Implementing and operationalizing AI isn’t cheap. Yet no enterprise can afford to ignore the value that AI can deliver. This means that understanding how to achieve a decent – and measurable and demonstrable – return on investment (ROI) from your enterprise’s AI initiatives is essential. The key to achieving this? API discipline.
Let us explain…
API discipline as a strategic enabler of AI ROI
APIs are the arteries of your AI systems. They feed data to your AI, playing a crucial role in its ability to succeed. Even the most sophisticated AI model can’t excel if the data feeding it – via APIs – isn’t reliable due to the APIs being mismanaged. Trash in, trash out.
This makes discipline in how you design, document and govern your APIs crucial, as it directly impacts the return on your AI investments.
With many CIOs and CTOs under pressure to justify AI spending, this focus on API governance is good news. API governance is a well-established discipline, with clear best practices to help guide your work in this area. It enables you to achieve everything from stronger API security to a culture in which developer creativity can thrive. It can also maximize your AI ROI by ensuring that inconsistent API practices don’t undermine AI performance and increase risks.
Siloed APIs can lead to poor data access, integration failures and even model bias, all of which can damage your ROI (and your enterprise’s reputation). API governance tackles this head-on. It acknowledges that API discipline isn’t a developer concern – it’s a strategic enabler for AI ROI.
The importance of people and processes
At the heart of any successful API governance strategy are people and processes. You establish what your business and teams need, translate those into processes and policies, then choose the right platform to enforce them. When these processes and policies are consistent, standardized, predictable and repeatable, you have a mature foundation on top of which to innovate.
This appreciation that API governance is not a technical problem or solution, but an enterprise-wide approach to delivering business outcomes through consistency and standardization, is the starting point of AI-readiness. Skip the API discipline and you’re setting your AI up to fail. Without it, you’re left battling the hidden costs of API complexity:
- Redundant integrations that drain resources
- Security vulnerabilities from undocumented endpoints
- Inconsistent inputs that lead to unreliable model outcomes
- Time lost to debugging and maintenance
With discipline in your API design, documentation and governance, you iron out these issues, replacing them with cleanly exposed data sources, reusable models and an infrastructure that is easier to maintain. This alignment and efficiency will impact noticeably on your AI ROI.
Measuring your AI ROI
A state-of-the-art AI model that can’t reliably consume data or produce outputs across teams is a sunk cost. Alternatively, one that is underpinned by well-disciplined, consistent AI governance stands an excellent chance of delivering a strong ROI.
Embracing open standards – specifically OpenTelemetry – can help you measure that ROI. You can apply observability practices to measure and trace the performance, usage and impact of your AI systems throughout their lifecycle. Instead of vague ROI estimates, telemetry data delivers clear, measurable and data-driven insights.
To achieve these, you’ll need to determine what ROI means in the context of your enterprise and your AI initiative. You can then instrument your observability tooling to measure relevant metrics. Some examples of these include (but are far from limited to):
- Operational efficiency (e.g. reduced time and/or cost per task)
- Accuracy/uplift (e.g. improved forecasting or decision-making)
- Adoption rates (e.g. how often models are used in production workflows)
- Cost to serve (e.g. compute/storage costs per inference)
- Business outcomes (e.g. increased sales or reduced churn)
Measuring AI ROI with OpenTelemetry means you can generate insights into data freshness, pipeline costs, training efficiency vs model performance, real-time model usage and operational costs, the impact of AI on your KPIs and so much more. There are trust, transparency and compliance benefits to be gained as well, meaning the benefits of visibility into your AI extend far beyond assessing its ROI.
Turn your API discipline into a force multiplier
When you’re planning how to get maximum value from your AI initiative, investing in API governance might not be the first thing that spring to mind – but it should be. You can use it as a force multiplier to boost interoperability, increase model reuse, reduce compliance risks and speed up time to market for your AI services. All as a result of embracing the foundational discipline of API governance and using it to implement your AI initiative in a sustainable, scalable and secure manner.
Ready to take the next step in accelerating your AI innovation? See if Tyk AI Studio can help.
