“I sat in yet another steering committee where everyone looked at the same dashboard and still argued for 45 minutes. Same charts. Same metrics. Same trend lines. Completely different interpretations. In that moment it struck me again: the bottleneck isn’t the data anymore. It’s the decision.”
We’ve spent years investing in BI platforms, clean data models, even AI pilots. Yet for many organizations, the reality is simple: If your dashboards don’t change decisions, they’re expensive wallpaper.
It’s not that analytics is obsolete. Far from it. It’s that analytics is just one layer inside a much broader decision framework, and most organisations never get past that first layer.
Beyond analytics: you don’t have a data problem, you have a decision problem
Many C-level leaders I speak to are in the same position:
They have dashboards.
They have data teams.
They’re experimenting with AI.
And yet day-to-day business still runs on:
Habit (“this is how we’ve always done it”)
Emotion (“I’ve got a bad feeling about this”)
Hierarchy (“we’ll go with what the most senior person thinks”)
People are stuck in business-as-usual mode and battle to elevate themselves to the point where they can confidently make decisions based on fact rather than noise.
The missing piece isn’t another layer of visualisation.
The missing piece is engineering the decision process itself: being explicit about who decides what, using which information, under what constraints, and with what expected outcome.
The shift from analytics to Decision Intelligence
What we really mean by “contextual recommendations”. “Contextual recommendations” can easily sound like marketing fluff, so let’s make it concrete. A contextual recommendation is not a red/green dot on a dashboard or a generic “sales are down, do better” alert.
It’s a suggestion that respects four things:
Who is deciding
A CFO, a regional manager and a call centre agent don’t need the same recommendation. Their authority, incentives and responsibilities are different.
What decision they’re making right now
Approve or decline? Increase budget or hold the line? Prioritise one customer over another? The recommendation must be specific to the actual choice in front of them.
Which constraints and risk appetite apply
Budget caps, headcount, regulatory limits, service levels, brand risk… In “growth mode” the right recommendation might be to lean in; in “cost containment” it might be to hold back, even on the same data.
Over what time horizon
A tactical “this week” decision looks very different to a strategic “this year” decision. Firefighting decisions and steering decisions are not the same thing.
In simple terms, a contextual recommendation says: Given who you are, the decision you’re facing, the constraints you’re under and how far ahead you’re looking, here are the top actions most likely to improve your outcome, and the trade-offs you’re making if you act or don’t act.
That’s where AI/ML actually becomes useful: not as a lab experiment, but as the engine that can scan patterns, predict likely outcomes and surface a small set of high-quality options, in context.
The Keyrus Decision Loop
So how do you move from dashboards to true decision support? At Keyrus, we’ve found a simple loop helpful, not as a grand theory, but as a practical way to structure work. We call it the Decision Loop:
1. Define the decision Be painfully clear:
What exactly is the decision?
Who owns it?
How often is it made?
What does “good” look like – revenue uplift, cost saving, risk reduction, customer experience?
If you can’t define the decision, you can’t improve it.
2. Instrument the context (data + constraints) Identify what really matters:
Which data points consistently influence this decision?
What thresholds or rules already exist (formal or informal)?
What constraints and policies must never be violated?
This is where your existing analytics, data engineering and platforms become inputs into a decision, not outcomes in themselves.
3. Generate recommendations (rules, ML, “what-if”) Now we can bring intelligence to the table
Use AI/ML models to predict outcomes (“if we do X, Y is likely to happen”)
Encode business rules and guardrails (“never recommend below this margin”, “always escalate above this risk score”)
Provide “what-if” scenarios so decision-makers can see the impact of different choices.
The goal isn’t a black box that replaces humans. It’s a smart co-pilot that narrows the field to a handful of good options, backed by evidence.
4. Learn from outcomes This is the step most organisations skip.
Did people follow the recommendations?
What happened when they did? What happened when they didn’t?
What did we learn about our assumptions, our models and our rules?
Feeding that back closes the loop. Over time, recommendations get sharper, and decisions become more consistent and less emotional.
None of this requires you to throw away your existing analytics. It simply insists that analytics serve a very clear purpose: better decisions, made faster, with greater confidence.
Are you ready for Decision Intelligence?
You don’t need a formal assessment to know whether this resonates. Start with these three questions:
Can you name your top five recurring decisions that materially affect revenue, cost or risk?
Not vaguely (“invest in growth”), but specifically (“approve or decline credit extensions above R500k”, “prioritise which region gets stock when supply is constrained”).
Do you know what information and rules currently drive those decisions?
Are people using clear, shared criteria, or a mix of experience, opinion, and last month’s crisis?
Do you have any feedback loop from decision to outcome?
Three or six months later, can you see whether those decisions actually made things better or worse?
If you’re struggling to answer these, your problem isn’t a lack of dashboards. It’s a lack of designed decisions.
Where Keyrus fits in
This is where we’ve been spending a lot of time with clients.
Because Keyrus is tool agnostic, we can work with the platforms you already have, while still bringing deep cloud and Microsoft expertise where that’s relevant. More importantly, we can span the full chain:
From strategy (which decisions matter most?)
To data architecture and engineering (how do we instrument the context?)
To analytics and AI (how do we generate useful recommendations?)
To operationalisation (how do we get this in front of real decision-makers, inside real processes?)
Practically, that often looks like:
A focused Decision Intelligence Assessment on two or three critical decisions.
A pilot recommendation engine around one high-impact use case, proving value quickly.
A Decision Intelligence roadmap tying your data, analytics and AI investments directly to how your organisation makes decisions.
Let’s talk about your decisions, not just your data
If you’d like to explore how to move beyond “death by dashboard” and bring accurate, contextual, fact-based recommendations closer to your decision-makers, let’s talk. Contact us at sales@keyrus.co.za.