#86 - Efficiency Gains for Blue Economy SMEs

Speaking to a family friend that operates in the "AI world", got me thinking how smaller businesses can get in the mixer.

AI isn’t just reshaping tech giants, it’s starting to quietly transform how small and medium-sized enterprises (SME) operate. From automating data reporting to predicting maintenance needs, what was once the domain of Silicon Valley is now becoming modular, affordable, and within reach for all SMEs (including in the Blue Economy).

Yes, i think there is a bit of an AI bubble right now, a rush of tools, promises, and noise. But the underlying shift in how organisations process information and make decisions is real and irreversible. For ocean-focused ventures, getting ahead of that curve could become the biggest competitive moat they ever build.

This post explores how AI could deliver efficiency gains, cutting waste, cost, and friction across operations, breaking down who it’s for, why it matters, and how to start implementing it across an SME.

Who This Is For

Not every SME needs to dive into AI right now, but for those battling inefficiencies, data overload, or slow manual processes (with scope to review and adopt), it’s becoming increasingly relevant.

Use case examples:

• Struggle to scale operations or manage data across multiple systems;

• Operate in environments where efficiency directly impacts costs (economic, environmental, social);

• Lack the in-house tech capacity to build AI solutions from scratch but are open to using modular tools.

Within the Blue Economy, three sectors stand out where AI can unlock the fastest and most measurable gains:

1. Marine Plastic Pollution – improving detection, sorting, and recovery efficiency.

2. Maritime Logistics & Port Operations / Data for Decarbonisation – optimising routing, fuel use, and scheduling.

3. Coastal Energy & Infrastructure – driving predictive maintenance and asset monitoring.

These are industries where time, precision, and resource allocation matter and where a smarter process can translate directly into lower emissions, lower costs, and better sustainability outcomes.

Why AI Matters for Efficiency

Most inefficiencies aren’t about effort, they’re about time, data, customer servicing and decisions. The gap between what’s happening and what’s known often determines how much waste, cost, or downtime a business absorbs. AI helps close that gap.

Here’s what that looks like in practice:

Pain Point: Manual monitoring or reporting processes

Efficiency Gap: Staff spend hours collecting data for compliance, impact reporting, or performance metrics.

AI Application: Use data automation and dashboard tools (e.g. Power BI, Notion AI, or Zapier integrated with sensors/spreadsheets) to standardise and visualise metrics in real time.

Result: Teams free up hours weekly and gain instant operational visibility and data to utilise further.

Pain Point: Reactive maintenance — fixing problems after they occur

Efficiency Gap: Equipment downtime leads to costly interruptions, especially in ports or energy infrastructure.

AI Application: Adopt predictive maintenance platforms like Uptime, SparkCognition, or AI-enhanced IoT sensors to forecast wear and prevent failures.

Result: Reduced maintenance costs, fewer unplanned outages, and improved asset life.

Pain Point: Energy inefficiency, waste, or idle time

Efficiency Gap: Systems and machinery often run outside optimal load ranges, wasting power or fuel.

AI Application: Implement smart energy management tools such as Siemens MindSphere or AWS IoT TwinMaker to monitor usage patterns and automate power balancing.

Result: Up to 10–20% energy savings and better alignment with decarbonisation goals.

Pain Point: Data trapped in silos or spreadsheets

Efficiency Gap: Different teams or partners collect data independently, making it impossible to compare or analyse effectively.

AI Application: Use data-cleaning and integration tools like OpenRefine, Microsoft Fabric, or no-code AI APIs to unify datasets and establish a single source of truth.

Result: Consistent, standardised data ready for analysis, reporting, or model training.

By addressing inefficiencies step-by-step, SMEs can build an operational layer where AI isn’t a buzz word, it’s simply how the work gets done faster, cleaner, and smarter.

BIG ADVANTAGE: Getting data layers standardised and composed means it can be utilised in manners you didn't even think possible. Big FMA even doing this step. Input = Output

Blue Economy example: Maritime Logistics & Port Operations

Pain Point:

Port congestion, inconsistent scheduling, and inefficient vessel routing increase fuel consumption and downtime.

Efficiency Gap:

Without predictive visibility into traffic, weather, and port asset status, operators rely on static timetables and reactive coordination.

AI Application:

• Predictive vessel routing using platforms like MarineTraffic AI, Spire, or IBM Watson for Marine Operations.

• Automated scheduling and berthing through port management systems integrated with machine learning.

• Asset performance monitoring via IoT analytics (e.g. Siemens MindSphere, Azure IoT Hub).

Result:

Reduced idle time, smoother port traffic flow, lower fuel costs, and improved emissions tracking, key for data-driven decarbonisation.

The Implementation Ladder

AI adoption doesn’t need to start with deep tech or big budgets. The most efficient ventures begin small, focusing on one repeatable process and building confidence step by step.

Here’s a simple ladder to climb:

1. Start Small — Find the Repetitive or Costly Process

Identify the task that drains the most time or resources each week: data entry, reporting, equipment checks, or route planning.

Example: A small aquaculture SME automates feed logging or temperature monitoring before expanding to predictive analysis.

Tip: Use simple automation tools like Zapier, Notion AI, or Power Automate to test impact quickly.

2. Data First — Clean and Structure What You Already Have

AI is only as good as the data it’s trained on. Spend time standardising and digitising your information, from spreadsheets and sensors to vessel logs or inspection reports.

Example: Clean and merge fragmented CSV files using OpenRefine or Microsoft Fabric to create a single, reliable data layer.

Tip: Don’t rush into AI before your data speaks a common language.

BIG ADVANTAGE: Once your data layers are standardised and composed, they can be utilised in ways you didn’t even anticipate, unlocking new insights, models, and partnerships. This foundational step is what even the biggest firms in maritime and energy are currently investing heavily in. Input = Output.

3. Leverage Tools — Use AI-as-a-Service or No-Code Platforms

Once your process is clear and your data is structured, plug in modular tools.

Example:

• Predictive analytics: UptimeAI, Google Vertex AI

• Operational dashboards: Tableau, Power BI, Causal

• AI assistants: ChatGPT Enterprise, Claude, or Notion AI for workflow drafting and reporting.

• Tip: Test integrations rather than custom-building — it’s faster and cheaper for SMEs.

4. Iterate — Measure, Adjust, Scale

Track improvements in time saved, accuracy, or cost reduction. Feed those results back into your operations and reinvest the gains.

Example: If predictive maintenance saves 10% in downtime, allocate part of that saving to automate a second process.

Tip: Keep it measurable — efficiency is your KPI, not the tech itself.

Closing Thoughts

AI isn’t here to replace the human element, it’s here to make it count. In the Blue Economy, where margins are thin and operations are complex, efficiency isn’t just a goal: it’s survival.

The biggest takeaway? You don’t need to build the next deep tech platform to benefit from AI. Start with your data, your processes, and your pain points. Build clean, standardised foundations, and let the technology do what it does best, amplify insight, automate repetition, and connect systems that have never spoken to each other before.

Getting your data layers in order is the most underrated move an SME can make. Once that foundation is set, AI becomes a multiplier, unlocking use cases and value streams you might not even have considered. Input = Output.

For ocean-focused SMEs, this shift could become the bridge between sustainability and profitability, where clean data drives clean growth.

If your venture is exploring ways to integrate AI for operational efficiency, I’d love to hear from you and potentially feature your story on OceanTech Insider.

OTI

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