Well, it depends.

When it comes to data protection and security, there is often a major difference between what an organisation is legally permitted to do with personal data and what its customers believe it should do.

And just because an organisation has the right to use someone’s data in a particular way doesn’t necessarily mean it should.

This tension between innovation, compliance, and customer expectations was one of the key themes of Clutch Events’ recent Data Protect and Security Summit in Sydney, where leaders in privacy, security, and data protection came together to discuss the intricacies of keeping data safe and secure.

As has been common at Clutch events recently, the key themes were highlighted in the final Think Tank session of the day, which focused on questions of permission, transparency, and ethics.

Those customer expectations can vary significantly depending on the type of organisation involved and the social licence that it carries. As has so often been proven, a fast-moving digital business that has staked its value on growth at all costs can get away with behaviours that a solid, stable, and trusted organisation would never dream of.

While this doesn’t mean any organisation is exempt from the law, the boundaries of acceptable behaviour can be very different.

On the flip side, it is also possible for more stable and trusted organisations to tick every box when it comes to regulatory compliance and still fall foul of public opinion when their actions fail to reflect consumer expectations, or when they fall victim to a data exfiltration attack despite their best efforts.

The discussion also touched on another key issue, namely the extent to which organisations can offload their responsibilities onto consumers through the use of complex consent documents. These documents tend to push consumers and employees into signing away rights and protections they should reasonably expect to retain over how their data is used.

While the push for plain English consent language has gained momentum in the past year, it is still far from common practice. If a consent document needs a lawyer to write it, then it probably requires a lawyer to interpret it, and has already failed the plain English test. Yes, it needs to stand up to legal requirements and scrutiny, but that should not be used as a smokescreen to give organisations an open licence to use people’s data as they choose.

This issue is likely to gain greater attention as we move into the era of personal AI agents, especially when those agents are supplied by organisations whose chief business model is to collect and monetise personal data. Who is that personal agent really working for?

Unfortunately, the companies at the forefront of developing such services are also among those whose pursuit of innovation-fuelled growth is likely to see them push the boundaries of what is legally permissible, let alone what is socially acceptable. ‘Move fast and break things’ is a fantastic strategy when the penalties are a rounding error compared to quarterly revenue – but possibly not so socially acceptable when the things you break are people.

Australia is equipped with a cohort of passionate, talented, and highly concerned professionals who are very much aware of the differences between what an organisation can do with personal data and what it should do.

The challenge now is ensuring those voices are heard early and loudly enough to shape innovation, rather than being left to sort through the mess after the damage has been done.

On that, we all depend.

One of the most frequently cited challenges organisations face when implementing probabilistic and agentic AI is determining a true return on investment. But one of the hardest questions behind that challenge is beguilingly simple. If an organisation is looking for a good outcome, how does it determine what “good” actually is?

This was perhaps the most important question posed at the recent Sydney AI Engineering and Infrastructure Summit, hosted by Clutch Events, and it was one that drew little consensus among the speakers.

Determining “good” in the era of deterministic systems was relatively straightforward. Performance could usually be measured in terms such as accuracy, speed, reliability, and cost.

But many of the challenges to which probabilistic and agentic AI systems are now being applied defy such simple measures.

A chatbot engaging with a human being might communicate more efficiently than a person, but that does not mean the experience is better. Organisations can measure proxies, such as whether a conversation leads to a desired action, but that bulk metric says little about the quality of the interaction itself. Sentiment surveys provide another approximation, but they are limited by who responds and by the inherently subjective nature of the feedback.

Determining what “good” looks like becomes an interesting new challenge for AI infrastructure and engineering professionals. It is not something they needed to think about to the same extent in the earlier generation of technology, where performance was more straightforward to provision, measure, and monitor.

It also points to a growing need for engineers and business leaders to work more closely together. The technical system cannot be separated from the use case it serves. Organisations need to connect what the AI is doing to the outcome the business is trying to achieve, and then determine whether that outcome represents genuine value.

And defining value is only one of the challenges organisations encounter as they try to move AI from experimentation into production.

Another challenge discussed throughout the day was skills availability, made more difficult by the reluctance of many organisations to invest sufficiently in the training and upskilling required to build those capabilities internally.

The challenge of scaling AI becomes more complicated again when organisations consider where their models should run. The default approach has been to consume frontier models through the public cloud. But rising costs are prompting some organisations to explore running open-weight models in their own environments or through third-party infrastructure providers.

The result is increasingly a hybrid model, along with the accompanying headache of constantly managing different cost structures. Running models outside the public cloud can reduce some costs and give organisations greater control, but it also introduces trade-offs. Security, governance, infrastructure management, and operational responsibility can shift back onto the organisation running the model.

When it came to the biggest hidden cost of AI infrastructure, however, there was much greater agreement, at least among the Think Tank panellists: engineering time spent on undifferentiated platform work rather than product development.

As one panellist observed, almost all of this is still new – which is part of the reason so many people are attending conferences like this one, to hear how others are approaching these challenges. The technology is changing rapidly, established patterns are still emerging, and the need for learning remains immense.

But this at least may become a solvable problem.

Knowledge gradually accretes within organisations, skills improve, and platforms mature. Over time, engineers should spend less effort solving the same foundational problems, while platforms become better able to accommodate changing capabilities without fundamental redesign.

That suggests the biggest costs of AI may eventually shift someplace else, and one likely candidate is data movement and management. AI systems have voracious appetites for data, while also generating enormous quantities of it through their outputs.

The infrastructure challenge of AI may therefore become less about simply providing enough compute, and more about understanding which costs genuinely contribute to better outcomes.

Which brings the discussion back to where it started.

Before organisations can decide whether AI is delivering value, they first need to answer the deceptively difficult question – what does good actually look like?

Can a finance function represent a competitive advantage for its organisation?

This was the final question I posed to the room at Clutch Events’ Melbourne Finance Technology and Transformation Summit in Melbourne.

The answer was a resounding ‘maybe’, with only a small number of hands going up when asked if they considered that they had already reached that level of value.

It’s easy to reason out why few would consider finance to be a competitive advantage. Modesty rules out a lot of potential positives immediately. Then there is also the consideration that many finance functions see themselves as being transaction-processing and record-keeping functions – hardly the place from which to lead an organisation towards its glorious destiny.

But we had spent the preceding six hours hearing stories of how artificial intelligence and related technologies are transforming the work of finance, by taking away much of the human-intensive manual processing work to allow those experts to focus on higher-value tasks.

The fact that so many finance functions are so early in their AI transformation journey means many leaders are yet to see the full potential of what AI might do for them.

When asked where attendees believed AI could bring the most benefits to finance, the strongest response was in automation. While this was borne out by the examples, it suggested two findings.

One – finance professionals are currently fixated on expediting existing processes using AI. They are yet to seriously consider how AI might fundamentally redesign processes to deliver ongoing value.

Second – they are also yet to plan for how that time saved might be reinvested in more valuable tasks – which might also be supported by AI.

Highly effective finance functions are about so much more than processing transactions and keeping records. They are sources of intelligence on business performance and insight into market trends and competitor actions. This puts finance in a strong position to both model possible future options and provide strategic advice on how to achieve them.

Ultimately, this is where AI can bring the most value to finance – not by helping it be better at what it already does, but by enabling it to be so much more.

Pic: Hemangini Tandel, Head of Identity and Access Management, Ramsay Health Care

There is an often-quoted phrase in cybersecurity that suggest security is like the brakes on a car, in that they enable an organisation to go faster because you know you can stop when you need to.

At the risk of stretching an already troubled analogy, if security is the brakes on the car, then identity and access management is the gearbox.

Throughout Clutch Events’ Melbourne Identity and Access Management Summit, one of the key challenges described by identity management professionals was that of over-provisioned access credentials, and the risk this creates when credentials are compromised.

When staff are given access to more systems than they need to perform their function, it can be akin to giving them a high-performance five-speed vehicle for tasks that amount to standard inner-city driving. This may not be a huge problem for them in the day-to-day, as they might rarely need to use that fifth gear.

But it is a huge problem when that car is stolen, and the attacker inherits access to systems that should never have been accessible through that identity in the first place.

The remedy often proposed is to restrict access, but this can have the opposite effect – perhaps likened to locking users into first gear and forcing them to ask permission every time they want to shift into second gear or higher.

This challenge is becoming even more acute as organisations adopt AI agents and other technologies that require privileged access to systems. The number of these so-called non-human identities (NHIs) is expanding rapidly within organisations. Organisations are no longer provisioning access only for people, but increasingly for identities that can act autonomously, at speed, and across multiple systems. Being able to effectively govern these NHIs is a challenge that few organisations have locked down.

The starting point for providing appropriate privileges for both human and non-human identities is visibility. As several speakers suggested, you can’t secure what you can’t see.

But it is important to remember that visibility works both ways. Having visibility of who has access to which systems, and how they are using that access, is one side of the equation. The other is visibility into how people actually work, and therefore what access they need to perform effectively.

In short, some people really do need a fast car and access to all five gears to perform effectively. Understanding who they are, and under what circumstances they need that level of access is critical for ensuring that identity does not become the choke point in operational performance.

But there is one other point to consider within all of this, which was well articulated by our final speaker, Ramsay Health Care Head of Identity and Access Management Hemangini Tandel, who said that when implementing an identity program, organisational change management matters.

Changing the gears that are available to workers is something that needs to be communicated, and its impact needs to be monitored and adjusted. Understanding the human reaction to change is another important aspect of identity and access management, as friction can be an important indicator of where identity controls are chafing against the needs of its users.

In the 30 years that I’ve spent observing business success and failure, there is a question that has puzzled me more than most: why do some organisations transform successfully, while others do not? And why do some leaders whittle away their organisation’s choices until the only ones left are whether to shut it down today or shut it down tomorrow?

For a long time I thought it was mostly a question of strategy, and in some instances it certainly has been. Nokia’s decline and eventual sale of its handset business to Microsoft under the stewardship of Stephen Elop is one example. It seems standing on a burning platform is not a great place from which to make existential strategic decisions.

But there are plenty of other instances where the strategy has been fine, and the business or project has failed anyway.

So if strategy alone doesn’t explain the difference, something must be happening between the decision and the outcome.

That missing piece is execution.

Because the best strategy is of little value if those tasked with implementing it can’t turn it into reality.

The concept of execution has also long fascinated my frequent collaborator Peter Fritz AM and the team at Global Access Partners, who have encompassed it in a concept they have developed called Execution Economics.

The principle behind Execution Economics is relatively simple. It asks whether those responsible for executing a decision have the genuine authority and control required to do so, whether they have access to the information needed to track its implementation, and if they have sufficient freedom from interference by others who might derail it.

These factors come together to determine Decision Sovereignty, which describes an organisation’s capacity to translate a decision into a realised outcome.

While the basic concepts behind Decision Sovereignty seem obvious, that is part of their strength. Its value lies in giving organisations a way to understand their execution capability and, when deficiencies become apparent, identify where they need to intervene.

Decision Sovereignty does not pass judgement on the quality of the strategy itself. Rather, it indicates the extent to which the value contained in that strategy can survive the journey from decision to outcome.

You’ll hear me talking a lot more about Execution Economics and Decision Sovereignty over the next year as we work to take the concept mainstream.

I introduced it recently in a presentation to a group of private healthcare executives at HAMBS in Adelaide. That presentation explored four factors that I believe are essential to successful transformation:

• Choice – the goal of transformation should be to increase the range of positive choices available to an organisation.
• Foresight – he ability to understand what might be coming and recognise the need to respond.
• Strategy – the codified response to that foresight.
• Execution – the translation of strategy into action in pursuit of an outcome.

If culture eats strategy for breakfast, then execution is where choices go to die.

All too often I have witnessed poor execution whittle away the choices available to an organisation, as cost overruns, skills gaps, supplier delays, competing priorities, and organisational interference erode the potential value of an outcome.

That matters because transformation is ultimately about preserving choice. Every failed implementation, blown budget, missed deadline, or compromised outcome narrows the range of choices available next time.

So perhaps the answer to the question I’ve been asking for 30 years isn’t simply that some organisations develop better strategies than others. It is that some are much better at preserving the value of a good decision as it passes from intention, to action, to outcome.

Execution Economics enables us to understand why.

The impact of AI on software engineering has been both swift and significant.

In some ways, engineering has become the coalminer’s canary for the impact AI might have on white-collar professions. We are already seeing the potential for enormous productivity gains, alongside growing questions about quality, security, skills, and ultimately, the future shape of the profession itself.

It is fair to say that the speed at which AI has disrupted software engineering has caught many organisations off guard, particularly when it comes to security. But the rapid announcements of supposedly AI-driven cuts to developer workforces, and the almost equally rapid walking back of some of those claims, have raised another set of questions.

If you are a junior software engineer entering the profession today, what skills should you actually be learning?

And what kind of job will be waiting for you when you have learned them?

These were among the issues raised in the Think Tank session that concluded Clutch Events’ recent Sydney Engineering & DevOps Summit, where Asif Gill (Head of Discipline – Software Systems and Director of the DigiSAS Lab at the University of Technology Sydney), Jeremy Burton (Chief Technology Officer at hipages Group), and Jovana Dunisijevic (Senior Technical Evangelist at Atlassian), discussed what the future might hold for engineering and software development in Australia.

The short answer is that nobody really knows. The pace of change is simply too fast for predictions based on today’s capabilities to remain reliable for very long.

What we can see more clearly are the changes already taking place in software delivery.

• Developers can produce code at a far greater rate than before.
• The increased use of generated code and pre-developed packages has created new security vulnerabilities.
• The bottleneck in software delivery has moved away from code creation towards testing, validation, and review.

But beneath these obvious changes sits a potentially more profound one.

AI is rapidly harvesting the low-hanging fruit of software development, and many of the lower-level, process-oriented tasks that once formed part of the training pathway for junior developers no longer need to be performed by human hands.

That creates an uncomfortable question: if we automate the work through which junior engineers traditionally learned their craft, how do they acquire the experience needed to become senior engineers?

What does “entry level” mean anymore?

Of course, software engineering has always evolved this way. Every era leaves skills behind. Few developers today need to write machine code or worry about many of the technical constraints that consumed earlier generations of programmers.

Perhaps AI simply pushes the definition of “entry level” further up the skills ladder.

But if entry-level work becomes more sophisticated, what sits at the top? What are the highest-value skills an engineer can aspire to develop in a world where increasingly capable AI systems can generate, test, explain, and potentially optimise large amounts of code?

Does the software engineering ladder extend upwards, or does the distance between its top and its bottom begin to compress? That question is much harder to answer because we don’t yet know where those limits lie.

One theme that did emerge clearly from the Think Tank discussion was that an engineer’s value increases significantly when they understand their work through a business lens. That might explain why the term “business engineer” is appearing more often.

The valuable engineer of the future may not simply be the person who can produce the code the fastest. It may be the person who best understands why the code should exist in the first place, how it interacts with the broader organisation, what risks it introduces, and what outcome it is intended to create.

I have a personal attachment to this question.

Many, many years ago, long before I entered technology journalism, I started a degree in digital systems and communications engineering at RMIT. I learned about things such as logic controllers, EPROMs, and differential calculus.

At the time, I couldn’t see what much of this had to do with the work I imagined myself eventually doing. I was told it was foundational knowledge, and that its relevance would become clearer as I progressed.

I never got the chance to find out. I dropped out by the end of my first year.

But what I remember is that each new piece of knowledge provided another fragment of the world I was entering. Even when something had little obvious practical value, it helped explain how the technologies around me had come to exist and how their different components fitted together.

That context matters.

Understanding why things are the way they are can be enormously valuable when trying to understand how different parts of a system interact. It contributes to what we now commonly describe as systems thinking, a capability that almost every employer seems keen to find.

And yet systems thinking is becoming harder to attain. Over the decades, science, technology, and engineering have become increasingly specialised. The depth of knowledge available within individual disciplines has grown enormously, but the ability for any one person to understand how those disciplines fit together has become much harder to develop.

Paradoxically, those who develop that ability may also be becoming more valuable.

This is where the impact of AI on engineering becomes more interesting than a simple discussion about productivity. If we only use AI only to produce more code, faster, we may be missing its greater value.

There is a risk that automation strips away some of the work through which engineers traditionally built knowledge, intuition, and experience. If that happens without us creating alternative pathways for developing those capabilities, we may eventually discover that we have automated away parts of the apprenticeship on which the profession has traditionally depended.

But the opposite outcome is also possible.

AI could remove enough of the repetitive, low-level work to give engineers more time to look beyond their immediate domain. It could allow specialists to explore adjacent disciplines, understand the businesses and systems within which their work operates, and develop the broader perspective that increasingly distinguishes high-value engineers from everyone else.

Perhaps, then, the goal for AI in software engineering should not simply be to help engineers produce more.

It should be to help them see more.

(pic credit Олег Мороз)

Sometimes your imposter can also be your friend.

One thing I hadn’t anticipated when I started running professional communications training was that I would not only be helping clients build skills, but also helping them overcome perceived shortcomings.

I would find myself sitting opposite people who appeared cool, calm, and accomplished, but who underneath were carrying a collection of jangled nerves and negative thoughts. They worried about being exposed as incapable, underqualified, or even being seen as frauds by their audience.

This concept of imposter syndrome is one of the most common challenges standing in the way of people becoming truly effective communicators.

But it can also be useful.

Imposter syndrome might make people underestimate their abilities and exaggerate the gaps in their knowledge, but the fear of being exposed can also drive them to prepare more thoroughly, examine their assumptions, and ensure they know their material.

The problems begin when preparation becomes over-preparation. That can lead to presentations that are too long, too detailed, and poorly suited to the needs of the audience. Speakers get lost in the detail, struggle to articulate their key point, and become increasingly anxious about forgetting something important.

There are three strategies people can use to ensure their imposter does not get the best of them:

• Know exactly what you are trying to achieve and stick to it ruthlessly. Having a clear objective and the discipline to stick to it is my most common advice. One objective, a small number of supporting messages, enough evidence to substantiate them, and a few compelling stories to bring them to life. It’s not a question of being prepared for everything, but of being confident in delivering what matters.
• Know the boundary of your expertise. You do not need to demonstrate everything you know. Staying inside the territory you can defend creates confidence and reinforces your authority. It is fine to concede there are areas where your expertise is lacking – provided you deliver value in the areas that matter.
• Remove the language of uncertainty. Cut unnecessary qualifiers such as “I think”, “I believe”, “hopefully”, “not necessarily”, “trying to”, and “sort of”. If you know something, say it. Confidence in delivery comes through what you say and how you say it. Getting to the point with confidence builds the audience’s confidence in you.

The goal isn’t to silence the imposter. Having a little doubt can be useful, because it makes us prepare, question ourselves, and avoid taking an audience for granted.

The trick is knowing when the imposter has done its job. It’s certainly done its job for me.

Prepare because your imposter tells you that you might not be good enough. Then, when it is time to communicate, leave it behind.

Great stories aren’t owned – they are share. Brand owners ignore this at their peril.

One of the defining qualities of a truly powerful story is that others want to retell it. In the act of retelling, stories inevitably change. They are adapted, embellished, and localised. This is one of the strengths of storytelling.

We see this in myths and legends. Stories travel across cultures, changing as they go. For instance, many societies have flood myths with strikingly similar elements – angry gods, chosen survivors, and the release of a bird to test whether the waters have receded (often referred to as the ‘bird scout’ motif). The core idea persists, but each culture makes it its own.

I often see this at conferences, where a story told on stage at one event is retold later, often with different details. One example is the story of the angry father who learned of his daughter’s pregnancy via the product recommendations she received – a story that most likely started out only as a hypothetical, before being translated into an ‘event’.

A story becomes more powerful precisely because it is no longer fixed – it is alive.

Fiction creators understand this. The popularity of a story is often measured by the fan fiction it inspires. Countless tales have been written about Sherlock Holmes that were never penned by Conan Doyle, including less obvious ones such as the TV series House (the clue is in the name – geddit?).

Entire fictional universes, from science fiction franchises to fantasy sagas, have expanded far beyond their original authors because fans wanted to continue the story. Were it not for E. L. James’ love of the Twilight novels we would not have 50 Shades of Grey (who do you think Mr Grey was based on …?). The worlds of science fiction are especially ripe for this phenomena, as seen through the thousands of stories that fans have written in the universes of Star Wars, Star Trek, and Doctor Who.

Brands are no different.

Two decades ago, the rise of user-generated content clearly showed how brands were no longer controlled solely by marketers. Today, thousands of creators and influencers tell brand stories every day, through videos, reviews, memes, and social media.

This creates a paradox for brand owners: they are not really in control, yet they are still responsible.

In reality, a brand exists only because people believe in it. A brand with no fans is just a name. A brand with millions of fans is a cultural force shaped as much by its community as by its originators and legal owners.

For anyone building a brand, the notion that others want to tell you story for you is not something to control – it is something to nurture and celebrate – and learn from. Third party stories are in some ways a better reflection of the brand than its owners themselves might see – it is a representation of the brand as others see it.

Understanding how the brand story changes through retelling can provide a clear indication of what parts of the story work most strongly, and a good indicator for areas of further development.

Letting go of control is hard for marketers. It requires courage, and careful stewardship. While brands cannot own their stories, they can influence them by clearly articulating their values. These values become the brand’s “canon” – the principles that guide how the story should evolve.

When brands betray that canon, fans notice, and react fiercely (search for the term ‘Holdo Manoeuvre if you’re keen to dive into a rabbit hole for how fandoms respond to such transgressions).

Fans are loyal, and brands crave loyalty. But loyalty is not a transaction – it is a relationship. Take it for granted, and you will discover that a betrayed fan can become your most relentless critic.

Yet when brands stop trying to control the story and instead choose to listen, learn, and participate in it, something extraordinary happens. The stories told by customers, creators, and communities often illuminate the true meaning of a brand far more clearly than any boardroom strategy ever could.

These third-party stories reveal what people actually care about, what they cherish, and what they are prepared to carry forward. They are not noise – they are the brand in its most authentic form.

So the task of brand building is not to lock the story down, but to set it free with intention.

If people want to tell your story for you, that is not a risk to manage – it is the highest form of success.

Because great brands don’t just make customers. They create fans.

And it is fans – not marketers – who turn brands into legends.

Why? Because every presentation should be judged from the audience’s point of view, and 15 seconds is about as long as you can reasonably assume someone will give their attention for free.

If your presentation doesn’t give them a reason to listen beyond the first 15 seconds, why would they?

At that point, for them, it’s already over.

Great presenters are ruthless about how they use those opening moments. In the first 15 seconds, the job is simple: establish value.

Some do this by stating the value exchange directly – their knowledge for your time. Others open with a question or a challenge that pulls the audience in. One of my favourite techniques is to make a promise – something specific the audience can hold you to.

Whatever the approach, it must earn the next 15 seconds. And then the next. And the next – until the presentation is done.

A lucky few can afford to burn precious seconds before delivering value, because their reputation has already done the work for them. This is most often the case for big name speakers, where the value lies as much in entertainment as it is in knowledge exchange.

In their case, the value is established long before they stepped on stage. But that still doesn’t mean they will have everyone’s attention at the 16 second mark.

For everyone else, there is no shortcut. It’s the hard grind of testing ideas, refining openings, and practising delivery until something consistently works, and then evolving it again.

This also means that if you have something important to say, you’d better say it early. The first 15 seconds are the only time you can be confident everyone is still listening.

Good presentations are exercises in holding attention. If you want the audience with you at the end, it’s far easier to keep them with you along the way than to win them back after you’ve lost them.

Yes, every presentation has highs and lows, and contrast matters. Even Mad Max movies need quiet moments so the audience can breathe.

But if that quiet moment comes at the beginning, you may never gain the momentum you need to carry your audience through to the end. Miss the first 15 seconds, and your presentation is effectively over before it’s begun.

Every presentation lasts 15 seconds.

Why? Because every presentation should be judged from the audience’s point of view, and 15 seconds is about as long as you can reasonably assume someone will give their attention for free.

If your presentation doesn’t give them a reason to listen beyond the first 15 seconds, why would they?

At that point, for them, it’s already over.

Great presenters are ruthless about how they use those opening moments. In the first 15 seconds, the job is simple: establish value.

Some do this by stating the value exchange directly – their knowledge for your time. Others open with a question or a challenge that pulls the audience in. One of my favourite techniques is to make a promise – something specific the audience can hold you to.

Whatever the approach, it must earn the next 15 seconds. And then the next. And the next – until the presentation is done.

A lucky few can afford to burn precious seconds before delivering value, because their reputation has already done the work for them. This is most often the case for big name speakers, where the value lies as much in entertainment as it is in knowledge exchange.

In their case, the value is established long before they stepped on stage. But that still doesn’t mean they will have everyone’s attention at the 16 second mark.

For everyone else, there is no shortcut. It’s the hard grind of testing ideas, refining openings, and practising delivery until something consistently works, and then evolving it again.

This also means that if you have something important to say, you’d better say it early. The first 15 seconds are the only time you can be confident everyone is still listening.

Good presentations are exercises in holding attention. If you want the audience with you at the end, it’s far easier to keep them with you along the way than to win them back after you’ve lost them.

Yes, every presentation has highs and lows, and contrast matters. Even Mad Max movies need quiet moments so the audience can breathe.

But if that quiet moment comes at the beginning, you may never gain the momentum you need to carry your audience through to the end. Miss the first 15 seconds, and your presentation is effectively over before it’s begun.