program execution 
4. In-production workshops. 

This framework enabled creativity to flourish within clear guardrails and ensured that promising ideas could transition into actionable prototypes. 

Watch the highlights

See the energy, creativity, and impact firsthand

This video features highlights from the live sessions, interviews with participants, and demos of the winning solutions.  

Customizing hackathons: What this means for the enterprise

A hackathon is not only a fantastic innovation hub, it’s also an opportunity to engage our employees from around the world. It provides insights into real-life use cases as well as upskilling knowledge and building culture, and shows how to stay ahead of the competition with new ideas. 

With our breadth of experience, ��ѻ��ý can work with clients looking to explore their own internal hackathon, helping define and prioritize agentic use cases specific to their needs, and upskill employees with challenge-based learning to accelerate skill development and adoption of emerging technologies, empowering teams to experiment and collaborate fosters long-term transformation. 

We can also help explore the possibilities and partner with Google Cloud for strategic collaboration to accelerate business outcomes. 

Looking ahead 

This competition brought together developers, designers, business analysts, and others to deliver multiple points of value: 

• Upskilled ��ѻ��ý talent with hands-on learning and certifications on the latest Google Cloud technologies 
• Expanded our AI agent gallery available to clients 
• Supported AI at scale as part of our Resonance Framework. 

Our goal is now to empower clients to accelerate transformation through intelligent, autonomous systems grounded in human-AI collaboration. A hackathon is just one of the many tools we have available to enable AI-powered enterprises. 

To explore how agentic AI and Google Cloud’s generative capabilities can accelerate innovation in your organization, reach out to our Google Cloud experts. Whether you’re looking to pilot a solution, scale a use case, organize a hackathon, or build a roadmap for transformation, we’re here to help you take the next step, wherever you are in the journey.  

The market reality 

Mobile networks are integral to society. Yet communications service providers (CSPs) face enduring challenges in unlocking the full potential of their infrastructure.  

Despite rapid data growth and geographic expansion, traditional pricing models no longer align with the evolving needs of the industry. To remain competitive, CSPs need innovative ways to monetize their networks and offer differentiated services.  

This calls for new business models that allow for a more dynamic use of network resources, enabling developers to create enhanced applications that leverage advanced network capabilities by decoupling existing stacks. 

The business opportunity 

The next wave of digitalization offers a significant business opportunity by enabling developers and enterprises to access the advanced capabilities of mobile networks through application programming interfaces (APIs). By making high-performing, programmable networks accessible, developers can create applications that enable CSPs to differentiate their offerings, evolve their business models, and generate new revenue streams.  

This shift toward API-driven services empowers developers to dynamically request network resources such as throughput, latency, quality of service (QoS), location, and device and service information which are critical for building innovative high-value applications. 

The market potential for network APIs is immense. As telecom providers expose their network capabilities through APIs, developers will be able to create better applications tailored to specific consumer and industry needs. This will lead to a surge in demand for network services. Research from Omdia predicts that from $161 million in 2023 to $8.7 billion by 2029.��

For this opportunity to be fully realized, an aggregator platform is essential to ensure global availability and scalability. Integrating network capabilities from individual CSPs has traditionally been a challenge, and a global platform will make network features accessible across various regions and networks. By providing developers the access to on-demand network resources, a global aggregator platform will make it easier for developers to innovate and deliver value-added services to enterprises, regardless of their location or the network they use. 

This new business model creates a win-win-win situation for CSPs, developers, and enterprises. By exposing advanced network capabilities via APIs, CSPs will open new avenues for monetization that will boost the return on their infrastructure investments. Meanwhile, developers gain the flexibility to create applications without needing a deep understanding of 5G networks, driving innovation and accelerating the pace at which new services are brought to market. As the telecom industry embraces this new era of programmability and network differentiation, the opportunity for growth and prosperity is vast, benefiting both industries and society. 

Accelerate the power of network APIs across industries 

As mobile networks become more programmable and open, a massive opportunity is emerging—not just for telecom providers, but for the entire digital ecosystem.  

Network APIs are changing the game in designing real best of quality end-to-end solutions, giving developers and enterprises real-time access to powerful network functions like adaptation of quality of service (QoS), verifying and using location, and device intelligence. But, to truly make these capabilities usable and valuable at scale, domain experts play a critical role in igniting industry innovation—leveraging underlying network capabilities as powerful sources of data and value to drive better use cases and solutions.  

On the other side, CSPs have invested heavily in their infrastructure which can now be made accessible via secured and standardized APIs. With deep experience in working across both IT and NT domains, domain experts can help CSPs standardize and expose network APIs, while also stitching them into end-to-end business solutions across different industries. 

What really drives impact is when these APIs are not just exposed, but also meaningfully integrated into real-world use cases and extend or even simplify daily operations. Take financial services, for example. Fraud prevention APIs that tap into network intelligence can be embedded into banking systems to stop fraud in real time by verifying the device or even the subscriber by using the intelligence from CSPs. It is a way to turn network data into real business value, without requiring banks to become telecom experts. 

In other sectors, like manufacturing, healthcare, or media, network APIs can improve automation, enhance capabilities in remote monitoring, and provide even better immersive experiences by providing adaptable frameworks to change latency and reliability exactly when and where it is needed. 

What’s next: Key takeaways for CSPs to tap the potential of network APIs

1. Standardize and scale API exposure. Standardize and securely expose network APIs across regions to create a consistent developer experience and accelerate adoption. 
2. Invest in aggregator platforms. Establish a global aggregator platform to make network capabilities and data accessible, scalable, and usable across different networks. 
3. Monetize programmability, not just infrastructure. Unlock new revenue streams from programmable services, recurring business models, and value-added API integrations. 
4. Fuel developer innovation with telecom intelligence. Provide developers with access to on-demand network features (e.g., QoS, latency, location, device info) to drive the creation of high-value, next-gen applications. 
5. Empower industry use cases in partnership with domain leaders. Collaborate with domain experts to leverage underlying network capabilities and data to create high-impact industry use cases and solutions. 

Take the leap in network APIs with ��ѻ��ý and Ericsson 

Ultimately, domain experts serve as the bridge between telecom capabilities and industry-specific innovation. By packaging network APIs into practical solutions, they help CSPs to monetize their networks while enabling enterprises or even full industries to move faster, reduce risk, and deliver better services. This new layer of programmability is not just a technical upgrade, but the very foundation of the next wave of mobile innovation. 

Ready to unlock the full potential of network APIs?  

Thanks to its deep telecom expertise and its leadership in all the global industries (telecom, financial services, manufacturing, healthcare, media, retail, and more), ��ѻ��ý supports both CSPs and enterprises design innovative use cases delivering value in the industry-specific context and deploysthem with end-to-end integration, and ecosystem enablement.  

Learn how we can help you accelerate innovation and monetization. 

Telco��ѻ��ý is a series of posts about the latest trends and opportunities in the telecommunications industry – powered by a community of global industry experts and thought leaders

Gen Garage is redefining innovation by harnessing AI to build real-world solutions in areas such as disaster management, sustainable farming, and environmental risk mitigation

By fostering talent transformation and embracing cutting-edge technologies, we empower tomorrow’s professionals to shape the future through hands-on impact. Step into Gen Garage, ��ѻ��ý’s ��ѻ��ý & Data innovation hub — where visionary minds, guided by expert mentors, craft transformative AI-driven solutions at the intersection of talent and technology. Fueled by generative AI, machine learning, and automation, Gen Garage accelerates operational excellence and delivers innovations that drive efficiency, inclusivity, and sustainability. As businesses lean into data-powered insights, our solutions stay aligned with evolving needs — helping organizations stay future-ready, while making a difference today.

DisasterX: AI-Powered Faster and Smarter Response for Disaster Management

DisasterX is a cutting-edge AI-powered application designed to revolutionize disaster response by enabling real-time, adaptive decision-making in high-pressure environments. Leveraging agentic AI, DisasterX overcomes the challenge of delayed responses by autonomously analysing vast amounts of real-time data from multiple sources. This allows for optimized rescue efforts, efficient resource allocation, and enhanced recovery strategies. By continuously learning from past disaster scenarios, DisasterX improves prediction accuracy, minimizes human error, and accelerates response times—ultimately saving lives and reducing economic and environmental impact. Its autonomous capabilities ensure scalability and resilience, seamlessly adapting to both local and large-scale emergencies. As the rise of agentic AI reshapes automation, DisasterX stands at the forefront of intelligent, proactive disaster management, delivering greater efficiency and reliability in crisis situations.

Picture 1 : An AI agent (Image credit – Pixabay)

KisanGPT: AI-Driven ��ѻ��ý for Smarter, Sustainable Farming

KisanGPT is an AI-powered platform designed to revolutionize farming by providing real-time insights on crop health, weather forecasts, and sustainable agricultural practices. Using advanced language models, satellite data, and weather analytics, it offers personalized recommendations to help farmers optimize yields, conserve resources, and tackle climate challenges. The platform supports multilingual access and speech recognition, ensuring inclusivity for farmers across diverse regions. By integrating market trends, government policy updates, and best farming practices, KisanGPT enhances decision-making, boosts profitability, and promotes eco-friendly agriculture. This AI-driven solution not only improves efficiency but also fosters a more resilient and sustainable farming ecosystem.

The Green Horizon: AI-Powered Vegetation Hazard Management

The Green Horizon is an AI-powered solution that detects and manages vegetation hazards near power lines, preventing wildfires, outages, and safety risks. Using satellite imagery, machine learning, and weather forecasting, it provides real-time monitoring, predictive insights, and proactive risk mitigation. With an intuitive chatbot and geospatial analytics, it empowers organizations to optimize resources, reduce costs, and ensure safer, more sustainable infrastructure. By automating hazard detection and integrating user feedback, it enhances decision-making for utility companies and environmental agencies. This innovative approach not only improves operational efficiency but also supports long-term sustainability and infrastructure resilience.

Market Trends / Key Opportunities and Developments:

Gen Garage strategically aligns its initiatives with prevailing market trends to address pressing societal and business needs.

The increasing investment in AI for disaster management presents a significant market opportunity for DisasterX to deliver innovative and data-driven solutions. With the rise of smart cities and the widespread adoption of IoT sensors in disaster-prone areas, vast amounts of real-time data can be leveraged for predictive analytics and rapid response. Gen Garage is at the forefront of this transformation, utilizing AI to enhance disaster preparedness and resilience. As climate change intensifies the frequency of natural disasters, the demand for intelligent, automated response systems continues to grow, positioning DisasterX as a key player in optimizing disaster mitigation and emergency management strategies.

KisanGPT taps into the growing demand for AI-driven agricultural solutions. By leveraging real-time analytics and precision farming techniques, Gen Garage maximizes market opportunities, helping farmers and agribusinesses adopt smarter, data-driven strategies. With advancements in AI and increasing support for sustainable farming practices, the platform positions itself as a game-changer in modern agriculture, driving innovation and long-term growth in the sector.

The Green Horizon initiative taps into the growing need for AI-driven environmental risk management. By integrating geospatial intelligence and predictive analytics, Gen Garage maximizes market opportunities, enabling utility companies and agencies to adopt smarter, data-driven strategies for sustainability and infrastructure resilience.

Gen Garage is where innovation gets hands-on — and where emerging talent learns by doing. By combining mentorship with real-world problem-solving, we’re helping young professionals grow into AI changemakers while delivering solutions that matter. From climate-smart farming to disaster response, the Garage proves that AI for good isn’t just a concept — it’s a daily practice. The challenges may be big, but with the right mix of curiosity, code, and collaboration, we’re building something that lasts. Stay tuned in the next edition of the Data-powered Innovation Review for more recent cases!

Start innovating now –

Empower Future Talent

Get involved in innovation projects that enhance AI skills and leadership capabilities, preparing young professionals for real-world challenges.

Leverage AI for Social Impact

Adopt AI solutions to drive sustainability, inclusivity, and efficiency across industries, from disaster management to smart farming.

Stay Ahead of Market Trends

Engage with cutting-edge technology and AI-driven insights to maintain a competitive edge in an evolving digital landscape.

Interesting read? ��ѻ��ý’s Innovation publication, Data-powered Innovation Review – Wave 10 features more such captivating innovation articles with contributions from leading experts from ��ѻ��ý. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.  Find all previous Waves here.

SaaS management is now a business problem – not only an IT issue

Today’s global economic landscape has made cost pressure concerns a significant topic in the agendas of most businesses, especially CFOs. At the same time, the costs of technologies and their associated supply chains are also rising – in particular for Software-as-a-Service (SaaS) and SaaS software management.

SaaS consumption is special in the sense that decision making is primarily in the hands of business lines and not only for IT organizations.  In fact, only 26.1% of SaaS applications are controlled by the IT organization, while around 70.1% are purchased by lines of businesses.¹ This means that, in reality, everyone is a buyer when it comes to SaaS. 

Considering that SaaS totals 60% of the average software spend budget for many organizations, prioritizing proactive SaaS management to protect operating expenses (OpEx) must now become a standard practice.² 

Going further, this is more than just cost savings and unlocking new value. It’s about addressing potential security and compliance risks that come with unused licenses, which may contribute to sensitive data leakage. It reinforces cybersecurity, while also optimizing management over SaaS tools, apps, and contracts. 

To put it simply: SaaS technologies and services are maturing, but their ways of management remain quite basic in most organizations, compared to what is invested.

The drivers of SaaS cost

SaaS costs are evolving due to several factors occurring side-by-side. The convergence of vendor price hikes, AI-enabled SaaS features, growing popularity in decentralized purchasing, and ongoing license complexity are increasing costs and consumption.

Take a proactive SaaS management approach

This is the moment where a SaaS management solution will facilitate the management of those drivers. Technologies constantly evolve, meaning it’s not enough to conduct cost-out on SaaS as a one-off activity – continuous SaaS optimization is critical to success. It’s imperative that organizations develop an active understanding of the inventory, licenses, and renewals, while keeping a vigilant watch on how these services are impacting costs. 

It’s also important to consider the functional overlaps between SaaS, as well as the limitations linked to a lack of SaaS integration where overlaps do occur. To be specific, where are SaaS applications covering similar business processes and are these teams sharing data to avoid siloes, data duplication, and inconsistencies?  

It requires an understanding down to the level of license type deployed to users, and what business metrics are tied to the value of these licenses by the purchasing department. Additionally, understanding how collaborative models work, so that they can discourage isolated purchasing behaviors.

However, this is only part of the story.

Take a pragmatic SaaS approach

It’s vital to analyze SaaS management needs from a business perspective. There is a critical need for executive sponsorship to understand the waste incurred by ineffective SaaS management.  

A practical first step for SaaS software management that any company can make is a “first-in, first-out” approach. In this case, it means examining the top SaaS contracts by proximity to their renewal, aggregated costs, and determining the volume of unused licenses. It can be tempting to look at the biggest contracts first, but our experience has shown that their complexity and length of negotiation can often degrade significant value from lower tier ones. 

That is why contracts near their renewal dates should be prioritized for review. This helps companies determine if a particular SaaS contract is being used to its fullest potential and if it is worth renewing or adjusting. It helps ease the process as well, given the abundance of SaaS contracts a company may have, which may mean dealing with a handful of renewals every week.

The case for a unified SaaS management strategy

This requires a complete analysis not only of the costs involved, but of the total value that a SaaS application is bringing to the business. If a SaaS app does have overlap between business processes, what is the total value it’s delivering for them?  

For example, if two separate business processes are leveraging a SaaS application, is this building greater value and efficiency for the company, and will the impact of reducing access to the SaaS application negatively impact that value? Will it lead to teams being locked out, unable to access the SaaS application at a critical moment to support another team? These kinds of questions need to be deeply considered during the analysis process. 

But this kind of approach, while highly rewarding, also requires specialized skills that may not be present within an organization or that are not readily available in the market. The best solution is to leverage an ecosystem of partners who can unlock value quickly, while also actively supporting and building additional SaaS management capabilities. 

Start your on-demand SaaS management evolution

Overall, re-thinking SaaS management is one part of a wider challenge in addressing all on-demand technologies, such as Cloud, Gen AI, AI infrastructure, and, of course, SaaS. It’s imperative to re-think this through a business lens to help control cost, consumption, security, and overall usage.

Capital expenditure governance systems are not structured in a way that can optimally navigate these continually evolving technologies. This is an era where everyone is a buyer, and every click is a micro-cost that can (and will) result in large costs later if left unchecked.

We’re ready to discuss how to drive greater value from your SaaS portfolio. We can support you with stronger insights and agile, proactive SaaS management advisory.

It’s time to stop asking, “What’s the cost of a click?”.

It’s time to know the cost of a click.

Reference: _{1. SaaS Management Index, 2025; 2. IDC Spending Guide, 2024}

The energy and utilities (E&U) industry is undergoing a dynamic transformation. Driven by emerging technologies from smart grids to the integration of renewable energy sources, the landscape is evolving rapidly.

Generative AI (Gen AI) is poised to play a pivotal role in accelerating this shift, fostering innovation, efficiency, and new opportunities across industries worldwide. You can read about this transformation and more in our “Future of” series, here.

What effect will Gen AI have on E&U end customers?

Customer complaints are a longstanding challenge for E&U suppliers, particularly during peak seasons or service outages. A recent revealed troubling statistics about the service quality of 17 major UK energy suppliers. Customers often face chaotic experiences marked by inconsistent messaging, long wait times, and unresolved issues leading to frustration and dissatisfaction.

The surge in call volumes (some suppliers reported a 300% increase since 2018), combined with a shortage of experienced agents, complex regulatory issues, and missing root cause analysis reports, has overwhelmed support teams. This results in high complaint rates, declining customer satisfaction scores, and reputational damage.

How Gen AI can vastly improve customer interactions with E&U businesses

Gen AI is already transforming customer support by streamlining complaint resolution, enhancing personalization, and reducing staff turnover. Key use cases include:

• Automated bill summaries: Gen AI can generate clear, concise summaries of complex bills, enabling faster query resolution and empowering customer self-service.
• Contextual routing: By analyzing historical queries, Gen AI can match current issues with agents who have relevant expertise, improving resolution speed and satisfaction.
• Real-time knowledge assistance: Gen AI can interpret technical manuals and guides, presenting information in simple language to both customers and staff.
• Sentiment analysis and personalized responses: Gen AI can assess customer tone and emotional state across channels, tailoring responses to foster empathy and clarity.
• Predictive maintenance support: In IoT-enabled environments, Gen AI can predict service disruptions and proactively suggest preventative actions.
• Email summarization: Gen AI can extract key information from lengthy emails, allowing agents to quickly understand and address issues.
• Routine task automation: AI-powered chatbots can handle scheduling, payments, and FAQs using natural language.
• Error reduction and consistency: Gen AI ensures accurate, consistent information across agents, improving service quality.
• Operational insights: Gen AI enables better call center audits, agent coaching, and customer-agent matching, reducing average hold times and boosting productivity.

These improvements not only enhance customer satisfaction but also drive profitability and reduce agent attrition. Root cause insights from Gen AI can inform future system and process design, creating a cycle of continuous improvement.

As highlighted in the����ѻ��ý Research Institute’s report Harnessing the value of generative AI: Top use cases across sectors, organizations are increasingly prioritizing Gen AI to elevate customer experience, with tools like ChatGPT becoming the preferred interface for product and service recommendations.

What agentic AI and embodied AI mean for the E&U industry

The next frontier is agentic AI, autonomous software agents that interact with their environment, gather data, and perform tasks to achieve defined goals. These agents leverage large language models (LLMs) to reason, act, and adapt dynamically.

In customer service, agentic AI can autonomously manage enquiries, request additional information, and resolve issues, sometimes even overriding standard procedures when necessary. This autonomy enhances customer satisfaction and allows human agents to focus on complex, high-value tasks.

Increasingly, we are also seeing the rise of embodied AI: AI systems integrated into physical or digital environments that can perceive, interact, and respond in real time. In the E&U context, embodied AI agents can be deployed in smart meters, grid management systems, and field service robotics to autonomously monitor, diagnose, and act on operational data. These agents combine Gen AI’s reasoning capabilities with sensor inputs and real-world feedback loops, enabling more adaptive and intelligent infrastructure.

��ѻ��ý and Gen AI in the E&U industry

Generative AI presents a transformative opportunity for the E&U sector to transform customer experiences, optimize operations, and drive sustainable growth. However, successful adoption requires careful governance to mitigate risks and maintain control over AI processes.

��ѻ��ý’s��Generative AI for Customer Experience��offering helps E&U companies unlock Gen AI’s potential by building tuned foundation models and navigating implementation complexities. By leveraging our global network of certified Gen AI for CX experts, we accelerate deployment of industry-specific use cases that deliver tangible business value.

With over 500 enterprise-ready use cases and demonstrators, and a track record of successful client engagements, we empower CxO leaders to drive high-impact transformation initiatives.

Gen AI is here to transform the customer satisfaction. Get in touch with us to learn how we can partner with you on your transformation journey.

Compiling and communicating upstream and downstream data on industrial batteries is key to increased circularity and transparency throughout the electric vehicle (EV) battery’s lifecycle. 

The metals used in an EV’s battery travel an average of 90,000 kilometers via multiple actors across the value chain before they are even incorporated into the battery (its components, cells, and stacks). This is just one example of how global and complex the EV battery supply chain is.  

Collecting, storing, and sharing data from every step of the product’s journey will be a complex, collaborative process. With the EU’s Digital Battery Passport (DBP) on the horizon for 2027, addressing this challenge has never been more pressing.  

As a result of this urgency, many suppliers are asking, “Which kind of data do I need to collect today to prepare, and how can I ensure compliance tomorrow?” 

Data enables upkeep and circularity  

The DBP is a set of regulations governing the collection and sharing of data for batteries relevant for the industrial and transportation sector, including those used in EVs. The data stored in the DBP will help provide transparency on raw materials impact, usage and wear of EV batteries, which today are relatively hard to track. Without this data, EVs are difficult to resell and maintain. 

A fully operational DBP will include upstream data to ensure due diligence, and downstream data for lifecycle management.  

Upstream data refers to information about the raw materials and production of the battery. Collected from various parties, including miners and traders, it accounts for a huge share of the battery’s overall carbon footprint. This poses a logistical challenge, as it is not data that battery manufacturers traditionally collect and share.  

Downstream data relies on a battery management system (BMS) to track the health and performance of the battery once it is installed in the EV. This data is either held locally in the car or communicated back to the manufacturer and is available for free to different stakeholders including the car owner, maintenance centers, recyclers, and legislators. This information can be stored safely in cloud- or blockchain-based systems, accessible via QR code. 

A transformational journey 

There is no doubt that collecting and communicating both upstream and downstream data poses a challenge, particularly at this pivotal moment in the automotive industry’s sustainability journey.  

But if they focus on the challenges, suppliers risk missing the forest for the trees.  

By providing an easily accessible cache of data on the battery’s origins, age, and performance over time, the DBP facilitates reselling, revamping, and upgrading. It tracks the level of wear on the battery in terms of residual autonomy, improving accuracy in circularity. 

In other words, the DBP will be a vital tool for facilitating the long-term growth and profitability of the EV market. It will push the automotive industry into its next phase, a more sustainable and transparent one.  

But first, DBP compliance requires an unshakeable foundation of comprehensive, reliable data so companies understand where they stand today. They also need data from across the value chain. For this, they must be able to rely on data from the entire ecosystem and in turn, share that information with consumers.  

Traceability fosters transparency 

��ѻ��ý can help create and foster data strategy, architecture, and communication. Working with upstream and downstream inputs, and leveraging our partnerships across the industry, we can ensure the BMS is compatible with data collection in the cloud. Our Product Traceability for Automotive offer creates efficiency and reduces costs. 

We are united in the effort to shape a more sustainable future. Now, we must work together to embrace regulations like the DBP, a key step along the way to achieving a more circular value chain.  

To learn more about how to collect and incorporate data into strategic decision-making, contact

Mobility, meet action. 

You can also meet me at the upcoming event to��discuss about how we can go beyond compliance with data-driven product lifecycle intelligence to increase circularity and transparency throughout the electric vehicle (EV) battery’s lifecycle.��

^{September 9-12, 2025 | Find us at Hall B1, Booth 22}