5 Reasons Why Your AI Deployment Isn't Delivering And what you can do to improve it.

European spending on artificial intelligence (AI) is predicted to reach $133 billion by 2028 – so clearly, companies are expecting strong returns from their investment. However, without the right data, infrastructure and strategy, even a seemingly flawless AI model can be rendered ineffective before roll-out even begins.

With that in mind, here are five of the most common pitfalls that are currently holding AI deployments back – and some practical steps that your enterprise can take to keep your investments in the green.

1. C-Suite vision doesn't align with technical execution

Ambitious executive visions for AI are most successful when they're grounded in a clear understanding of the technical requirements. Without the input from the teams responsible for implementation, even the best strategies can lose the momentum or miss the mark.

Open communication and cross-function collaboration are simple yet effective ways to build more alignment and unlock the full potential of your AI initiatives. Giving representatives from across your workforce a seat at the table opens the conversation to a rich pool of insights and expertise. And when employees feel heard and respected, they are more likely to embrace the strategy and help drive its successful implementation.

Solution: Generate buy-in by establishing an inclusive AI strategy that factors in the opinions of all your stakeholders – particularly IT engineers. That way, you can encourage a dialogue in which joint decision-making, budgetary alignment, shared KPIs and collective responsibility become the norm.

2. Data quality isn't up to scratch

For AI to produce meaningful outcomes, the information it analyses must be accurate, complete and relevant. But when data collection and movement vary from team to team, region to region or system to system, the inconsistency will prevent AI from fulfilling its true potential. Put simply: the better data is handled, the better the AI.

This is also a key ethical consideration as the world readies for widespread AI adoption – so much so that the imminent EU Artificial Intelligence Act mandates that moving forwards, data sets shall be relevant, sufficiently representative, free of errors and complete in view of the intended purpose. On both an operational and legislative level, prioritising data quality today is a much more cost-efficient option than troubleshooting imprecise or problematic AI outputs tomorrow.

Solution: To standardise data collection, deploy automating cleansing processes or build a central data repository for your organisation. Likewise, implement strong governance principles that ensure your AI models are consistently fed with clear and safe information.

3. Tool sprawl is overwhelming IT teams

As IT landscapes grow, so does the number of monitoring and management tools. And even if each AI platform does offer useful insights, the collective complexity and cost they incur make it difficult for IT teams to keep track. It's like packing all your belongings into a giant suitcase, only to waste time locating one single item when you need it most.

Case in point: new research from Riverbed Technology has revealed that organisations use 13 observability tools from 9 different vendors on average. Clearly, relying on a patchwork of applications is too common – and layering AI over an already fragmented digital toolkit will only increase pressure and delay decision-making. [Editor's note: The author is Director of Solutions Engineering, UK & I, at Riverbed Technology.]

Solution: Where possible, consolidate single-purpose tools into one integrated platform. Unified observability solutions can streamline multiple workflows into an end-to-end dashboard that provides real-time, cross-domain visibility into network health.

4. Non-standardised telemetry data

Every aspect of a network generates data – and vast amounts of it. Recent projections estimate that an incomprehensible 402.74 million terabytes are now created each day worldwide. However, if each data point arrives in a different format, it can be difficult to analyse. What's more, blindly feeding an incoherent mass of data into an AI model will skew the results it produces.

That's why reliable and consistent data pipelines are more important than ever in the age of AI. You need to be confident that compatible, easy-to-interpret inputs are receivable from across your entire digital estate.

Solution: Embrace Open Telemetry (OTel), a framework designed to establish common protocols for collecting, routing and formatting data. This is rapidly emerging as a foundational industry standard for AI-enablement, with a staggering 95% of business leaders agreeing that OTel's cross-domain interoperability makes it critical to observability.

5. Unified communications are being ignored

Despite the understandable hype around AI, the backbone of daily operations within a business is still its unified communications (UC) stack. Platforms like Google Workspace or Zoom are the most-used applications for many employees, which also means they're the biggest driver of helpdesk tickets.

When you realise that operating at 99.9% uptime in a contact centre running 10 hours a day means companies could miss out on 219 minutes of productivity per year, the incremental cost of short-lived downtime becomes a more pressing issue. Addressing even the most minor UC obstacles can help you unlock higher levels of performance, job satisfaction and cost-efficiency.

Solution: Proactively monitor and optimise your UC performance to improve user experience. Troubleshooting issues quickly and predictively can save countless IT manhours that can then be rededicated to other initiatives, like AI innovation.

Turning ambitions into outcomes

AI is already spearheading productivity, innovation and user engagement across Europe. But as these examples indicate, a good algorithm isn't enough on its own. Successful AI deployment demands clean data, streamlined solutions, standardised telemetry and a business strategy that bridges the gap between executive ambition and technical execution.

If you can put those foundations into place, your business will move toward a future in which your AI systems are supplied with superior inputs and backed by a cohesive digital infrastructure. This translates into the kind of competitive advantages, operational efficiency and sustainable growth that you sought from your investment in the first place.