Operationalising AI: Observability and Business Readiness

Operationalising AI: Observability and Business Readiness

AI is no longer a distant vision, it’s already shaping how engineering teams build, ship, and scale technology. But turning promise into practice is where the real challenge lies.

From keeping models reliable in production to balancing governance with innovation, leaders are under pressure to operationalise AI in a way that drives measurable value without creating unnecessary complexity. That’s why we gathered a select group of engineering leaders for a candid discussion on observability, business readiness, and what it truly takes to make AI work day-to-day.

🔗 Download the summary sheet

Our moderator for the afternoon was Robert Heywood, AI Engineering @ Portia AI.

This was an invite-only, discussion-style event, allowing our attendees to expand their networks and bounce ideas off of other seasoned Engineering Leaders in the tech community.

Topics:

• Keeping AI systems trustworthy and effective day to day, avoiding models drifting off course, data quality slips or bias.
• Moving from demo to something reliable & scalable in production. Where have practices helped, or hindered, getting AI into real-world use?
• What does it really take to roll out AI safely and cost-effectively across a company? From guardrails and human-in-the-loop feedback to choosing low-risk use cases (like chatbots).

Key Take aways:

💡 AI adoption works best when it’s owned across the business
Ambassador programmes and cross-functional “AI guilds” are driving genuine uptake, not just pockets of experimentation. Visibility, clear objectives, and leadership backing make the difference.

⚖️ Governance can’t be an afterthought
Introduce AI-specific procurement boards or “SWAT team” to evaluate tools safely. It keeps experimentation fast but structured.

🧱 The prototype-to-production gap is real

Building demos is easy, making them scale isn’t. Focus is now learning to treat prototype code as disposable, rebuild clean, and move from “vibe coding” to reliable production.

💬 Prompting is becoming a core skill
Developers are spending more time in natural language than code. Strong prompting, clear specs, and modular systems are key to keeping AI-generated code maintainable.

🔒 Security is evolving fast
Prompt injection, data leakage, and misuse are new frontiers. Teams are sandboxing environments, restricting free text inputs, and defining review points for high-risk outputs.

What are the most common AI adoption challenges engineering leaders face and the solutions that are working in practice.

Why do AI pilots fail to scale across organisations?

Problem: AI experiments stay in silos. Teams run isolated AI experiments, but they don’t spread beyond a single function.

Solution: Create AI ambassadors in each department to share use cases, test tools, and prevent siloed learning. Cross-functional “AI guilds” build momentum and visibility.

How do you get employees to actually use AI?

Problem: Employees can see AI as extra work.
When AI projects are “side of desk,” adoption can fizzle out.

Solution: Make AI part of formal objectives. Allocate time (e.g. 10%) for exploration and ensure leadership support is visible.

How can you build confidence in AI at work?

Problem: Teams often don’t know how to start.

Solution: Provide prompt engineering training, publish guidelines on what’s acceptable, and showcase success stories so employees see clear value.

How do you manage too many AI tools at once?

Problem: The majority of SaaS platforms we are seeing, are all adding AI, which can lead to tool overload.

Solution: Standardise with clear rules: is it really worth adopting a whole new tool for just 10% increase in output? Prioritise interoperability and become expert in fewer tools.

Why don’t AI prototypes make it to production?

Problem: Prototypes impress, but can’t scale

AI demos are easy to build but rarely production-ready.

Solution: We're seeing a lot of time and energy going into productionising something that can take not long at all to produce. Treat prototype code as disposable and rebuild clean for production.

Is AI code production good quality?

Problem: AI-generated code is often bloated or inconsistent.

Solution: Use AI for rapid ideation, but enforce strict engineering standards. Invest in modular design systems and clean data structures to make AI outputs more reliable.

Should you build or buy AI tools?

Problem: Often teams want to build everything, but costs can be unpredictable.

Solution: Build quick internal productivity tools, but buy or carefully evaluate customer-facing AI, especially where inference costs at scale could hit margins.

Is traditional development too slow for AI?

Problem: Traditional cycles (wireframe → code → test → release) can not fit AI prototyping.

Solution: Shift to AI-first cycles: prototype with AI → test with users → refine → production. This brings design and engineering together earlier.

How can you measure if AI is actually working?

Problem: No clear ROI for AI initiatives. Without metrics, AI adoption becomes guesswork.

Solution: Track edit rates, run A/B tests, measure conversation depth, and monitor return usage. These metrics show whether AI is delivering business value.

What are the biggest AI security risks?

Problem: Lack of awareness around prompt injection and data leakage

AI tools can create new vulnerabilities.

Solution: Build AI-specific security testing frameworks, sandbox new tools, and restrict free-text inputs where possible.

Should AI always identify itself as non-human?

Problem: Ethical concerns about AI that feels “too human”

Voice AI and empathetic responses risk manipulation or brand damage.

Solution: Set UX and ethics rules. Is it worth looking into requiring AI to disclose it’s non-human for your business? Test voice AI carefully.

Practical next steps for AI adoption

• Launch an AI ambassador programme across departments

• Create a dedicated AI procurement process

• Develop an observability framework for production AI

• Roll out training in prompt engineering

• Define an AI security testing framework

• Provide sandbox environments for experimentation

Engineering leaders searching for questions like “why do AI pilots fail to scale?”, “how do I measure AI ROI?”, or “is AI code production-ready?” are tackling the same issues discussed in this roundtable.

By sharing real-world examples, we hope more organisations can adopt AI in a secure, scalable, and effective way.

At Burns Sheehan we're passionate about our community-driven initiatives and host a variety of technology events with our network. If you are a technical leader and would like to get involved, please reach out to our event coordinator and Engineering Leadership Consultant, Simon Evans.