The Understanding Gap - Why technological capability still needs human clarity
Technology can now model entire systems, simulate failure and generate content in seconds but capability alone does not create understanding. A digital twin contains knowledge, but still needs translating. Risk must be shown, but carefully framed. AI can generate content, but human experience gives it relevance. A script contains information, but human direction turns it into a narrative.
In this edition, we explore the space between information and understanding and why clear, strategically considered communication is what ultimately turns technical capability into meaningful action.
Put your communication to the test
Before you read on, consider these five questions:
Could your customers, investors or internal teams clearly explain your capability after seeing it once?
Can you demonstrate how your technology responds when something goes wrong without creating unnecessary alarm?
Are you adapting the same technical information for audiences with different priorities, concerns and levels of knowledge?
Is AI supporting human judgement and creativity within your communications or simply generating more content?
Do your scripts explain what you do, or do they give your audience a compelling reason to care?
If any of those questions gave you pause, the four stories below explore where the communication gap can appear and how thoughtful visual storytelling can help close it.
Digital Twins and Their Own Language
Human twins can develop their own shorthand: expressions, references and ways of communicating that make perfect sense to each other. Their parents may understand much of it too, having watched that shared language develop.
To everyone else, however, the meaning may not be nearly as obvious.
Digital twins can face a similar communications problem.
The model and the engineering team speak fluently to one another through data, variables, simulations and system behaviours. The organisation of the digital twin’s “mum and dad” understands what it has created, why it matters and what the results reveal.
But customers, investors, operators and procurement teams may not speak that same technical language.
A digital twin can contain an extraordinary amount of knowledge, but information alone does not create understanding. It becomes a communications tool only when someone identifies which behaviour matters, selects the right scenario and translates the output into a clear visual narrative.
The communication challenge is not merely theoretical. NIST identifies human comprehension as a central consideration in digital-twin interfaces, noting that visual technologies can make complex modelling, simulation and monitoring more accessible to users.
Animation and carefully designed visuals can act as the interpreter, turning complex outputs into something each audience can quickly see and understand.
An engineer, investor and operator may all examine the same model, but each needs a different route into its meaning.
The twin already knows how to speak to the system.
The challenge is helping it speak to the wider world.
Source: NIST IR 8356: Security and Trust Considerations for Digital Twin Technology
How to Show Risk Without Creating Alarm
Most organisations show their technology working perfectly: the aircraft completes its mission, the production line runs without interruption and the network remains connected.
But decision-makers also want to know what happens when conditions change. Where is the redundancy? When does someone intervene? How quickly can normal operation be restored?
Showing failure can feel risky. Real-world footage may appear dramatic, alarming or too focused on the incident itself. Animation creates a degree of visual distance, allowing sensitive or high-risk scenarios to be examined in a controlled and less intimidating way without minimising their seriousness.
The audience can follow the sequence clearly: what changed, how the system identified it, which safeguards were activated, where human intervention occurred and how normal operation was restored.
Evidence from risk communication research supports this approach. In a 2024 randomised study, participants given visually enhanced risk information felt significantly less overwhelmed and more confident in their ability to explain the risks themselves. Although the research was conducted in a medical setting, the underlying principle is highly relevant: carefully designed visuals can make serious information easier to examine without diminishing its importance.
The failure provides the context, but resilience remains the story.
Visualising risk in this way can demonstrate preparedness rather than vulnerability. It removes unnecessary spectacle and directs attention towards the strength of the response.
Sometimes the most persuasive way to demonstrate a capability isn’t simply to show that it works ist’s to show how it responds when something goes wrong.
Source: Frontiers in Surgery: Improving risk communication through visual aids
AI, Experience and Human Value
“Why can’t we simply use AI for this? Why do we need you?” Like everyone, we are excited by AI and the possibilities it presents. We use it to support some of our administrative work, but it does not form part of our creative process. Every idea, script, design and animation is thoughtfully shaped and brought to life by our talented human creatives.
It is a fair question and one we are increasingly asked.
AI can research a subject, generate a script and create images in seconds. It can process enormous amounts of information and identify patterns far more quickly than any individual person.
But generating content is not the same as creating communication.
AI does not possess lived experience. It has never sat in a stakeholder meeting and sensed that the conversation was moving in the wrong direction. It has never watched an audience lose interest, challenged a brief that was solving the wrong problem or seen the moment when a complex idea finally clicks.
AI can recognise patterns, but it cannot read the room.
Experience helps us identify the unasked question, the technical distinction that really matters and the apparently minor detail that could change how an entire message is received.
Your CEO might allow AI to support the preparation of company accounts—but would they publish them without experienced financial professionals checking, challenging and taking responsibility for the result?
Communications deserve the same consideration.
Messaging is money. It influences whether an investor recognises the opportunity, whether procurement understands the difference, whether a customer trusts the claim and whether talented people want to join the organisation.
AI can accelerate research, scripting, animation and content production. Used intelligently, it is a valuable part of the creative process.
Research increasingly suggests that how we use AI matters as much as whether we use it. In a 2026 study, people who drafted their own material before using AI to refine it retained significantly more ownership and connection to the work than those who simply copied AI-generated content.
The evidence points towards collaboration rather than substitution: AI can accelerate the process, while people retain responsibility for judgement, meaning and the final result.
But technology does not replace the need to understand the audience, find the central idea and make deliberate decisions about what to say—and what not to say.
AI can generate the content.
Experience makes it resonate.
Sources: Scientific Reports: Active collaboration with AI and Royal Society Open Science: Generalisation bias in AI summaries
AI Can Write a Script. Can It Find the Story?
AI can generate 500 words in seconds. But 500 words are not necessarily a story.
A script contains words, information and instructions. A narrative gives them direction.
It establishes who the story is for, what they need to understand, why it should matter to them and what should change by the end.
That distinction is important.
A script can be technically correct, grammatically polished and logically structured while still failing to connect with its audience. It might explain every feature but never establish why those features matter. It might present the right information in entirely the wrong order. It might communicate what a product does without creating a reason to care.
Finding the narrative requires more than arranging words. It requires imagination, our distinctly human ability to picture something that does not yet exist.
AI works by predicting what might come next. A storyteller can begin at the other end: imagining the final page, the feeling they want to leave behind and the change they want to create, then working backwards to find the right beginning.
That sense of destination is what transforms a sequence of words into a narrative with purpose.
It means identifying the tension at the centre of the story: the problem that needs resolving, the perception that needs changing or the opportunity the audience has not yet recognised.
It also requires emotional understanding.
An investor assessing risk, an engineer evaluating performance and an employee learning a new process may all receive the same underlying information, but they will not respond to it in the same way. Each arrives with different priorities, expectations and concerns.
Human direction helps recognise those differences and decide where the story should reassure, challenge, inspire or create urgency.
Cultural understanding matters too. Language that feels confident and persuasive to one audience may appear exaggerated, insensitive or untrustworthy to another. Humour, symbolism, tone and even the way authority is expressed can change meaning across industries, organisations and cultures.
AI can draw on patterns found in enormous quantities of existing content, but it does not share the lived context of the people it is addressing. It cannot independently know which organisational history should be handled carefully, which technical distinction carries political weight or why a seemingly minor phrase could undermine trust.
Those judgements require someone to understand the room, even when the audience is not physically in it.
Research demonstrates both the opportunity and the limitation. In a study involving 293 writers and 600 independent evaluators, access to AI-generated ideas improved average novelty and usefulness scores by around 8–9%. However, the AI-assisted stories also became more similar to one another.
AI can therefore strengthen an individual piece of writing while simultaneously pulling different stories towards familiar patterns. Without clear human direction, efficiency can easily become sameness.
The most important creative decision is often not what to include, but what to leave out, what to emphasise and which sequence will make the meaning clear.
This is where the difference between scriptwriting and storytelling becomes visible.
AI can help produce the sentences. Human insight establishes the purpose behind them, considers how they will make the audience feel and ensures they reflect the cultural and commercial context in which they will be received.
The emerging research points towards a hybrid model: technology contributes speed and generative capacity, while people provide intention, emotional intelligence, cultural understanding and direction.
AI can help write the script.
Finding the story still requires someone to decide why it deserves to be told.
Sources: Science Advances: Generative AI enhances individual creativity but reduces collective diversity and Frontiers in Communication: AI storytelling across media