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Many enterprises are experimenting with artificial intelligence, but too often projects stall in proof-of-concept mode, with many AI projects failing to deliver measurable value. The challenge isn’t building models; it’s operationalising AI to deliver measurable business outcomes.
At Colibri Digital, our mission is simple: make AI real, scalable, and aligned to business value. Applied AI is not about chasing hype or research vanity projects - it’s about embedding trustworthy, explainable intelligence into business operations where it makes a difference.
Applied AI is the shift from experimentation to execution. The last 18 months have seen many small advantages with AI, but we are now starting to move this forward. We’re moving from pilots to production, embedding explainable, outcome-driven AI into everyday workflows.
This is applied AI, meaning we are:
“We need to stop thinking about building an AI for a task and start thinking about employing an AI to take on a role - like a junior researcher or analyst,” says Marv Gillibrand, Colibri’s Head of Product.
“That’s when it becomes embedded in the flow of work, rather than sitting on the side.”
While many firms run “AI labs” exploring abstract possibilities, Colibri takes a pragmatic, outcomes-first approach, bridging models and management decisions.
Despite enthusiasm, most organisations face significant barriers to productionising AI. The industry is split between promises of billion-to-trillion-dollar efficiencies of AI and stories of AI failures.
Too many proofs-of-concept are built without clear business metrics - nearly a third have no measurable outcomes defined. Without a “proof of value” lens, AI initiatives struggle to justify investment.
“We’d never hire an employee without setting objectives or a budget,” says Marv. “Yet companies routinely spend millions on AI without defining what good looks like.”
Colibri advocates a proof of value approach, identifying decisions and workflows to improve, setting measurable KPIs, and tying every experiment to ROI from the outset.
Legacy infrastructure, siloed data, and slow governance processes make scaling AI hard. Even when a model works in isolation, integrating it into live systems can expose brittle architectures and overstretched teams.
Many organisations face “integration debt”, the cumulative drag of disconnected tools and manual workarounds that prevent smooth deployment.
Poor data quality, fragmented silos, and weak integration pipelines undermine even the best models. The reality is that data engineering and model operationalisation are often more complex than model building.
“What used to take months can now be prototyped in hours, but moving from prototype to production is often three times harder in terms of new security concerns, emerging governance, and uncertainty around how to scale,” Marv explains.
Generative and predictive AI systems are inherently probablistic, meaning the same input may not always yield the same output. Boards, regulators, and auditors are rightly asking how to ensure fairness, explainability, and control.
With the EU AI Act and UK Responsible AI Framework coming into force, organisations must build trustworthiness and auditability into their AI lifecycle from day one, not as an afterthought.
Transitioning from experimentation to applied AI requires structure, discipline, and a clear link between technical delivery and business value. At Colibri, we often remind clients that building a model is the easy part. Operationalising it at scale is where true competitive advantage lies.
Marv said: “There’s a degree of naivety in assuming that because a proof of concept works in a sandbox, it can be productionised quickly. What used to take months to build can now be built in hours, but turning that prototype into something governed, secure, and scalable is where most organisations stumble.”
Here’s how to move past that barrier and make AI real in your organisation.
Every AI initiative must begin with a definition of value. What problem are you solving -and how will success be measured? Whether it’s better decisions, faster processes, reduced costs, or new insights, metrics should be agreed at the outset and tied directly to business objectives.
Too many organisations start with a model in search of a use case. Colibri advocates starting instead with a proof of value, not a proof of concept. That means identifying:
AI is only as strong as the data it’s built on. At the proof-of-concept stage, data is often manually curated or simplified. In production, it must come from multiple live systems, governed and traceable end-to-end. That means building robust data pipelines, with:
“Without solid infrastructure and data governance, AI runs on sand,” notes Marv. “Our role is to help clients build the foundations first, because a great model is worthless if it can’t be trusted or integrated.”
Trust is the currency of applied AI. Regulators, boards, and end-users all need confidence in how AI-driven decisions are made. This is why explainability, bias detection, and governance frameworks must be embedded from the start, not bolted on later.
Marv stresses that traditional software engineering practices don’t fully apply here:
“Generative AI is non-deterministic. The same input might not produce the same output twice. That’s a huge shift for organisations used to deterministic systems. Governance, monitoring, and human oversight are non-negotiable.”
Embedding governance means:
The final, and often most underestimated, stage is operationalising AI at scale. This is increasingly referred to as AI Ops and refers to the discipline of monitoring, maintaining, and optimising AI systems throughout their lifecycle.
Marv comments: “AI systems need to be managed like digital employees - reviewed regularly, retrained when necessary, and retired when they no longer add value. That continuous oversight is where most AI projects fail today.”
AI Ops brings together:
“We need to think of AI systems the way HR thinks about people - with regular reviews, retraining where necessary, and even retirement when they no longer add value,” says Marv.
Applied AI is already delivering measurable impact across industries:
“AI gives banks the opportunity to move beyond fragmented data to offer genuine customer insight - even something as ambitious as a personalised financial advisor for every customer,” Marv explains.
Turning AI from a promising experiment into a practical business capability demands more than great technology. It requires experience, structure, and a value-driven mindset. This is where Colibri Digital stands apart.
While many organisations are still caught in the cycle of prototypes and pilots, Colibri has built its reputation on making AI operational - bridging the gap between data science and real-world decision-making.
Our approach combines engineering rigour, business context, and a relentless focus on measurable outcomes.
At Colibri, we don’t sell “AI experiments.” We deliver applied AI that moves the needle.
The next 18 months promise to be transformative for applied AI - not just in what’s possible, but in how organisations deploy and sustain it responsibly. We’re entering an era where AI moves from experimentation to embedded capability, but the path forward is not without constraints.
“Global compute capacity is struggling to keep pace with AI demand due to GPU and energy constraints,” warns Marv. “There simply aren’t enough data centres or processors on the planet right now to meet the future demand. It’s a real-world bottleneck that could slow innovation unless we find smarter, more efficient ways to train and run models.”
This global compute crunch is already shaping investment patterns. Across the UK and Europe, billions are being channelled into hyperscale data centre construction and advanced chip design to keep pace with AI’s energy and performance requirements.
There’s even a growing hope that AI itself will help solve its own efficiency problem, by optimising energy grids, designing next-generation semiconductors, and accelerating breakthroughs in material science.
Despite these challenges, the outlook remains overwhelmingly optimistic. Three shifts are defining the next phase of applied AI:
AI’s ability to detect patterns beyond human perception is unlocking breakthroughs across science, healthcare, and engineering. From early cancer detection to drug discovery and materials innovation, applied AI is enabling discoveries that were once thought impossible.
Marv says: “The ability to recognise unrecognisable patterns at scale is fascinating. We’re starting to see discoveries that scientists would have missed or never even thought to look for.”
By 2026, we’ll see the emergence of agentic AI workflows - systems where AI tools don’t just support humans but collaborate with each other across departments and platforms. Instead of manually moving data between tools, AI systems will coordinate end-to-end processes, acting almost as digital middle managers.
“It’s not unrealistic to imagine AI managing AI. Think about a laboratory running hundreds of tests each day. Humans will still perform the experiments, but an AI might handle the scheduling, planning, and orchestration. That’s middle management, reimagined.” says Marv.
This shift will redefine organisational design, introducing AI orchestration layers that manage workflows, allocate resources, and flag decisions requiring human oversight.
As AI becomes ubiquitous, its limitations and fragilities will come sharply into focus. From model drift to bias and infrastructure strain, the coming year will be a period of maturity where businesses move from blind optimism to structured accountability.
Marv commented: “Every major technology wave brings its own growing pains. We’re already seeing mistakes and overreliance on AI systems. The next phase will be about catching up and building resilience, governance, and clear-eyed realism into AI programmes.”
Applied AI is no longer a future ambition. It’s a baseline capability. The organisations that succeed will be those that marry technical ambition with operational discipline: scaling AI sustainably, transparently, and with measurable business value at its core.
“By 2026, agentic AI workflows - where applications and AI systems collaborate with each other - will be the big shift,” predicts Marv. “Firms that fail to scale responsibly will quickly fall behind.”
The message is clear: start now. Move beyond proofs of concept. Partner with experts who know how to scale responsibly and turn AI into a trusted driver of business value.
At Colibri Digital, we don’t just imagine the future of AI. We make it real.
If you’re ready to move beyond experiments, Colibri can help you operationalise AI – safely, sustainably, and at scale. Contact our team today to find out how we can help you.
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