Artificial intelligence (AI) is already gathering insights within your systems. You enabled it by giving it access to your most valuable data. In return, it promised speed, scale and competitive advantage. But AI doesn’t take sides. It makes decisions based on what it’s been fed by you – whether that’s good or bad.
Sometimes AI behaves like Dr Jekyll – thoughtful, consistent and trusted. Other times, without warning, it becomes Mr Hyde – biased, opaque and dangerously confident. And too often, leaders don’t know which version they have until it’s too late.
Around 42% of AI initiatives are being scrapped in 2025 – not because the models didn’t work. It’s because they were unleashed into environments riddled with outdated data, disconnected systems and no clear ownership.
Poor data quality is a leading cause, with studies indicating that roughly 85% of failing AI projects are linked to the AI models underperforming. When those data flaws meet unchecked automation, the consequences can be severe – from reputational damage to lost customers.
Laying the foundations for AI
AI offers major advantages – speed, scale and competitive edge. But these benefits only show up when an organisation is truly ready to use the technology properly. Too often, companies move too fast, which can create problems that are not just technical but also reputational.
Salocin Group’s latest paper, The Shadow Inside the Machine, tackles this issue head-on. Challenging the hype and being the voice of reason and asks a harder question: are you building value or just building risk?
We’re using our experience to offer a realistic way to think about AI – not as a shiny new tool but as something that reflects the systems it learns from.
This isn’t about opposing AI – it’s about recognising that its success relies on strong foundations: clean data, stable infrastructure and effective governance.
What you’ll learn
The Shadow Inside the Machine highlights three major priorities for deploying AI responsibly and effectively:
1
Good AI starts with clean, reliable data
AI systems only work as well as the data they’re trained on. Many organisations still rely on inconsistent, disorganised or non-compliant data. The paper shows how bad data doesn’t just weaken results – it can introduce real risks.
2
Most cloud setups aren’t ready for AI
The paper explains what real readiness looks like; having cloud infrastructure doesn’t automatically mean you can support AI at scale. You need strong performance, clear governance and reliable architecture. Many organisations assume they’re ready but in practice they’re not.
3
Practical AI matters more than flashy pilots
It’s common for businesses to experiment with AI in controlled settings without moving to meaningful deployment – the paper warns against that. Instead, it promotes real use cases embedded into operations, measured properly and governed carefully.
Download the paper
Explore the full boardroom horror story and learn how to stop your systems turning against you by downloading The Shadow Inside the Machine – you can either read the paper or listen to the audiobook.
