Digital | AI Insights

Explore articles from the Best of Digital Transformation newsletter on LinkedIn, diving deeper into:
– The latest technology trends and their impact on transformation
– The specific role of AI in shaping digital transformation
– Thought-provoking book reviews in the field
– Insights from recent publications and research
This section also reflects my ongoing learning journey in digital transformation, sharing personal discoveries and perspectives to inspire and guide you in your own transformation efforts.

GAINing Clarity – Demystifying and Implementing GenAI

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Herewith my final summer reading book review as part of my newsletter series.
GAIN – Demystifying GenAI for Office and Home by Michael Wade and Amit Joshi offers clarity in a world filled with AI hype. Written by two respected IMD professors, this book is an accessible, structured, and balanced guide to Generative AI (GenAI), designed for a broad audience—executives, professionals, and curious individuals alike.

What makes GAIN especially valuable for leaders is its practical approach. It focuses on GenAI’s real-world relevance: what it is, what it can do, where it can go wrong, and how individuals and organizations can integrate it effectively into daily workflows and long-term strategies.

What’s especially nice is that Michael and Amit have invited several other thought and business leaders to contribute their perspectives and examples to the framework provided. (I especially liked the contribution of Didier Bonnet.)

The GAIN Framework

The book is structured into eight chapters, each forming a step in a logical journey—from understanding GenAI to preparing for its future impact. Below is a summary of each chapter’s key concepts.

Chapter 1 – EXPLAIN: What Makes GenAI Different

This chapter distinguishes GenAI from earlier AI and digital innovations. It highlights GenAI’s ability to generate original content, respond to natural-language prompts, and adapt across tasks with minimal input. Key concepts include zero-shot learning, democratized content creation, and rapid adoption. The authors stress that misunderstanding GenAI’s unique characteristics can undermine effective leadership and strategy.

Chapter 2 – OBTAIN: Unlocking GenAI Value

Wade and Joshi explore how GenAI delivers value at individual, organizational, and societal levels. It’s accessible and doesn’t require deep technical expertise to drive impact. The chapter emphasizes GenAI’s role in boosting productivity, enhancing creativity, and aiding decision-making—especially in domains like marketing, HR, and education—framing it as a powerful augmentation tool.

Chapter 3 – DERAIL: Navigating GenAI’s Risks

This chapter outlines key GenAI risks: hallucinations, privacy breaches, IP misuse, and embedded bias. The authors warn that GenAI systems are inherently probabilistic, and that outputs must be questioned and validated. They introduce the concept of “failure by design,” reminding readers that creativity and unpredictability often go hand in hand.

Chapter 4 – PREVAIL: Creating a Responsible AI Environment

Here, the focus turns to managing risks through responsible use. The authors advocate for transparency, human oversight, and well-structured usage policies. By embedding ethics and review mechanisms into workflows, organizations can scale GenAI while minimizing harm. Ultimately, it’s how GenAI is used—not just the tech itself—that defines its impact.

Chapter 5 – ATTAIN: Scaling with Anchored Agility

This chapter presents “anchored agility” as a strategy to scale GenAI responsibly. It encourages experimentation, but within a framework of clear KPIs and light-touch governance. The authors promote an adaptive, cross-functional approach where teams are empowered, and successful pilots evolve into embedded capabilities.

One of the most actionable frameworks in GAIN is the Digital and AI Transformation Journey, which outlines how organizations typically mature in their use of GenAI:

  • Silo – Individual experimentation, no shared visibility or coordination.
  • Chaos – Widespread, unregulated use. High potential but rising risk.
  • Bureaucracy – Management clamps down. Risk is reduced, but innovation stalls.
  • Anchored Agility – The desired state: innovation at scale, supported by light governance, shared learning, and role clarity.

This model is especially relevant for transformation leaders. It mirrors the organizational reality many face—not only with AI, but with broader digital initiatives. It gives leaders a language to assess their current state and a vision for where to evolve.

Chapter 6 – CONTAIN: Designing for Trust and Capability

Focusing on organizational readiness, this chapter explores structures like AI boards and CoEs. It also addresses workforce trust, re-skilling, and role evolution. Rather than replacing jobs, GenAI changes how work gets done—requiring new hybrid roles and cultural adaptation. Containment is about enabling growth, not restricting it.

Chapter 7 – MAINTAIN: Ensuring Adaptability Over Time

GenAI adoption is not static. This chapter emphasizes the need for feedback loops, continuous learning, and responsive processes. Maintenance involves both technical tasks—like tuning models—and organizational updates to governance and team roles. The authors frame GenAI maturity as an ongoing journey.

Chapter 8 – AWAIT: Preparing for the Future

The book closes with a pragmatic look ahead. It touches on near-term shifts like emerging GenAI roles, evolving regulations, and tool commoditization. Rather than speculate, the authors urge leaders to stay informed and ready to adapt, fostering a mindset of proactive anticipation.posture of informed anticipation: not reactive panic, but intentional readiness. As the GenAI field evolves, so must its players.

What GAIN Teaches Us About Digital Transformation

Beyond the specifics of GenAI, GAIN offers broader lessons that are directly applicable to digital transformation initiatives:

  • Start with shared understanding. Whether you’re launching a transformation program or exploring AI pilots, alignment starts with clarity.
  • Balance risk with opportunity. The GAIN framework models a mature transformation mindset—one that embraces experimentation while putting safeguards in place.
  • Transformation is everyone’s job. GenAI success is not limited to IT or data teams. From HR to marketing to the executive suite, value creation is cross-functional.
  • Governance must be adaptive. Rather than rigid control structures, “anchored agility” provides a model for iterative scaling—one that balances speed with oversight.
  • Keep learning. Like any transformation journey, GenAI is not linear. Feedback loops, upskilling, and cultural evolution are essential to sustaining momentum.

In short, GAIN helps us navigate the now, while preparing for what’s next. For leaders navigating digital and AI transformation, it’s a practical compass in a noisy, fast-moving world.

Fusion Strategy – How real-time Data and AI will Power the Industrial Future

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This book by Vijay Govindarajan and Venkat Venkatraman gives excellent insights on how industrial companies can become leaders in this Data and AI-driven age.

Rather than discarding legacy strengths, the book shows how to fuse physical assets with digital intelligence to create new value, drive outcomes, and redefine business models. It gives a compelling and well-structured roadmap for industrial companies to get ready and lead through this digital transformation

From Pipeline to Fusion: A New Strategic Paradigm

Traditional industrial firms have long operated with a pipeline mindset – designing, building, and selling physical products through linear value chains. But in a world where customer needs change in real-time, and where data flows continuously from connected devices, this model is no longer sufficient.

Fusion Strategy introduces a new playbook: combine your physical strengths with digital capabilities to compete on adaptability, outcomes, and ecosystem value. It’s about integrating the trust and scale of industrial operations with the intelligence and speed of digital platforms.

Competing in the Four Fusion Battlegrounds

At the core of the book is a powerful matrix: four battlegrounds where industrial firms must compete – and four strategic levers to win in each: Architect, Organize, Accelerate, and Monetize.

Fusion Products – Embedding intelligence into physical products

This battleground focuses on evolving the traditional product into a smart, connected version that delivers value through both physical functionality and digital enhancements. It shifts the value proposition from one-time transactions to continuous value creation.

  • Architect: Build connected products with embedded sensors and software.
  • Organize: Create cross-functional product-data-software teams.
  • Accelerate: Use real-world usage data to improve iterations and performance.
  • Monetize: Shift to usage-based pricing, subscription models, or data-informed upgrades.

Example: John Deere integrates GPS, sensors, and machine learning into its agricultural equipment, enabling precision farming and monetizing through subscription-based services.

Fusion Services – Creating new layers of customer value

This battleground addresses the transformation from product-centric to outcome-centric offerings. Services become digitally enabled and proactively delivered, increasing customer stickiness and long-term revenue potential.

  • Architect: Design service layers that improve uptime, efficiency, or experience.
  • Organize: Stand up service delivery and customer success capabilities.
  • Accelerate: Leverage AI to scale and automate service interactions.
  • Monetize: Offer predictive maintenance, remote diagnostics, or outcomes-as-a-service.

Example: Caterpillar offers remote monitoring and predictive maintenance for its heavy equipment fleet, increasing operational uptime and generating recurring service revenues.

Fusion Systems – Transforming internal operations

This battleground focuses on using data and AI to reengineer internal processes, improve agility, and reduce cost-to-serve. Real-time operational intelligence becomes a source of competitive advantage.

  • Architect: Digitize plants, supply chains, and operations with real-time visibility.
  • Organize: Break down functional silos; design around data flows.
  • Accelerate: Use AI to optimize scheduling, energy use, or resource allocation.
  • Monetize: Drive efficiency gains and free up capital for reinvestment.

Example: Schneider Electric uses digital twins and data-driven energy management to optimize operations and reduce downtime in its global manufacturing network.

Fusion Solutions – Building platforms and ecosystems

This battleground is about building broader solutions that integrate products, services, and partners. It opens new avenues for value creation through platforms, data sharing, and co-innovation.

  • Architect: Offer modular solutions with open APIs and partner integration.
  • Organize: Orchestrate partner ecosystems that create mutual value.
  • Accelerate: Foster external innovation through developer communities.
  • Monetize: Sell analytics, data products, or platform access.

Example: Tesla is reimagining mobility not just as a product (cars) but as an integrated solution combining electric vehicles, software, energy management, autonomous driving, insurance and charging/energy infrastructure.

The Role of Data Graphs in Fusion Strategy

One of the foundational concepts emphasized throughout Fusion Strategy is the importance of data graphs. These are strategic tools that connect data across silos and enable intelligent, real-time insights.

A data graph is a semantic structure that maps relationships between entities—machines, sensors, people, processes, and locations—into a flexible and navigable format. In fusion strategy, data graphs link physical and digital domains, enabling smarter operations and decisions.

How to build a data graph:

  1. Collect data from operational systems – sensors, ERP -, CRM systems, etc.
  2. Define key entities and relationships – focus on what matters most.
  3. Create semantic linkages – use metadata and business context.
  4. Ensure real-time updates – to maintain situational awareness.
  5. Enable access – for both humans and AI systems.

Why data graphs matter:

  • Provide context for AI and analytics.
  • Enable real-time visibility across assets and systems.
  • Power predictive services, digital twins, and platform innovation.

According to the authors, data graphs are essential for scaling fusion strategies. Without them, it’s difficult to unify insights, drive automation, or deliver integrated digital experiences

Why This Book Stands Out

This is book does not start from the successful digital native companies, but from the leader of the industrial age point of view, describing on how they can become leaders in the digital age.

The structure is what makes it so useful:

  • It gives executives a language to discuss digital opportunities in operational and financial terms.
  • It balances the long-term vision with near-term execution levers.
  • It connects customer value, technology, organization, and monetization in one integrated model.

It’s a strategy-led, boardroom-level guide to competing in the AI era.

My Reflections

  • Applying Fusion Strategy is a shift in how to re-architect your products and business. It requires rewiring how you create, deliver, and capture value.
  • You don’t need to become a tech company. You need to become a fusion company – one that blends operational excellence with digital innovation.
  • Winning in Fusion means rethinking strategy, governance, talent, and incentives – all at once in other words, enabling full transformation.

Fusion Strategy is essential reading for any industrial executive seeking to lead their company through this era of accelerated transformation. It’s not about jumping on the latest AI trend – it’s about designing a future-ready business, grounded in strategy.

The battlegrounds are clear. The tools are available. The time is now.

Amplifying the Human Advantage over AI – Lessons from Pascal Bornet’s Irreplaceable

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For this holiday season I had, Pascal Bornet’s book Irreplaceable: The Art of Standing Out in the Age of Artificial Intelligence on top of my reading list. His work delivers a clear and timely message: the more digital the world becomes, the more essential our humanity is.

For executives and transformation leaders navigating the impact of AI, Bornet provides a pragmatic and optimistic blueprint. This article summarizes the core insights of Irreplaceable, explores its implications for digital transformation, and offers a practical lens for application (Insights).

AI as Enabler, Not Replacer

Bornet challenges the zero-sum narrative of “AI vs. Humans.” Instead, he positions AI as an enabler: capable of handling repetitive, structured tasks, it liberates humans to focus on what machines can’t do—leading, empathizing, creating, and judging. AI, in this view, is not the destination but the vehicle to a more human future.

Insight: Use AI to augment human roles—especially in decision-making, customer experience, and creative problem-solving—rather than replacing them.

The “Humics”: Redefining the Human Advantage

At the heart of Irreplaceable lies the concept of Humics: the uniquely human capabilities that define our irreducibility in an AI-powered world. Bornet identifies several:

  • Genuine Creativity – The capacity to generate novel ideas and innovations by drawing on intuition, imagination, and deeply personal lived experiences that machines cannot emulate.
  • Critical Thinking – The ability to evaluate information critically, reason ethically, and make contextualized decisions that reflect both logic and values.
  • Emotional Intelligence – A complex combination of self-awareness, empathy, and the ability to manage interpersonal relationships and influence with authenticity.
  • Adaptability & Resilience – The readiness to embrace change, learn continuously, and maintain performance under stress and uncertainty.
  • Social Authenticity – The human ability to create trust and meaning in relationships through transparency, shared values, and emotional connection.

Insight: Elevate Humics from soft skills to strategic assets. Build them into hiring, training, and leadership development.

The IRREPLACEABLE Framework: Three Competencies for the Future

Bornet proposes a universal framework built on three future-facing competencies:

  • AI-Ready: Develop the ability to understand and leverage AI technologies by becoming fluent in their capabilities, applications, and ethical boundaries. This involves not just using AI tools, but knowing when and how to apply them effectively.
  • Human-Ready: Focus on strengthening Humics—the inherently human skills like empathy, critical thinking, and creativity—that make people indispensable in roles where AI falls short.
  • Change-Ready: Build resilience and adaptability by fostering a growth mindset, embracing continuous learning, and staying flexible in the face of constant technological and organizational change.

Insight: These competencies should be embedded into your workforce strategies, talent models, and cultural transformation agenda.

Human-AI Synergy: The New Collaboration Model

Bornet advocates for symbiotic teams where AI and humans complement each other. Rather than compete, the two work in tandem to drive better outcomes.

  • AI delivers scale, speed, and precision.
  • Humans provide context, ethics, judgment, and empathy.

Insight: Use this pairing in high-impact roles like diagnostics, content creation, customer service, and product design.

Avoiding “AI Obesity”: The Risk of Over-Automation

Bornet warns against AI Obesity: a condition where organizations over-rely on AI, leading people to lose touch with essential human skills like critical thinking, empathy, and creativity. The solution? Regularly exercise our Humics and ensure humans remain in the loop, especially where oversight, ethics, or trust are required.

Insight: Define clear roles for human oversight, especially in ethical decisions, people management and policy enforcement.

Real-World Application: Individuals, Parents, and Businesses

Bornet offers tailored strategies for:

  • Individuals: Blend digital fluency with human depth to future-proof your career. Learn how to partner with AI tools to enhance your strengths, stay adaptable, and lead with human judgment in an increasingly automated environment.
  • Parents & Educators: Teach kids curiosity, resilience, and emotional intelligence alongside digital skills. Equip the next generation not only to use technology responsibly but also to cultivate the uniquely human traits that will help them thrive in any future scenario.
  • Businesses: Redesign roles and culture to embed AI-human collaboration, with trust and values at the core. Shift from a purely efficiency-driven mindset to one that sees AI as a co-pilot, empowering employees to do more meaningful, value-adding work.

Note: This is not just about new tools; it’s about new mindsets and behaviors across the organization.

Implications for Digital Transformation Leaders

Irreplaceable aligns seamlessly with modern transformation priorities:

  • Technology as Amplifier: Deploy AI to expand human capabilities, not to replace them.
  • Human-Centric KPIs: Add creativity, employee experience, and trust metrics to your dashboards.
  • Purpose-Driven Change: Frame digital transformation as an opportunity to become more human, not less.

How to Apply This in Practice

Start with a diagnostic: Where is human judgment undervalued in your current operating model? Then:

  1. Redesign roles with AI + Human pairings
  2. Invest in Humics through people development and learning journeys
  3. Update metrics to track human and AI impact
  4. Communicate the purpose: Align AI initiatives with a human-centered narrative

Conclusion

Pascal Bornet’s Irreplaceable offers more than optimism. It provides a strategic lens to ensure your organization thrives in the AI age—by amplifying what makes us human. For digital and transformation leaders, the message is clear: being more human is your greatest competitive advantage.

For more information you can check out: Become IRREPLACEABLE and unlock your true potential in the age of AI

If AI Is So Smart, Why Are We Struggling to Use It?

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The human-side barriers to AI adoption — and how to overcome them

In my previous newsletter, “Where AI is Already Making a Significant Impact on Business Process Execution – 15 Areas Explained,” we explored how AI is streamlining tasks from claims processing to customer segmentation. But despite these breakthroughs, one question keeps surfacing:

If AI is delivering so much value… why are so many organizations struggling to actually adopt it?

The answer isn’t technical — it’s human.

In this edition, I explore ten people-related reasons AI initiatives stall or underdeliver. Each barrier is followed by a practical example and suggestions for how to overcome it.

1. Fear of Job Loss and Role Redundancy

Employees fear AI will replace them, leading to resistance or disengagement. This is especially prevalent in operational roles and shared services.

Example: An EY survey found 75% of US workers worry about AI replacing their jobs. In several large organizations, process experts quietly slow-roll automation to protect their roles.

How to mitigate: Communicate early and often. Frame AI as augmentation, not replacement. Highlight opportunities for upskilling and create pathways for digitally enabled roles.

2. Loss of Meaning and Professional Identity

Even if employees accept AI won’t replace them, they may fear it will erode the craftsmanship and meaning of their work.

Example: In legal and editorial teams, professionals report reluctance to use generative AI tools because they feel it “cheapens” their contribution or downplays their expertise.

How to mitigate: Position AI as a creative partner, not a substitute. Focus on use cases that enhance quality and amplify human strengths.

3. Low AI Literacy and Confidence

Many knowledge workers don’t feel equipped to understand or apply AI tools. This leads to underutilization or misuse.

Example: I’ve seen this firsthand: employees hesitate to rely on AI tools and default to old ways of working out of discomfort or lack of clarity.

How to mitigate: Launch AI literacy programs tailored to roles. Give people space to experiment, and build a shared language for AI in the organization.

4. Skills Gap: Applying AI to Domain Work

Beyond literacy, many employees lack the applied skills needed to integrate AI into their actual workflows. They may know what AI can do — but not how to adapt it to their role.

Example: In a global supply chain function, team members were aware of AI’s capabilities but struggled to translate models into usable scenarios like demand sensing or inventory risk prediction.

How to mitigate: Invest in practical upskilling: scenario-based training, role-specific accelerators, and coaching. Empower cross-functional “AI translators” to bridge tech and business.

5. Trust and Explainability Concerns

Employees and managers hesitate to rely on AI if they don’t understand “how” it reached its output — especially in decision-making contexts.

Example: A global logistics firm paused the rollout of AI-based demand forecasting after regional leaders questioned unexplained fluctuations in output.

How to mitigate: Prioritize transparency for critical use cases. Use interpretable models where possible, and combine AI output with human judgment.

6. Middle Management Resistance

Mid-level managers may perceive AI as a threat to their control or relevance. They can become blockers, slowing momentum.

Example: In a consumer goods company, digital leaders struggled to scale AI pilots because local managers didn’t support or prioritize the initiatives.

How to mitigate: Involve middle managers in co-creation. Tie their success metrics to AI-enabled outcomes and make them champions of transformation.

7. Change Fatigue and Initiative Overload

Teams already dealing with hybrid work, restructurings, or system rollouts may see AI as just another corporate initiative on top of their daily work.

Example: A pharmaceutical company with multiple digital programs saw frontline disengagement with AI pilots due to burnout and lack of clear value.

How to mitigate: Embed AI within existing transformation goals. Focus on a few high-impact use cases, and consistently communicate their benefit to teams.

8. Lack of Inclusion in Design and Rollout

When AI tools are developed in technical silos, end users often feel the solutions don’t reflect their workflows or needs.

Example: A banking chatbot failed in deployment because call center staff hadn’t been involved in the design phase — leading to confusion and distrust.

How to mitigate: Involve users early and often. Use participatory design approaches and validate tools in real working environments.

9. Ethical Concerns and Mistrust

Some employees worry AI may reinforce bias, lack fairness, or be used inappropriately — especially in sensitive areas like HR, compliance, or performance assessment.

Example: An AI-based resume screener was withdrawn by a tech firm after internal concerns about gender and ethnicity bias, even before public rollout.

How to mitigate: Establish clear ethical guidelines for AI. Be transparent about data usage, and create safe channels for feedback and concerns.

10. Peer Friction: “They Let the AI Do Their Job”

Even when AI is used effectively, friction can arise when colleagues feel others are “outsourcing their thinking” or bypassing effort by relying on AI tools.

Example: In a shared services team, tension grew when some employees drafted client reports with AI in minutes — while others insisted on traditional methods, feeling their contributions were undervalued.

How to mitigate: Create shared norms around responsible AI use. Recognize outcomes, not effort alone, and encourage knowledge sharing across teams.

Final Thought: It’s Not the Tech — It’s the Trust

Successful AI adoption isn’t about algorithms or infrastructure — it’s about mindsets, motivation, and meaning.

If we want people to embrace AI, we must:

  • Empower them with knowledge, skills, and confidence
  • Engage them as co-creators in the journey
  • Ensure they see personal and professional value in change

Human-centered adoption isn’t the soft side of transformation — it’s the hard edge of success. Let’s create our transformation plans with that in mind.

Where AI is Already Making a Significant Impact on Business Process Execution – 15 Areas Explained

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After exploring a wide range of expert sources—and drawing from my own experience—I collaborated with AI tools (ChatGPT, Gemini, Claude) to create a concise overview of where AI is currently having the biggest impact on business processes. The aim: to bring together the most referenced success areas across functions and reflect on why these domains are leading the way. Recognizing these patterns can help us anticipate where AI is likely to deliver its next wave of value (at the end of the article).

Below are 15 high-impact application areas where AI is already delivering significant value—each explained with clear benefits and real-world examples.

Marketing & Sales

1. Smarter Customer Service Automation
AI-powered chatbots and virtual agents are now central to handling customer inquiries. They can resolve a majority of tickets without human intervention, enabling 24/7 service while reducing costs and improving customer experience. Beyond just scripted replies, these agents learn from interactions to provide increasingly accurate and personalized support, allowing human teams to focus on complex or emotionally sensitive requests.
Example: Industry-wide AI adoption in contact centers, with 88% of firms reporting improved resolution times and reduced overhead (Statista, McKinsey).

2. Personalised Marketing at Scale
AI recommendation engines tailor content and product offerings based on individual browsing behavior, purchase history, and contextual data. This creates more relevant experiences for users and lifts conversion rates. Example, Amazon’s recommendation engine contributes over a third of its e-commerce revenue, proving the model’s commercial impact.

3. Sales Acceleration with AI
AI is transforming sales operations by taking over repetitive tasks like data entry, scheduling, and opportunity scoring. It also enables more informed decisions through predictive analytics, guiding sales teams to focus on leads with the highest conversion potential. Example: Salesforce research reveals that 83% of AI-enabled sales teams saw revenue growth versus 66% without AI. Besides this Salesforce example, I also can share from my personal working experience at Brenntag that AI solutions to guide “next best actions”  for salespeople drives significant impact.

Operations, Manufacturing & Supply Chain

4. Predictive Maintenance Efficiency
Traditional maintenance schedules often lead to unnecessary downtime or surprise equipment failures. AI flips the model by continuously analyzing sensor data to detect anomalies before breakdowns occur. This helps manufacturers schedule maintenance only when needed, extending equipment life and minimizing disruption.
Example: Mitsubishi and others use predictive maintenance tools that have led to up to 50% reduction in unplanned downtime.

5. AI-Powered Quality Control
In industries where product consistency is crucial, AI-enhanced computer vision inspects goods in real time for even the tiniest defects. These systems outperform the human eye in speed and accuracy, ensuring higher product quality and reducing waste from production errors.
Example: Automotive and electronics manufacturers now use AI to identify surface defects, alignment issues, or functional flaws instantly on the line.

6. Smarter Inventory Optimization
AI brings new precision to inventory planning by factoring in historical sales, seasonal trends, macroeconomic indicators, and real-time customer demand. This ensures businesses maintain optimal stock levels—avoiding both overstock and stockouts—while reducing working capital.
Example: Companies using AI in supply chain forecasting report inventory reductions of up to 35% (McKinsey).

7. Logistics Route Optimization
AI’s real-time route planning considers traffic, weather, delivery windows, and driver availability to suggest the most efficient routes. This leads to faster deliveries, fuel savings, and higher customer satisfaction. It also helps logistics providers scale without proportionally increasing operational complexity.
Example: DHL’s AI-driven routing platform reduces mileage per package and improves on-time delivery.

Finance, Accounting & Risk Management

8. Touchless Document Processing
Invoice entry and document reconciliation are among the most repetitive and error-prone tasks in finance. AI automates these workflows by reading, validating, and recording data with high accuracy, drastically reducing processing time and human error.
Example: Large enterprises report cutting invoice processing time by 80% and lowering cost per invoice by over 60%.

9. Smarter Fraud Detection Systems
Modern fraud schemes evolve too rapidly for traditional rules-based systems to catch. AI models can continuously learn from new data and detect suspicious behaviors in real time, flagging anomalies that might otherwise go unnoticed.
Example: A global bank using AI to process checks in real time saw a 50% drop in fraud and saved over $20M annually.

10. Automating Financial Controls
AI supports internal audit and compliance by automatically flagging unusual transactions, reconciling financial data, and generating traceable logs for auditors. This not only boosts confidence in regulatory reporting but reduces the burden on finance teams.
Example:  Deloitte finds AI-led controls improve accuracy, reduce audit costs, and streamline compliance workflows.

HR & Administration

11. Accelerated AI Recruitment
Hiring at scale is time-consuming and prone to bias. AI now supports end-to-end recruitment by screening CVs, analyzing video interviews, and predicting candidate-job fit based on past data. This enables faster, fairer hiring decisions and a better candidate experience.
Example: Unilever’s AI-powered hiring cut time-to-hire by 90%, reduced recruiter workload, and increased hiring diversity by 16%.

12. AI-Powered Admin Assistance
Whether it’s helping employees navigate HR policies or resetting passwords, AI bots respond instantly to internal requests. They resolve issues efficiently and learn from interactions to improve over time, reducing dependency on HR and IT service desks.
Example: AT&T’s HR bot answers thousands of employee questions per month, freeing up support teams and reducing internal wait times.

Software Development & IT Operations

13. AI Code Generation & Testing
AI-assisted development tools help engineers write code, suggest improvements, and run automated tests. This shortens development cycles, reduces bugs, and improves overall code quality. It also democratizes coding by assisting less-experienced developers with best practices.
Example: Enterprises report 20–30% faster feature delivery using AI-assisted development environments.

14. Intelligent IT Service Management
From incident triage to root cause analysis, AI is embedded in IT Service Management platforms to help resolve tech issues automatically. Predictive insights help prevent outages and minimize disruption across business-critical systems.
Example: Leading digitally enabled firms see average resolution time drop by 50%, with improved system reliability and user satisfaction.

15. AI-Driven DevOps Optimization
By analyzing telemetry data and past deployments, AI optimizes build pipelines, monitors production systems, and predicts future resource needs. This ensures smoother rollouts and better infrastructure planning.
Example: Cloud-native companies use AI to reduce deployment failures and improve performance-to-cost ratios in real time.

Why AI Wins in These Areas

Despite the diversity of domains, these success stories share clear commonalities:

  • High process volume: Tasks that are frequent and repetitive gain the most from automation.
  • Structured and semi-structured data: AI performs best where input data is clean or can be normalized.
  • Clear Return On Investment (ROI) levers: The efficiency gains are measurable—reduced cycle time, lowered cost, or increased accuracy.
  • Repeatable workflows: Standardized or rules-based processes allow for predictable automation.

In essence, AI is most effective where complexity meets scale. As more enterprises embed AI into their operations, it is not just making processes faster—it’s reshaping them for quality, agility, and scale in a digital-first world.

Looking ahead, the next wave of AI impact is likely to emerge in areas where unstructured data and human judgment still dominate today. Examples include:

  • Legal and contract management, where AI is starting to support contract drafting, review, and risk flagging.
  • Strategy and decision support, where generative AI can synthesize market trends, customer feedback, and financial data to help leaders shape better strategies.
  • Sustainability tracking, where AI can analyze supply chain and operational data to monitor and reduce environmental impact.

As models become more capable and context-aware, these higher-value and less-structured domains may soon follow the path of automation and augmentation already seen in the 15 areas above.

What We Can Learn from Lego: Three Transformation Lessons

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During my recent visit to Lego House in Billund, I was reminded just how much more this iconic brand represents than simply being a maker of plastic bricks. Lego is a great example of smart design, purposeful transformation, and digital innovation. Organizations aiming to stay relevant in a changing market can learn a lot from Lego’s ability to reinvent itself.

In this article, I explore three interconnected dimensions of Lego’s success: how its design principles mirror modern architectural thinking, how the company has transformed while staying true to its purpose, and how it is leveraging digital and AI to lead in both product and operational innovation.

1. Architecture & Design Thinking: From Bricks to Platforms

As a child, I already experienced the smart design of Lego—how one collection of components allowed me to create countless structures. Each brick is designed with a standard interface that guarantees compatibility—regardless of shape, size, or decade of manufacture. This is the physical-world equivalent of APIs in digital architecture: enabling endless creativity through constraint-based design.

Beyond modularity, Lego also embodies platform thinking. With Lego Ideas, they invite users into the design process, allowing them to co-create and even commercialize their models. This open innovation model has helped extend Lego’s reach beyond its internal capabilities.

Lego also uses digital twins to simulate the behavior of physical Lego components and production systems. This enables the company to test product performance, optimize assembly processes, and reduce waste—before anything is physically produced.

Lesson: Embrace modularity—not only in your product and system design but in your organizational setup. Invest in simulation and digital twin technology to test, iterate, and scale with greater speed and lower risk. And treat your users not just as consumers but as contributors to your platform.

2. Organizational Transformation: Reinvention with Purpose

Lego’s transformation journey is a great example of how established companies under pressure can reinvent themselves without losing their DNA. In the early 2000s, Lego faced a financial crisis caused by over-diversification and lack of focus. The turnaround required painful choices: divesting non-core businesses, simplifying product lines, and reconnecting with the company’s core mission—”inspiring and developing the builders of tomorrow.”

But Lego didn’t stop at operational restructuring. It also launched a broader innovation strategy to stay commercially relevant to changing customers. This included launching new experiences like The Lego Movie, which reinvented the brand for a new generation, and partnering with global content leaders such as Disney, Star Wars, and Formula 1 to create product ranges that merged Lego’s design with beloved franchises. These moves helped strengthen the brand and attract new audiences without alienating loyal fans.

Sustainability has become another important dimension—especially for a company built on plastic. Lego has committed to making all core products from sustainable materials by 2032 and is investing heavily in bio-based and recyclable plastics.

Lesson: Transformation isn’t about discarding the old; it’s about strengthening your core value and building on that foundation. Focused innovation, clear communication, and a culture that supports learning, sustainability, and adaptation are crucial.

3. Digital & AI Integration: Enhancing Experience and Performance

As a customer, I’ve already experienced how Lego.com tracks and rewards my purchases. For the younger user group, they’ve developed the Lego Life platform. Here, AI is used to moderate content and create engaging digital experiences for children. Personalization engines recommend content and products based on individual preferences and behaviors.

Lego has embraced digital not just to modernize, but to structurally improve its value chain. Robotics and automation are widely implemented in both production and warehousing. Their supply chain uses real-time data, predictive analytics, and machine learning to forecast demand, optimize production, and manage global inventory.

Perhaps the most innovative example is LegoGPT, an AI model developed with Carnegie Mellon University. It allows users to describe ideas in natural language and receive buildable Lego models in return. By converting abstract intent into tangible design, LegoGPT showcases the power of generative AI to bridge imagination and engineering.

Lesson: Use digital and AI to create meaningful impact—whether by enhancing customer experiences, increasing operational agility, or unlocking new creative possibilities.

Conclusion: Building with Intent

Lego teaches us that true transformation lies at the intersection of smart innovation, strong organizational purpose, and enabling technologies. Its enduring success comes from continually reinterpreting its core principles to meet the needs of a changing world.

For transformation leaders, Lego is more than a nostalgic brand—it’s a masterclass in building the future, one brick at a time.

The Battle of the Agents – Choosing the Right AI Platform for Your Enterprise

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The market for AI agents is booming — and it’s just getting started. According to a 2024 McKinsey report, the market for AI agent applications across customer service, sales, operations, and IT support is expected to exceed $100 billion by 2030. Business and technology leaders are rapidly embracing AI agents to automate tasks, enhance decision-making, and create seamless interactions across digital and human channels.

Amidst this explosion, two distinct categories of AI agents are emerging:

  • Embedded agents, seamlessly integrated into existing enterprise platforms.
  • Universal agents, developed to operate across diverse systems and workflows.

This article explores this critical divide by comparing two landmark examples: Agentforce from Salesforce, and Agentspace from Google Cloud.

Embedded Agents: Agentforce by Salesforce

Agentforce is Salesforce’s embedded AI agent framework, designed to supercharge user productivity across its Sales, Service, and Marketing Clouds. Powered by Einstein GPT and native CRM data, Agentforce deploys autonomous agents that can automate tasks, recommend next-best actions, or even interact with customers directly. These agents operate safely within Salesforce’s governance, security, and compliance standards — making them ideal for organizations seeking low-risk, high-impact AI augmentation without leaving their CRM ecosystem.

The promise of Agentforce is simple: smarter workflows, better outcomes, minimal disruption.

Universal Agents: Agentspace by Google Cloud

Agentspace is Google Cloud’s flexible development environment for creating powerful, domain-specific AI agents. It allows organizations to orchestrate multiple AI models (including Gemini and open-source options), integrate enterprise data sources, and define complex multi-step task flows. Agents built with Agentspace can operate across cloud, on-premise, and SaaS environments, offering vast flexibility.

Unlike embedded solutions, Agentspace demands a stronger technical investment — but in return, it enables businesses to build sophisticated, enterprise-wide AI capabilities tailored to their unique environments and strategies.

Comparison Table: Agentforce vs. Agentspace

FeatureAgentforce (Salesforce)Agentspace (Google Cloud)
Primary FocusEnhancing customer service, sales, and marketing productivity within Salesforce.Building custom, autonomous AI agents for a wide range of enterprise applications.
Target UserBusiness users (e.g., customer service agents, sales managers) and Salesforce administrators.Developers, AI engineers, and cloud architects.
IntegrationDeep integration within the Salesforce CRM and Service Cloud environment.Flexible integration across cloud services, APIs, and enterprise systems.
Core FunctionalityReal-time assistance, task automation, knowledge suggestions, next-best actions, customer interaction support.Multi-step task execution, information retrieval, workflow orchestration, natural language understanding, integration with external systems.
Customization LevelConfiguration and workflow adjustments within Salesforce boundaries.High customization — full control over logic, models, tools, and integrations.
AI FoundationSalesforce Einstein GPT and proprietary AI models tied to CRM data.Google’s Gemini models, open-source models, and Google’s AI/ML toolsets.
Use CasesCustomer support, case management, sales assistance, marketing automation.Custom automation, digital assistants, chatbots, research agents, operational task agents.
Development EffortLower — focuses on using built-in features and low-code tools.Higher — requires development, model orchestration, and cloud engineering expertise.
Autonomy LevelPrimarily agent-assisted — supports and augments human workflows.High autonomy — agents capable of independent, multi-system operations.
EcosystemSalesforce platform ecosystem.Google Cloud Platform and broader multi-cloud and SaaS ecosystem.
Deployment SpeedFast — minimal technical lift for Salesforce customers.Moderate to slow — requires planning, development, and integration efforts.
ScalabilityScales within Salesforce CRM and Service Cloud contexts.Cloud-native scaling across diverse workloads and environments.
Governance & Risk ManagementStrong governance built into Salesforce’s compliance and security frameworks.Flexible — enterprises must implement their own governance, security, and monitoring.
Cost ConsiderationsPredictable — tied to Salesforce licensing models.Variable — dependent on cloud consumption, storage, and AI compute costs.

Conclusion: Choosing the Right Path for AI Agents

Selecting between Agentforce and Agentspace ultimately depends on your organization’s strategic priorities, technology landscape, and appetite for AI innovation.

Choose Agentforce when:

  • You are heavily invested in Salesforce and want seamless AI augmentation within existing sales, service, and marketing operations.
  • Your primary goal is to boost employee productivity, reduce response times, and enhance customer satisfaction through AI-powered guidance and automation.
  • You need quick deployment with minimal technical lift, leveraging Salesforce’s built-in governance, security, and compliance frameworks.
  • Your focus is on agent enablement — empowering human workers with intelligent tools, not fully automating complex workflows.

Key takeaway: Agentforce is ideal for organizations seeking trusted, low-risk AI augmentation embedded into the Salesforce environment, driving faster operational improvements without disrupting existing workflows.

Choose Agentspace when:

  • You require highly customized, autonomous AI agents capable of performing complex, multi-system tasks.
  • Your ambition is to build AI solutions that extend far beyond CRM — into operations, product development, knowledge work, and customer experience innovation.
  • You have technical resources (developers, AI engineers, cloud architects) to design, deploy, and govern sophisticated agent architectures.
  • You want to leverage Google’s powerful AI models and enjoy the flexibility to orchestrate bespoke workflows across your technology stack.

Key takeaway: Agentspace is suited for enterprises seeking strategic AI innovation — building differentiated, high-autonomy agents that drive transformation across the business, provided the necessary expertise and investment are in place.

Annex: Extending the Comparison Beyond Salesforce and Google

While this article focuses on the comparison between Salesforce’s Agentforce and Google Cloud’s Agentspace, it is important to note that similar considerations apply when evaluating other leading AI agent solutions.

Other Embedded AI Agent Platforms:

  • ServiceNow: ServiceNow’s Generative AI Controller and Virtual Agent capabilities offer embedded AI assistance across IT Service Management, HR, and customer workflows.
  • SAP: SAP’s Joule AI integrates conversational AI into ERP, CRM, and supply chain systems to drive workflow automation and insights.
  • Oracle: Oracle’s Digital Assistant delivers pre-built AI agents across CX, HCM, and ERP applications with deep process integration.
  • Pega: Pega’s AI and decisioning capabilities embed adaptive intelligence into customer service, case management, and workflow automation platforms.

These embedded solutions share common traits:
✅ Deep integration within their respective ecosystems.
✅ Faster deployment with lower technical lift.
✅ Strong governance, but less flexibility outside the platform boundaries.

Other Customizable AI Agent Platforms:

  • Microsoft Azure AI: Azure OpenAI Service, Azure AI Studio, and Cognitive Services provide a robust environment to build, orchestrate, and deploy enterprise-grade AI agents across the Microsoft ecosystem and beyond.
  • AWS AI Services: AWS offers powerful capabilities through Bedrock, SageMaker, and Lambda-based orchestration to create highly customized and scalable AI agents.
  • IBM Watson: IBM Watsonx platform enables businesses to build domain-specific AI agents with strong support for enterprise governance, regulatory compliance, and hybrid cloud environments.

These customizable platforms share characteristics:
✅ High degree of design flexibility and autonomy.
✅ Potential for cross-system orchestration at enterprise scale.
✅ Require greater investment in technical resources, governance, and lifecycle management.

Key Takeaway:

Whether you evaluate embedded solutions or custom platforms, the core decision principles remain the same:

  • Speed and simplicity versus flexibility and autonomy.
  • Platform integration versus ecosystem-wide orchestration.
  • Operational augmentation versus strategic innovation.

As the agent economy matures, technology leaders must align their agent strategy with their overall digital transformation roadmap, balancing immediate needs with future ambitions.

How AI Changes the Digital Transformation Playbook

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I recently revisited David L. Rogers’ 2016 book, The Digital Transformation Playbook. This work was foundational in how I approached digital strategy in the years that followed. It helped executives move beyond viewing digital as a technology problem and instead rethink strategy for a digital enabled business. As I now reflect on the accelerating impact of artificial intelligence—especially generative and adaptive AI—I found myself asking: how would this playbook evolve if it were written today? What shifts, additions, or reinterpretations does AI demand of us?

Rogers identified five strategic domains where digital forces reshaped the rules of business: customers, competition, data, innovation, and value. These domains remain as relevant as ever—but in the age of AI, each requires a fresh lens.

In this article, I revisit each domain, beginning with Rogers’ foundational insight and then exploring how AI transforms the picture. I also propose three new strategic domains that have become essential in the AI era: workforce, governance, and culture.

1. Customers → From Networks of Relationships to Intelligent Experiences

Rogers’ Insight (2016):
In the traditional business model, customers were treated as passive recipients of value. Rogers urged companies to reconceive customers as active participants in networks—communicating, sharing, and shaping brand perceptions in real-time. The shift was toward engaging these dynamic networks, understanding behavior through data, and co-creating value through dialogue, platforms, and personalization.

AI Shift (Now):
AI enables companies to move beyond personalized communication to truly intelligent experiences. By analyzing vast datasets in real-time, AI systems can predict needs, automate responses, and tailor interactions across channels. From recommendation engines to digital agents, AI transforms customer experience into something anticipatory and adaptive—redefining engagement, loyalty, and satisfaction.

2. Competition → From Industry Ecosystems to Model-Driven Advantage

Rogers’ Insight (2016):
Rogers challenged the notion of fixed industry boundaries, arguing that digital platforms enable competition across sectors. Businesses could no longer assume their competitors would come from within their own industry. Instead, value was increasingly co-created in fluid ecosystems involving customers, partners, and even competitors.

AI Shift (Now):
Today, the competitive battlefield is increasingly defined by AI capabilities. Winning organizations are those that can develop, fine-tune, and scale AI models faster than others. Competitive advantage comes from proprietary data, high-performing models, and AI-native organizational structures. In some cases, the model itself becomes the product—shifting power to those who own or control AI infrastructure.

3. Data → From Strategic Asset to Lifeblood of Intelligent Systems

Rogers’ Insight (2016):
Data, once a by-product of operations, was reimagined as a core strategic asset. Rogers emphasized using data to understand customers, inform decisions, and drive innovation. The shift was toward capturing more data and applying analytics to create actionable insights and competitive advantage.

AI Shift (Now):
AI transforms the role of data from decision-support to system training. Data doesn’t just inform—it powers intelligent behavior. The focus is now on quality, governance, and real-time flows of data that continuously refine AI systems. New challenges around data bias, provenance, and synthetic generation raise the stakes for ethical and secure data management.

4. Innovation → From Agile Prototyping to AI-Augmented Co-Creation

Rogers’ Insight (2016):
Rogers advocated for agile, iterative approaches to innovation. Instead of long development cycles, companies needed to embrace experimentation, MVPs, and customer feedback loops. Innovation was not just about new products—it was about learning fast and adapting to change.

AI Shift (Now):
AI amplifies every step of the innovation process. Generative tools accelerate ideation, design, and prototyping. Developers and designers can co-create with AI, testing multiple solutions instantly. The loop from idea to execution becomes compressed, with AI as a creative collaborator, not just a tool.

5. Value → From Digital Delivery to Adaptive Intelligence

Rogers’ Insight (2016):
Value creation, in Rogers’ view, moved from static supply chains to fluid, digital experiences. Companies needed to rethink how they delivered outcomes—shifting from products to services, from ownership to access, and from linear value chains to responsive platforms.

AI Shift (Now):
With AI, value is increasingly delivered through systems that learn and adapt. Intelligent services personalize in real time, optimize continuously, and evolve with user behavior. The value proposition becomes dynamic—embedded in a loop of sensing, reasoning, and responding.

Why We Must Expand the Playbook: The Rise of New Strategic Domains

The original five domains remain vital. Yet AI doesn’t just shift existing strategies—it introduces entirely new imperatives. As intelligent systems become embedded in workflows and decisions, organizations must rethink how they manage talent, ensure ethical oversight, and shape organizational culture. These aren’t adjacent topics—they are central to sustainable AI transformation.

6. Workforce → From Talent Strategy to Human–AI Teaming

AI is not replacing the workforce—it is changing it. Leaders must redesign roles, workflows, and capabilities to optimize human–AI collaboration. This means upskilling for adaptability, integrating AI into daily work, and ensuring people retain agency in AI-supported decisions. Human capital strategy must now include how teams and algorithms learn and perform together.

7. Governance → From Digital Risk to Responsible AI

AI introduces new dimensions of risk: bias, security, and regulatory complexity. Governance must now ensure not only compliance but also ethical development, explainability, and trust. Boards, executive teams, and product leaders need frameworks to evaluate and oversee AI initiatives—not just for effectiveness but for responsibility.

8. Culture → From Digital Fluency to AI Curiosity and Trust

The mindset shift required to scale AI is cultural as much as technological. Organizations must foster curiosity about what AI can do, confidence in its potential, and clarity about its limits. Trust becomes a cultural asset—built through transparency, education, and inclusive experimentation. Without it, AI adoption stalls.

Conclusion: A Playbook for the AI Era

Rogers’ original playbook gave us a framework to reimagine business strategy in a digital world. That foundation still holds. But as AI redefines how we compete, create, and lead, we need a new version—one that not only shifts the lens on customers, competition, data, innovation, and value, but also adds the critical dimensions of workforce, governance, and culture. These eight domains form the new playbook for transformation in the age of intelligence.

Rewiring the Workforce: Aligning HR and IT in the Age of AI

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AI is already changing how teams operate, how leaders make decisions, and how value is delivered to customers. For organizations, this means rethinking not just what work gets done, but how it’s done, and by whom. Therefor it is crucial to think about how we bring Human and AI resource management together.

The 2023 MIT Sloan / BCG study, The Rise of AI-Powered Organizations, found that the most successful companies with AI are those where HR and IT work closely together to redesign processes and roles. That collaboration is critical. If AI is deployed without rethinking how humans and machines collaborate, companies risk missed value, employee resistance, and ethical missteps.

Designing the Hybrid Workforce: Teams, Tasks, and Talent

AI doesn’t eliminate jobs—it changes them. To prepare, organizations need to break down roles into specific tasks:

  • What can be automated?
  • What can be enhanced by AI?
  • What should remain Human-led?

From there, teams can be redesigned around how people and AI tools work together. In practice, this might mean:

  • A customer service team using AI to summarize queries while humans resolve complex issues
  • A product development team using AI to generate design options that humans refine

The HBR article Collaborative Intelligence: Humans and AI Are Joining Forces (Wilson & Daugherty, 2018) highlights five human roles in human-AI collaboration, such as AI trainers, explainers, sustainers, amplifiers and translators. These roles are already emerging in forward-looking teams and should be reflected in new job descriptions and team capabilities.

Organizational Change: Leading Through Disruption

Adding AI isn’t just a tech upgrade—it changes how decisions are made, who makes them, and what leadership looks like. For example:

  • Middle managers might now focus more on coaching and less on reporting, as AI handles data consolidation.
  • Teams may need to consult AI before acting, introducing a new rhythm to collaboration.

Gartner’s 2023 report How to Measure AI-Augmented Employee Productivity stresses that success in AI transformation isn’t just about productivity—it’s about how well teams adapt, collaborate, and trust AI tools. That requires strong change management, hands-on leadership, and clear guidance on when to trust AI versus when to override it.

Performance and Culture in an AI-Augmented Workplace

With AI in the mix, traditional performance reviews fall short. Leaders need to ask:

  • How are employees using AI tools to improve their work?
  • Are decisions more consistent, inclusive, and data-informed?
  • Is the AI system fair and explainable?

The Stanford HAI Annual AI Index Report 2024 shows that AI systems are improving technically, but companies often lack the tools to measure human impact—such as employee trust or the inclusiveness of AI-driven decisions. Stanford HAI provides several frameworks that can be leveraged to measure Human + AI teams performance.

HR + IT: From Functional Silos to Strategic Workforce Partners

To make AI work, HR and IT must be in lockstep. Here’s what that looks like:

  • Shared strategy: Joint planning on where AI will impact jobs and what new skills are needed
  • Reskilling programs: Co-owned initiatives to help employees build digital and AI literacy
  • Data and governance: Shared ownership of tools that measure workforce readiness and ensure responsible AI use

IBM’s 2023 Enterprise Guide to Closing the Skills Gap highlights that companies closing the skills gap at scale have strong HR–IT alignment. It’s not about HR specifying training needs and IT buying tools. It’s about building workforce capabilities together, with shared accountability.

Practical Implementation Guide

Step-by-Step

  1. Start with a vision: What does AI mean for how your people work?
  2. Create joint ownership: HR and IT should lead together from day one
  3. Map current tasks and roles: Where can AI add value or remove friction?
  4. Pilot hybrid teams: Run experiments in one area (e.g., marketing, finance) and scale what works
  5. Define ethical rules: Decide where AI should assist, and where humans must retain control
  6. Track impact: Use KPIs that include both productivity and human experience

Avoid These Pitfalls

  • Launching AI without involving HR
  • Treating AI as an isolated IT solution
  • Ignoring cultural resistance or trust issues
  • Failing to update roles, reviews, or incentives

Patterns That Work

  • Embedding AI in learning programs led by both HR and IT
  • Using AI to support—not replace—human decision-making in recruiting
  • Creating workforce councils to oversee AI ethics and inclusion

Conclusion: Time to Rewire

AI is a shift in how people, teams, and organizations operate. Making that shift successful requires deep collaboration between HR and IT, clear direction from leadership, and a willingness to rethink everything from team design to performance reviews.

Organizations that embrace this challenge with practical steps and shared ownership will not only manage AI’s impact—they’ll harness its full potential to build a smarter, more adaptive workforce.

Revolutionizing Finance with AI Automation

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Finance is one of the business functions most primed for disruption through AI. With its high volume of repetitive transactions, rich data environments, and structured processes, the finance and controlling function is uniquely positioned to benefit from automation and intelligent analytics. As AI technologies mature, they are enabling a shift from transactional finance to strategic finance, unlocking new efficiencies, predictive capabilities, and business insights.

The implications are not just technological but organizational. With automation potential ranging from 30% to over 80% across various activities, it is likely that finance teams of the future will require less than 50% of the current workforce for traditional roles. The roles that remain will be more analytical, advisory, and technology-driven.

This article explores the key activities of the Finance function, how AI is transforming each area, and what the finance function could look like in 5–10 years. We also outline two distinct transformation scenarios—one focused on rapid implementation and another on sustainable foundations—to help finance leaders chart their course.

Key Activities of the Finance & Controlling Function and AI’s Impact

  1. Financial Planning & Analysis (FP&A)
    • Activities: Budgeting, forecasting, scenario modeling, variance analysis.
    • AI Impact: Predictive forecasting, automated scenario generation, and anomaly detection increase speed and accuracy.
  2. Management Reporting
    • Activities: Internal performance reports, dashboards, KPI tracking.
    • AI Impact: Natural language generation and self-service analytics personalize insights and automate commentary.
  3. Controlling
    • Activities: Cost control, investment analysis, policy compliance.
    • AI Impact: AI uncovers cost drivers, monitors ROI, and enforces compliance rules automatically.
  4. Accounting & Financial Close
    • Activities: AP/AR, reconciliations, journal entries, intercompany close.
    • AI Impact: OCR, bots, and smart matching drastically reduce manual work and cycle times.
  5. Treasury & Cash Management
    • Activities: Cash forecasting, liquidity management, FX risk, banking.
    • AI Impact: Predictive models optimize cash positions and detect fraud in real time.
  6. Tax & Compliance
    • Activities: Tax classification, filings, regulatory adherence.
    • AI Impact: Automated tax coding, real-time compliance monitoring, and AI-driven audit trails.
  7. Audit & Risk Management
    • Activities: Internal/external audit support, control testing, risk management.
    • AI Impact: Continuous audit monitoring, real-time risk scoring, and policy breach alerts.
  8. Financial Systems & Data Management
    • Activities: ERP management, data quality, automation enablement.
    • AI Impact: Data cleansing, process mining, and AI copilots transform finance operations.

AI Automation Potential Across Finance

AreaAutomation PotentialComments
FP&AModerateForecasting and analysis are automatable, but strategic planning remains human-led.
Management ReportingHighReport generation and commentary can be mostly automated.
ControllingModerateRoutine cost analysis is automatable; investment decisions are not.
Accounting & CloseHighReconciliations and entries are ideal for automation.
TreasuryModerateForecasting and fraud detection can be automated; decisions require oversight.
Tax & ComplianceModerateClassification and monitoring are automatable; legal interpretation is not.
Audit & RiskLow to ModerateMonitoring can be automated; assessments need human judgment.
Financial SystemsHighData tasks and support functions are highly automatable.

The Finance Function in 5–10 Years: AI-Augmented and Insight-Driven
Finance in the future will be lean, real-time, and forward-looking. The traditional role of finance as a scorekeeper will evolve into that of a strategic partner. Key shifts will include:

  • Near real-time closing and continuous forecasting
  • Proactive risk management through AI-driven monitoring
  • AI copilots supporting analysts with real-time insights
  • Self-optimizing processes and embedded business advisory

This transformation also entails a significant redefinition of workforce composition. Many routine roles will be phased out or reshaped, with the remaining talent focused on analytics, business partnering, and data stewardship. Finance teams may operate with less than half the current headcount, but with higher impact and strategic relevance.

Two Roadmap Scenarios for AI Transformation

1. Go Fast: Rapid AI Deployment in High-Impact Areas

  • Focus on automating high-volume, repetitive tasks for fast ROI.
  • Prioritize areas like reporting, financial close, and forecasting.
  • Launch an AI Center of Excellence to scale use cases.
  • Upskill teams in AI tools and data literacy.
  • Risks: Process fragmentation, change fatigue.

2. Build to Last: Strengthen Foundations Before Scaling AI

  • Begin with standardizing processes and modernizing ERP/data.
  • Use process mining to identify where AI fits best.
  • Pilot AI while building trust in systems and data.
  • Drive long-term scalability through structured change management.
  • Risks: Slower benefits realization, loss of momentum.

Conclusion: Finance Leaders Must Shape the AI Journey
AI offers unprecedented potential to elevate finance from an operational function to a strategic powerhouse. Whether choosing to go fast or build to last, success will require clear vision, strong governance, and continuous upskilling.

But leaders must also prepare for the workforce transformation ahead. With many transactional roles set to disappear, reskilling, talent planning, and organizational redesign must become part of the AI roadmap. Finance leaders who act now—balancing ambition with structure—will define the future of the profession and unlock new value for their organizations.