An exciting announcement for N16.

You may have seen the announcement yesterday from Small Business and Manufacturing Minister Chris Penk and Minister of Science, Innovation and Technology Dr Shane Reti about an AI advisory pilot programme.

I am super excited to share that N16 is one of a small number of confirmed providers.

The pilot will be delivered through the Regional Business Partner Network (RBPN) and is currently available to existing customers. Invitations will be sent out by RBP partners starting next week. It is set to run for at least 6 months, offering practical support to SMEs so that they can access and use AI tools that lift productivity.

There are a few things I really like about the pilot:

1. They are starting small as a proof of concept, gathering the data and evidence to prove that it is making an impact – just as all successful AI pilots should.
2. It’s about solving real business problems first. We are not just implementing AI tools, but first identifying business challenges, assessing benefits and ROI before implementing.
3. It shows the government understands the real challenges SMEs are facing in NZ (particularly around productivity) and are looking at innovative ways to support.

For N16 – perhaps it was luck or a bit of good planning – but the AI Navigator I have been building fits almost perfectly with what the pilot is wanting to achieve.

If you’re a business owner keen to explore the wide range of support available through the RBPN, reach out to your local RBP partner for more details.

Looking forward to sharing more about this over the coming months.

New Zealand has one of the highest concentrations of small and medium enterprises in the developed world. We also consistently rank near the bottom for productivity among OECD nations. Coincidence? Not quite.

While our SME-heavy economy isn’t the only factor dragging down productivity, it creates specific vulnerabilities that, when combined with distinctly Kiwi characteristics, make low productivity almost inevitable. The encouraging news? These aren’t permanent conditions. They’re fixable problems that most business owners simply haven’t recognised yet.

Why SME-Dominated Economies Face Productivity Headwinds

There’s nothing inherently wrong with small businesses. But size does create structural disadvantages that affect productivity:

Economies of scale work against you. When you’re running a ten-person operation, every additional cost hits your bottom line directly. Large firms spread fixed costs across thousands of units of output. You feel every software license, every compliance requirement, every price increase immediately.

Technology and systems investment requires critical mass. A $50,000 investment in automation is a rounding error for a large firm. For an SME, it’s a major capital decision that might take years to justify. The result? You keep doing things manually while your larger competitors automate.

Capital access isn’t equal. Large firms can borrow at favourable rates or raise equity capital. SME owners typically bootstrap from personal savings or operate on razor-thin margins, leaving little room for the kind of investment that drives productivity gains.

Management bandwidth is your scarcest resource. Big firms have dedicated teams for operations, finance, HR, and strategy. SME owners are juggling sales calls, managing staff, doing quotes, chasing invoices, and trying to think strategically — all in the same afternoon. Something has to give, and it’s usually process improvement.

Process consistency falls apart. Larger organisations standardise how work gets done. Small firms vary by person, by day, and by situation. When knowledge lives in people’s heads rather than in systems, productivity becomes unpredictable.

What Other Countries Get Right

Some countries manage to maintain both high SME participation and strong productivity. Germany’s Mittelstand companies are famous for combining small size with world-class efficiency. Switzerland, Denmark, and the Netherlands all have robust SME sectors and high productivity. What do they have that we don’t?

They invest heavily in management capability and continuous improvement. Their SMEs adopt technology faster. They focus on high-value specialisation rather than competing on price. And critically, they have strong industry associations and support networks that help smaller firms access expertise and share best practices — something New Zealand’s geographically dispersed businesses struggle with.

What Makes New Zealand SMEs Different

Our productivity gap isn’t just about size. It’s about how our small businesses operate:

Technology adoption. Walk into many Kiwi SMEs and you’ll find businesses still running core processes in Excel spreadsheets. I’ve seen companies tracking inventory, managing projects, and even handling customer relationships in spreadsheets that four different people update, creating version control chaos and errors that cascade through the business.

Management capability. Many SME owners are excellent tradespeople, salespeople, or subject matter experts who’ve never had formal management training. They’re running businesses on instinct rather than systems. They don’t know what good looks like because they’ve never seen it, and they’re too busy fighting fires to step back and learn.

Innovation investment. Research shows New Zealand businesses invest less in R&D and process innovation than comparable OECD countries. When margins are tight and capital is scarce, innovation feels like a luxury rather than a necessity.

We’re concentrated in lower-value sectors. New Zealand’s SME economy skews heavily toward farming, tourism, construction, and retail — sectors with inherently lower productivity potential than technology, advanced manufacturing, or specialised business services. There’s nothing wrong with these industries, but they don’t generate the same value per hour worked as higher-complexity sectors.

The Real-World Cost of Staying Manual

Here’s what this looks like in practice. I recently worked with a business that had several salespeople making site visits throughout the region. At the end of each day, they’d return to the office with customer requirements then once a week, they would batch up all the quotes and send them out in one go.

Meanwhile, their competitors were using mobile quoting tools that generated quotes on-site, often while still sitting with the customer. By the time they sent their quote — sometimes several days after the initial visit — the customer had already signed with someone else. They weren’t losing on price or quality. They were losing on speed.

This isn’t an isolated example. Process variation is everywhere. One person does quotes one way, another person does them differently. Customer data lives in email inboxes. No one can quickly answer “How many active quotes do we have?” or “What’s our conversion rate by customer type?” The information exists, but it’s scattered across systems, spreadsheets, and people’s memories.

The Path Forward Is More Accessible Than You Think

Here’s the genuinely good news: the barriers to productivity improvement have never been lower. Ten years ago, implementing proper business systems meant six-figure investments in enterprise software and lengthy implementation projects. Today, you can get started for a few hundred dollars a month with tools that work out of the box.

But technology alone isn’t the answer. The starting point is understanding your end-to-end processes: enquiry to quote to delivery to invoice to payment. Map out how work actually flows through your business. Where are the handoffs? Where does information get stuck? Where do things sit waiting? You’ll find opportunities to streamline even before you add any technology.

Once you understand your processes, you’ll discover that someone has already solved most of your problems. There are off-the-shelf tools for nearly every type of business and workflow. The key is being willing to change how you work to suit the technology, rather than trying to make technology replicate your existing messy processes.

The Biggest Opportunities Right Now

AI-powered booking and customer service. AI chatbots can now handle initial customer enquiries, book appointments, answer common questions, and even qualify leads — 24/7, without human intervention. A customer visiting your website at 9pm can have a conversation, check availability, and book a time slot instantly. The technology has matured dramatically in the past year — these aren’t the clunky chatbots of five years ago.

Invoice and document automation. If someone on your team is still manually typing up invoices, processing supplier bills, or entering data from PDFs into your accounting system, you’re wasting thousands of dollars a year. Modern tools can extract data from documents automatically, match invoices to purchase orders, route approvals, and sync everything to your accounting system. Most SMEs see payback in under six months.

Real-time inventory and stock management. If you’re holding physical inventory and still doing stock takes with clipboards, you’re burning cash. Real-time inventory systems tell you what you have, what you need, what’s moving, and what’s not — right now, not at the end of the month.

Integrated collaboration and communication tools. When your field staff are texting updates, emailing photos, calling the office for information, and updating spreadsheets when they get home, coordination becomes chaos. Modern collaboration platforms integrate messaging, file sharing, task management, and real-time updates in one place.

Knowledge management and documentation. When critical information lives only in people’s heads, you’re one resignation away from chaos. Start by documenting your core processes, building simple how-to guides, and creating a searchable repository where staff can find answers without interrupting someone else.

Where to Start

If you’re reading this and thinking “That’s us — we’re doing things the hard way,” here’s what I’d recommend:

Pick one process that frustrates you or your team most. Map it out completely. Calculate what it actually costs you in time and errors. Then research what tools exist to fix it. Talk to other businesses in your industry about what they use. Most importantly, commit to changing your workflow to match the tool, not the other way around.

The technology is ready. The question is whether you’re ready to acknowledge that the way you’ve always done things is holding you back. New Zealand’s productivity problem is real, but it’s not destiny. It’s a choice we make every day when we decide whether to keep working harder or start working smarter.

For years, automation and now generative AI have been marketed as shortcuts to efficiency: adopt the right tools, plug them in, and you’ll reduce admin, speed up delivery, and improve consistency. The reality has been far less inspiring. A recent MIT-sponsored study found that 95% of generative AI pilot projects fail to deliver measurable value in P&L or business impact. The technology isn’t broken — the issue is that too many businesses are trying to deploy AI on top of brittle, outdated, or unclear processes. Instead of solving problems, they end up automating chaos.

It’s a sobering reminder:

If a new hire can’t follow your process, what makes you think a bot will?

The Trap of Automating Messy Processes

When businesses rush to automate without addressing the foundations, they simply lock in inefficiency. Processes that are undocumented, inconsistent, or reliant on a handful of experts don’t magically improve with technology. In fact, the opposite happens. Automating fragile processes multiplies the problems: projects cost more, take longer, and often fail under pressure.

This isn’t a failure of ambition, it’s a failure of readiness. Businesses are not doing anything wrong by exploring AI — but most are not yet set up to succeed with it. The first step is to make processes clear, simple, and resilient.

Why Optimisation Isn’t Enough

Most organisations approach process improvement by asking how to optimise what they already do: streamline steps, remove waste, and then automate. That’s fine for incremental gains. But if you stop there, you risk making small improvements to processes that are no longer fit for purpose.

The harder — and more valuable — question is: “Is this still the best way to do this at all?”

Many business processes were designed for a different time: when teams were smaller, customers had different expectations, or technology was more limited. Over years and decades, those processes evolved reactively, not strategically. They may have made sense once, but they don’t necessarily represent the best way forward.

This is where leaders need to step back. If you were building your business from scratch today, how would you design it? What skills would you prioritise? What core processes are truly essential? How would you deliver efficiently and with quality to the customer? These are systems-level questions, not just efficiency tweaks.

Sketching an AI-First Future

Thinking about processes differently naturally leads to a bigger consideration: what does an AI-first organisation look like? The answer isn’t crystal clear yet — the landscape is shifting too quickly. But that’s not an excuse to avoid the question. The businesses that start sketching scenarios now will be the ones most prepared to adapt.

An AI-first organisation won’t look like today’s business with a few bots plugged in. It will have workflows fundamentally redesigned to take advantage of AI agents. It will require new roles, new governance, and new ways of capturing and sharing knowledge. And while we can’t draw the final blueprint yet, we can explore options and make the early investments that create agility.

What Success Looks Like

The few AI projects that succeed already show us the pattern. They are:

• Integrated deeply into workflows, not bolted on.
• Focused on domain-specific applications, not generic experiments.
• Designed with resilience — including human oversight and fallback paths.
• Measured against clear business outcomes.

These organisations don’t just automate existing processes. They reimagine them. They design for clarity first, then use AI to scale and accelerate that clarity.

Conclusion: From Optimising to Reimagining

The promise of AI isn’t in making yesterday’s ways of working slightly faster. It’s in building entirely new ways of operating that are designed for the world we’re moving into. That requires leaders to shift mindset: from optimising what exists, to reimagining what’s possible.

The leaders who thrive will be those who:

• Reimagine, not just optimise.
• Design resilient, clear processes.
• Capture and share knowledge rather than letting it live in a few heads.
• Use AI to accelerate clarity, not multiply confusion.

AI won’t replace humans entirely. But it will expose weaknesses in systems and amplify the advantages of well-designed organisations.

So the real question is: where in your business are you still relying on “the old way”? And is it time to reimagine before AI forces your hand?

Having seen first-hand how transformational AI can be when it’s done well, I’ve been surprised at the level of scepticism.

So when I read the new MIT report (The GenAI Divide: State of AI in Business 2025) showing that 95% of enterprise AI projects fail to deliver results, I was shocked.

On closer inspection, the report echoes what I’ve been saying for months:

The businesses that have streamlined and simplified processes are the ones reaping the benefits. The ones trying to bolt AI onto messy systems are stalling at the first hurdle.

MIT highlights three main reasons why most projects fall short:

1. Brittle workflows – AI struggles when processes are rigid or broken.
2. Lack of contextual learning – deployments don’t adapt or improve over time.
3. Misalignment with daily operations – AI is often layered onto the wrong tasks, adding little real-world value.

In short: AI isn’t failing. Our systems and processes are.

What Businesses Can Do About It

The good news? Businesses can avoid becoming part of that 95% by focusing on the foundations first. Here are three practical steps:

1. Simplify Workflows

Before introducing AI, map how work actually happens. Where are the bottlenecks, the duplications, the unnecessary steps? Simplify and standardise processes first. AI only multiplies what’s already there. If you start with chaos, you’ll just get faster chaos.

2. Structure Your Data

AI is only as good as the information it can access. If your data is scattered across systems, locked in spreadsheets, or poorly maintained, AI will struggle to deliver meaningful insights. Clean, structured, accessible data is non-negotiable.

3. Target the Right Problems

Not everything needs an AI solution. Start by identifying specific, high-value problems where AI can genuinely help:

• Automating repetitive admin
• Summarising or interpreting large volumes of text/data
• Supporting decision-making with predictive insights

When you focus AI on clearly defined pain points, ROI comes into view.

The Bottom Line

AI isn’t a magic wand. It’s a multiplier.

If your systems are simple, standardised, and aligned — AI will accelerate your progress. If they’re messy, rigid, or misaligned — AI will just amplify the cracks.

The MIT study is a warning, but it’s also an opportunity. If 95% of projects are failing, there’s a huge advantage for the 5% who get it right.

At N16, this is where we work with business leaders: building clarity in their operations so every investment — whether in people, tools, or AI — delivers real returns.

Artificial Intelligence is everywhere right now — but for many businesses, the real question isn’t whether to use AI, it’s where it will actually make a difference.

Some leaders rush to adopt tools without a plan. Others hesitate, worried about wasting money or disrupting their team or what if we spend money on something that is obsolete in a few months. Both end up frustrated, because AI without clarity usually creates more noise, not less.

That’s why we built the AI Navigation Assessment.

It’s a free online tool designed to help you:

• Pinpoint the areas of your operations where AI could drive the most value
• Identify the foundations you need in place before automating anything
• Get a clear, tailored snapshot of your AI readiness in under 10 minutes

The assessment works like a short guided diagnostic. You’ll answer a few questions about your business operations, workflows, and current systems. Then, you’ll receive a personalised report highlighting your strengths, gaps, and the opportunities to use AI more effectively.

It’s not about pushing a one-size-fits-all solution. It’s about showing you where AI will help, and where you’d be better off focusing on processes or strategy first.

Take the AI Navigation Assessment →

For decades, Lean practitioners have relied on frameworks like TIMWOOD and DOWNTIME to identify and eliminate waste in business processes. These acronyms have stood the test of time, helping teams uncover inefficiencies and continuously improve. But as AI moves from the fringe into the fabric of daily work, it’s time we asked an important question:

Are we overlooking a new kind of waste?

As someone working closely with businesses on practical ways to integrate AI, I’ve seen a consistent pattern: people are still doing work that AI could already do — faster, better, and more reliably. Think of tasks like summarising meetings, drafting standard content, rewriting SOPs, or extracting patterns from customer feedback. Not only are these repetitive tasks consuming valuable time, they’re also preventing people from focusing on what only humans can do: creative thinking, relationship building, judgment-based problem solving.

In Lean terms, this is waste.

From DOWNTIME to ADOWNTIME

The DOWNTIME acronym captures the 8 classic wastes in Lean:

• D – Defects
• O – Overproduction
• W – Waiting
• N – Non-utilised talent
• T – Transportation
• I – Inventory
• M – Motion
• E – Extra-processing

Each waste points to an inefficiency that reduces value for the customer. But none of them reflect the opportunities presented by new technologies.

That’s why I believe we need to add a 9th waste:

A – AI-ready work still done manually

Tasks that are predictable, repeatable, and ideally suited for AI — yet are still done by humans due to habit, lack of awareness, or resistance to change.

Adding “A” to the front of the acronym gives us ADOWNTIME — a small but significant update that recognises the reality of modern work. It doesn’t replace Lean principles. It builds on them.

Why This Matters

The addition of AI ready work as a formal waste creates a powerful lens for improvement:

• It invites leaders to ask: Where are we still doing things the hard way?
• It helps teams recognise what could be automated or augmented with minimal effort.
• It reinforces that continuous improvement now includes digital fluency and tool awareness.

The point isn’t to automate everything. It’s to get clear on where human effort is being misapplied.

Looking Ahead

Lean has always evolved to fit the context it’s applied in — from factories to hospitals to software teams. As we enter an era where AI tools are readily available and increasingly user-friendly, Lean needs to evolve again.

ADOWNTIME is a simple but timely update that reflects how work is changing. It helps bridge the gap between operational excellence and digital transformation.

And most importantly, it reminds us that improvement isn’t just about removing waste — it’s about being open to new ways of working.

With the rise of AI, we need to be honest about the future of consulting.

Many of the tasks that used to take days, or at least long, coffee-fuelled hours, can now be done much more quickly. Preparing for a workshop, analysing interview data, finding key themes across disparate sources, crafting a coherent story in a slide deck… These are things we still need to guide, but they no longer need to consume hours of effort.

Case Study

I recently worked on a Target Operating Model (TOM) design project. I’ve been part of large-scale TOM initiatives before – the kind that take months and cost high six or even seven figures. While some deliver great outcomes, others end up overly complex, impossible to implement, or simply out of date by the time they’re finished.

This time, using AI throughout the process, we co-designed a complete TOM, end to end, with all the layers, in around six weeks and at a fraction of the cost. The client is already preparing to implement. That’s disruption.

The Industry Needs to Catch Up

Consultancies that aren’t rethinking their delivery models or their pricing risk falling behind. Not because their work is poor, but because they’re not keeping pace with the tools that make consulting smarter, faster, and more actionable.

What this shift means for the industry… that’s a post for another day.

What This Means for You

If you’re engaging a consultancy, it’s time to revisit your assumptions. You might get the same value in half the time, or twice the value for the same spend.

At N16, we’re not running a half-price sale – but we are delivering work in half the time, with twice the impact. Got two projects in mind? Let’s talk. You might just find it feels like a 2-for-1 deal.

Most organisations don’t have a knowledge problem. They have a findability problem.

The answers to your biggest operational questions — how to handle a tricky customer scenario, why a particular process was designed a certain way, what was learned from a failed project two years ago — almost certainly exist somewhere inside your business. The challenge is that “somewhere” usually means buried in an email chain, locked in someone’s head, or saved in a SharePoint folder that nobody can navigate.

This is a pattern I see repeatedly. Businesses invest heavily in generating knowledge — through projects, workshops, reviews, and day-to-day problem solving — but invest almost nothing in making that knowledge accessible. The result? Teams solve the same problems over and over. New starters take months to become productive. And decision-makers operate without the full picture.

When AI Meets a Well-Structured Knowledge Base

This is where AI becomes genuinely transformative — not as a replacement for human expertise, but as a way to unlock it. Modern AI tools can search, summarise, and surface information across large volumes of unstructured content. But they only work well when the underlying knowledge is reasonably well structured.

Think of it this way: AI is the search engine, but your knowledge management system is the library. If the library is a mess — books out of order, no catalogue, entire sections missing — even the best search engine won’t help.

The businesses that will get the most value from AI are the ones that first invest in organising what they already know: documenting processes, capturing lessons learned, creating structured repositories for operational knowledge, and making tacit expertise explicit.

What I’ve Seen in the Field

In workshops with customers, I regularly ask teams: “Where does your team’s knowledge live?” The most common answers are:

• “In people’s heads.”
• “In a shared drive somewhere.”
• “We used to have a wiki, but nobody updates it.”

This isn’t a technology problem. It’s a culture and structure problem. And it affects businesses of every size.

I’ve worked with manufacturing plants where decades of process improvement knowledge was held by a handful of senior operators — none of it written down. When those people retire, the knowledge retires with them. I’ve seen professional services firms where every new project starts from scratch because there’s no systematic way to build on previous work. And I’ve seen growing businesses where the founder is the single point of knowledge for almost everything — a bottleneck that limits scale.

The Opportunity

The good news is that fixing this doesn’t require a massive technology investment. It starts with three things:

1. Identify your critical knowledge. What information, if lost tomorrow, would cause the biggest disruption? Start there.
2. Create simple, consistent structures. Templates, categories, naming conventions. The goal is to make it easy to contribute and easy to find.
3. Build the habit. Knowledge capture needs to be part of how work gets done, not an afterthought. Bake it into project close-outs, team handovers, and regular reviews.

Once the foundation is in place, AI tools can multiply the value. Imagine being able to ask a question like “What did we learn from the last time we onboarded a client in this sector?” and getting a useful, accurate answer in seconds. That’s not science fiction — it’s achievable today, with the right groundwork.

The Bottom Line

Your organisation already knows the answer to most of the questions it’s asking. The challenge is making that knowledge findable, usable, and enduring.

Before you invest in new AI tools, invest in understanding what you already know — and make it accessible to everyone who needs it. That’s where the real competitive advantage lies.

When a key team member hands in their notice, most leaders feel it immediately — the disruption, the gap in capability, the scramble to redistribute work. But there’s a less visible loss that often does more long-term damage: the knowledge that walks out the door with them.

Not just what they knew, but how they knew it. The context behind decisions. The shortcuts that made processes work. The relationships with suppliers or customers that were never formally documented. The lessons from failures that only existed in conversation.

This is the knowledge management challenge that most organisations underestimate — until it’s too late.

The SharePoint Problem

Most organisations have some form of knowledge repository. SharePoint, Google Drive, Confluence, a shared network folder — the technology isn’t the issue. The problem is that these tools become dumping grounds. Files are saved with inconsistent names. Folders are nested five layers deep. There’s no clear ownership, no curation, and no way to distinguish between what’s current and what’s obsolete.

The result is a system that technically contains knowledge but practically hides it. People stop looking because they know the search will take longer than just figuring it out themselves. And so the cycle continues: knowledge is created, poorly stored, and eventually lost.

Four Aspects of Knowledge Management That Matter

Effective knowledge management isn’t about choosing the right software. It’s about getting four things right:

1. Structure

Knowledge needs a home. Not just a folder, but a logical, consistent structure that people can navigate intuitively. This means clear categories, naming conventions, and a taxonomy that reflects how the business actually works — not how IT thinks it should be organised.

2. Capture

The most valuable knowledge is often the hardest to capture: tacit knowledge, context, and experience. This requires deliberate effort — structured handovers, documented lessons learned, recorded decision rationale. It needs to be built into the rhythm of work, not treated as an occasional exercise.

3. Curation

Knowledge that isn’t maintained becomes noise. Someone needs to own the quality of what’s stored: archiving outdated content, updating procedures, flagging gaps. Without curation, any knowledge system will degrade over time.

4. Access

Knowledge is only valuable if the people who need it can find it quickly. This means intuitive search, clear tagging, and — increasingly — AI-powered retrieval that can surface relevant information from across the organisation.

Why AI Makes This Urgent

The rise of AI tools hasn’t reduced the importance of knowledge management — it’s amplified it. AI systems like retrieval-augmented generation (RAG) can dramatically improve how people access organisational knowledge. But they depend entirely on the quality and structure of the underlying data.

If your knowledge base is a mess, AI will simply give you faster access to mess. If it’s well structured, AI can turn it into a genuine competitive advantage — enabling employees to get accurate, contextual answers in seconds rather than hours.

The organisations that invest in knowledge management now will be the ones best positioned to leverage AI effectively. Those that don’t will find themselves stuck in the same cycle: losing knowledge, repeating mistakes, and wondering why their AI investments aren’t delivering results.

What You Can Do Today

You don’t need a major programme to start improving. Here are three practical steps:

1. Audit your critical knowledge. Identify the top 10 things that would cause the most damage if the person who knows them left tomorrow. Start capturing those first.
2. Simplify your structure. Reduce the number of places where knowledge lives. Create clear, simple categories. Make it obvious where things should go.
3. Make it a habit. Build knowledge capture into project close-outs, onboarding, and regular team routines. If it’s not part of the process, it won’t happen.

Knowledge is one of the most valuable assets any business has. The question is whether you’re protecting it — or letting it walk out the door.

I recently sat down with ChatGPT for a 30-minute interview on a topic I’ve been thinking about for a while: New Zealand’s productivity challenge. Rather than writing a traditional article, I wanted to explore the issue in a conversational format — treating AI as an interviewer that could push me on my thinking and draw out perspectives I might not have articulated otherwise.

What follows is an edited version of that conversation. It covers the systemic, structural, and cultural factors that contribute to New Zealand’s productivity gap — and some practical ideas for what businesses and leaders can do about it.

The Interview

Q: New Zealand consistently ranks below the OECD average on productivity. What do you think is really going on?

It’s a combination of things, and none of them are simple. The obvious factors are well documented — geographic isolation, a small domestic market, relatively low levels of capital investment. But I think the bigger issue is structural. We have an economy dominated by small and medium-sized businesses, many of which operate in industries that are inherently difficult to scale.

Agriculture, tourism, construction — these are important sectors, but they don’t naturally lend themselves to the kind of productivity gains you see in tech or advanced manufacturing. And because our businesses tend to be small, they often lack the resources or expertise to invest in the systems and tools that drive productivity improvement.

Q: So is it a scale problem?

Scale is part of it, but I don’t think it’s the whole story. I’ve seen plenty of small businesses that are incredibly productive — lean, focused, and well-run. The issue is more about capability and mindset. Many NZ businesses are still operating with manual, paper-based, or heavily people-dependent processes. They haven’t adopted the tools and technologies that are standard in other countries.

And it’s not because the tools don’t exist or aren’t affordable. It’s often because there’s a cultural resistance to changing the way things are done. The “she’ll be right” mentality can work against us when it comes to operational improvement.

Q: What role does management capability play?

A huge role. This is something that doesn’t get talked about enough. In many NZ businesses, people are promoted into management roles because they’re good at the technical work, not because they have management skills. So you end up with managers who are essentially senior practitioners — still doing the work themselves rather than building systems, developing their teams, and improving processes.

Good management is a force multiplier. When managers understand how to set clear objectives, establish routines, use data to make decisions, and create an environment where people can do their best work — productivity improves significantly. But when management is ad hoc, reactive, and personality-dependent, you get inconsistency and waste.

Q: How does technology adoption fit into this picture?

Technology is a huge lever, but it’s often applied poorly. I see businesses that have invested in expensive software systems but are using maybe 20% of the functionality. Or they’ve adopted a tool but haven’t changed their underlying processes to take advantage of it. The technology ends up as a digital version of the same inefficient process.

The businesses that get the most value from technology are the ones that start by understanding their processes first, then select and implement tools that genuinely improve how work gets done. Technology should serve the process, not the other way around.

Q: What about AI specifically — is that part of the solution?

AI is absolutely part of the solution, but not in the way most people think. The narrative around AI tends to focus on dramatic transformation — replacing entire roles, reinventing industries overnight. The reality is much more practical. AI can make a meaningful difference in everyday tasks: drafting documents, analysing data, summarising meetings, automating routine communications.

For a typical NZ SME, even modest AI adoption could save hours per week per person. Multiply that across a team and you’re talking about significant productivity gains. But you have to start with the basics — having clear processes, good data, and people who are willing to work differently.

Q: What would you say to a business owner who wants to improve productivity but doesn’t know where to start?

Start with visibility. Most business owners don’t have a clear, honest picture of how their business actually operates day to day. They know the outcomes — revenue, profit, customer complaints — but they don’t always understand the processes that produce those outcomes.

Map your core processes. Understand where time is being spent. Look for the bottlenecks, the rework, the handoffs that create delays. Talk to your team — they usually know exactly where the problems are, they just haven’t been asked.

Once you have that visibility, you can make informed decisions about where to invest: whether that’s in training, technology, process redesign, or simply removing unnecessary steps.

Q: Any final thoughts on what it will take to shift the dial on NZ productivity?

It needs to be a collective effort. Government policy, industry bodies, education providers, and individual businesses all have a role to play. But if I had to pick one thing, it would be management capability. If we can lift the quality of management in NZ businesses — particularly SMEs — the productivity gains will follow.

That means investing in management development, not just technical training. It means creating cultures where continuous improvement is valued, not just tolerated. And it means being honest about where we’re falling short, rather than accepting mediocrity as the norm.

New Zealand has enormous potential. We just need to be more deliberate about how we work, not just how hard we work.

One of the most powerful things a leader can do is create an environment where their team is empowered to solve problems. Not just escalate them, not just report them — but genuinely own them and work through them systematically.

This is at the heart of continuous improvement. And yet, in many organisations, problem solving is something that happens reactively — when things go wrong, when a customer complains, when a deadline is missed. The rest of the time, teams are too busy firefighting to step back and address root causes.

The Hospital Nurses Example

I often use an example from a hospital setting to illustrate how powerful team-based problem solving can be. A ward was struggling with medication errors — not catastrophic ones, but a consistent pattern of small mistakes that created risk and consumed time to correct.

The traditional response would have been to add more checks, more sign-offs, more oversight. Instead, the ward leader brought the nursing team together and asked them to map the medication process from start to finish. Not a theoretical process map — the actual process, as it really happened on a busy shift.

What they found was illuminating. The root cause wasn’t carelessness or lack of training. It was interruptions. Nurses were being interrupted an average of six times during each medication round — by colleagues, patients, phone calls, and other tasks. Each interruption increased the probability of an error.

The team designed their own solution: a simple visual signal (a coloured vest) that indicated when a nurse was on a medication round and should not be interrupted unless it was urgent. The result was a significant reduction in errors, achieved without adding any new processes, technology, or costs.

The key insight isn’t the solution itself — it’s that the people closest to the work were the ones who identified the problem and designed the fix. That’s continuous improvement in action.

What Continuous Improvement Really Means

Continuous improvement isn’t a programme or a project. It’s a way of working. It means building the expectation — and the capability — that every team, at every level, is constantly looking for ways to do things better.

This requires a few things to be in place:

• A common language for problem solving. Whether you use A3 thinking, PDCA, 5 Whys, or something simpler — the team needs a shared approach to identifying problems, understanding root causes, and testing solutions.
• Time and permission. If people are running flat out all day, they won’t have the headspace to improve. Leaders need to create time for improvement activities and signal that it’s valued, not a distraction from “real work.”
• Visibility. Problems need to be visible — not hidden. This means creating an environment where it’s safe to surface issues without fear of blame. Visual management tools like huddle boards, Kanban boards, and performance dashboards all help.
• Follow-through. Nothing kills a continuous improvement culture faster than asking people for ideas and then doing nothing with them. When a team identifies a problem and proposes a solution, there needs to be a clear process for testing, implementing, and reviewing it.

The Role of Leadership

Leaders set the tone for continuous improvement. If a manager’s default response to a problem is to find someone to blame, the team will quickly learn to hide problems rather than surface them. If a leader consistently bypasses the team to impose their own solutions, people will stop trying to contribute.

The most effective leaders in a continuous improvement culture do a few things consistently:

• They ask questions rather than give answers. “What do you think is causing this?” is more powerful than “Here’s what you need to do.”
• They coach rather than direct. They help their team develop problem-solving skills rather than solving every problem themselves.
• They celebrate the process, not just the outcome. Recognising the effort of investigating a problem thoroughly is as important as celebrating the result.
• They make improvement visible. They share wins, track progress, and create forums where teams can learn from each other.

Bringing It All Together

Problem solving and continuous improvement aren’t separate from the day job — they are the day job. The businesses that build this into their DNA don’t just get incremental efficiency gains. They build more engaged teams, better customer experiences, and organisations that can adapt to change rather than being overwhelmed by it.

If you’re not sure where to start, pick a problem that your team knows about but hasn’t been able to fix. Bring them together, map the process, find the root cause, and let them design the solution. You might be surprised at what they come up with.

This is the second in a series on management systems. In the first article, I introduced the concept of a management system and why it matters. In this piece, I want to get practical — starting with how to set objectives and establish the routines that turn good intentions into consistent execution.

Setting Objectives That Actually Work

Most businesses have goals. Fewer have objectives that are genuinely useful — the kind that focus effort, drive decisions, and create accountability. The difference matters.

Good objectives share a few characteristics:

• They’re specific enough to act on. “Grow revenue” is a goal. “Increase recurring revenue from existing clients by 15% in the next 12 months” is an objective. The second version tells people what to focus on and how to measure success.
• They’re limited in number. If everything is a priority, nothing is. Most teams can realistically focus on 3–5 key objectives at any one time. Beyond that, you get dilution and confusion.
• They connect to the day-to-day. An objective that sits in a strategy document but doesn’t translate into weekly actions is just aspirational thinking. People need to see how their work contributes to the bigger picture.

The simplest framework I’ve found for setting objectives is to ask three questions:

1. What are we trying to achieve? (The outcome)
2. How will we know we’re on track? (The measure)
3. What do we need to do differently? (The actions)

If you can answer those clearly for each objective, you’re in good shape.

Establishing Routines

Objectives without routines are just wishful thinking. The management system needs a rhythm — a set of regular activities that keep the organisation focused, aligned, and accountable.

Here are the core routines that make a management system work:

Daily Huddles

Short, focused team stand-ups (10–15 minutes) that cover: what’s the priority today, what’s blocking us, and does anyone need help? These aren’t status meetings — they’re alignment and problem-solving sessions. Done well, they prevent small issues from becoming big problems.

Weekly Reviews

A slightly longer session (30–60 minutes) to review progress against objectives, discuss key metrics, and address any emerging issues. This is where the team checks whether they’re on track and adjusts their plans accordingly.

Monthly Performance Reviews

A deeper dive into how the business or team is performing against its objectives. This should include a review of leading and lagging indicators, root cause analysis on any areas that are off track, and decisions about where to focus improvement efforts.

Quarterly Strategic Reviews

A step back to look at the bigger picture. Are the objectives still right? Has the context changed? What needs to shift for the next quarter? This is where the management system connects back to strategy.

Making Priorities and Measures Work

One of the biggest pitfalls I see is businesses that set objectives and then measure the wrong things — or measure too many things. The key is to distinguish between:

• Leading indicators — measures that predict future performance (e.g., number of proposals sent, training hours completed, process adherence rates).
• Lagging indicators — measures that confirm past performance (e.g., revenue, profit, customer satisfaction scores).

Most businesses focus on lagging indicators because they’re easy to measure. But by the time you see them, it’s too late to change the outcome. The real power is in tracking leading indicators — the activities and behaviours that drive results.

Dashboards: Keep It Simple

A good dashboard tells you three things at a glance:

1. Are we on track? (Performance against target)
2. What’s the trend? (Getting better, worse, or flat)
3. What needs attention? (Exceptions and red flags)

The temptation is to cram every possible metric onto a dashboard. Resist that. A dashboard with 30 metrics is not a dashboard — it’s a spreadsheet. Focus on the critical few measures that tell you whether your objectives are on track, and drill deeper only when something needs investigation.

The best management systems I’ve seen use a single page — physical or digital — that the whole team can see and understand. If it takes more than a few seconds to read the dashboard, it’s too complicated.

What Comes Next

Setting objectives and establishing routines are the foundation of a management system. In the next article in this series, I’ll look at how to build accountability, handle escalation, and create a culture where the system is maintained and improved over time.

As a business grows, the things that used to be simple become complex. Decisions that were once made by one person now involve three. Processes that worked with five staff break down at fifteen. Communication that happened naturally in a small team requires deliberate effort in a larger one.

This is the point where many business owners feel overwhelmed. Not because they lack ambition or capability, but because the business has outgrown its informal systems. What got you here won’t get you there.

The antidote is a management system.

The Growing Complexity Challenge

In the early stages of a business, the founder or a small leadership team holds everything together. They know every customer, every process, every problem. Knowledge lives in their heads, decisions are made on the fly, and things work because the people involved are close enough to the work to course-correct in real time.

But as the business scales, this model breaks. The symptoms are familiar:

1. Things fall through the cracks. Tasks that everyone assumed someone else was handling don’t get done.
2. Inconsistency creeps in. Different people do the same thing differently, leading to variable quality and customer experience.
3. Communication breaks down. Teams or departments stop sharing information effectively, creating silos.
4. The founder becomes the bottleneck. Every decision, every escalation, every question comes back to one person.
5. Firefighting becomes the norm. The team spends so much time reacting to problems that there’s no time left for improvement or strategic work.
6. People feel disengaged. Without clear objectives and feedback, team members lose sight of how their work contributes to the bigger picture.
7. Performance is invisible. Nobody really knows whether the business is on track until the end of the month — or the end of the quarter.
8. Growth stalls. The business hits a ceiling because its systems can’t support further expansion.

If any of these feel familiar, you don’t have a people problem. You have a systems problem.

Why You Need a Management System

A management system is the set of structures, routines, and tools that help a business operate consistently, make good decisions, and improve over time. It’s not bureaucracy. It’s not a rigid framework imposed from above. Done well, it’s the thing that gives everyone — from the leadership team to the front line — clarity on what matters, how things should work, and where to focus their energy.

Think of it as the operating system for your business. Just as a computer needs an operating system to coordinate its hardware and software, a business needs a management system to coordinate its people, processes, and priorities.

The Six Building Blocks

Every effective management system is built on six core components. These aren’t optional extras — they’re the foundations that make everything else work.

1. Clear Objectives

The business needs a small number of clearly defined objectives that everyone understands. These should cascade from the overall strategy down to team and individual level, so people can see how their daily work connects to the bigger picture.

2. Defined Processes

Core processes need to be documented, understood, and followed consistently. This doesn’t mean rigid procedures for everything — it means being clear about how the important things should work, so quality and efficiency are built in rather than relying on individual heroics.

3. Meaningful Measures

You need a small set of metrics that tell you whether the business is on track. These should include both leading indicators (activities that predict outcomes) and lagging indicators (results). The key is choosing measures that drive the right behaviour, not just the ones that are easy to track.

4. Regular Routines

The management system needs a rhythm: daily huddles, weekly reviews, monthly deep dives, quarterly strategic check-ins. These routines create the cadence for communication, decision-making, and accountability. Without them, the system is just a set of documents that nobody looks at.

5. Visual Management

Making performance visible — through dashboards, boards, or simple displays — keeps everyone aligned and creates a shared understanding of how things are going. If people can’t see the current state, they can’t improve it.

6. Continuous Improvement

A management system isn’t static. It needs built-in mechanisms for identifying problems, testing solutions, and making things better over time. This is where problem solving, feedback loops, and a culture of learning come together.

Getting Started

You don’t need to build the perfect management system overnight. Start with the area that’s causing the most pain. If decisions are bottlenecked with the founder, start with clearer objectives and delegation frameworks. If quality is inconsistent, start with process documentation. If the team feels disconnected, start with regular routines.

The important thing is to start — and to treat the management system itself as something that will evolve and improve over time. Because that’s exactly what it should do.

In the next article in this series, I’ll go deeper into the first two building blocks: setting objectives and establishing routines.