The AI Momentum Point | Momentum Series Part 4

The final instalment in our Momentum Series explores how AI represents a powerful momentum point that can transform organisations if they are willing to challenge their constraints and reimagine how work gets done.

In the first three parts of this momentum series, we explored how momentum points emerge when teams collectively recognise that long-accepted constraints are actually flexible.  We discussed how to identify and remove blockers that prevent action and how to scale momentum by embedding capabilities into your organisation's operating system.

Now, we face what may be one of the most significant momentum points of our era.  AI is rapidly emerging as a powerful catalyst, offering unprecedented opportunities to enhance efficiency, accelerate innovation and improve decision-making.  However, to fully harness this transformative power and ensure sustained momentum, organisations must recognise and challenge the constraints that their current operating systems place on leveraging AI fully.

This fourth instalment builds directly on the momentum framework we have outlined in this series, applying its core principles to the specific challenges and opportunities presented by AI.  In this post, we will explore how to recognise AI-driven momentum points, consider the blockers that prevent action and identify ways to scale the transformative power of AI throughout your organisation.

Recognising AI as a Momentum Point Within a Rapidly Evolving Landscape

As explored in Part 1, true momentum in business often emerges at pivotal moments when teams recognise that long-accepted constraints are actually flexible. AI represents such a momentum point, though it is important to acknowledge that the technology is still rapidly evolving. In this evolution, the concept of 'AI experts' is also relatively new and it is fair to say that the longevity and experience of technologists and developers play just as important a role in this journey to mastery of AI. Organisations that started their AI journey six months or a year ago likely have significantly more practical knowledge than those just starting now. That said, the technology and ecosystem around it is evolving week by week. Delaying further only means falling further behind the learning curve.

Recognising the Constraints AI Can Break

When we examine AI as a momentum point through the lens established in Parts 1-3 of this series, we can identify several key constraints that organisations have long accepted as fixed but are now becoming flexible:

• The human-machine division of labour: traditionally, we have accepted a clear division between tasks that humans perform and those that machines handle.  AI is blurring this boundary, enabling machines to perform tasks that were previously thought to require human judgment, creativity and intuition.

• The speed of knowledge work: we have accepted constraints around how quickly certain types of work can be completed, particularly in knowledge-intensive domains.  AI is challenging these assumptions, enabling dramatic acceleration of tasks from content creation to code development to customer interactions.  The ability to operate at a higher level of abstraction allows teams to solve problems faster and build better products.

• The scale of personalisation: organisations have traditionally faced trade-offs between standardisation (efficient but impersonal) and customisation (personal but resource-intensive). AI enables mass personalisation at previously impossible scales, breaking this constraint and allowing organisations to deliver custom experiences cost-effectively.

• The learning curve of expertise: we have accepted that developing specialised expertise requires years of training and experience. AI is now making specialised knowledge more accessible to non-experts, democratising capabilities from design to coding to advanced analytics.

• The boundary between human and digital experiences: traditionally, we have accepted limitations in how natural and intuitive digital interfaces can be.  AI is eliminating these constraints through natural language processing, creating avatars and other technologies that make digital interactions more human-like.

By recognising these constraints as flexible rather than fixed, organisations can identify powerful momentum points in their structures, processes and business offerings where AI can transform what is possible.

First Step is to Identify the Initial Blockers to Remove

In Part 2 of this series, we introduced a team-based approach to identifying and overcoming blockers. The process begins by convening a cross-functional team to establish a shared understanding of the opportunity, desired business outcomes and how AI might help achieve them.

Through these initial discussions, the team can move beyond surface-level symptoms to identify the underlying problems or blockers that are truly preventing progress. By exploring these root causes, the team can determine which obstacles must be addressed to initiate momentum. When applying this framework to AI initiatives, consider these key questions:

• Where are the current data bottlenecks and how might they be addressed?

• Which AI skills do we need to develop or acquire in our team?

• What governance mechanisms should we establish for responsible AI use?

• How can we ensure ethical, secure and unbiased implementation?

The following are examples of symptoms that may inhibit momentum:

• Data limitations: many organisations struggle with fragmented, low-quality or inaccessible data, making it difficult to fully leverage AI capabilities.

• Technical debt: legacy systems and infrastructure were not designed for AI integration and so may significantly slow adoption and limit the potential value of AI solutions.

• Skill gaps: teams will likely lack the necessary expertise to effectively implement, customise and govern AI systems.

• Change resistance: AI represents a fundamental shift in how work gets done, leading to natural resistance from teams concerned about their roles and value.

• Governance uncertainty: unclear structures and protocols for ensuring AI embedded in a controlled manner with clear organisation oversight.

• Financial constraints: there are significant costs associated with AI implementation, both in initial set-up and ongoing maintenance once embedded in the organisation's operating system. These costs need to be budgeted appropriately.

• Security, Privacy and Bias Concerns: AI systems can perpetuate or amplify existing biases, create new security vulnerabilities or compromise privacy if not carefully designed and monitored.

• Legal and ethical uncertainties: emerging challenges around intellectual property rights, model provenance and data ownership create legal complexity. Questions about who owns AI-generated content, whether training data was ethically sourced and how to properly attribute the origins of AI outputs can significantly slow adoption.

• Digital workforce considerations: as AI creates new forms of "digital employees" or digital twins of workers, organisations face novel ethical questions about the relationship between human and AI labour, including how to manage this new hybrid workforce effectively.

  
  

Categorise the blockers discovered, considering these dimensions:

• Data challenges (quality, accessibility, governance).

• Technical limitations (infrastructure, integration, security).

• Organisational barriers (skills, processes, change management).

• Governance issues (ethics, compliance, risk management).

• Resource constraints (budget, talent, time).

Creating Initial AI Momentum

Once the team has identified key blockers, they should prioritise and sequence them, then begin methodically removing these impediments. This structured approach helps set the momentum flywheel in motion.

The Crawl Phase, Starting Small

For AI initiatives, effective "crawl" phase applications might include:

• A narrowly-defined use case that addresses a visible pain point.

• Implementation that can be achieved within days or weeks rather than months.

• Solutions that do not require complicated approval processes.

• Projects that will deliver clear, measurable improvements.

• Initiatives achievable with reasonable initial investment.

While solving for the pain point, ensure to:

• Focus on data foundations first: rather than attempting organisation-wide data transformation, start by cleaning and organising specific data needed for the initial AI use case.

• Document the before state: capture metrics and pain points associated with the current process to create a baseline for demonstrating AI value.

• Build in responsible AI practices from the start: establish clear protocols for addressing security, privacy and bias concerns early as it is much easier than retrofitting them later.

The Walk Phase, Building on Success

Once an initial AI initiative shows results, that success can be used to build broader support and tackle more significant opportunities:

• Celebrate visibly: sharing success stories widely, focusing not just on what changed but on how the team identified and questioned the constraints that AI helped overcome builds momentum.

• Document the return: measuring and communicating the benefits of the change, whether in time saved, quality improved or new capabilities enabled provides evidence for further investment.  Being transparent about the full costs involved, including ongoing maintenance, builds trust.

• Sequence the next move: rather than expanding to multiple parallel initiatives, selecting the next most impactful AI opportunity and removing associated blockers creates a coherent story of progress.  Each successive win should build on previous achievements.

• Expand the data foundation: based on the success of the initial project, incrementally expanding data infrastructure and master data management frameworks supports additional AI use cases.

• Develop a sustainability framework: creating guidelines for using AI resources efficiently and responsibly includes both environmental considerations and ensuring AI is applied to genuinely valuable use cases.

The Run Phase, The Flywheel Spins

As organisations develop both capability and appetite for more significant AI-driven momentum and continue to remove blockers inhibiting transformation:

• Connect the dots: helping everyone see how individual AI improvements are part of a larger momentum story reveals patterns of constraint-breaking that could apply more broadly.

• Prioritise depth over breadth: rather than pursuing multiple parallel AI initiatives, focus on increasing the scope and complexity of AI applications in specific business areas.  This builds deeper capabilities while maintaining focus.

• Complete in-flight activities before starting new ones: resist the temptation to launch new AI projects before completing current ones.  Half-finished AI implementations create cynicism about the technology's value and drain resources without delivering momentum.

• Focus on constraint-breaking patterns: as teams gain experience with AI, they begin recognising similar constraints across different business areas. These patterns help identify where AI can create the most significant impact next.

• Create a feedback loop: successful AI implementations generate insights that inform future initiatives, creating a self-reinforcing cycle of continuous improvement and innovation.

Scaling the AI Capability

In Part 3 of this series, we explored how to scale momentum by embedding capabilities into an organisation's operating system. These same principles apply to scaling AI-driven momentum across enterprises.

To effectively scale AI momentum, organisations need a thoughtful approach that aligns with their culture, structure and readiness. Here are six distinct approaches to consider, each with its own strengths and suitable contexts.

The Collaborative Approach

This approach emphasises shared learning and collective adaptation.  It creates an environment where experimentation is encouraged, knowledge is shared openly and teams collectively develop their AI capabilities.

Key characteristics:

• Emphasises broad involvement of employees in understanding and applying AI.

• Prioritises the sharing of knowledge, experiences and best practices across teams.

• Focuses on the organic growth of AI literacy through collective effort.

This approach works best in organisations with a strong existing culture of teamwork and open communication, where knowledge sharing is already ingrained.  It is particularly effective for democratising AI understanding across diverse departments and fostering a sense of ownership.

Prerequisites for success:

• Open communication channels.

• Cross-functional teams.

• Accessible general AI training.

• Supportive leadership encouraging experimentation.

• Platforms for internal knowledge sharing.

The Federated Approach

This model combines central oversight with distributed implementation.  A central AI team sets strategy, standards and provides core infrastructure, while individual business units have dedicated AI resources who apply AI to their specific needs.

Key characteristics:

• Balances centralised strategy with decentralised execution.

• Maintains consistent standards while enabling adaptation to local needs.

• Creates a hub-and-spoke model of AI expertise.

This approach works best in larger, more complex organisations with diverse business units that have unique challenges and opportunities for AI.  It allows for tailored solutions while maintaining consistency and governance.

Prerequisites for success:

• A strong central AI team with clear authority.

• Well-defined standards and guidelines.

• Robust communication channels between central teams and business units.

• Dedicated AI resources within business units.

• A collaborative culture that balances autonomy with alignment.

The Direct Challenge Approach

This approach emphasises individual accountability and urgent action.  It creates a sense of personal responsibility for adapting to AI-driven change and encourages proactive skill development.

Key characteristics:

• Frames AI adoption as an imperative rather than an option.

• Emphasises individual responsibility for upskilling.

• Creates clarity around the changing nature of work.

• Provides clear pathways for action.

This approach works best in organisations with a highly motivated, results-oriented workforce where individual initiative is valued.  It can be useful when there's a clear need for rapid upskilling in specific areas where AI can provide immediate productivity gains.

Prerequisites for success:

• Clear individual goals related to AI adoption.

• Access to targeted training and resources.

• Integration of AI skills into performance management.

• Strong individual learning motivation within the workforce.

• Supportive environment that rewards proactive adaptation.

The AI Champions Network (Approach)

This approach identifies and empowers passionate individuals across the organisation to become advocates for AI within their teams. These champions drive awareness, facilitate local experimentation and act as a bridge between technical AI expertise and business needs.

Key characteristics:

• Leverages enthusiastic early adopters as change agents.

• Creates a peer-to-peer influence network.

• Distributes AI advocacy throughout the organisation.

• Builds community around AI adoption.

This can be a valuable supplementary strategy in organisations of any size, particularly those looking for a more organic and less top-down approach to adoption.  It is especially effective in dispersed teams or organisations with a strong sense of community.

Prerequisites for success:

• A process for identifying and supporting AI champions.

• Training and resources for champions.

• Platforms for champions to connect and share experiences.

• Leadership support and recognition for champion contributions.

• Time allocation for champions to fulfil their advocacy role.

The Centralised Excellence Approach

This involves creating a dedicated AI centre responsible for driving AI strategy, developing core capabilities and deploying AI solutions across the organisation.  This team often holds significant expertise and resources.

Key characteristics:

• Concentrates AI expertise and resources in a dedicated team.

• Establishes clear governance and standards.

• Builds deep technical capabilities.

• Drives organisation-wide AI initiatives.

This is often the initial approach for organisations making significant strategic investments in AI and needing a strong central capability to guide development and ensure quality and ethical considerations.  It is useful for building foundational AI capabilities and deploying complex, organisation-wide solutions.

Prerequisites for success:

• A skilled central AI team with diverse expertise.

• A clear mandate and authority.

• Robust governance frameworks.

• Centralised AI infrastructure and platforms.

• Strong communication with business units to understand needs.

The Ecosystem Engagement Approach

This strategy involves actively partnering with external AI vendors, research institutions and startups to access specialised expertise, cutting-edge technologies and innovative solutions.

Key characteristics:

• Leverages external partnerships to accelerate AI adoption.

• Accesses specialised expertise without building it all internally.

• Stays connected to emerging AI developments.

• Balances building versus buying AI capabilities.

This can be beneficial for organisations that lack specific internal AI expertise or want to accelerate innovation by tapping into external advancements.  It is particularly useful for accessing niche AI capabilities or exploring emerging technologies without significant internal investment.

Prerequisites for success:

• A clear strategy for identifying and engaging with external partners.

• Processes for evaluating and integrating external AI solutions.

• Legal and procurement frameworks for partnerships.

• Internal teams to manage external collaborations effectively.

• Ability to integrate external solutions with internal systems and processes.

Determining The Ideal Approach

While these are presented as distinct approaches, the most effective strategy for scaling AI momentum typically combines elements from multiple models, tailored to the specific context, competitive landscape and organisational culture.

Many organisations find value in starting with a more centralised approach to build core capabilities and governance, then evolving toward a more federated model as AI maturity grows.  By its nature, the momentum approach outlined in this series focuses a small cross functional team on removing blockers and implementing solutions.  This model lends itself to adapt into any of the six outlined here.

The key is recognising that the approach to scaling AI should evolve as an organisation's AI maturity progresses.  What works in the early stages of adoption may not be optimal as AI becomes more deeply embedded in operations.

The Changing Nature of Work in an AI-Enhanced Organisation

As AI becomes more deeply integrated into organisations, it fundamentally changes how work gets done.  Understanding these shifts through the momentum lens helps teams see them not as threats but as opportunities to break through long-accepted constraints.

The most successful organisations recognise that AI's greatest value comes not from replacing humans but from creating new forms of collaboration that leverage the unique strengths of both.  This shift manifests in several important ways:

• From task executors to workflow designers: AI is becoming the default starting point for many tasks, with humans operating at a higher level of abstraction. Teams spend less time on routine execution and more time designing effective workflows and systems that AI can execute.

• From information gatherers to insight interpreters: as AI takes over the collection and processing of information, human value shifts toward interpreting insights, applying context and making judgment calls that require emotional intelligence and ethical reasoning.

• From specialised knowledge to integrated wisdom: as AI increasingly handles specialised tasks, humans add value through their ability to integrate knowledge across domains, see the bigger picture and connect technical capabilities to human needs.

• From rigid processes to adaptive systems: traditional work processes often followed fixed sequences designed for consistency and control.  AI enables more adaptive, responsive systems that can optimise for changing conditions and needs.

These shifts represent significant momentum points for individuals and teams, allowing them to break through constraints that have limited their impact.  As discussed throughout this series, recognising these constraints as flexible rather than fixed is the first step toward transformation.

Five Key Principles for the AI Momentum Journey

Based on the momentum framework developed throughout this series, here are five principles to guide an organisation's AI journey:

• Question AI constraints continuously: the ecosystem of AI is evolving rapidly, with capabilities that were impossible last year becoming routine today.  Making it a regular practice to revisit assumptions about what AI can and cannot do helps teams continuously discover new possibilities.

• Balance augmentation and automation:  The most successful organisations do not simply automate existing processes but thoughtfully redesign work to leverage the complementary strengths of humans and AI.  Focusing on how AI can enhance human capabilities rather than just replace tasks creates more sustainable value.

• Build data foundations incrementally: rather than attempting a massive data transformation all at once, building data infrastructure progressively as AI use cases are implemented is more effective.  Starting with the data needed for initial AI momentum points, then expanding as larger challenges are tackled creates sustainable progress.

• Cultivate AI learning habits: creating organisational rhythms that support continuous learning about AI capabilities enhances adaptation.  This might mean dedicating a percentage of time to experimentation and learning or establishing formal knowledge-sharing mechanisms.

• Focus on value, not technology: always keeping the emphasis on the business outcomes and human needs that AI can address, rather than implementing AI for its own sake ensures meaningful progress.  Using AI wisely and sustainably, applying it only where it creates meaningful value delivers lasting impact.  The true momentum point is not the technology itself, but the constraints it helps overcome.

Address Risks Systematically

While AI offers tremendous potential, organisations must proactively address key risk areas:

• Security: AI systems can introduce new attack vectors and vulnerabilities. Implement robust security protocols and regularly testing AI applications for potential weaknesses to mitigate these risks. Setting time-limited and task-specific access controls are increasingly recognised as an important safeguard when implementing AI systems.

• Privacy: many AI applications require access to sensitive data. Establish clear data governance policies that protect privacy while enabling AI capabilities.

• Bias: AI systems can perpetuate or amplify existing biases. Implement rigorous testing frameworks to identify and mitigate bias in training data and model outputs.

• Ethical use: create clear guidelines for the ethical application of AI. Considering impacts on employees, customers and society more broadly helps prevent unintended negative consequences.

• Regulatory compliance: stay informed about evolving AI regulations and ensuring implementations remain compliant with relevant laws and standards reduces legal risk.

• Sustainability: consider the environmental impact of AI implementations, particularly those involving large-scale compute resources.

• Transparency: AI systems can often operate as "black boxes" where decisions and processes are not easily explainable or understandable to users and stakeholders. This lack of transparency can reduce accountability and make it difficult for organisations to ensure compliance with regulations and ethical standards.

• Financial governance: AI systems can lead to unexpected cost escalations if not properly managed. Establishing robust financial controls, monitoring computational resource usage, reviewing systems alongside evolving technology improvements and implementing regular cost-benefit reviews helps prevent budget overruns. As AI becomes more deeply embedded in operations, organisations need mechanisms to track total cost of ownership, including ongoing maintenance, retraining requirements and necessary infrastructure investments.

Addressing these risks is not just about mitigation it is about building trust in AI systems across organisations and with external stakeholders.

Looking Into The Future

As we explore AI as a momentum point, there is an even more profound possibility to consider: what if AI's most transformative impact is not just helping us overcome the constraints we recognise but revealing constraints we do not even know we are operating under?

AI systems can process massive amounts of information, know things we do not and keep getting smarter. They might help us see that many things we have always accepted as 'just how business works' are actually limitations we could change or eliminate entirely.

Perhaps the most exciting possibility is that AI can explore solution spaces more comprehensively than humans, considering combinations and approaches we might dismiss due to cognitive biases or traditional thinking. An AI system is not constrained by professional training, industry norms or cultural assumptions about "how things work."

This capacity for unconstrained exploration could lead to entirely new organisational forms, business models and ways of working that we cannot yet imagine. Not because they are technologically impossible, but because our human thinking has not questioned the constraints that make them seem impossible.

This perspective suggests a meta-level of momentum: AI not only helps us break through identified constraints but can help us identify constraints we did not even recognise we were operating under. As AI systems increasingly develop capabilities for self-improvement and research, this potential grows exponentially. When AI becomes deeply embedded in an organisation's operating system, it creates a natural evolution in the momentum flywheel itself. Rather than momentum being driven solely by human recognition of constraints, the AI-enhanced operating system continuously identifies new opportunities for constraint-breaking. This creates a self-reinforcing cycle where each AI-driven insight leads to new implementations, which generate new data, which enables further insights and a truly sustainable momentum. This is not about AI replacing human creativity or insight. It is about AI complementing human thinking by approaching problems without our inherent limitations and biases, helping us see beyond the horizons of our current understanding. The true momentum point may come when AI helps us ask questions we did not know to ask, revealing constraints we never knew were holding us back.

AI as a Significant Momentum Multiplier

Throughout this series of posts, we have explored how momentum emerges when teams collectively recognise that constraints they have long accepted as fixed are actually flexible. We have discussed how to identify and remove blockers that prevent action and how to scale momentum by embedding capabilities into an organisation's operating system.

AI represents a significant momentum point.  What makes AI particularly powerful is its ability to challenge multiple constraints simultaneously and to enable entirely new ways of working.  The organisations that will thrive in the coming years are those that can harness this momentum, using AI not just as a tool but as a catalyst for reimagining what is possible.

The approach outlined in this series provides a framework for this journey:

• Starting by questioning the constraints that organisations have accepted as fixed, particularly around how work gets done, who can do it and how quickly it can happen.

• Identifying and removing the blockers that prevent action, whether they are related to data, skills, infrastructure or organisational resistance.

• Scaling momentum throughout organisations by building team capabilities, creating supportive architectures and establishing rhythms that sustain transformation.

By following this approach, organisations can avoid the common pitfalls of AI adoption: the hype cycles, the isolated proofs of concept that never scale and the focus on technology at the expense of human needs.

Successful organisations will not just ask how to implement AI. They will ask what becomes possible when we challenge our assumptions. This mindset shift, more than any technology, is what will drive real progress with AI.