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23 stories
Forbes

Board-level AI strategy: closing the gap between ambition and execution

Why it mattersThis article examines the gap between board approval of an AI strategy and the understanding and support needed to execute it. It helps CEOs assess readiness, accountability and realistic expectations.
Original sourceForbesLeadership and the C-suite
Leadership and the C-suite23 September 2026 · 7 min read

Board-level AI strategy: closing the gap between ambition and execution

The article reports that in 2025, 83% of S&P 500 companies recognised AI as a material risk, while fewer than 3% of board members had AI expertise. The resulting execution gap appears in accountability structures, expectations of progress and the board’s actual mandate.

Key points
  • Unclear accountability for AI outcomes often leaves developers carrying responsibility that belongs with senior leadership.
  • Pressure to overstate AI progress creates a cycle of unrealistic expectations and decisions based on inflated information.
  • Boards need to understand the organisational changes, roles and processes required to deliver an AI strategy.
  • Leadership must create the conditions for implementation, giving teams support and clear direction alongside strategy approval.
Conclusion

CEOs should ensure that boards and senior leaders understand the organisational structure, accountability and culture required to deliver the AI plans they approve.

Read the original · Forbes
KPMG

KPMG creates a group to build AI-led businesses: lessons for corporate innovation

Why it mattersThis example offers CEOs practical insight into organising AI innovation and keeping large companies agile and competitive.
Original sourceKPMGAI and transformation
AI and transformation22 September 2026 · 4 min read

KPMG creates a group to build AI-led businesses: lessons for corporate innovation

KPMG LLP announced a Client Technology & Innovation group to build AI-led businesses and scale new products quickly. The move highlights the need to think differently and make bolder innovation investments as technology changes rapidly.

Key points
  • The CT&I group will work like a startup to experiment and move faster while protecting the core business.
  • Each business at the edge will have a mandate to build and scale, with possible outcomes including reintegration into the core business.
  • KPMG argues that the pace of change requires different thinking and faster, larger AI investments.
  • The group brings together alliance partners, startup investments and venture-capital relationships.
  • Its aim is to accelerate new business development and co-create products with clients.
Conclusion

CEOs can consider innovation at the edge and internal startups to accelerate AI-led development, balancing agility with the needs of the core business and engaging external partners.

Read the original · KPMG
Brookings Institution

Why AI is contentious: the moral and social challenges for business

Why it mattersThe article explores the reasons for public opposition to AI beyond economic and existential concerns. Its focus on data used without consent matters for leaders building ethical, sustainable AI strategies.
Original sourceBrookings InstitutionPeople and culture
People and culture24 September 2026 · 5 min read

Why AI is contentious: the moral and social challenges for business

Despite its economic potential, AI provokes considerable public concern. Research suggests that this reflects not only fears about job losses, but also moral objections to using people’s data, work and experiences without their consent.

Key points
  • Many people object to AI because it was built using people’s words, creative work and experiences without consent.
  • Around half of US adults are more concerned than excited about the growing use of AI in everyday life.
  • Policies focused only on economics, such as universal basic income or retraining, remain incomplete without addressing moral concerns.
  • Addressing these objections requires restoring a degree of ownership and attribution to the people whose work contributed to AI.
  • AI affects identity as well as job security when it appropriates aspects of people’s lives for training.
Conclusion

Responsible AI adoption requires leaders to consider more than technology and economics. Addressing moral questions about data use and human identity is central to trust and lasting public acceptance.

Read the original · Brookings Institution
CEPR · VoxEU

Before looking outside for talent, companies need to see the talent within.

Why it mattersInformation about employees’ capabilities becomes a management asset when leaders beyond the current team can use it.
Original sourceCEPR · VoxEUPeople and culture
People and culture22 September 2026 · 1 min read

Before looking outside for talent, companies need to see the talent within.

In a CEPR article published on 22 September, researchers analyse Finnish company data: more productive firms fill vacancies internally more often, through both promotions and lateral moves.

Key points
  • Among higher-level professionals, the productivity association appears mainly through lateral moves; at lower levels, through promotions.
  • The authors suggest that more productive companies may be better at recognising how internal candidates fit new roles.
  • The study does not establish that better talent assessment causes this relationship: direct evidence about management practices is limited.
  • External hiring remains necessary when new knowledge is needed or a stronger candidate works elsewhere.
Conclusion

Editorial takeaway: internal mobility is a system for sharing information about people as well as a career benefit. Review the internal talent pool before opening a vacancy.

Read the original · CEPR · VoxEU
IBM Institute for Business Value

Who has the right to disagree with AI? An organisational design question.

Why it mattersEmployees need clear authority to challenge AI recommendations and accountability for decisions as well as tools.
Original sourceIBM Institute for Business ValueAI and transformation
AI and transformation21 September 2026 · 1 min read

Who has the right to disagree with AI? An organisational design question.

IBM Institute for Business Value research shows a gap between technology strategy and work design. In 46% of surveyed organisations, the CHRO is not involved in defining AI strategy.

Key points
  • The study surveyed 1,500 HR leaders and 8,800 employees between April and June 2026.
  • Some 60% of employees fear skills erosion. This is respondents’ perception, not a measured effect of AI.
  • Some 43% say they are blamed for AI errors; 41% of HR leaders believe employees may feel unsafe challenging a system.
  • Only 28% of HR leaders report a shared plan with IT supported by an agreed operating rhythm.
Conclusion

Editorial takeaway: involve HR while AI strategy is being designed. Establish which decisions remain human and how employees can stop a flawed recommendation.

Read the original · IBM Institute for Business Value
Gartner

AI governance begins with shared responsibility.

Why it mattersEven strong rules will fail if the business sees data as IT’s responsibility alone.
Original sourceGartnerManagement systems
Management systems21 September 2026 · 1 min read

AI governance begins with shared responsibility.

Gartner predicts that by 2027, 60% of organisations that ignore cultural barriers in data governance will fail to govern AI successfully. This is a forecast, not an observed failure rate.

Key points
  • In a survey of 223 data and analytics leaders, cultural resistance was cited as a leading cause of failed initiatives more often than limited funding: 60% versus 40%.
  • Gartner recommends connecting data governance to measurable business outcomes and strategic objectives.
  • Business and technology teams should share responsibility.
  • Data literacy, AI literacy and change-management skills need to be embedded in daily work.
Conclusion

Editorial takeaway: before approving another AI policy, define decision owners, working relationships and the outcome for which they are accountable.

Read the original · Gartner
Forbes

An operating-model problem, not a shortage of product talent

Why it mattersThe article challenges the tendency to blame product failures on talent, arguing that operating models and systemic dysfunction are often the root cause.
Original sourceForbesManagement systems
Management systems17 September 2026 · 5 min read

An operating-model problem, not a shortage of product talent

Companies often blame employees for product failures when ineffective operating models prevent even talented teams from succeeding.

Key points
  • The article states that 80% of features in an average cloud product are rarely or never used, pointing to a systemic problem.
  • Problems include withholding inputs, assigning responsibility without authority and ignoring analysis that challenges established views.
  • Adding AI to outdated workflows can amplify dysfunction and produce unnecessary features faster.
  • The remedy includes redesigning workflows, restoring customer access, making financial metrics transparent and stabilising priorities.
  • AI adoption requires a rethink of the platform, clear workflows and team alignment alongside automation.
Conclusion

Leaders should identify and repair systemic weaknesses in operating models instead of searching for employees to blame.

Read the original · Forbes
KPMG

KPMG Global Family Business Report 2026: challenges and transformation amid uncertainty

Why it mattersThe report offers strategic insight for family and founder-led businesses navigating economic uncertainty, accelerating technology and leadership transitions, including AI governance and talent management.
Original sourceKPMGLeadership and the C-suite
Leadership and the C-suite16 September 2026 · 10 min read

KPMG Global Family Business Report 2026: challenges and transformation amid uncertainty

KPMG’s 2026 report examines the trends and challenges facing family businesses worldwide, highlighting the need to adapt traditional governance models to AI and generational change.

Key points
  • Family businesses show strategic confidence but expect growth to become harder over the next decade.
  • There is a structural shift from a family-operated business towards a family enterprise with professional management and independent boards.
  • AI adoption is outpacing governance: 64% are adopting AI, while 38% lack corresponding governance frameworks.
  • Talent and succession pressures are growing, with attracting external talent the leading workforce challenge.
  • Risk readiness lags behind complexity: only one third have a comprehensive risk-management system.
Conclusion

To remain resilient and influential, family businesses need to adapt governance, invest in professional management, develop robust AI governance and address talent and succession challenges.

Read the original · KPMG
Forbes

AI governance as a strategic differentiator: control, traceability and accountability

Why it mattersEffective AI governance goes beyond compliance and can distinguish market leaders. The article explains how to approach governance strategically to support sustainable growth and reduce risk.
Original sourceForbesAI and transformation
AI and transformation18 September 2026 · 5 min read

AI governance as a strategic differentiator: control, traceability and accountability

As companies accelerate AI adoption, the real challenge is to scale it safely, sustainably and under control. Leadership in the AI era will depend on demonstrating control, traceability and accountability in AI systems.

Key points
  • The ability to scale AI safely and under control matters more than adoption speed alone.
  • Governance must address which models are used, what data they consume, who has access, how behaviour is monitored and who owns adverse outcomes.
  • Many executive teams have not yet made AI governance part of their oversight agenda, creating a gap between adoption and control.
  • Companies that neglect AI safety face operational, regulatory and reputational risks.
  • Market leadership will depend on demonstrable control, traceability and accountability.
Conclusion

Leaders should treat AI governance as a strategic priority and embed control, traceability and accountability throughout development and deployment. This supports trust and lasting value as well as reducing risk.

Read the original · Forbes
Deloitte Insights

The COO’s role in agentic AI transformation

Why it mattersCOOs can turn AI ambition into operational results at scale by connecting adoption with business outcomes and coordinating work.
Original sourceDeloitte InsightsManagement systems
Management systems16 September 2026 · 10 min read

The COO’s role in agentic AI transformation

COOs play a central role in translating agentic AI ambition into results. Four priorities can help close the gap between aspiration and operational reality.

Key points
  • Align adoption with business goals and outcomes to identify where agents can have the greatest impact.
  • Organise accountability and workflows to coordinate people and agents effectively.
  • Rethink service-delivery models and introduce cross-functional co-creation.
  • Use the modular nature of agentic AI: start small, scale incrementally and keep reconfiguring systems.
Conclusion

COOs can act as architects of agentic AI transformation by aligning technology with business goals, improving workflows and using modularity to sustain operational gains.

Read the original · Deloitte Insights
KPMG

Leading transformation in the AI era: from ambition to execution

Why it mattersThe material explores the leadership capabilities needed for continuous AI-led transformation and the gap between ambition and delivery.
Original sourceKPMGLeadership and the C-suite
Leadership and the C-suite17 September 2026 · 5 min read

Leading transformation in the AI era: from ambition to execution

KPMG’s transformation leadership programme argues that successful organisations build enterprise-wide capabilities, embed trust and redesign work for an AI-enabled future alongside delivering projects.

Key points
  • Leading organisations build enterprise-wide capabilities and embed trust in transformation.
  • They redesign work for AI and develop cultures that help change scale and endure.
  • Some 58% of leaders see enterprise-wide capabilities as critical, while only 12% implement them effectively.
  • Only 19% believe their workforce is ready for human–AI collaboration despite expectations of significant benefits.
Conclusion

CEOs should approach transformation as a leadership challenge and build connected, resilient leadership capable of turning continuous change into sustained progress.

Read the original · KPMG
KPMG International

AI governance as a strategic pillar: from compliance to value creation

Why it mattersKPMG connects AI governance with value creation, risk management and organisational transformation, making it a strategic priority beyond compliance.
Original sourceKPMG InternationalManagement systems
Management systems17 September 2026 · 7 min read

AI governance as a strategic pillar: from compliance to value creation

As adoption grows, organisations need to integrate AI governance into business strategy rather than treating it as a separate function. Effective governance supports lasting value and reduces risk.

Key points
  • AI governance connects value creation, risk and organisational transformation.
  • It requires board-approved ambition and risk appetite, a value-led portfolio and an inventory of AI applications.
  • Lifecycle controls need to cover data quality, security, testing, monitoring and incident response.
  • Workforce policies, training and clear guidance on responsible use matter.
  • Boards should request evidence of realised value, emerging risks and effective controls as well as technical metrics.
Conclusion

Leaders should integrate AI governance into business strategy, assign clear risk ownership and involve boards actively in oversight.

Read the original · KPMG International
Boston Consulting Group

Five AI lessons for CEOs from Asia-Pacific

Why it mattersThe article draws on Asia-Pacific companies to offer practical guidance on leadership, business context, model choice and common barriers to AI adoption.
Original sourceBoston Consulting GroupAI and transformation
AI and transformation17 September 2026 · 7 min read

Five AI lessons for CEOs from Asia-Pacific

CEOs can accelerate AI transformation by learning from Asia-Pacific businesses that are using AI to reshape how they operate.

Key points
  • CEOs should lead AI initiatives rather than delegate ownership entirely to IT.
  • Focus on transforming the organisation’s most important functions and set ambitious KPIs.
  • Build business context into AI through proprietary data and processes to create a lasting advantage.
  • Use multiple models, including those that are good enough for the task, instead of waiting for a perfect model.
  • Avoid endless pilots and waiting for perfectly clean data: pursue initiatives at scale using available data.
Conclusion

CEOs should lead actively, concentrate on core business functions, combine models and avoid the familiar traps that delay scaling.

Read the original · Boston Consulting Group
IBM

Why CHROs must play a central role in AI transformation

Why it mattersCHROs need to join AI strategy discussions early to redesign work, define human–AI collaboration and govern a hybrid workforce.
Original sourceIBMPeople and culture
People and culture15 September 2026 · 7 min read

Why CHROs must play a central role in AI transformation

AI transformation is a transformation of work as well as technology. CHROs should be involved from the outset to shape the workforce, define how people and AI collaborate and support responsible governance.

Key points
  • AI reaches its potential when technology is accompanied by work redesign.
  • Bringing HR in after technology decisions leaves it managing consequences instead of shaping them.
  • CHROs should help decide what to automate, where human judgement remains essential and how people and AI work together.
  • Early HR involvement shapes recruitment, learning, performance assessment, accountability and delivery.
  • Workforce management will encompass both people and AI systems, requiring clear responsibilities and decision rights.
Conclusion

CHROs should be strategic partners from the beginning, helping redesign work and manage the integration of people and AI.

Read the original · IBM
Deloitte Insights

Four COO priorities for agentic AI transformation

Why it mattersThe article sets out leadership priorities for redesigning operating models and organisational structures to create lasting value from agentic AI.
Original sourceDeloitte InsightsAI and transformation
AI and transformation16 September 2026 · 5 min read

Four COO priorities for agentic AI transformation

Agentic AI can fundamentally change how work gets done, but scaling it remains a leadership challenge. COOs need to focus on four priorities to guide their organisations through the transition.

Key points
  • Rethinking operating models and organisational structures is essential to integrating agentic AI and creating lasting value.
  • Leadership priorities include accountability, risk and performance in an environment involving AI agents.
  • The gap between AI ambition and readiness for adoption at scale requires deliberate leadership.
  • Employees need reskilling to work effectively with agents, supported by careful transition management.
Conclusion

COOs need to redesign operating models and structures while managing the workforce transition, so that agentic AI supports accountability, risk reduction and productivity.

Read the original · Deloitte Insights
Brookings Institution

Workforce policy in the AI era: focus on the value of human work

Why it mattersBrookings offers a nuanced view of AI’s labour-market impact beyond the extremes of mass unemployment or universal augmentation. This matters for talent strategies that reflect changes in expertise and economic opportunity.
Original sourceBrookings InstitutionPeople and culture
People and culture15 September 2026 · 12 min read

Workforce policy in the AI era: focus on the value of human work

Economic evidence suggests sharply divergent outcomes: AI may rapidly displace some forms of work and gradually complement others. The authors argue for focusing on how AI changes the value of human work and access to opportunity.

Key points
  • Evidence points towards divergent labour-market outcomes rather than either universal augmentation or mass unemployment.
  • The central question is how AI changes the value of human expertise, beyond whether it creates or destroys jobs.
  • Successful adoption requires knowing when to trust AI as well as how to use it.
  • Workforce policy should adapt, support people who are actually displaced and widen access to new opportunities.
  • Uncertainty is not a reason for inaction: decisions are needed now to prepare for the transition.
Conclusion

Leaders should adapt workforce strategies to changing expertise, roles and skills, support people through transitions and broaden access to new economic opportunities.

Read the original · Brookings Institution
EY

AI governance: closing the trust gap in autonomous systems

Why it mattersEY examines the gap between formal AI governance policies and confidence in operational controls, particularly as agentic AI emerges, and recommends stronger accountability and oversight.
Original sourceEYAI and transformation
AI and transformation15 September 2026 · 6 min read

AI governance: closing the trust gap in autonomous systems

EY’s report finds that many organisations face a trust gap in the effectiveness of their controls despite widespread adoption of formal governance policies.

Key points
  • Nearly all surveyed organisations have formal AI governance policies, but 69% worry about insufficient internal expertise to evolve controls effectively.
  • Some 47% have previously bypassed AI governance processes for urgent deployments.
  • Agentic AI raises the stakes, yet 49% have not updated governance frameworks to account for its risks.
  • Closing the gap requires visibility across AI systems, clear ownership of autonomous activity, controls embedded in workflows and regular evidence of effectiveness.
Conclusion

CEOs should invest in operational implementation, staff training and continuous monitoring as well as policies, to maintain control over autonomous AI systems and prevent unauthorised deployment.

Read the original · EY
Forbes

Sustainable growth starts with customers: deeper relationships, lasting success

Why it mattersThe article invites CEOs to treat existing customer relationships as a source of sustainable growth, with practical ways to deepen those relationships and identify new opportunities.
Original sourceForbesStrategy and growth
Strategy and growth15 September 2026 · 6 min read

Sustainable growth starts with customers: deeper relationships, lasting success

Sustainable growth depends heavily on the quality and depth of existing customer relationships. Alongside seeking new markets, CEOs should reassess customer priorities, challenge old assumptions and engage beyond familiar contacts.

Key points
  • Growth often comes from relationships already built, as well as from new markets.
  • Do not assume alignment: customers’ priorities change, so revisit their needs and challenge established assumptions.
  • Build relationships beyond existing contacts to understand needs more fully and spot opportunities.
  • Focus on long-term value as well as immediate transactions.
  • Shared understanding of customer priorities across teams creates a consistent experience and reveals opportunities that might otherwise be missed.
Conclusion

CEOs should invest in deeper customer relationships, continually reassess needs and adapt. Challenging assumptions, broadening engagement and focusing on lasting value can uncover new growth opportunities.

Read the original · Forbes
European Central Bank

AI as a driver of growth and sovereignty: strategic challenges for business

Why it mattersThe ECB president’s speech provides macroeconomic context for AI investment, productivity and technological sovereignty, helping leaders think about long-term growth and capital allocation.
Original sourceEuropean Central BankStrategy and growth
Strategy and growth14 September 2026 · 8 min read

AI as a driver of growth and sovereignty: strategic challenges for business

Christine Lagarde describes AI as a key European project that could raise productivity by up to 4% over a decade and requires substantial investment. She calls for faster adoption, European computing capacity and AI models to strengthen technological sovereignty.

Key points
  • AI could raise the level of productivity by up to 4% over a decade, with major implications for public finances and the economy.
  • Rapidly growing AI investment, particularly in the US, increases pressure on Europe to adopt faster.
  • European sovereignty requires domestic computing capacity and open AI models.
  • Companies should be able to adopt AI without concerns over where their data is stored.
  • Europe needs to remain indispensable at critical bottlenecks in the AI supply chain to protect access to advanced technology.
Conclusion

Leaders should treat AI investment as a strategic priority for productivity and competitiveness, while considering geopolitics and developing capabilities that reduce dependency.

Read the original · European Central Bank
BCG

The COO in the AI era: reimagining operations

Why it mattersThe article explains how AI is changing the COO role and which capabilities and approaches matter for operational transformation.
Original sourceBCGAI and transformation
AI and transformation15 September 2026 · 7 min read

The COO in the AI era: reimagining operations

AI-led business transformation depends heavily on the COO’s ability to design an AI-centred operating model. COOs need to rethink operations rather than simply add AI to existing workflows.

Key points
  • CEOs should look for COOs who actively redesign operations and integrate AI to improve productivity.
  • Successful COOs question long-standing operational problems and explore new AI-enabled solutions.
  • Pattern recognition, operational discipline, resilience and adaptability become more valuable.
  • CEOs should support the capabilities, teams and mindsets needed to lead an AI-centred operating model.
  • The article suggests potential productivity improvements of more than 30% in industrial operations within two to three years, and potentially a tripling within five years.
Conclusion

To realise AI’s value, CEOs should support their COOs in rethinking operations and treating AI as a driver of innovation as well as automation.

Read the original · BCG
Forbes

A leadership framework for responsible AI in healthcare: lessons across sectors

Why it mattersThe article offers a structured approach to responsible AI adoption centred on outcomes, ethics and people, with lessons for trust and effective integration across industries.
Original sourceForbesAI and transformation
AI and transformation14 September 2026 · 8 min read

A leadership framework for responsible AI in healthcare: lessons across sectors

The Forbes article presents five leadership priorities for responsible AI integration, focused on healthcare but applicable more broadly.

Key points
  • Evaluate AI by improvements in clinical or business outcomes rather than the number of tools deployed.
  • Embed cybersecurity in safety strategies and strengthen data governance to support resilience and trust.
  • Assign responsibility for validation, bias assessment, regulatory compliance and ongoing performance monitoring.
  • Use AI to support people, reduce administrative work and leave professionals more time for expertise and empathy.
  • Leadership determines whether technology is used ethically and effectively and whether it earns trust.
Conclusion

Responsible AI leadership needs a framework spanning intelligence, cyber resilience, data trust, governance and human-centred design, with emphasis on effective integration rather than deployment alone.

Read the original · Forbes
Forbes

The future of business intelligence: from reactive dashboards to proactive AI and ontologies

Why it mattersThe article invites leaders to rethink business intelligence, moving from dashboards to proactive, agent-based systems that use AI and ontologies to anticipate needs and automate decisions.
Original sourceForbesAI and transformation
AI and transformation14 September 2026 · 6 min read

The future of business intelligence: from reactive dashboards to proactive AI and ontologies

Traditional business intelligence focuses on visualising data in dashboards and answering existing questions. AI and ontologies are enabling proactive systems that anticipate needs, detect anomalies and support decisions in real time, changing how businesses use data.

Key points
  • Traditional BI is reactive, answering questions through dashboards; proactive systems anticipate needs.
  • AI agents can automate complex processes and support real-time decisions by continually evaluating metrics and detecting anomalies.
  • Ontologies formally describe concepts and their relationships, giving AI business context beyond data tables.
  • Combining AI and ontologies can move BI from displaying data to understanding and responding to business events.
Conclusion

Leaders should consider investing in semantic layers and ontologies to enable proactive AI-powered business intelligence and improve decision-making and operational efficiency.

Read the original · Forbes
EY

AI transformation starts with people: leadership, mindsets and behaviour

Why it mattersSuccessful AI transformation depends on workforce readiness, changing behaviours and leaders who support continuous learning and responsible use.
Original sourceEYPeople and culture
People and culture11 September 2026 · 7 min read

AI transformation starts with people: leadership, mindsets and behaviour

AI adoption requires deeper cultural change alongside technology investment. Leadership is essential to building trust and helping employees prepare for new ways of working.

Key points
  • Adoption stalls when workforce readiness lags behind investment; mindsets, behaviours and culture determine success.
  • Lasting value emerges when leadership, governance and workforce practices support responsible everyday use.
  • Leaders need to demonstrate AI fluency, encourage responsible experiments and remove organisational barriers.
  • Leaders demonstrate an AI culture rather than delegating it.
Conclusion

CEOs should lead the cultural transition, invest in learning, set clear norms and show their own willingness to experiment.

Read the original · EY