Your HR Tech stack only amplifies what your culture allows

Nov 13, 2025
4 min read

Summary: Your HR Tech must amplify an adaptive culture. Fix hierarchy by decentralising decisions, increasing transparency, and granting autonomy before investing in software to avoid automating dysfunction.

I often get asked: “Barry, What tech stack should we be using now in this new, emerging world of AI-first organisations?”

Wrong question.

Whilst I’ll tackle it in this article, tech should always be an (important) afterthought. If your structure, organisational system and therefore culture can’t handle complexity, no software will save you.

Your Real Starting Point

Before you sign another SaaS contract, ensure you understand what makes organisations adaptive in the face of today’s complexity (i.e., an acceptance that we embrace the unpredictable in people and the external market).

And with that challenge cracked, the associated design principles for success should reflect the following:

Decentralisation. Decisions made where the information (and the client) resides.
Transparency. Context flows freely, no information hoarding, especially at the top.
Autonomy. Teams own outcomes, not just tasks. Bin delegation.
Value creation at the edge. The people closest to customers shape the work.

These aren’t HR buzzwords. They’re essential structural requirements.

And because AI doesn’t respect hierarchy, it works horizontally, iteratively, autonomously.

Just like the people you claim you want to hire.

The Structural Problem: We’re addicted to the old and obsolete.

McKinsey’s recent research on operating models proved what many of us already suspected: 89% of organisations still use traditional hierarchical structures.

Read it here. Matrix management. Functional siloes. Command-and-control with some prettier language.

Meanwhile, those organisations using emergent structures (still decades late!) such as product platforms, enterprise agile, or decentralised networks, significantly outperform these traditional models in readiness to handle AI, digitisation, and innovation.

The data is clear: structure determines whether your tech investment creates value or just exposes how broken your current workflows are.

You can’t automate dysfunction. You can only make it faster.

The Categories that matter.

So, what about this Tech stack Barry?

I’ve been around long enough to see the evolution of our HR Technology. From automating the filing cabinet; the HRIS, LMS and PMS enterprise cookie cutter, the arrival of SaaS point solutions to a landscape of eye-watering fragmentation you see below.

Bring on the wave of consolidation.

“Choose carefully” is my message.

Move beyond the single source of data thinking, or the ‘next shiny toy’ and build a stack that amplifies consistently what the organisational needs to be successful.

To help you get insight & feedback across the lifecycle (in a decentralised and adaptive business model), you’ll need Worktech that should deliver on the following features:

Devolution of talent attraction, hiring and onboarding. Let candidates and teams self-select, ensure alignment with values & role clarity. Capture feedback early about candidate experience, values fit. Early pulse surveys, mentors; the ability to customise onboarding per group; visibility into first days’ feedback.
Outcome-driven performance platforms with continuous feedback loops. Rather than top-down appraisal, more frequent, peer and manager feedback; ability for self-assessment; strong clarity on goals and outcomes (not inputs).
Culture, engagement, and recognition tools. Pull feedback from various sources. Make culture visible and ensure remote/hybrid doesn’t mean disconnected. Pulse surveys; sentiment analysis; recognition tools; well-being / psychological safety tools; anonymous channels.
Real-time feedback, sentiment; transparency. Tools that make sentiment visible so teams can act, not wait for HR to interpret.
Democratised and decentralised learning ecosystems. Peer-led skill growth through adaptive pathways, not top-down training. Differing learning needs; emergent roles; people adapting. Transparent paths & growth focused.
Analytics & tools to surface emergent patterns, not top-down metrics. Enable local intelligence and shared learning through knowledge bases. Pulse feedback loops spot problems before they escalate. Performance data shows where goals misalign across teams. Learning analytics reveal skill gaps in real time. Recognition patterns surface who’s driving collaboration. Open repositories that turn local experiments into reusable playbooks. The key? Information flows sideways, not just upward. Teams see the same data leadership sees and everyone adjusts together.
Distributed decision making & accountability . What separates high-performing SMEs from the rest: they use data to learn, not to control.

That’s transparency. That’s trust. That’s how you scale without centralising.

Rethink and unlearn your old habits and see that this isn’t a HR reporting stack. It’s the digital nervous system of a learning organisation.

Move Beyond AI Playtime: The Dayforce Example

So, how might this look in practice?

Dayforce recently shared how they scaled AI internally, and their approach mirrors exactly what decentralised organisations do well.

They didn’t run a top-down rollout. They followed a three-wave model: democratise access by putting simple AI assistants in everyone’s hands early; integrate locally by embedding AI inside each function’s natural workflow; transform collectively by using insights from local experiments to reshape work everywhere.

The lesson? You scale AI the same way you scale decentralisation: through distributed experimentation, not central planning.

Here’s how SMEs should do it:

Appoint local stewards, not project managers. Each team has someone who helps others experiment and shares learning openly.
Run peer pilots. Two or three teams start small, document what works, invite others to adapt.
Curate knowledge. Don’t hoard it. Use an open platform as your shared brain. Visible to all, updated continuously.
Lead from the front. If you’re the CEO, use the same tools as your teams. Nothing kills credibility faster than “AI for them, not for us.”
Keep it human. Data without empathy erodes trust. Feedback loops must lead to conversations, safety and not just dashboards.

The Hard Truth

Most AI initiatives fail not because the software isn’t good enough, but because the system isn’t ready for it.

Before you sign another software contract, ask yourself:

Does our structure allow this technology to make us smarter, or just more visible?

Because AI isn’t here to replace you. It’s here to reveal how well your organisation works.

And that, my friends, is the part no software can do for you.

What next?

Want to understand how your current culture stands up to scrutiny with a free 8-minute cultural readiness self-assessment. Access it here and find out.

If I might be able to support your SME build out the right cultural infrastructure. Book a free consultation here.

Until next time.