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For the past 30 years, Can!do has improved the performance of businesses by driving user adoption.

For the past 30 years, Can!do has worked with some of South Africa’s leading organisations to improve the performance of their businesses by driving user adoption of new business processes and systems.

28/10/2026

A new AI feature may be technically ready to activate, but that does not always mean the business is ready to use it.

Before switching it on, check these five things:

1️⃣ Is the process clear?
If teams already handle the same task differently, decide what the expected process should be before adding AI to it.

2️⃣ Is the data reliable enough?
Consider whether the information the feature relies on is good enough for the task you are asking it to perform.

3️⃣ Who owns the feature?
Make sure it is clear who investigates problems, makes changes or steps in when something goes wrong.

4️⃣ Where is human judgement still needed?
Decide which outputs can move forward and which still need someone to review, question or approve them.

5️⃣ Can users flag a problem easily?
Give people a clear way to report an output that looks wrong so issues can be picked up and addressed.

These checks do not need to make the rollout complicated. They simply help establish how the feature should work in practice before people start building habits around it.

21/10/2026

ERP release notes can contain dozens of changes, many of which will have little or no effect on the people using the system every day. The difficulty is spotting the few that will.

Without a simple review process, users often become aware of an update at the least convenient moment, when a screen looks different, a familiar step has moved, or a workflow no longer behaves as expected.

A quarterly check can prevent much of that confusion. Someone reviews the release from the user's point of view, identifies which roles are likely to notice a change, and decides what support is actually needed. Sometimes a short message is enough, while in other cases, an updated job aid or a quick demonstration will save people time later.

It does not need to become another project. The value comes from making the review part of the normal rhythm of managing the system, rather than waiting for questions and support tickets to reveal what changed.

14/10/2026

There is usually a lot of focus on ERP training in the lead-up to go-live, when materials are current, sessions are scheduled and the project team is still close at hand to answer questions and help people find their feet.

The experience can look quite different six months later when a new employee joins. By then, they may be sent an old training link, paired with a colleague or simply shown the quickest way to get the job done. That colleague can be an invaluable source of practical knowledge, but informal handovers can also carry across the shortcuts and workarounds that have developed since the system went live.

This is why system training should not end with the rollout. Keeping a simple onboarding path in place gives every new starter the same foundation, while still allowing them to learn from the people around them. The core guidance needs to stay current, someone needs to take responsibility for it, and system learning should become part of normal employee onboarding.

The project team will eventually move on, but the need to help people use the system well does not disappear with it.

08/10/2026

AI is already making its way into ERP systems, but switching on a new feature is only part of the rollout.

When a system starts suggesting GL codes, flagging anomalies or generating forecasts, employees need more than instructions on where to click. They need to know when to trust the recommendation, when to question it and what to do when something doesn’t look right.

That changes the training conversation considerably.

Our latest article looks at what AI in ERP means for employees, why human judgement becomes even more important, and what organisations should consider before new AI functionality goes live.

Read the full article here: cando.co.za/ai-has-arrived-in-your-erp-are-your-people-ready/

07/10/2026

Most system training follows a familiar pattern: people are shown the screen, taken through the fields and taught the steps they need to complete a task correctly. For a process with a predictable sequence, that approach makes sense.

AI-assisted work changes the nature of the task. The system may flag an anomaly, recommend a vendor or pre-fill information, but the person using it still needs to decide whether that suggestion makes sense in the situation in front of them. Knowing where to click is no longer enough. People also need enough understanding of the process to recognise when an answer looks right, when something feels off and when they should stop and check.

That changes what good training needs to prepare people for. Instead of only practising the correct process, users should have opportunities to work through AI-generated suggestions that are accurate, clearly wrong and, perhaps most importantly, plausible but incorrect. Those are the situations that require real judgement.

People also need to know what happens when they are unsure. A clear route for questioning or escalating an AI-generated answer is just as important as knowing how to use the feature itself.

As AI becomes part of everyday systems and workflows, training needs to develop judgement alongside system knowledge. The technology can make a recommendation, but the person using it still needs to know what to do with that recommendation.

30/09/2026

Change fatigue gets treated as a volume problem: too much change, with not enough recovery time between initiatives.

That diagnosis is often wrong, and it can lead to fixes that don't address what's actually happening.

Picture two organisations, each running five significant initiatives this year. In the first, someone maps which teams are already stretched, spaces the rollouts so teams aren't facing several major changes at once, and waits for adoption before adding the next one.

In the second, five different sponsors secure budgets on their own timelines. Nobody looks across the organisation to see that Finance is already in the middle of an ERP migration when HR rolls out a new performance system.

It's the same number of initiatives, but a completely different experience for the people in the middle of them.

The difference is whether anyone is looking at the combined load. When the same people sit on multiple steering committees and the same teams are expected to absorb several changes at once, that's a coordination problem.

It also changes how leaders respond. If the exhaustion is treated as a communication problem, the answer is often more engagement sessions, more updates and more change champion activity. None of that reduces what the team is carrying. In fact, it can add to it.

When someone says they can't take on another change right now, that may be an accurate description of their workload, not resistance that needs to be overcome.

The real fix is, in most cases, a sequencing decision. Someone needs to have a view across the organisation and understand which teams are already carrying a heavy change load. And “settled” needs to mean that the previous change has actually been adopted, not simply that its go-live date has passed.

Sometimes that means telling a project sponsor that their rollout needs to wait.

It's a harder conversation than sending another communication, but it may also be the conversation that makes the difference.

23/09/2026

Global ERP rollouts are complex for all the obvious reasons: data migration, integrations, different regulatory requirements and the challenge of coordinating a single timeline across multiple countries.

But there is another source of complexity that is easier to overlook. A change approach designed in one cultural context does not automatically translate to another.

The assumptions behind the approach can be surprisingly deep. They affect how much explanation people expect before they are comfortable with a change, whether a group training session encourages discussion or makes people reluctant to speak up, and how comfortable people feel raising concerns or asking questions, particularly where hierarchy plays a strong role.

These aren't fixed rules for every country, and culture is never as simple as putting people into national categories. But ignoring these differences can leave you with a rollout that works well in one region and struggles in another.

That doesn't mean creating a completely different change strategy for every country. The things that need to stay consistent, such as the system, core processes, timelines and definition of success, should remain consistent.

What needs to flex is the way the change reaches people: the tone and format of communication, the training approach, the language and the examples used.

A global standard is only useful if people in different regions can actually engage with it. Adapt the delivery, not the outcome.

16/09/2026

AI features embedded in ERP systems are becoming standard. The technology is there, the feature is switched on, and the product demo looks impressive.

Six months later, the usage data can tell a very different story.

The problem isn't necessarily the technology. Usually, people simply haven't been given a clear reason to use it, they haven't built enough trust in the output, or they haven't been trained for the kind of work AI-assisted systems now require.

🚩 No clear use case
People know the feature exists, but not necessarily when to use it, what a good result looks like, or how to check whether the output is right. Without that clarity, most people stick with the process they already know.

🚩 No trust
On tasks where mistakes carry real consequences, people tend to rely on methods they know they can verify. Trust in AI doesn't come from one training session. It develops through repeated, lower-risk use where people can see what the system gets right, where it falls short, and when they need to question the result.

🚩 Training hasn't caught up
AI-assisted work requires people to assess and challenge system-generated suggestions rather than simply follow a set process. That's a different skill, and training needs to reflect that change.

Switching on an AI feature doesn't make it adopted. The technology provides the capability, but people still need a reason to use it, the confidence in the result and the skills to use it well.

10/09/2026

The same ERP rollout produced strong adoption in one region and quiet non-compliance in another. Same system, go-live date, and training deck.

The gap wasn't the technology, but rather the assumptions built into how the training was delivered: how much context people needed before they'd act, whether a group session felt collaborative or like a performance in front of colleagues, and how directly a concern could be raised and to whom.

Some teams want the full case before they'll act on a change, asking why this system, why this process, and what alternatives were ruled out. Others will move readily on a clear instruction from someone they trust, while a long justification can come across as a lack of confidence in the decision.

None of this means rebuilding the rollout five times. The process, configuration and go-live timelines can stay fixed. What should change by design is the tone, format, language and training delivery for each region the rollout reaches.

We break down what to keep standard and what to adapt in our latest article: cando.co.za/how-to-train-a-global-workforce-across-different-cultures/

09/09/2026

Super users are one of the most valuable investments in an ERP or change project. They learn the system deeply, support colleagues through training, answer the questions that don't make it to the service desk, and provide a practical link between the project team and the people doing the work.

The problem is what happens to that network when the project ends.

In most cases, the project board closes, the super user role is formally ended, and everyone's time goes back to business-as-usual. A network that took months to build is effectively disbanded just as the organisation enters a different phase of change.

The need for super users doesn't disappear at go-live. New employees will join who weren't part of the project, teams will find their own workarounds when something isn't working as expected, and system updates will change workflows that people have only just become comfortable with. During hypercare, those issues might have been picked up quickly. Once the project team has moved on, the same questions can become quiet, persistent frustrations.

A continuing super user network gives the organisation somewhere for those issues to go. It can catch workarounds early, help new starters find their feet and give teams a familiar point of contact when the next system update lands.

It doesn't need to become a major ongoing programme. A named structure, a communication channel and regular check-ins can be enough to keep the network active.

The investment in building your super user network has already been made. Don't let the return on that investment disappear when the project does.

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