11 Reasons Your AI Investment Isn't Paying Off (And What to Do About It)

Last updated: June 2026

Most organisations that are struggling to get returns from AI are not dealing with a technology problem. The evidence is now clear enough to state plainly: if your AI investment is not paying off, the gap is almost certainly in your leadership, your culture, your strategy, or your processes, not in the tools you chose. As of June 2026, PwC's 29th Global CEO Survey of 4,454 chief executives across 95 countries found that 56% report seeing neither revenue gains nor cost reductions from AI, while only 12% have achieved both. That is not a vendor problem. It is an organisational one.

The uncomfortable truth running through all the major research in this space is that the technology is rarely the issue. The issue is whether the organisation around the technology is ready to change. According to BCG's AI Radar Survey, 70% of AI's impact depends on workforce adoption, workflow integration, and cultural alignment, while only 10% comes from the algorithms themselves. A further 20% comes from technology and data infrastructure. When leaders pour resources into the 10% and neglect the 70%, the result is exactly the ROI gap most organisations are now experiencing.

This blog walks through eleven reasons AI investments fail to deliver, along with the practical shifts that separate the small group of organisations actually seeing results. Jonno White, author of Step Up or Step Out (10,000+ copies sold), Certified Working Genius Facilitator, and executive team facilitator who works with schools, corporates, and nonprofits around the world, works with leadership teams on exactly the alignment, culture, and team dynamics that determine whether transformation of any kind actually lands.

To explore how this applies to your organisation, email jonno@consultclarity.org.

Why the AI ROI Gap Exists at All

The AI ROI gap exists because organisations are buying technology but not building the organisational conditions that make technology valuable. This section sets the context by identifying the structural mismatch at the heart of the problem.

The research picture is stark. McKinsey's 2025 State of AI survey of nearly 2,000 organisations found that only around 5.5% attributed meaningful EBIT impact to their AI use. MIT's independent research arrived at essentially the same number from a different direction, finding that only 5% of AI pilot programmes generate measurable P&L impact. The Atlassian AI Collaboration Index put an even sharper number on the problem, finding that 96% of companies have not seen meaningful business value from AI despite widespread investment.

These are not numbers that suggest the technology needs more time to mature. These are numbers that point to a consistent, structural gap in how organisations approach implementation. The organisations in the top 5-12% who do see real returns share a set of characteristics that have nothing to do with the AI tools they chose and everything to do with how they lead. They have redesigned workflows end-to-end rather than adding AI to existing processes. Their senior leadership is visibly and actively engaged, not just strategically approving. They have built genuine governance so that ownership, measurement, and accountability are clear. And they have invested in their people before demanding returns.

For more on how change leadership determines whether organisational transformation lands, check out the blog post 'Leading Your Team Through Change' at https://www.consultclarity.org/post/leading-team-change.

Reason 1: You Are Solving the Wrong Problem

The most fundamental reason AI investments fail is that organisations deploy AI to address a problem they have not clearly defined. A Gartner study found that 85% of AI projects fail due to unclear objectives and poor project management. Research by the RAND Corporation identified misunderstanding of project purpose and domain context as the most common single reason for AI project failure. The pattern is consistent: organisations see what AI can do in demonstrations or competitor announcements and reverse-engineer a problem for it to solve, rather than starting with a sharp definition of the actual bottleneck and asking whether AI is genuinely the right response.

The test is simple. Before committing to any AI implementation, a leadership team should be able to state in plain language the specific decision AI is meant to improve, the current cost or impact of making that decision poorly, and how they will know when AI has improved it. If those three questions cannot be answered clearly before implementation begins, the initiative is built on ambiguity and is already at significant risk. Leaders who bypass this diagnostic step rarely do so because they are careless. They do so because the pressure to act on AI is intense and the social cost of being seen to move slowly is high. But speed without direction is not an advantage.

Reason 2: Your C-Suite Is Not Aligned

Leadership misalignment is one of the most consistently cited structural causes of AI failure, and it is also one of the most systematically underestimated. Grant Thornton's 2026 AI Impact Survey of 950 business leaders found a striking divergence within the same organisations: 39% of CIOs and CTOs reported that their workforce was fully ready to adopt AI, while only 7% of COOs agreed. Forty-four percent of CIOs said AI was accelerating innovation, while only 20% of COOs and 22% of CFOs agreed. These are not different organisations with different cultures. These are different functions within the same leadership teams looking at the same AI initiatives and describing a completely different reality.

When the C-suite is not aligned, the results are predictable. Technology gets installed that the operations function has no capacity to support. Finance funds initiatives that the CEO has not connected to a clear business outcome. IT and business leadership develop opposing accounts of what is working, creating a governance gap that no one has the explicit authority to close. As Grant Thornton Business Consulting Partner Jennifer Morelli put it, the lack of alignment across leadership leads directly to lack of ownership, lack of accountability, and ultimately lack of ROI at an enterprise level. The technology was never the constraint. The conversation the leadership team was not having was.

Engage Jonno White to run an executive offsite specifically designed to create AI alignment across your leadership team. Jonno works with C-suite teams to surface misalignment early and build the shared language, priorities, and ownership structures that AI initiatives depend on. Email jonno@consultclarity.org. International travel is often far more affordable than organisations expect.

For practical guidance on building executive alignment more broadly, the blog post '11 Proven Ways to Get Your Leadership Team Aligned' at https://www.consultclarity.org/post/leadership-team-aligned is a strong starting point.

Reason 3: You Treated It as an IT Project

The single most common strategic error in AI implementation is handing the initiative to the technology team and treating deployment as the primary success metric. Fast Company's reporting on this pattern put it plainly: deploying AI is a workforce strategy that demands behaviour change and a new operating model, not a technology rollout. When organisations install AI tools and count the number of licences activated, they are measuring the wrong thing. Counting tool installations says nothing about whether AI is creating value. What matters is whether behaviour has changed and whether the change is sustainable.

CapTech's 2025 research, conducted in collaboration with the Harris Poll through interviews with senior executives at the C-suite and VP levels, found that up to 70% of AI-related change initiatives fail due to employee pushback or inadequate management support. Writer.com's 2026 Enterprise AI Adoption survey found that 54% of C-suite executives admitted that adopting AI was tearing their company apart. These are not technical failure rates. They are organisational failure rates that trace directly back to the assumption that AI implementation is an IT responsibility rather than a whole-of-organisation leadership responsibility.

The most effective AI implementations have a business owner, not just a technology owner. The business owner defines the outcome being targeted, holds accountability for adoption, and creates the feedback loops that tell leadership whether the initiative is working. Without that structure, even technically successful deployments produce nothing the CFO can point to.

Reason 4: You Layered AI on Top of Broken Processes

One of the most underappreciated dynamics in AI failure is the workflow trap: taking an inefficient, approval-heavy, manually intensive process and adding AI to speed it up slightly rather than redesigning the process end-to-end. The result, as BCG's research describes it, is a faster broken process. McKinsey's 2025 State of AI data found that only 21% of organisations have redesigned their workflows alongside AI deployment, yet those that do are nearly three times more likely to achieve meaningful financial returns. The gap between those two groups is not explained by the quality of their AI tools. It is explained by whether they were willing to change how work gets done.

The practical test is whether AI is genuinely changing the decision a human makes, or simply accelerating the production of an output that then needs significant human review and correction. Workday's 2026 research found that 37 to 40% of the time AI ostensibly saves is consumed by reviewing, correcting, and verifying AI-generated output. When this happens at scale, efficiency gains disappear and the leadership team finds itself questioning whether the investment was worthwhile at all. The answer to that question is almost never to stop using AI. It is to go back upstream and ask whether the process was designed for AI to operate inside it effectively.

Reason 5: You Have Not Built the Culture AI Requires

AI does not work in cultures built on fear, compliance, and performance pressure. The reason is straightforward: AI requires experimentation, and experimentation requires psychological safety. If employees believe that trying something new and having it not work will be held against them, they will not try. They will install the tools and find workarounds to avoid using them. The adoption statistics will look acceptable on a dashboard while actual behaviour remains unchanged. This is what researchers have come to call adoption theatre, the appearance of usage without the substance of change.

BCG's 10-20-70 rule is the clearest summary of what this means for investment allocation. Ten percent of organisational AI effort should go to algorithms, 20% to technology and data, and 70% to people, including change management, culture, training, and workforce readiness. Most organisations have this exactly backwards. Grant Thornton's 2026 AI Impact Survey found that only 6% of executives named change leadership and workforce enablement as a top skill essential for success in an AI-driven environment. That number is the problem in a single figure. The organisations that perform best under AI transformation are not those with better models. They are those with leadership teams that have invested in building the psychological safety, trust, and honest communication that make any kind of genuine change possible.

Bring Jonno White in to facilitate a Working Genius session with your leadership team. Understanding how team members are wired for different types of work is directly relevant to designing AI rollouts that people actually engage with. Email jonno@consultclarity.org.

More on how team frameworks apply to sustained transformation is in the blog post 'Working Genius Model: Complete Implementation Guide for Teams and Leaders' at https://www.consultclarity.org/post/working-genius-implementation-guide.

Reason 6: Middle Management Has Been Left Behind

Middle managers translate strategy into daily action. When they are confused about AI, unsure of their own role in relation to it, or quietly anxious about what it means for their authority and relevance, implementation stalls regardless of how committed the executive team is. Gartner's 2023 research found that 82% of HR leaders agreed managers were not equipped to lead change, and this finding applies with particular force to AI change, which asks managers to advocate for tools they may not fully understand and that some of their people fear.

The practical failure mode here is predictable. Executives announce AI initiatives at all-hands meetings. The initiative lands with middle managers as a mandate to implement, without the training, context, or authority they need to support their teams through the transition. Employees look to their managers for interpretation and guidance. The managers have no clear message to give. Resistance grows quietly, not as an organised rebellion but as the accumulated drag of thousands of small decisions to avoid, minimise, or work around the new tools. S&P Global's 2025 survey found that 42% of companies abandoned AI initiatives entirely that year, up from 17% the year before.

Hire Jonno White, author of Step Up or Step Out (10,000+ copies sold) and host of The Leadership Conversations Podcast (230+ episodes reaching listeners in 150+ countries), to deliver a keynote or workshop that builds the communication skills and change confidence your middle managers need. Email jonno@consultclarity.org.

Reason 7: No One Owns the Outcomes

Ambiguity around ownership is one of the most consistent structural causes of AI project failure, and it operates at multiple levels simultaneously. At the C-suite level, when the CIO leads implementation and the COO runs operations, it is easy for each function to assume the other is accountable for results. At the project level, when AI pilots run as innovation experiments rather than as committed business initiatives with a named owner, accountability diffuses across teams and no one is in a position to make the call to scale, pivot, or stop. At the team level, when employees use AI tools without guidance on expected outcomes or norms around usage, individual productivity gains accumulate without translating into business value.

The organisations that see meaningful AI returns have resolved this. They assign a specific business owner to every AI initiative, distinct from the technology owner, who is personally accountable for whether the initiative delivers its defined outcome. They set time-bounded milestones with clear success criteria. They run quarterly reviews with the authority to stop initiatives that are not performing and redirect resources to those that are. BCG's 2025 global study of 1,250 companies found that only about 5% create substantial AI value at scale, while 60% generate no material value despite meaningful spending. The 5% are not smarter or better resourced. They are more disciplined about ownership and accountability.

Reason 8: You Are Not Measuring the Right Things

Most organisations measure AI activity, not AI impact. They count tool activations, hours of training completed, number of use cases piloted, and percentage of employees with access to AI tools. None of these metrics tell a CFO or CEO whether AI is creating value. A Gallup survey found that only 15% of US employees reported that their workplace had communicated a clear AI strategy, and the measurement gap is a direct consequence of the strategy gap. When organisations have not defined what success looks like in business terms, they fall back on measuring what is visible and countable, which is usually activity.

The meaningful metrics for AI ROI work at three levels. At the process level, they measure whether a specific decision or output has improved in quality, speed, or cost. At the function level, they measure whether the team using AI is achieving better business outcomes than before. At the enterprise level, they measure whether AI investment is contributing to EBIT, revenue growth, or cost reduction in a way that is traceable and defensible to the board. Reaching all three levels simultaneously requires the governance infrastructure to be in place before the AI initiative launches, which is why Grant Thornton's 2026 survey found that the organisations performing best were those that built measurement infrastructure first.

Reason 9: Your People Are Not Ready and Do Not Feel Safe to Say So

Workforce readiness is one of the most consistently underfunded elements of AI investment, and the gap between what leaders believe and what frontline employees experience is striking. CapTech's 2025 research found that less than 20% of employees' AI usage is for work purposes. Fourteen percent of individuals report feeling uncomfortable using AI tools. Among the drivers of this disengagement are lack of training, distrust of data privacy and security, and lack of meaningful opportunities to engage with AI in their specific role. When employees are not given role-specific guidance on how AI applies to their actual work, general AI training produces general AI awareness that does not translate into changed behaviour.

The more subtle problem is that employees who feel uncomfortable or underprepared often do not say so, because in cultures where competence is highly valued and admitting uncertainty carries social cost, people do not advertise their discomfort with new tools. The result is a layer of quiet non-adoption that is invisible to leadership and shows up only in the ROI numbers, typically many months after the decision to invest was made. Leaders who want to close this gap need to create explicit permission for employees to name what they do not understand and what they are not using, without penalty. This is not a technology problem. It is a psychological safety problem, and it sits squarely in the domain of leadership.

Engage Jonno White, founder of The 7 Questions Movement (6,000+ participating leaders), for a leadership team offsite where your executive team can surface the real patterns in your organisation's AI adoption and build the communication rhythms that create genuine safety. Email jonno@consultclarity.org.

For a broader look at how executive team offsites create lasting alignment, the blog post '21 Effective Tips for Executive Team Offsites' at https://www.consultclarity.org/post/executive-team-offsites is a useful starting point.

Reason 10: Your Data Is Not Ready

Even when leadership, culture, and strategy are well-positioned, AI requires clean, accessible, and well-governed data to produce reliable outputs. Many organisations discover this only after committing to an AI initiative: their data exists in silos, is inconsistently formatted, is incomplete, or lacks the governance structures to make it trustworthy input for AI decision-making. The AI systems themselves surface the problem by producing unreliable or contradictory outputs that erode user trust and slow adoption. What looks like a cultural resistance problem often has a data quality driver underneath it.

This is not primarily a technology department problem. It is a leadership and governance problem. Data exists the way it exists because of the decisions leaders made, or did not make, about how information flows across the organisation, who owns it, and what standards it is held to. Fixing the data layer for AI requires the same cross-functional leadership alignment that fixing any structural organisational problem requires: clear ownership, agreed standards, and the authority to enforce them. Organisations that treat data readiness as a prerequisite for AI investment rather than an afterthought to it consistently perform better on every measure of AI maturity.

Reason 11: You Are Expecting Returns Too Fast

AI transformation is not a twelve-month investment cycle. The organisations seeing real returns have typically been building the conditions for AI value for two to three years, often before committing to their largest AI investments. They built governance early. They cleaned their data. They redesigned their workflows. They built the culture of experimentation and psychological safety that makes people willing to try things and honest about what is not working. Then, when they deployed AI at scale, they had the infrastructure to capture the returns. PwC's research identifies a vanguard group of roughly one in eight companies achieving both additional revenues and lower costs from AI. What separates that group is not a single decision. It is a sustained, disciplined approach to building the preconditions for AI success over time.

The leadership implication is not that patience is a strategy. It is that the timeline for AI ROI should be honest when it is presented to boards and executive teams. Organisations that promise transformative returns in 12 months and deliver none are not just missing their targets. They are eroding the trust and credibility that would allow them to sustain investment long enough to achieve the returns that are genuinely available. Honest expectation-setting, combined with clear early-stage milestones that demonstrate meaningful progress even before enterprise-level P&L impact arrives, is what separates organisations that sustain AI investment from those that abandon it.

For a deeper look at how leadership alignment prevents strategy from stalling mid-execution, the blog post '37 Practical Tips for Leadership Alignment Consulting' at https://www.consultclarity.org/post/leadership-alignment-consulting covers the underlying principles in detail.

What the Organisations Getting ROI Are Doing Differently

The 5-12% of organisations achieving meaningful AI returns share a consistent set of practices that are worth naming plainly, because they reframe the problem from 'why is AI failing' to 'what specifically needs to change.' McKinsey's analysis found that AI high performers are three times more likely to have strong senior leadership engagement, 3.6 times more likely to be targeting transformative rather than incremental change, and significantly more likely to have redesigned their workflows end-to-end rather than adding AI to existing processes. They set outcome-based objectives tied to business KPIs rather than activity metrics. They build governance infrastructure before scaling. And they invest heavily in capability, with more than a third of high performers allocating over 20% of their digital budgets to AI.

The common thread is that these organisations treat AI as an organisational transformation requiring leadership commitment, not as a technology project requiring vendor management. PwC's Global Chairman Mohamed Kande framed the current moment plainly: a small group of companies are already turning AI into measurable financial returns, while many others are still struggling to move beyond pilots, and that gap is starting to show up in confidence and competitiveness. It will widen quickly for those that do not act.

Common Mistakes to Avoid

The most common mistake organisations make with AI investment is starting with the technology and working backwards to a problem, rather than starting with a sharp definition of the problem and testing whether AI is genuinely the right solution. This produces initiatives with impressive-sounding use cases that have no clear owner, no success metric, and no practical connection to the decisions that matter most to the business.

A second closely related mistake is running AI as a portfolio of disconnected pilots that never graduate to production. Gartner projected that 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, and the pattern persists. Proof of concept proves that the technology works in a controlled environment. It does not prove that the organisation has the change capacity to adopt the capability broadly, or that the cultural and structural foundations for meaningful transformation are in place. Organisations that accumulate pilots without building the pathway to production are not building AI capability. They are building AI theatre.

A third mistake is measuring deployment instead of adoption. Counting tool installations, licences activated, or hours of training completed says nothing about whether behaviour has changed. Real adoption shows up in whether the specific decisions AI was deployed to improve have actually improved, and whether the people using AI tools are genuinely integrating them into how they work rather than using them occasionally for low-stakes tasks. Leaders who are not measuring this are not managing their AI investment. They are hoping it works.

A fourth mistake is treating AI as the solution to a culture problem. AI will not fix disengagement, poor communication, unclear accountability, or low trust. When these problems exist in an organisation, AI tends to amplify them. The teams that resist change most strongly are the most likely to resist AI adoption. The leaders who struggle to have honest conversations about performance are the most likely to avoid the difficult conversations about what AI is and is not delivering.

Jonno White, Certified Working Genius Facilitator and host of The Leadership Conversations Podcast (230+ episodes), facilitates executive team sessions and leadership workshops that address the underlying culture and communication issues that determine whether any transformation, including AI transformation, actually succeeds. Email jonno@consultclarity.org.

A Practical Implementation Guide for Leaders

If your AI investment is not paying off, the starting point is an honest diagnostic, not another vendor conversation. The diagnostic has five questions that every member of your leadership team should be able to answer independently and consistently. If their answers differ significantly, you have found the root of the problem.

The first question is whether your leadership team agrees on what problem AI is being used to solve and what success looks like in specific, measurable terms. If different members of the C-suite give different answers, the initiative lacks the strategic foundation it needs.

The second question is whether you have assigned a named business owner, distinct from the technology owner, to every active AI initiative. This person should be accountable for adoption, business outcomes, and the decision to scale or stop. If ownership is diffuse, accountability is absent.

The third question is whether your middle managers understand what AI is doing in their teams, can answer their people's questions about it honestly, and have been given the training and authority to support adoption. If managers are uncertain or quietly anxious, frontline adoption will stall.

The fourth question is whether you have redesigned the workflows AI is meant to support, or whether AI has simply been added to existing processes. This question almost always reveals whether the investment will return value or not.

The fifth question is whether your measurement framework tracks business outcomes, not activity. If your AI dashboard shows tool activations and training completions but no connection to EBIT, revenue, or specific decision quality, you are measuring the wrong things.

Once this diagnostic is complete, the path forward almost always involves a leadership team conversation that most organisations have been avoiding.

Bring Jonno White in to facilitate that conversation as part of an executive team offsite or leadership workshop. Jonno is a Certified Working Genius Facilitator, author of Step Up or Step Out (10,000+ copies sold), and founder of The 7 Questions Movement (6,000+ participating leaders). He has facilitated executive team offsites and alignment sessions for leadership teams navigating exactly this kind of organisational challenge. Email jonno@consultclarity.org to discuss what this would look like for your organisation.

Frequently Asked Questions

What percentage of AI investments actually pay off?

The data as of 2026 points consistently to a small minority of organisations achieving meaningful returns. PwC's 29th Global CEO Survey of 4,454 CEOs across 95 countries found that 56% report seeing no revenue gains or cost reductions from AI, while only 12% have achieved both. McKinsey's 2025 State of AI analysis found that roughly 5.5% of organisations attribute meaningful EBIT impact to AI. MIT's independent research found that only 5% of AI pilot programmes generate measurable P&L impact. These numbers are consistent across methodologies and point to the same conclusion: AI ROI at scale remains the exception rather than the rule, and the organisations achieving it are doing so through organisational discipline, not superior technology.

Is AI ROI failure a technology problem or a people problem?

The weight of evidence points clearly to people, leadership, and organisational factors as the primary causes of AI ROI failure, not the technology itself. BCG's AI Radar Survey found that 70% of AI's impact depends on workforce adoption, workflow integration, and cultural alignment, with only 10% attributable to the algorithms. CapTech's 2025 research found that up to 70% of AI-related change initiatives fail specifically due to employee pushback or inadequate management support. The technology overwhelmingly works when the organisational conditions are right. Building those conditions is a leadership responsibility.

How long does it take for AI investment to pay off?

The organisations achieving real AI returns have typically been building the preconditions for AI value over two to three years before committing to large-scale deployment. This includes building data governance, redesigning workflows, developing the culture of experimentation and psychological safety that enables genuine adoption, and building measurement infrastructure. Organisations that expect enterprise-level P&L impact within twelve months of first deployment are setting themselves up for disappointment and, often, premature abandonment of investments that would have paid off with greater patience and more sustained organisational work.

What do the organisations achieving AI ROI do differently?

McKinsey's research identifies six consistent patterns among AI high performers. They target transformative change, not just efficiency. They invest heavily, with more than a third allocating over 20% of digital budgets to AI. Their senior leadership is deeply and visibly engaged. They have redesigned workflows end-to-end. They set outcome-based objectives tied to business KPIs. And they invest seriously in capability building. The differentiator is not the tools chosen. It is the combination of leadership commitment, organisational redesign, and measurement discipline applied consistently over time.

What is the role of leadership in AI ROI?

Leadership is the single most important variable in whether AI investments deliver returns. McKinsey found that AI high performers are three times more likely to have strong senior leadership engagement. Grant Thornton's 2026 survey found that C-suite misalignment, with different functions describing the state of AI adoption in contradictory terms, is a primary driver of the accountability gaps that prevent ROI from materialising. Leadership alignment, clear ownership, visible modelling of AI use, and the communication culture that enables honest feedback are all leadership responsibilities that have a direct bearing on whether AI investment converts to business value.

Final Thoughts

The AI investment problem that most organisations are experiencing in 2026 is not new. It is the same problem that has characterised every major technology transition in the past several decades: organisations buy the technology and underfund the organisational change. The reasons AI feels different are real. The pace is faster, the stakes are higher, and the competitive pressure to be seen to be moving is more intense than it has ever been. But the underlying failure mechanism is the same, and the solution runs through the same place: leadership.

The organisations that will close the AI ROI gap over the next two to three years are not those with the most sophisticated tools or the largest technology budgets. They are those with leadership teams that are genuinely aligned on what they are trying to achieve, honest about what is and is not working, and willing to invest in the culture, communication, and team dynamics that make any transformation possible. If your AI investment is not paying off, that is the conversation worth having.

Jonno White, Certified Working Genius Facilitator, author of Step Up or Step Out (10,000+ copies sold), and host of The Leadership Conversations Podcast (230+ episodes, 150+ countries), facilitates executive team offsites and leadership workshops that build the alignment, culture, and communication foundations that AI transformation depends on. Whether your team needs a strategic offsite, a Working Genius session, or a keynote that reframes the AI challenge in terms your people will actually act on, reach out at jonno@consultclarity.org. Whether virtual or face to face, many organisations find that international travel is far more affordable than expected.

About the Author

Jonno White is a Certified Working Genius Facilitator, author of Step Up or Step Out, and leadership consultant who has worked with schools, corporates, and nonprofits around the world. His book Step Up or Step Out has sold over 10,000 copies globally, and his podcast The Leadership Conversations has featured 230+ episodes reaching listeners in 150+ countries. Jonno founded The 7 Questions Movement with 6,000+ participating leaders and achieved a 93.75% satisfaction rating for his Working Genius masterclass at the ASBA 2025 National Conference. Based in Brisbane, Australia, Jonno works globally and regularly travels for speaking and facilitation engagements. Organisations consistently find that international travel is far more affordable than expected.

To book Jonno for your next keynote, workshop, or facilitation session, email jonno@consultclarity.org.

Sources

BCG AI Radar Survey (Boston Consulting Group). Grant Thornton 2026 AI Impact Survey (Grant Thornton). McKinsey State of AI 2025 (McKinsey & Company). PwC 29th Global CEO Survey (PricewaterhouseCoopers, January 2026). MIT GenAI Divide: State of AI in Business 2025 (MIT). Atlassian AI Collaboration Index (Atlassian). CapTech Executive Research with Harris Poll, August 2025 (CapTech). Writer Enterprise AI Adoption 2026 (Writer). S&P Global AI adoption survey 2025 (S&P Global). Gartner generative AI research 2025 (Gartner). Workday AI productivity research 2026 (Workday).

Next Read

Every AI transformation challenge is also a change leadership challenge. The same dynamics that cause AI initiatives to stall, including leadership misalignment, middle management uncertainty, and culture that penalises experimentation, are the same dynamics that cause any organisational change to fail.

The blog post '25 Proven Keys to Leading Your Team Through Change' at https://www.consultclarity.org/post/leading-team-change covers the communication, trust-building, and leadership strategies that determine whether transformation of any kind actually sticks.