A Practical Guide to AI Solution Assessment

Your business has established a clear strategy for leveraging AI to drive value, be it through newfound revenue streams or significant cost efficiencies. The next crucial step is to translate this vision into a tangible plan. This requires a meticulous assessment of the technical and operational aspects of different AI solutions, ensuring the chosen path aligns not just with your goals but with your technical capabilities and security requirements.

This article provides a framework for this critical evaluation, detailing the key considerations and steps necessary to move from abstract strategy to concrete implementation.

Where can you run it? The Edge vs Cloud

The first and most fundamental decision is where your AI model will execute. The choice between edge (on device or internal hardware) and cloud computing is a trade-off between latency, cost, scalability and data privacy. Your use case for AI will dictate the tolerable thresholds for those attributes.

Cloud Computing This is the most common and often the simplest approach. Models are deployed on remote servers managed by a cloud provider like Google Cloud, AWS or Azure.

• Pros: High scalability (you can provision immense compute power on demand), simplified management and access to a vast ecosystem of tools and services. It's ideal for complex, large-scale models that require significant computational resources.

• Cons: Latency can be a factor as data must be sent to the cloud for processing. Data transfer and compute costs can add up.  For highly sensitive data, security and data residency concerns will need to be managed.

Edge Computing This involves running AI models directly on local devices or hardware, such as manufacturing robots, surveillance cameras or mobile phones.

• Pros: Near-zero latency as model execution happens locally without needing to send data to the cloud. It also enhances data privacy as sensitive information never leaves the device or local network. This is crucial for applications where real-time decisions are paramount e.g. autonomous vehicles or real-time quality control.

• Cons: Limited computational power on local hardware which restricts the size and complexity of the models you can run. Initial hardware costs can be high.  Also, managing and updating models across multiple edge devices, especially when heterogeneous, can be a logistical challenge.

Your choice should be dictated by the specific use case. E.g. if you're analysing quarterly reports, a cloud-based solution is perfect. If you’re detecting anomalies in a real-time manufacturing process edge computing is likely the only viable option.

Selecting the Right Model: AI provider, Open Source, Size and Performance

Once you’ve defined your deployment environment the next task is selecting the model itself. When choosing a large language model (LLM) for your application you have several key factors to consider. The decision often comes down to a trade-off between control, cost and convenience.

Open-Source Model Selection Evaluate models based on their license (e.g., Apache 2.0, MIT), community support and suitability for your specific task (e.g., a computer vision model for image analysis, an NLP model for text classification). A vibrant community can provide invaluable support and regular updates, while a permissive license ensures you can use the model commercially without legal risk.

Model Provider Services These services provide access to powerful, pre-trained and continuously updated models through a simple API. This will significantly reduce the complexity of deployment and maintenance. Forecasting usage of the providers' model is critical to understand the cost vs the benefit of the larger models offered.

Then compare the forecast of the provider usage cost with the development, testing and run costs of hosting and maintaining the models yourself.

Model Size and Hardware Requirements A model's size, measured in parameters, directly correlates with its computational demands. Larger models (e.g., Llama 3 with 70B parameters) are more powerful but require extensive hardware, typically high-end GPUs. A smaller, more specialised model might be a better fit for a constrained edge device. Assess your available hardware, whether on-premise servers or cloud instance to ensure it can handle the model's processing and memory needs. Don't over provision; a smaller, fine tuned model that meets 90% of your needs is often more cost-effective than a massive, general purpose one.

Integrating with Data Sources: The Fuel for Your AI 

An AI model is only as good as the data it's trained on and the data it's given to analyse. Crap in, crap out. If the data the model needs to analyse to produce an output is dynamic, then integration with your existing data sources may be required. Otherwise, frequent fine-tuning of the model with the latest data will be required.

Connectivity and APIs Your AI solution must be able to securely connect to and retrieve data from your business systems such as CRM, ERP, and data warehouses. This typically involves using well documented APIs or dedicated data connectors. Ensure the chosen solution supports the necessary authentication and authorisation protocols.

Data Quality and Preprocessing Data is rarely in a pristine state. The integration process must include robust data cleaning and preprocessing steps. This could involve handling missing values, standardising formats and transforming data into a structure the AI model can understand. This stage is critical for the accuracy and reliability of the model’s output.

Security, Privacy and Data Policy 

Perhaps the most critical, yet often overlooked, aspect of AI implementation is the security and data governance framework. An AI solution must be built on a foundation of trust and compliance.

Data Sensitivity and Policy Identify the sensitivity of the data that the AI solution will handle. Is it customer data, financial information or proprietary business secrets? Your internal data policy should dictate how this data is handled, stored and processed. This includes policies on data anonymisation, encryption and access controls.

Cybersecurity Framework Implement a multi-layered cyber security strategy to protect the AI system and the data it uses.

Data in Transit Ensure all data moving between your systems, the AI model and the cloud is encrypted using protocols like TLS/SSL.

Data at Rest All data stored, whether in a cloud data lake or on an edge device, must be encrypted.

Access Control Follow the principle of least privilege. Only grant individuals and automated systems the minimum access necessary to perform their functions. This is crucial for both data access and model deployment pipelines.

Threat Modelling Conduct regular threat modelling to identify potential vulnerabilities in your AI system, from adversarial attacks on the model itself to unauthorised access to data.

Making AI work for You

Choosing the right AI solution is more than just a technical decision; it's a strategic one. By carefully considering edge vs. cloud deployment vs third party, picking the right model, ensuring smooth data integration, and building a strong cybersecurity framework, you can move confidently from strategy to successful implementation. This is how you turn your AI investment into real, measurable value.

Ready to find the right AI vehicle for your business? Contact Next Phase Consultancy for a chat and let's make sure your strategy leads to a successful outcome.