Data Archives - Argility Technology Group

The AI Strategy Dilemma: Are You Ready for More Than Just a Pilot?

ajustus Nov 27, 2025 AI Strategy, Data, Gen AI, Governance, Technology

By Andrej Hudoklin, Executive Head of Data and AI: Europe

Most businesses have now dipped their toes into AI, but dipping toes will not drive real transformation. And here’s the part we don’t like to talk about:

Up to 90% of AI initiatives never make it beyond the pilot phase – not because the technology fails, but because there’s no plan for ownership, scaling, or value realisation.

Pilots often start strong, attract interest, maybe even deliver encouraging early results. And then… nothing. The pilot wraps, everyone claps, and the model gets quietly parked.

It’s not a technology problem, it’s a strategy problem.

We’ve seen this happen too many times across our work in retail and route-to-market in Europe, MENA, and Africa. Businesses invest time, money, and talent into pilots, but without the clarity, ownership, or structure required to turn them into something scalable.

Pilots Are Good. But They’re Not the Strategy.

Let’s be fair, pilots serve a valuable purpose. They help organisations learn, test assumptions, de-risk decisions, and explore what AI can do in a relatively safe environment. We run pilots ourselves and recommend them when they make sense.

But increasingly, we see companies treating pilots like endpoints rather than stepping stones.

A pilot is not a win. It’s the beginning. And unless it’s designed with a clear path to production, scale, and ownership, it doesn’t matter how clever the model is. It’s just a prototype with better PR.

If your team doesn’t know what happens after the pilot, who will use it, where it fits, how it evolves, then you don’t have a strategy. You have a science project.

What a Real AI Strategy Looks Like

There’s no shortage of AI frameworks out there. McKinsey, BCG, Gartner, Microsoft,… they’ve all published layered models, value chain diagrams, and maturity curves. Most of them are pretty good.

But here’s my advice:

Don’t follow any single framework to the letter. Pick two or three that fit your business reality, and apply them pragmatically.

Adapt them to your culture, your teams, and your systems. Build for what works and not just what looks good in theory.

Within our team, we rely on a practical, layered approach based on what we’ve seen succeed and fail on the ground. We think of it as the five layers of a scalable, sustainable AI strategy, and it’s become a common lens for assessing our own roadmap and how we support clients.

Five-layers-AI-Strategy

1. Business Alignment

Everything starts here. AI must solve a real problem tied to a real objective like revenue, cost, margin, execution, efficiency, or customer experience. If your AI model can’t tie back to a KPI, business process, or behavioural outcome, it doesn’t matter how technically sound it is. It won’t stick. Strategy starts by answering: what’s the point?

2. Operating Model

This is where many pilots collapse. The operating model defines ownership, usage, monitoring, and integration into business rhythms. You can’t just “plug in AI” and hope it runs. Risk management and governance need to be embedded here too:

Who is accountable when the model fails?
How do you handle model drift, bias, or compliance issues?

Without clear operating models, AI projects gather dust rather than gaining momentum.

3. Data, Technology, and Trust Foundations

Yes, you need the right data and tech, but that’s only the starting point. Usability, adaptability, and trust are non-negotiable. Focus on:

Modern pipelines and data governance
Version control and retraining
Real-time risk, security, and compliance monitoring (TRiSM)
Building explainability and transparency into every model

Trust is not an add-on. It’s the foundation that determines if AI scales or fails.

4. People, Change Enablement, and Ethics

Even the best models fail if no one trusts them, understands them, or knows what to do with them. Change enablement isn’t just training, it’s about:

Communication
Trust-building
Clear support structures
Mindset shifts around working with AI

Responsible AI design ensures fairness, transparency, and minimises bias, which must be embedded from day one. Ethics isn’t an afterthought. It’s part of how you build AI that earns adoption and survives scrutiny.

Scaling AI is often less a technical problem than a behavioural and trust problem.

5. Experimentation-to-Scale Loop

Pilots are necessary, but they are only the beginning. Success depends on having a clear scaling path:

Who owns the pilot’s output once it succeeds?
How is it funded, integrated, monitored, and evolved?

Without these answers, even the best pilots turn into “another thing” on the shelf.

What Changes When You Actually Scale

We often use this table with clients to explain the shift in mindset and mechanics between experimenting and scaling.

Pilot Trap vs Scaling for Success

Scaling means thinking differently about where AI lives, who owns it, and how it becomes part of daily execution and not something extra that needs to be “used”.

Want Trust? Then Build Governance.

Governance isn’t bureaucracy. It’s the safety system that prevents you from crashing once AI speeds up. It answers essential questions early:

Who owns the model once it’s live?
How do we manage updates, risk, and bias?
What happens when something breaks?

Without trust, there’s no adoption. Without adoption, AI is just code.

Good governance doesn’t slow AI down. It enables AI to scale safely, sustainably, and with confidence. It’s less about setting up committees and more about building lightweight but real structures for ownership, versioning, bias management, and incident response before the system becomes too critical to fail.

Governance is not a barrier to AI innovation. It’s the bridge that turns experiments into lasting outcomes.

Where We’re Putting This Into Practice

At Smollan Technologies, we’ve had to work through all of this ourselves, and we’re still evolving. We’re building AI capabilities across three key tracks: generative AI, predictive intelligence, and image recognition. But we’re doing it with a strong bias toward real-world integration, not experimentation for its own sake.

We’re working with field and planning teams across markets to build tools that actually help them make better decisions. Our GenAI agents, for example, are designed to surface insights through natural language so that anyone can ask questions and get clear, context-relevant answers. Our PredictRetail and PredictManufacturer products use forecasting and pricing models to support commercial teams with real-time trade-offs. Our Data-Driven Execution solution for field teams delivers daily execution alerts and short-term demand signals to the front lines, so people can fix problems before they become losses. And we’re combining image recognition with execution logic to reduce the in-store reporting burden.

But all of this, no matter how smart or sophisticated, is ultimately designed to answer one question: “What is my next best action?”

If your AI isn’t helping people at different levels of the business, answer that, it’s just “another thing” that sits on the shelf. The real challenge is not building the model, it’s making sure it lands.

Final Thought

We’ve seen too many clever pilots die quietly. Not because they failed. But because they were never designed to live. So before you greenlight another proof-of-concept, ask the hard questions:

What happens if this works?
Who owns it after the demo?
How does it scale, evolve, and become part of how the business actually runs?

If you can’t answer these questions, you don’t have a strategy. And without a strategy, no amount of AI will stick.

Your Turn

Scaling AI is messy, complex and absolutely worth it when done right. If you’re thinking about AI and are not sure where to start, let’s connect and figure it out together – data@smollan.tech

Using data to discover the future of retail

ajustus May 19, 2022 Artificial Intelligence, Data, Digital Transformation, PredictRetail, Retail

As business digitalisation continues apace, more and more data is being generated. Retailers need to understand its value and how to leverage it.

By Tanya Long, Chief Operating Officer, Argility Technology Group

Digitalisation has been growing for many years, and the social and business changes driven by the global response to the COVID-19 pandemic have accelerated it.

Hybrid working models that involve increased working from home get a lot of the limelight, but equally significant has been the definitive growth in online retail.

For example, e-commerce in the US increased from $431.6 billion in 2020 to $469.2 billion in 2021, with shopping habits looking like they have permanently changed to include a growing online proportion.

The move towards digitalisation across all industries has resulted in record levels of IT spend. Gartner forecasts that worldwide IT spending will jump by 5.5%, its biggest increase in more than a decade, to reach $4.5 trillion in 2022.

Unsurprisingly, given that data is the major by-product of digitalisation, a hefty proportion of that spending is going on big data and business analytics. Investment was projected to reach $215.7 billion at the end of 2021, with a compound annual growth of 12.8% until 2025.

These are all big numbers but, as always, much of that spend does not necessarily result in bottom-line benefits. There’s a lot of focus on collecting data but turning it into insights that generate business benefits is rather more hit and miss.

Effecting that transformation requires not only a profound understanding of the technology involved but, even more critically, how the particular industry sector works.

Goldilocks technology

It seems as though we’ve been talking about data warehouses and business intelligence forever, and business is littered with projects that never really lived up to expectations.

What’s changed is the emergence of new technologies that make it possible not only to collect and store vast amounts of data, but also to process it to extract valuable information.

Thus far, predictive analytics is not a crystal ball, but if one believes half the hype one reads, that could happen in the distant future.

Perhaps the most important of these is the cloud itself, because it provides a way for companies to access the plentiful and cost-effective storage they need to deposit huge and increasing amounts of data, and also the sheer computing power needed to process and analyse it − think artificial intelligence (AI) and machine learning (ML), both used to turn data into information.

As always, though, technology is only part of the story − and not necessarily even the most important part. The real point is that the cloud’s resources make it possible to mine data for patterns, deploy sophisticated statistical models and create models on which to base decisions.

In short, the real story is data and what it can be used to do. There are basically three ways in which data can be used, namely: to describe, to diagnose and to predict. The first two are backward looking: using data to establish what happened (descriptive), and then to understand why it happened (diagnostic). Both are obviously very valuable and can be used profitably to improve future performance.

However, the real benefits come when one starts to be able to use data to look to the future − what is usually called predictive analytics. It uses AI, ML, data modelling and the rest of them to analyse current data and make predictions about the future.

Of course, one has to understand this approach is predicated on the assumption that the future derives from the past, and thus that the past contains the seeds of the future.

Predictive analytics thus becomes less useful when change is highly disruptive, and also the further into the future one goes. The further forward in time one goes, the greater the tendency to regress to the mean, to become more and more generalised.

Clear benefits

Thus far, predictive analytics is not a crystal ball, but if one believes half the hype one reads, that could happen in the distant future.

But it does offer an evidence-based − or data-driven, to quote the industry jargon − way to identify how the near future is likely to unfold, and what actions a specific business should be taking to defend its current position, or to take advantage of new opportunities.

In this way, it can help an organisation to move away from the challenge of choosing between a large number of 50% probabilities, to choosing between a small number of high probabilities.

This is an extremely valuable strategic tool given the growing complexity of the business environment, and the intense competition fuelled by the globalisation of markets and a volatile economic environment.

All of these combine to create what is known as a VUCA world − one that is volatile, uncertain, complex and ambiguous − and so the company that is able to develop a likely-to-succeed strategy based on evidence rather than intuition is already better positioned than competitors.

In certain environments, organisations have experienced that business intelligence delivers a return on investment of 80%, which just goes to show the importance of data-driven decision-making − but the return can shoot up to 250% when predictive analytics is used.

The reason for this startling increase is that decisions are taken based on the rigorous analysis of fact and not on intuition.

The frustration for CFOs when considering the investment in AI and ML is that actual guarantees on ROI is not set in stone, the statistics come from passageway talk about successful projects. But there is no doubt the success is there and that the benefits abound.

Retail is a particularly VUCA sector because it straddles both the real and digital worlds, and consumers are growing ever-more demanding. For them, as I will explore in my next article, predictive analytics is especially attractive.

Source: IT Web

Tanya Long, Chief operating officer, Argility Technology Group.

Long has 30 years of industry experience. Her career in the IT sector started in 1988 in IT support for point-of-sale solutions. She moved formally into software development with UCS/Argility in 1989 as a developer and progressed through to team leader, account management, project and development management roles, which led her into various industries and corporations.

In 2017, Long returned to Argility (having previously worked at the company in a technical capacity) as human capital executive, where she reunited her retail, IT, leadership and HR knowledge to drive her zeal for transformation.

In her capacity as COO, Long is responsible for overseeing operations, with a specific focus on human capital, sales and marketing, and ensuring the company culture and vision shows up daily for customers through an engaged technical team of experts.