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At Dovetail Digital, we believe in bringing ideas, people, processes, and technology together.

14/05/2026

Most AI assessments give you a number between one and five.

Our new AI Impact Report gives you six things.

01 — Your workforce at a glance. Baseline view of team size, structure, and current AI exposure.
02 — Where you stand today. Your maturity level mapped against practical benchmarks.
03 — The capacity gap. A calculation of time and output being lost to inconsistent AI use right now.
04 — Training uplift. What structured training could realistically recover, in hours and quality.
05 — What your team could improve or build. Specific capability areas from your workforce inputs.
06 — Your recommended next step. One clear, evidence-based recommendation, not a generic strategy deck.

Built from your inputs. Delivered within 48 hours. No technical knowledge required.

[Link in first comment]

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05/05/2026

There's a line that's stayed with us from John Munsell at INGRAIN.

"This reframes the conversation in the right way. Not 'are we using AI?' but 'are we building the capability to get results from it?"

That question is the whole argument of the final blog in our AI Capability Series.

Most AI rollouts treat humans as reviewers, someone who approves or rejects what AI produces. The businesses getting durable results treat humans differently. Not as a checkpoint at the end of the process but as the person setting the direction, adjusting for conditions, and deciding where the whole thing goes next.

A reviewer can be bypassed. A captain can't.

AI can do a great deal. It handles the drafting, the structuring, the first pass at analysis. But without someone at the helm, someone who understands the work, owns the output, and knows when to override, what you get is faster drift, not faster progress.

Our latest blog unpacks what staying at the helm actually looks like in a service business.

Blog link in the first comment below.

04/05/2026

If you've seen one AI readiness quiz, you've probably seen enough.

A handful of questions. A score out of five. A recommendation to "improve your digital maturity" that could apply to any business in any industry anywhere.

We've seen them too. And we built something different specifically because of that experience.

The AI Impact Report isn't a quiz and it doesn't produce a score. It's a custom analysis built from your workforce inputs: team size, structure, salary level, current AI maturity. From those inputs it calculates your actual capacity gap: the hours and output quality being lost to inconsistent AI use right now, and what structured training could realistically recover.

Six sections. One specific recommended next step. Delivered within 48 hours.

If you've been waiting for something that gives you a business case rather than a rating, this is it.

Link in first comment below.

28/04/2026

We keep seeing the same thing in the businesses we work with.

Someone has been using an AI tool for months. Nobody approved it. Nobody knows what's going into it client data, internal documents, commercially sensitive context. And nobody has thought about what happens when that output ends up in front of a client or a regulator.

This isn't a failure of intention. It's a failure of structure.

Shadow AI emerges when the organisation hasn't created a clear, approved path for people who want to use AI well. The absence of a policy isn't neutral. It pushes experimentation underground where it's harder to catch, harder to correct, and impossible to build on.

The fix isn't a crackdown. It's a framework. Something clear enough that people know what's safe, simple enough that they'll actually use it, and specific enough that it holds up when something goes wrong.
Our latest blog covers what Shadow AI looks like in practice, why it's more common than most leaders realise, and what a sensible first step looks like.

Blog link in the first comment below.

27/04/2026

Something comes up in almost every conversation we have with leaders about AI.

Not "should we be using it?" That question is mostly settled.

The one that keeps surfacing is quieter: "How do we know if what we're doing is actually working?"

Usage is up. Tools have been added. Some people are clearly getting value. But confidence in the overall picture is low. Nobody's quite sure whether the organisation is building something durable or just staying busy.

That uncertainty isn't a failure of ambition. It's usually a signal that the foundations haven't been made explicit yet. What AI is for in this business. What good output looks like. Where human judgement stays in the process and where it steps back.

When those things are shared rather than assumed, the answer to "is it working?" gets a lot clearer.

What does that conversation look like in your organisation right now?

24/04/2026

Today, we pause to honour the courage, sacrifice, and service of our ANZACs past and present.

Their legacy reminds us that leadership isn't about noise. It's about integrity, discipline, and heart.

23/04/2026

Inconsistent AI use has a cost. Most organisations just haven't measured it yet.

It shows up as rework. Outputs that vary depending on who produced them. Decisions made twice because the first version wasn't trusted. Briefs that take longer than they should because the process lives in someone's head, not in a shared workflow.

Individually, none of it looks serious. Collectively, it's hours. And in a service business where time is the product, those hours have a number attached to them.

The AI Impact Report calculates that number. It takes your team size, structure, salary level, and current AI maturity, and returns a specific view of what inconsistent use is costing you right now, and what structured training could realistically recover.

Not a benchmark figure. Not an industry average. Your capacity gap, built from your workforce data.

For more info, link in the first comment below.

21/04/2026

What actually separates the businesses getting results from AI and the 90% that aren't?

It's not the model. It's not the budget. It's not even the prompts.

It's structured training.

A 2026 NBER study of approximately 6,000 executives found that each percentage point of workforce spend on AI training delivers 5.9 percentage points of productivity gain. The multiplier is real. But it only activates when training is structured, shared, and tied to how the work actually runs, not delivered as a one-off session and left to individuals to figure out.

The businesses seeing nothing from AI aren't doing less. They're doing it without the scaffolding that makes progress compound.

Our latest blog "The Training Multiplier" covers what that scaffolding looks like and why the sequence matters as much as the investment.

Read the full blog in the comment below

16/04/2026

We've spent a good part of the last few months building something we think fills a real gap.

Not another quiz. Not a generic scorecard. A personalised report that takes your workforce inputs: team size, structure, current AI maturity, salary level, and returns a specific view of what inconsistent AI use is costing your organisation right now, and what structured training could realistically recover.

We called it the AI Impact Report.

Most businesses we work with aren't starting from zero with AI. They're somewhere in the middle, some people using it well, others not at all, nobody quite sure what the whole picture adds up to. That gap has a cost attached to it. The Report makes that cost visible, in your numbers, not industry benchmarks.

It takes 5 to 10 minutes to complete. The report is personalised and delivered within 24-48 hours.

Link in first comment below.

15/04/2026

The thing most AI rollouts miss isn't a tool.

It's shared understanding of what good looks like.

One person on a team gets excellent results from AI. Another tries the same approach and produces something that needs to be redone entirely. A third isn't using it at all because they're not sure what's safe.

That variation isn't a capability problem. It's a fluency problem and it doesn't fix itself with more tools or more time.

The organisations making steady progress have done something specific: they've made explicit what was previously assumed. What AI is used for. What the output needs to look like. Where human judgement stays in the process.

When that's shared across a team rather than held by one or two people, AI stops being fragile. It starts being reliable.

Blog 1 covers how to build that foundation before scaling anything.

Read the full blog link below.

14/04/2026

Our proposal process used to take three to four hours from brief to first draft.

It now takes under forty minutes. The quality doesn't vary by who's doing it that week. And the business lead who reviews it is focused on the offer and the framing not rebuilding the shell from scratch.

That shift didn't come from finding a better AI tool. It came from stopping to understand what the work actually involved before we touched anything.

Most AI implementations skip that step. They identify tasks, assign tools, and call the pilot a success when output arrives faster. What they get is faster inconsistency results that are hard to repeat, harder to defend, and nearly impossible to hand off.

Starting with the work means asking a different question. Not "what can we automate?" but "what does this output actually need to be, and who needs to trust it?"

The sequence matters more than the tools. Blog 1 unpacks why.

Blog link in first comment below.

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