Category: 3. Business

Published on 08/12/2025 – 11:47 GMT+1
•Updated
11:48

Shares in L’Oréal slipped almost 1.5% during morning trading in Paris after the firm announced that it would double its stake in Swiss skincare firm Galderma.

Galderma shares had risen almost 2% in Zurich by around 11:00 CET, dropping from a more dramatic high seen earlier in the day.

L’Oréal will purchase 24 million extra shares in the Swiss firm, bringing its ownership to 20%, from a consortium led by the Swedish private equity group EQT.

The purchase price is undisclosed and the deal is expected to close in the first quarter of 2026. Galderma will also consider nominating two L’Oréal representatives to its board, replacing the consortium led by EQT.

“Our initial strategic investment made in 2024 in Galderma has proven very successful and therefore we are eager to solidify and extend the partnership further,” said L’Oréal CEO Nicolas Hieronimus.

The firm stressed that it did not plan to increase its stake further.

Flemming Ørnskov, CEO of Galderma, said: “Galderma continues to deliver impressive growth, strong innovation and category leadership across its broad, science-based dermatology portfolio…We are pleased with L’Oréal’s increased investment, which affirms our direction and the meaningful value creation we expect in the years ahead.”

A shift towards innovation

Galderma was originally set up by L’Oréal and Swiss food group Nestlé in 1981. In 2014, Nestlé bought out its partner’s 50% stake, and it then sold Galderma to private equity group EQT in 2019.

Last year, L’Oréal then acquired a 10% stake in Galderma, estimated at €1.7 billion.

The move notably signalled a shift towards more cutting-edge beauty technology, as Galderma is focused on the skin aesthetics market, offering a broad portfolio of dermo-cosmetics, dermatologic drugs, and hyaluronic acid fillers, among other products.

“Aesthetics is a key adjacency to our core beauty business that we are keen to continue to explore,” said CEO of L’Oréal Nicolas Hieronimus on Monday.

L’Oréal’s dermatological portfolio already includes major brands such as La Roche-Posay and CeraVe, brands boosted by an increased interest in science-driven skincare promoted on social media.

The fresh deal with Galderma is the latest in a series of acquisitions made by the French beauty giant in recent years. In October, L’Oréal announced it would buy Kering’s struggling beauty business, including perfume maker House of Creed. L’Oréal will also enter into 50-year licensing arrangements for some of Kering’s most iconic brands, specifically Gucci, Bottega Veneta, and Balenciaga.

Other brands that L’Oréal has added to its portfolio in recent years include UK skincare brand Medik8 and Australian soapmaker Aesop.

London-listed miner Anglo American has dropped plans to award its bosses multimillion-pound bonuses if its planned $50bn mega-merger with a Canadian rival goes through, after a backlash from its investors.

The FTSE 100 miner had sought shareholder approval for a plan to award its chief executive, Duncan Wanblad, a huge share bonus if the deal to buy Teck Resources to create a copper producing giant is completed.

Other senior executives were also incentivised through the plan, which would have updated long-term awards made in 2024 and 2025 to hand them a minimum of 62.5% of share awards when the merger was finalised.

The company argued that its pay structures needed to be “fully aligned to … a successful delivery of the merger”, which it said would “require exceptional performance by the Anglo American group’s senior management”.

It also said it wanted to “support the retention of senior management through a period of significant change”.

However, it said on Monday that shareholders had “raised a number of concerns” and that it would now abandon the plan.

Wanblad’s share bonus would have been worth about £8.5m, according to the Times.

The move comes after several influential voices objected to the proposed bonuses. Institutional Shareholder Services (ISS), the advisory group, recommended investors vote in favour of the merger, but said “the linking of variable incentives to the completion of transactions is not considered good practice” and that the high proposed payout “undermines the other performance criteria”.

Anglo said it would now engage further with investors over director pay before its AGM next year.

The U-turn comes just a day before investors in Anglo and Teck vote on the proposed merger, which if approved would form one of the biggest copper producers in the world.

The deal emerged after Wanblad fought off a series of takeover attempts by its larger rival BHP last year, pushing it to radically restructure the group, including seeking the sale of its famous diamonds business De Beers.

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Anglo, which was founded in 1917 by the entrepreneur Ernest Oppenheimer and also owns the troubled Woodsmith fertiliser mine project in North Yorkshire, rebuffed the £39bn takeover from BHP while Teck rejected an offer from Glencore in 2023 for £16.6bn.

Last month, Anglo rejected another takeover tilt from BHP, as its rival mounted a last-minute attempt to disrupt the planned merger with Teck. Under City takeover rules, BHP is now blocked from making another bid for Anglo for six months unless there is a significant change in circumstances.

If approved, the Anglo-Teck deal would be one of the biggest ever agreed in the mining sector. The largest deal on record is the Glencore-Xstrata merger in May 2013, which was valued at $90bn.

It would also mark a multibillion-dollar bet on the global copper market, with the mineral an important building block for low-carbon technologies such as solar farms and electric cars.

Shares in Anglo American slipped by 0.9% in early trading on Monday, although it is up by more than 40% so far this year.

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Data centers – the vast, physical warehouses where IT servers and systems are kept – are experiencing a boom in demand, particularly across the USA. Driven by the rapid ascent of AI, analysts project that the global electricity demand for data centers is expected to double by 2030, reaching consumption levels that rival entire developed nations. Still, the challenge goes beyond the sheer volume of power needed. Data centers operate 24/7 and experience pronounced swings in demand which legacy grids simply aren’t engineered to handle.

Luckily, answers are emerging. Grid-scale batteries can respond quickly enough to tame this volatile demand, and when properly coordinated by a sophisticated operating system – like Kraken – they can contribute to building a healthier, better-balanced grid overall.

Data centers have uniquely volatile demand profiles

The fundamental nature of data center electrical loads distinguishes them from traditional industrial consumption, being not just especially large, but unusually “spiky” and unpredictable. When tech companies launch AI training algorithms or massive computing clusters activate, the resulting power draw is instantaneous and intense. This poses a pressing stability problem. The grid’s legacy generators, such as slow-ramping gas-fired peaker plants, aren’t merely relatively expensive and slow to build, but are ultimately technically incapable of matching huge demand spikes that occur in milliseconds.

This critical mismatch between fast demand and slow supply results in immediate frequency instability, severe stress on local transmission and distribution networks and significantly higher balancing costs for grid operators (if they can meet that demand at all). And this volatility is only compounded as clusters of data centers concentrate in particular geographical regions. A faster, smarter solution is clearly needed.

Coordinating grid-scale storage to tame demand

Fortunately, the flexibility afforded by large batteries is well-suited to addressing pronounced immediate swings, charging up whenever energy is cheapest and cleanest and discharging instantaneously to flatten out spikes and maintain critical grid frequencies.

But installing batteries along the grid isn’t enough. These flexible assets must be intelligently optimized to keep up with data center demand. To achieve this, operators need a few things:

• Digital operating systems that can provide real-time visibility into both grid signals and market pricing
• Accurate forecasting that accounts for changes in data center load, broader grid constraints and the wider availability of electricity
• Automated, synchronized dispatch across entire portfolio battery assets

When these batteries are intelligently managed, they also allow owners to ‘value-stack’ – unlocking new, overlapping, non-speculative monetization opportunities for their owners through energy arbitrage, balancing markets and capacity services.

This sophisticated, real-time coordination between dynamic grid signals, fast-moving market pricing and on-site operational demands requires robust software solutions. Today’s digital operating systems are ready and able to deliver this support and Kraken’s Generational Flexibility capability is a case in point.

Co-locating batteries and data centers creates overlapping benefits

Grid-scale batteries are especially useful when ‘co-located’ together with data centers (situated alongside a data center behind its grid connection). These batteries can then charge up at times when the grid is least constrained and energy is cleanest and cheapest, and in turn, discharge to save data centers from relying on more expensive grid electricity, especially during peaks in demand.

Data centers themselves can use next-generation operating systems to internally balance the energy they draw from the grid and any stored electricity that they draw from their batteries to meet demand most efficiently. Additionally, these batteries could also be used to help balance the grid outside of peak times, providing other sources of income for data centers, while enhancing grid flexibility. By coupling a facility with storage and allowing it to participate in local or national flexibility markets, operators ultimately offset operational costs and dramatically enhance both their own resilience and that of the wider grid.

This also opens the door to building more data centers in places where grid capacity is constrained.  Electricity distribution infrastructure is built to handle a maximum electrical load at peak times during the day, and will have spare, underutilized capacity the rest of the time. In fact, a EU Joint Research Center study calculated that some grid infrastructure is used between just 2-20% of the time. Many data center projects are denied or delayed because utilities and grids can only supply the required power for (say) 95% of the year. In this case, grids would normally aim to upgrade their infrastructure to meet capacity 100% of the time, which is costly, and time-consuming.

However, where data centers are co-located with batteries, they could use their own backup storage to cover those 5% of time periods that the grid alone cannot meet, using an intelligent operating system to forecast and schedule this off-grid generation. This would delay, or remove, the need for upgrades, allowing many more data-center projects to go ahead, without putting additional build costs and pressure on utilities, and ultimately, consumers.

Data center demand will define the grid’s next decade

Data centers will undoubtedly define the next decade of grid planning. It’s not a question of whether grids accommodate them, but how. If this new, volatile demand is managed intelligently – with responsive distributed assets and smart, optimizing software – then data centers could become an integrated part of tomorrow’s smarter, cleaner energy system.

This company is my father’s legacy. He founded it with my mother in 1997 as a small family workshop. Over the years, it grew steadily to more than 100 employees. We produce stainless steel salad bowls, oil strainers, filter racks, and fruit baskets.

The COVID-19 pandemic and the war in Ukraine changed everything. Sales dropped by half, inventories piled up, and cash flow was scarce. My father was old, and the management team was not willing to make changes because they had stayed in their comfort zone for too long.

I knew that transformation was the only way forward, but I didn’t have a clear plan.

© Ying Huaye/ILO

In July 2024, the International Exchange and Cooperation Centre of the Ministry of Emergency Management and the Emergency Management Bureau of Yongkang City co-launched a SCORE pilot programme, and our factory was selected to participate.

At the time, our sales had dropped sharply and internal management was in disarray – production efficiency was low, product quality inconsistent, cost accounting difficult, and the factory environment messy. We were plagued by problems both inside and outside the company.

After SCORE Training, I was deeply inspired and convinced that this project could help us transform and upgrade. I decided to implement it in our factory, and it provided the professional knowledge we had long lacked.

© Ying Huaye/ILO

The biggest change was the transformation of our employees. Their motivation grew. Now, they think from the company’s perspective. They proactively identify and report problems and suggest practical solutions that save time and money.

Our sales have increased by 20 per cent. At the Canton Fair in April, for the first time in our 27-year history, we played a video of our factory workshop at our booth, and our high-quality products attracted many new customers and major retailers.

Employees have also reaped the benefits of the company’s growth. This year, our average salary increased by about 10 per cent, far above the industry average of 3–5 per cent. Next year, over 10 employees will see raises of 20 per cent, and three to five may see increases of up to 50 per cent.

Respect is the foundation. When workers see their ideas adopted, they gain a sense of achievement that motivates them even further.

Ying Changbin, Manager, LIVSHEW

Communication also strengthened. With guidance from SCORE trainers, we established an Enterprise Improvement Team that meets weekly. Employees contribute suggestions through QR codes or directly to managers, and every idea is discussed.

Respect is the foundation. When workers see their ideas adopted, they gain a sense of achievement that motivates them even further.

I grew up in the factory, and as a child I witnessed accidents and cried when I saw them. Our factory used to have three to four safety incidents annually. Since SCORE Training, we haven’t had a single safety incident. This is remarkable for a hardware factory.

Before the pandemic, our biggest markets were Russia, Ukraine, and the Middle East. After our transformation, our products moved from mid- to low-end to mid- to high-end. Our export markets expanded to Europe, North America, Japan, and South Korea. Recently, a Japanese client inspected our factory several times. Their standards were extremely high, but we became the first company in our industry to pass.

© Ying Huaye/ILO

When employees’ suggestions are put into practice, their work becomes safer and more comfortable, the workplace environment improves, and they are rewarded – sometimes with a bonus, sometimes with a raise or promotion. Monthly recognitions such as “SCORE Star”, “Discovery Star” and “Cleanliness Star” also serve as great motivators.

Our female workers have seen additional benefits. Women can apply for special leave during difficult menstrual periods, and we provide thermos cups for comfort in winter. These initiatives, encouraged by SCORE, have improved workplace equality and wellbeing.

© Ying Huaye/ILO

We also became more concerned about the physical and mental health of employees. We have even built a rooftop garden, open to everyone.

My long-term plan is stable growth. If we achieve this, we will buy a new factory building to provide an even more comfortable and better working environment. I will also create a safety fund to support employees.

Employees have reaped the benefits of the company’s growth. This year, our average salary increased by about 10 per cent, far above the industry average.

Ying Changbin, Manager, LIVSHEW

I have grown personally as well. I’ve learned that workers need recognition and respect – not just a salary. Through open dialogue, they have also come to understand that rules and systems are essential for ensuring quality.

The most important lessons I have learned are communication, trust, and respect. In the end, everything comes down to the human part.

Tokyo, December 8, 2025 – Mitsubishi Heavy Industries, Ltd. (MHI), together with Worley and Heidelberg Materials, has entered the execution phase of the Padeswood Carbon Capture and Storage (CCS) project in Flintshire, North Wales in the United Kingdom. The project will be the first in Europe to deploy MHI’s proprietary Advanced KM CDR Process™ to capture around 800,000 tonnes of carbon dioxide (CO₂) annually from cement production operations at Heidelberg Materials’ Padeswood plant. The CO₂ will be transported via pipeline for permanent storage in depleted gas fields under Liverpool Bay, as part of the HyNet North West cluster.

The news follows Heidelberg Materials’ final investment decision (FID) in September 2025, made in collaboration with the UK Government under Track-1 of its CCUS cluster sequencing program. The new CCS facility is set to be operational in 2029.

MHI and Worley had been awarded a front-end engineering design (FEED) study in 2024. In the execution phase, MHI and its regional representative MHI-EMEA via its London headquarters will provide the engineering and procurement under the Advanced KM CDR Process™ for the CO₂ capture technology, together with other associated plant including compressors. Worley will deliver engineering, procurement, and construction management for the balance of plant.

Cement production is responsible for around 7-8% of CO₂ emissions globally. Since most of these emissions come from the chemical process (calcination), they cannot be avoided by switching to clean energy sources. This leaves CCS as the only viable option for fully decarbonized production.

Tatsuto Nagayasu, Senior Vice President (CCUS) of GX (Green Transformation) Solutions at MHI, said: “We are proud to support Heidelberg Materials in realizing the UK’s first full-scale carbon capture facility in the cement sector. Using our Advanced KM CDR Process™, this project will play a leading role in decarbonizing one of the most challenging industrial sectors. Together with Worley, we look forward to delivering this landmark CCS facility that will contribute to the long-term resilience of UK industry and help fulfill the country’s net zero ambitions.”

Simon Willis, CEO at Heidelberg Materials UK, said: “This is the next major milestone in our plans to build the UK’s first carbon capture facility at a cement works. We have established an excellent working relationship with Worley and MHI during the completion of the front-end engineering design (FEED) for our Padeswood project. This, along with their proven track record in delivering this type of complex facility, makes them the perfect partner to take our groundbreaking project to the next stage.”

Chris Ashton, Chief Executive Officer of Worley, said: “This project is a landmark for industrial decarbonisation in the UK and Europe and part of the HyNet carbon capture cluster. We’re proud to be working alongside Heidelberg Materials and MHI to deliver a facility that will help transform cement production and support the UK’s net zero ambitions. Our role in this project reflects our ability to enable sustainable industrial solutions and leverage our global expertise in delivery for complex energy and infrastructure projects.”

The Padeswood CCS project is expected to create approximately 50 new permanent jobs and secure over 200 existing roles, in addition to supporting up to 500 jobs during construction. As part of the HyNet North West cluster, the project will also contribute to building a long-term carbon management infrastructure in the UK, while enabling Heidelberg Materials to supply low-carbon cement to the construction industry.

About Heidelberg Materials
Heidelberg Materials is one of the world’s largest integrated manufacturers of building materials and solutions with leading market positions in cement, aggregates, and ready-mixed concrete. We are represented in more than 50 countries with around 51,000 employees at almost 3,000 locations. At the centre of our actions lies the responsibility for the environment. As the front runner on the path to carbon neutrality and circular economy in the building materials industry, we are working on sustainable building materials and solutions for the future. We enable new opportunities for our customers through digitalisation.
heidelbergmaterials.com

In the UK, Heidelberg Materials (formerly Hanson UK) is split into five business lines – aggregates (crushed rock, sand and gravel), concrete, asphalt and contracting, cement and recycling – which together operate over 300 manufacturing sites and employ more than 4,000 people. The company is leading the decarbonisation of the cement sector through carbon capture and storage and has reached a final investment decision with the UK Government to build the world’s first carbon capture facility to enable fully decarbonised cement production at its Padeswood works in north Wales. Construction is underway and the new facility will enable the production of evoZero carbon captured near-zero cement in 2029.
heidelbergmaterials.co.uk

About Worley
Worley is a leading global professional services company of energy, chemicals, and resources experts. We partner with customers to deliver projects and create value over the life of their assets. We’re bridging two worlds, moving towards more sustainable energy sources, while helping to provide the energy, chemicals and resources needed now. Worley Limited is headquartered in Australia and listed on the Australian Securities Exchange (ASX: WOR).
www.worley.com

About MHI Group’s CO₂ capture technologies
MHI Group has been developing the “KM CDR Process™” (Kansai Mitsubishi Carbon Dioxide Recovery Process) and the “Advanced KM CDR Process™” in collaboration with The Kansai Electric Power Co., Inc. since 1990. As of December 2025, the Company has delivered 18 plants adopting these processes, and two more is currently under execution. The Advanced KM CDR Process™ adopts the “KS-21™” solvent, which offers superior regeneration efficiency and lower deterioration than the “KS-1™”, and has been verified to provide excellent energy saving performance and reduced operating costs.

Further information on MHI Group’s CO₂ capture plants:
https://www.mhi.com/products/engineering/co2plants.html

Customer service doesn’t break down because people don’t care. It breaks down when systems don’t talk to each other fast enough, or when teams are stretched thin without the right tools. That’s where AI and the future of automation are starting to matter.

The numbers show just how high the stakes are. Global surveys this year suggest that 71% of customers actually want AI to be implemented into their shopping experience, and many employees are embracing AI automation tools too, looking for ways to cut down repetitive tasks.

But most organizations aren’t ready for the next wave. McKinsey says only 1% of companies believe they’ve achieved AI maturity. 79% of companies fail to automate just one process – like sales. The problem is a lack of preparation. Companies are scrambling to adopt more intelligent tools, but they don’t have the foundations in place to ensure measurable ROI.

The Evolution of AI and the Future of Automation in CX

The days when automation meant bots filling forms or running basic scripts are gone. Today, it’s living, learning, active systems, tied into every part of the CX ecosystem.

At the heart of this shift is a stack: CRM automation routing leads or flagging risks; workflow tools making work happen behind the scenes; and customer data platforms (CDPs) weaving profiles together, so the next interaction makes sense.

Holmes Murphy added CRM automation to its workflows and saved $6.9 million while clearing 44,000 hours from manual workflows.

On the CDP front, companies that build unified profiles see better engagement. Vodafone, for instance, reported a 30% lift in customer engagement when using a CDP to unify digital behavior and service history. That’s the kind of foundation AI automation needs to stay sharp – without fragmented context.

The drive to prepare for AI and the future of automation is also growing, thanks to the “agentification” of the enterprise. Every major vendor is investing more in agentic AI, from Salesforce, with its acquisition of Bluebirds, to Google with its AI-mode agents, and Microsoft with Intent agents.

The success of all of these digital colleagues will rely on one thing: preparation.

The Disconnect: Why AI Automation Isn’t Plug-and-Play

Lots of organizations treat AI automation like a magic wand – flip the switch and watch efficiency drop in, bills get paid, and customers smile. Reality has a different tone.

Many teams wake up to fragmented data across apps – no single view of the customer. According to the 2025 Jitterbit report, 71% of enterprises still lack an end-to-end automation platform, and 70% of the automation burden still lands on IT.

That’s not a foundation for transformation; that’s a recipe for burnout.

Even when the tech is in place, there’s rising pressure to automate everything – what Gartner calls “limitless automation.” Executives push for it hard, hoping to shed headcount, but expectations often outpace truth. Many contact center leaders expect generative AI to cut headcount significantly, but others point out the demand for human agents is rising, not falling.

Here’s the real issue: automation built on shaky data or without a clear purpose falls flat. When agents lose trust in bots or escalation paths aren’t well defined, disruption follows, long hold times, frustrated customers, and damaged morale.

So if AI and the future of automation is going to scale, understanding this gap, that readiness matters as much as technology, is essential.

Preparing for AI and the Future of Automation: A Practical Playbook

The real challenge isn’t buying the tools, it’s getting the foundation right. That means thinking ahead about how data, systems, and teams all connect. Without that groundwork, even the most capable AI can fall flat or frustrate staff.

In practice, automation preparation starts with asking basic questions:

• What still belongs with people?
• Where does AI make sense, and where does it introduce unnecessary risk?
• What parts of your tech stack aren’t talking to each other today?

Starting with clarity lets teams move fast, and scale without crashing.

1. Define Goals and Guardrails

Every automation story starts with the same temptation: “Let’s automate everything.” On paper, it sounds efficient. In practice, it spreads teams too thin and creates quick wins that rarely scale. The better approach is choosing a handful of journeys where automation delivers both impact and reliability.

Think of things like account password resets, claims updates, or high-volume “where is my order” queries. These are predictable, measurable, and low-risk. They also free human agents for calls that carry more emotion or nuance.

Redwood Software found that 73% of enterprises increased automation spend in 2025, and almost 40% cut costs by a quarter or more. But the same research showed failures happened when departments automated in isolation or with no clear ROI framework.

This is where guardrails matter. Gartner has warned of the push toward “limitless automation,” where leaders roll out bots without fallbacks or escalation plans. The smarter route is to set boundaries up front: define what shouldn’t be automated, document how humans step in when needed, and measure success on outcomes, not just volume.

2. Build the Data Layer

Ask anyone who has been through a failed automation rollout, and they’ll tell you: the issue wasn’t the bot, it was the data. If systems can’t deliver consistent, trusted information, the automation simply repeats errors at scale. That’s why building a strong data layer comes second only to setting goals.

In practical terms, this means consolidating data across CRM, contact center platforms, billing systems, and knowledge bases. A Customer Data Platform (CDP) often becomes the backbone here, because it gives AI access to a unified view of behavior and service history.

Data integrity matters just as much. That means regular checks on accuracy, freshness, and duplication. Without them, an agentic AI could route a call to the wrong team or surface the wrong answer. That’s one of the reasons companies like Microsoft are introducing new solutions to help with data preparation, like the Data Security Posture Management system for AI.

The work here isn’t glamorous: running audits, setting quality dashboards, and agreeing on governance rules for what data feeds which model. But it’s the piece that decides whether AI and the future of automation scales gracefully, or collapses under its own weight.

3. Master Orchestration

Too often, enterprises stack new tools on old processes and call it progress. The result? Bots that can answer a question, but not update an order. AI that can route a query, but not pull the right customer history. Orchestration is the answer.

It’s not about having dozens of automations; it’s about making them work in concert. Platforms like NICE CXone Orchestrator now aim to manage the flow between channels, knowledge sources, and human agents, ensuring that AI doesn’t act in isolation.

Orchestration means setting clear escalation paths: what happens if the AI fails to answer confidently, or if a customer insists on human contact? It also means defining guardrails for actions- like who approves a refund, or how sensitive data is handled.

Without it, enterprises end up with islands of automation. With it, CX becomes seamless, and teams stop firefighting disconnected journeys. If AI and the future of automation is going to deliver measurable ROI, orchestration is the conductor that holds the performance together.

4. Set Compliance and Governance Standards

No serious enterprise deploys AI at scale without asking: who’s watching the system? Compliance and governance are survival mechanisms.

AI systems process sensitive data, make decisions at speed, and scale faster than traditional oversight can handle. That’s why monitoring tools have become a category of their own. Solutions like Scorebuddy’s oversight for agentic automation show how enterprises are beginning to track what AI agents say and do, giving leaders confidence that outputs remain within policy.

Microsoft and others are also pushing data security posture management (DSPM) frameworks to spot compliance gaps before they become fines. Regulators are tightening expectations too, the EU AI Act and ISO 42001 are both shaping the standards enterprises must follow.

Practical steps help here: create an AI Use Policy for your organization, set up a cross-functional AI governance board, and log every model update or prompt change. Run red-team tests quarterly, simulate prompt injections, data leaks, or malicious misuse, to see where cracks appear.

In a market where one wrong answer can damage trust overnight, governance isn’t bureaucracy. It’s the cost of building AI automation that customers and regulators can believe in.

5. Embrace Customization

Preparing for the future of AI and automation shouldn’t mean just readying teams to hit play on a generic deployment. Not every system is built for every use case. Many standard LLMs still misunderstand industry terminology, mishandle compliance, or deliver answers that feel tone-deaf to the brand voice.

This is why enterprises are leaning into customization. Domain-specific models and tailored orchestration flows reduce errors and build confidence. Graia has already highlighted the dangers of “generic automation,” saying that one-size-fits-all agents often frustrate customers instead of helping them.

Customization can take many forms. For some, it’s training smaller industry-specific LLMs rather than relying on massive general-purpose models. For others, it’s adapting retrieval sources so that AI answers only come from curated and compliant knowledge bases.

The key is to design systems that reflect your workflows, compliance needs, and customer language. That’s how Toyota, for instance, tuned its proactive AI agents to schedule service appointments – earning a 98% satisfaction rate. Get customization right, and AI and automation stop being blunt instruments. They become tools crafted for the way your business really works.

6. Train People and Manage Change

The conversation around AI and the future of automation often focuses on tools. But the real challenge lies with people. Agents, supervisors, and managers need to understand how their roles will evolve, and why automation isn’t about replacing them, but about removing repetitive work.

Research shows that workforce engagement improves when AI handles routine tasks, leaving employees with more meaningful work. Lowe’s, for example, reported higher job satisfaction after adopting workforce management tools that gave agents clearer schedules and better support.

Change management should start early. Appoint “automation champions” within teams, so staff learn from peers instead of feeling dictated to. Build training programs around new micro-skills – like maintaining knowledge bases, testing AI responses, or managing exceptions.

Give agents transparency: show them how automation decisions are made, and where they can step in to correct errors. Handled poorly, automation creates fear and pushback. Handled well, it creates a more confident, skilled workforce ready to thrive alongside intelligent systems.

7. Redefine Metrics

For decades, contact centers judged success on speed -average handle time, calls per hour, queue abandonment. But those numbers don’t tell you whether the customer problem was solved. In a world shaped by AI automation, measuring the wrong things can mislead leaders and mask risk.

Forward-looking organizations are already shifting. Instead of legacy metrics, they track:

• Containment quality: Did the automation resolve the issue fully?
• Precision: Was the action or answer accurate?
• Adoption: Are agents and customers actually using the tools?
• Risk reduction: Did the system avoid policy breaches or compliance errors?

Case examples show why. Simba Sleep tied AI-driven automation directly to £600k in monthly revenue, proving that financial impact is the ultimate measure, but they also reported a reduction in employee burnout and customer churn.

8. Monitor, Optimize, Iterate

Even the best-designed automation systems drift over time. Customer expectations shift, regulations tighten, and models change as new data flows in. Without ongoing monitoring, small cracks can quickly become major gaps in service.

Continuous oversight is now non-negotiable. Enterprises are introducing quarterly “automation audits” where teams stress-test bots with new scenarios, review escalation logs, and adjust routing flows. Frontier Airlines, for example, has been scaling automation by 30% annually without increasing headcount, thanks to constant review and optimization cycles.

Realize automation is never finished. Build feedback loops, run red-team tests to expose vulnerabilities, and refresh training data regularly. Treat AI agents like products that need updates, not projects to tick off.

With this mindset, AI and automation stop being experiments. They become living systems that deliver measurable returns year after year.

Case Studies: Proof Points of AI Automation Done Right

Theory only gets you so far. The clearest way to understand what works is to look at organizations already experiencing AI and the future of automation.. These examples show the difference between pilot projects and business transformation.

• Atlassian: Building Champions, Scaling Impact: Using Workato, Atlassian saved 100,000 hours of manual work, cut finance processes by 75%, and made employee requests 98% faster. What stands out is not just the efficiency, but the way business users became “champions,” training peers and driving adoption across teams.
• Nexo: From Pilot to 2,600 Hours Saved: Nexo used Salesforce’s Agentforce to design, test, and tune their automation step by step. The result: more than 2,600 hours of manual work eliminated, and a framework that keeps improving as customer demand evolves.
• Wyndham Hotels: Millions Saved with 62% Automation
  Wyndham leaned on Five9 to automate high-volume, routine interactions, using a connected data platform as the foundational layer. The outcome: a 62% automation rate, saving millions annually, and freeing agents for higher-value guest conversations.
• Great Southern Bank: Faster Answers, Happier Customers: With NICE CXone Mpower, Great Southern Bank cut wait times to about 29 seconds and lifted NPS by eight points. Better orchestration and routing helped customers in urgent situations reach the right team quickly.
• Pluxee Romania: Efficiency Meets Experience: Working with Genesys, Pluxee Romania used AI to streamline interactions while improving customer experience. Productivity increased by 140%, first contact resolution rates grew by 11%, and average handling time fell by 41%, all with careful orchestration.

These cases highlight a common thread: success comes when automation is introduced with clear goals, clean data, orchestration, and human buy-in. Each organization approached automation preparation differently, but all built a framework that scales.

AI and the Future of Automation in CX

The ground under CX leaders is shifting quickly. A year ago, many were still testing pilots. Now, automation and AI are becoming part of the core stack. The question isn’t if to adopt, but how to keep up as the landscape changes.

• Consolidation and Platform Power: The big vendors are moving fast. NICE announced plans to acquire Cognigy. Genesys rolled out its AI Studio. Salesforce keeps expanding Agentforce. Each move points the same way: automation is no longer about single bots. It’s about suites that can run the entire journey end to end.
• The Rise of Agentic AI Factories: Companies aren’t just buying chatbots anymore. They’re setting up “agent factories,” using studios to design and test multiple agents, each tuned to a specific role. The goal is speed and scale – a system where new use cases can be launched in weeks, not months. That’s how AI automation is maturing.
• Flexible Model Strategies: One model won’t fit every task. CX leaders are mixing large general models with smaller domain-specific ones. It cuts costs, improves accuracy, and avoids the “generic automation” trap. Graia’s warning on this is clear: off-the-shelf agents often create more friction than they remove.
• Governance Moves to the Front: Compliance used to be an afterthought. Now it’s a front-page issue. The EU AI Act is setting strict rules, and ISO 42001 is on the horizon. Vendors like Microsoft are pushing DSPM (data security posture management) to help enterprises monitor risk in real time. Oversight tools such as Scorebuddy are becoming standard kit for automation teams.
• The Workforce Redesign: AI is also reshaping jobs. IBM and others predict a shift to skills-based structures, where automation clears the routine and people handle empathy, judgment, and escalation. For contact centers, that means redefining training, metrics, and career paths.

Preparing for Next-Level Automation in CX

The direction we’re moving in is obvious. AI and the future of automation is something that matters to every organization. The pressure is already here – rising costs, higher expectations, and a market where loyalty can shift overnight.

The steps that matter aren’t complicated, but they take discipline. Pick the right journeys. Build a clean data layer. Put orchestration at the center, and make governance non-negotiable. Train people early, change what you measure, and keep tuning the system. None of this is glamorous, but it’s what separates a pilot that fizzles from a program that pays back.

The results are there for anyone willing to look. Enterprises investing in AI automation are saving millions, lifting satisfaction, and even driving new revenue streams. Those who delay will feel the gap widen.

The task for CX leaders now is simple: treat automation preparation as a board-level priority. Start small, prove value, and scale with intent. The future is already arriving. The only question is who’s ready.