The future of on-demand tech skills in a world shaped by data, insights, analytics and AI
We are entering an era where the speed of technological change outpaces the speed of traditional hiring.
Data platforms evolve continuously. Insights and analytics are becoming central to how organisations understand demand, performance and risk. AI, including Agentic AI, is moving rapidly from experimentation into execution. At the same time, skills are becoming more specialised, more time bound and harder to secure through permanent workforce models alone.
In this environment, on demand tech skills are no longer a tactical resourcing option. They are becoming a core operating model for how organisations deliver outcomes.
At Brightbox, we have a clear point of view on where this is heading. We are bringing that vision to life incrementally and responsibly, combining data, insights, analytics and AI with human judgement, trust and experience.
The Liquid Workforce Operating Model explained
The Liquid Workforce Operating Model is not a linear hiring process. It is a continuous operating model made up of interconnected capabilities that use data, insights and analytics to sense demand, identify expertise, build trust, mobilise skills and learn over time.
These capabilities do not operate in sequence. They reinforce one another. Insight from delivery improves future matching. Signals from demand shape discovery. Learning strengthens foresight. Technology, analytics and AI support every capability, while human judgement and accountability remain central throughout.
What follows are the core capabilities of this model, with explicit examples that show how it works in practice.
Capability 1: Sensing skills demand using data, insights and analytics
The model begins with visibility into current and future demand.
Examples
Analysing delivery pipelines, transformation roadmaps and programme plans to generate insight into upcoming skills requirements
Using analytics to identify recurring bottlenecks, such as repeated delays caused by shortages in data engineering or security expertise
Producing forward looking insights that forecast demand as technology adoption accelerates, for example increased AI usage driving demand for analytics, governance and model oversight skills
Insights inform planning. Humans set priorities.
Capability 2: Discovering expertise through insight led talent intelligence
Talent discovery moves beyond CVs and job titles to evidence and relevance.
Examples
Using structured skills data and insight models to identify experts with the right mix of technical depth, domain context and delivery experience
Applying analytics to historical engagements to surface individuals who have delivered similar outcomes in comparable environments
Using AI to generate ranked shortlists based on relevance and evidence rather than keyword matching
Insights improve confidence and relevance. Humans assess nuance and suitability.
Capability 3: Building trust through human led vetting supported by technology
Trust cannot be automated, but it can be supported by insight.
Examples
Using video conferencing to pre vet candidates, assessing communication, clarity of thinking and problem solving approach early
Combining interview observations with historical feedback and performance insights
Recording interviews to enable peer review and consistent assessment across stakeholders
Technology improves speed and consistency. Final trust decisions remain human.
Capability 4: Matching skills to outcomes using analytics and Agentic AI
On demand tech skills deliver the greatest value when aligned to outcomes rather than roles.
Examples
Using analytics to match expertise to specific outcomes such as platform modernisation, analytics uplift or AI deployment
Applying AI to consider skills relevance, availability, delivery context and ways of working
Using Agentic AI to propose optimal combinations of expertise across teams or initiatives, for example recommending a temporary uplift in data, analytics and security skills ahead of a major release
AI recommends. Humans approve and remain accountable.
Capability 5: Mobilising skills quickly through automated, insight driven onboarding
Speed to productivity is critical to value.
Examples
Automating onboarding workflows to provision access, tools and documentation based on role and security requirements
Using analytics to identify onboarding steps that typically cause delay and removing friction
Providing consistent onboarding journeys while tailoring access and tooling using insight
Insights reduce delay. Teams focus on delivery from day one.
Capability 6: Learning and improving through continuous insights and analytics
A Liquid Skills Workforce improves through use.
Examples
Analysing delivery outcomes to generate insight into which skills and profiles perform best in different environments
Using structured feedback data to understand collaboration patterns and team dynamics
Applying analytics to assess culture alignment based on communication styles, ways of working and engagement signals rather than subjective opinion alone
Insights strengthen conversations and decisions. Humans remain accountable.
Capability 7: Anticipating future skills demand using Agentic AI and predictive insights
The most advanced capability in the model is foresight.
Examples
Using Agentic AI to continuously analyse demand signals across programmes and generate predictive insights
Running scenario models to understand how changes in strategy, technology or regulation affect future skills demand
Highlighting emerging capability gaps early, enabling organisations to prepare rather than react
Insights enable preparation. Leadership retains control over strategy, ethics and accountability.
A human centred vision powered by insight and delivered incrementally
The future of on demand tech skills is not about automation for its own sake.
It is about turning data into insight, insight into action, and action into outcomes, while preserving trust, transparency and human responsibility at every point.
At Brightbox, we are building towards this Liquid Workforce Operating Model step by step. Each capability is designed to deliver clearer insight, better decisions and stronger outcomes as organisations navigate constant change. Because the organisations that succeed in a world shaped by data, insights, analytics and AI will not be those that automate the most.
They will be the ones that use insight to empower people, access the right skills at the right time, and turn constant change into sustained advantage.
