DOES YOUR ORGANISATION HAVE AN AI ADOPTION CHALLENGE

July 15, 2026

Speaking with our clients and peers over the last few months about AI, we've heard a multitude of different stories about where organisations are with AI, from it being accepted and adopted to others who are still grappling with what it could mean for their people, the business, their jobs and the future of who their work gets done. What seems to be common though, is that AI is being used in organisations at the moment, sometimes in approved tools, but often in quiet workarounds. It raises the question as to whether that AI uptake is deliberate, or accidental - and if it's accidental, what could that mean?

A lot of the organisations we've been speaking to seem to be getting stuck not because the tech is immature, but because the business hasn’t made room for it, and they are uncertain about the role AI could (or should) be playing, not just in their organisation, but in their industry.

At Spring Point, we've decided to treat AI as an operating model and a behaviour change challenge, so we're now looking at how we design the decisions, roles, routines and leadership habits that make AI usable at scale, then defining the change work required for it not to evaporate after launch.

There is a real opportunity in "Structured AI" adoption

In most sectors, the pattern of AI adoption seems to be familiar:

Small pockets of AI pop up; one team here, one champion there
Tools land on desktops faster than governance, security and data controls can respond
Leaders can’t tell which initiatives are worth backing, and which are noise
Risk rises quietly: operational errors, reputational hits, workforce backlash

These scenarios have played out over the past couple of years and will continue to do so. What it means is that AI spreads in ways that are fragmented, reactive and hard to see (and manage). So, after some internal discussion we decided to tackle the challenge of AI adoption by developing an adoption framework that we believe can be universally applied within an organisation to help plan and better manage AI in the workplace, with a view to not only establishing the playing field and rules, but also ensuring that the advantages and benefits of AI can be effectively leveraged to create value for the organisation. Our aim was to ensure that AI becomes intentional, prioritised and governable, without killing momentum.

Defining the AI Adoption Framework (Beyond the Theory)

We decided that to be useful, this framework needed to be more than a poster on a wall or a slide in PowerPoint deck. It needed to earn its keep in three ways:

It needed to tie AI effort to real business outcomes (not “cool demos”)
It needed to make the next decision obvious—and stops you funding the wrong things
It needed to get the organisation ready (think processes, roles, skills and habits) not just the tech stack

So, the Spring Point AI adoption framework (below) integrates six elements:

Vision & Strategic alignment
Value & ROI prioritisation
Data & Technology foundations
Capability, Culture & Change
Risk & Governance

What’s different about this approach is the focus on how the organisation will run once AI sits inside everyday workflows. We translate the framework into operating model design (decision rights, forums, measures and ways of working), leadership habits (what leaders do differently week-to-week), and a transformation plan that takes it beyond pilots, to repeatable performance uplift.

There are plenty of organisations out there that can help you pick tools or stand up governance. We decidied to focus on the harder part: redesigning how decisions get made and how work gets done so AI becomes business‑as‑usual, and value actually scales.

Our framework is deliberately phased and pragmatic, and recognises that AI maturity is built, not bought.

1. From the “shiny object” to Strategic Value

Lets face it, a lot of AI initiatives get up because they're strongly anchored to board, management or individual enthusiasm! Have you ever been in a board meeting where someone asks "so what are we doing with AI?" This is one of the main reasons we hear of AI initiatives falling over - not through lack of enthusiasm,, but because they’re not anchored to strategy.

A strong framework forces organisations to be explicit about:

Which business outcomes AI should support
Where AI can realistically shift performance
What success actually looks like

This means that AI investment becomes a strategic choice, rather than a series of disconnected experiments designed to demonstrate that something is being done with AI!

2. Better Decisions About Where to Invest (and Where Not To)

One of the most under‑appreciated benefits of an AI adoption framework is what it helps organisations say no to.

By assessing use cases against:

Strategic alignment
Value potential
Feasibility
Time‑to‑value
Organisational readiness.

Leaders can confidently prioritise a small number of initiatives that matter. This means that resources are focused, pilots deliver learning faster, and AI stops competing with core priorities for attention.

3. Technology Foundations are Necessary, but Not the Whole Story

Yes, AI frameworks must address:

Data foundations
Platforms and architecture

But treating AI primarily as a technology or risk problem is where many organisations go wrong. Technology enables AI. It does not ensure adoption, trust or value. The framework puts technology in its rightful place: essential, but not central.

4. Culture, Capability & Change: where the battle is won or lost

Culture

AI changes far more than how work is executed though. It changes how decisions are formed, challenged, trusted, and acted upon, which makes culture (not technology) a more decisive factor in whether AI creates value or stalls.

In organisations without a deliberate cultural shift, AI will undoubtedly cause friction rather than improvement. This could lead to leaders overriding AI‑informed insights because they don’t trust the data, (in fact, the June 2026 AI Adoption Insights bulletin put out by the National AI Centre, mentions that 65% of SME organisations that haven't adopted AI cite trust in the output as a key factor). Or, teams may treat AI outputs as suggestions they can ignore or more likely, threats they need to defend against. Without a deliberate shift in culture to enable the change, decision‑making will likely slow as people debate whether AI is “allowed” rather than whether it can bring value.

Our AI adoption framework needed to treat culture as an operating condition, not a soft add‑on, so that it made explicit the behaviours required for AI to work in practice, including:

Leaders modelling data‑informed judgment: Not deferring blindly to AI, but visibly using AI‑generated insights alongside experience, and explaining how trade‑offs are made. This signals that AI augments judgment rather than replaces it.
Comfort with probabilistic answers: Teams learning to work with recommendations, confidence levels, and ranges, rather than waiting for certainty that never comes. This requires a shift away from “perfect answers” toward “better decisions, faster.”
Permission to test, learn, and iterate: AI improves through use. Organisations need to normalise small experiments, fast feedback, and adjustment, without treating early imperfection as failure and 'throwing the baby out with the bathwater.'
Clear boundaries that build trust: People engage more confidently with AI when they understand what it will and won’t be used for (e.g. decision support vs performance monitoring). Explicit guardrails reduce fear and prevent quiet resistance.

The framework creates the space for organisations to define and reinforce these expectations deliberately, rather than leaving teams to guess what they might be based on an organisation's inconsistent signals. Culture becomes something leaders design, not something they hope will adapt on its own.

When culture is addressed explicitly, AI can augment judgment instead of being resisted, ignored, or quietly bypassed, and decision‑making becomes faster, clearer, and more consistent across the organisation.

Capability

AI doesn’t just introduce new tools either, it reshapes roles, decisions, and expectations. We think that a mature framework should treat capability as an enterprise system (not a training program). It clarifies:

The capability model: what “good” looks like (e.g., AI literacy, judgement & decision-making ability, data fluency, change) by cohort.
The gap view: role-and-skill assessment focused on critical roles and priority use cases.
The build plan: explicit build / buy / borrow / partner decisions with timelines.
The scaling mechanism: role-based learning in the flow of work (playbooks, patterns, communities) with clear ownership and metrics tied to adoption and outcomes.

This makes the workforce impacts more visible and manageable, rather than surprising and disruptive. Capability is built deliberately, and fear is replaced with clarity.

Change & adoption

AI only creates value when people use it, and people use it when they understand what’s changing, why it matters, and how to work safely with it.

The change effort needs the same discipline as the technology: clear decisions, a deliberate plan, and repeatable communication that builds confidence. Adoption accelerates when decision rights are clear, leaders have visible routines, teams know the safe path, and measures reinforce the new way of working. In practice, role-level interventions that help people redesign real workflows and run pilots can be a high‑leverage mechanism to accelerate adoption.

A clear narrative and guardrails: What we’re doing, why now, what success looks like, and how AI will be used responsibly.
Visible leadership sponsorship: Leaders role‑model usage, demonstrate judgement, make decisions, and allocate time and funding.
Clear ownership and cadence: named owners (business + tech + risk), local champions, and a rhythm of feedback and iteration.
Role-based rollout: redesign real workflows, provide hands‑on support, and scale what works.
Measures that matter: track usage, confidence, and workflow outcomes (time, quality, incidents) and adjust accordingly.

This matters because people will often translate “AI adoption” into “job loss.” If leaders don’t address what will change, what won’t, and what support is available directly, trust erodes, and adoption becomes performative.

This means AI solutions move beyond pilots and actually change how work gets done.

5. Risk & Governance:

The last pillar in the framework is Risk & Governance - not a footnote, but a necessity when it comes to safe, explainable, and scalable adoption. We know that sometimes good governance is misunderstood and seen as a roadblock that slows progress; but when it's properly considered, good governance can actually increase speed by removing uncertainty and making it safe for teams to act. Our view here is that this shouldn't be seen as a new layer of bureaucracy, but an opportunity to embed clear guardrails and proportionate assurance into everyday work.

Clear guardrails (“in” and “out”): acceptable use, data handling, and approved tools, so teams know what’s allowed.
Fast decision pathways: thresholds that match the level of risk, with named review paths and agreed turnaround times.
Reusable patterns and lightweight assurance: reference architectures and control patterns that teams can reuse to scale safely.
Security, privacy and compliance embedded into delivery (not bolted on after deployment).
Model and operational risk managed with proportionate testing, monitoring, and escalation.

So why is this important?

Inevitably, your organisation will adopt AI at some point, whether you plan for it or not. The only real choice organisations have in the matter is whether adoption happens by design, or by drift.

When there’s no framework, it's difficult for leaders to “stay neutral”; ambiguity creates a vacuum that gets filled with unmanaged experimentation, a plethora of individual AI tools, poor judgement, inconsistent decisions, and often vendor-led solutions looking for problems to solve that expand faster than the organisation can absorb or govern.

What happens if you don’t employ a framework

Tool and vendor sprawl becomes the strategy, multiple overlapping solutions, inconsistent contracts, and escalating security and support burden.
Shadow AI grows as teams use unapproved tools to move fast, increasing the risk of data leakage and inconsistent practices.
Pilots proliferate, but value doesn’t scale because changes to process, roles, data, and controls were never designed.
Workforce trust erodes as communication is inconsistent and people fill the gaps with worst‑case assumptions about jobs, performance, and surveillance.
Risk is discovered late. Privacy, bias, IP and security issues surface after deployment, when they are costlier to remediate and harder to explain.
Executives lose line of sight. Spend increases while accountability decreases, making it harder to defend decisions to boards, regulators, customers, and employees.

By contrast, when organisations implement a fit‑for‑purpose AI adoption framework, we see clear, tangible shifts:

Clearer prioritisation of AI investment
Faster movement from pilot to scale
Reduced operational and reputational risk
Stronger workforce engagement, capability development, and confidence
AI embedded into “how we operate,” not treated as an add‑on

Challenge for executives

If AI is already being used in your organisation through hundreds of daily decisions, who is designing those decisions? Is it your leadership team, your vendors or your individual teams? A framework is how you make that answer intentional. We believe that the organisations that treat this as an enterprise transformation - operating model, leadership, behaviours, and culture built around a small set of priority use cases, rather than a standalone technology program - will likely see the most significant upside. Most importantly though, leadership teams and boards will stop asking “what are we doing with AI?” and start asking “where can AI make the biggest difference?"

The bottom line

An AI adoption framework isn’t bureaucracy; it’s leadership intent made operational. It allows organisations to move forward with confidence, focus and control, while bringing their people with them. For executive teams, the most important consideration when it comes to AI adoption isn’t whether you have the technology bedded in, but whether the organisation is set up to leverage and manage AI well.

Want to read more? Check out our Capability 2030 white paper to find out more about how you can use our AI Adoption Framework to support your AI adoption challlenges. Or if you want to find out more about how we could help, let us know. We’d love to catch up and chat.