Derisking AI is where the real work begins - Image by Tung Nguyen from Pixabay Artificial Intelligence (AI) is starting to write its own success story. The more it matures, the more quantifiable use cases emerge. And if the numbers are to be believed, then the recent report by NewVantage Partners highlighting that 77.8% of respondents stated their companies are either using AI widely or at least in limited production, notes a staggering 65.8% increase from last year.

The significance of this can’t be overstated. It speaks clearly to the fact that AI is here, it is becoming pervasive, and its proven value is fuelling further investment into the technology. But, and yes, there’s always a but – organisations experiencing AI success, those who have used it for innovation and business transformation, are facing new risks. In short, their very success with AI deployments set the stage for future challenges. No matter where you are in your AI journey, these risks will also bear mitigation now.

Danger: Critical AI risks

Notably, there are two primary risks to getting business value from AI, or as we like to term it, “double trouble.” The first is the risk from talent shortages. The second is the reliance on non-compliant or badly executed black box solutions.

Defining a new approach to derisking AI – ModelOps

Let’s first define the approach I’m recommending, ModelOps. Most business leaders have never encountered ModelOps – and that’s okay. But it is important to understand how it enables AI as businesses need the innovation and optimisation AI and machine learning provide.

To oversimplify what ModelOps is and does, it enables the management of key elements of the AI and decision model lifecycle. Bear in mind that AI models are machine-learning algorithms. They are trained on real or synthetic data. They emulate logical decision-making based on the data that teams “feed” or make available to them.

Data scientists carefully design these models to gain insight into solving a specific business or operations problem, identified in partnership with analytics and data management teams. Now, let’s look at how ModelOps derisks AI.

Look to ModelOps to derisk AI productivity challenges

When it comes to securing AI talent, it’s extremely challenging. LinkedIn research estimates a 74% job growth in the US and a #1 “hottest job” ranking in the UK. And when you do get a data scientist or data science team on board, they are usually inundated with requests that lead to a severe project request backlog. When data science resources are limited,  it may be impossible for an organisation to scale AI-fuelled insights and decision-making.

One approach that can reduce the pressure on limited data science resources is through ModelOps. It supports the automation of model management and the democratisation of model production and deployment. ModelOps also helps derisk talent constraints and thus addresses productivity challenges. Instead of constantly making your data science team swim against the tide of increasing demand, this approach helps free them to focus on their highest value to the organisation.

A ModelOps approach keeps your data science unicorns focused on exploring, testing, and innovating through AI. It allows your data engineers and analysts to put more models into production.

A related challenge that ModelOps meets head-on is scalability. Creating models at scale doesn’t only refer to the number of models. Scale relates to how broadly AI is used in a business’s systems and processes.

Remember the talent challenge? More integration means more models are required to unlock the benefits of AI. It requires more data scientists to support model development and deployment. Without ModelOps, you run the risk of deployment challenges that will result in failed AI. By using ModelOps to democratise model deployment at scale, you have a catalyst that will move the business from incremental to breakthrough advantages from AI.

Look to ModelOps to derisk AI via improved transparency

The second risk is reliance on “black box” AI solutions. We know that regulation is coming that will make the best practices of interpretability and transparency for all AI systems a must. Even if a partner or vendor provides your AI technology, your organisation could be liable for biased or flawed outcomes. Forthcoming and current regulations also pose real risks of legal issues, fines, non-compliance, and even reputational loss.

The Stanford Institute for Human-Centered Artificial Intelligence noted in its 2022 AI Index report, “Globally, AI regulation continues to expand. Since 2015, 18 times more bills related to AI were passed into law in legislatures of 25 countries around the world and mentions of AI in legislative proceedings also grew 7.7 times in the past six years.”

It’s imperative to understand current and planned AI use, and plan for AI transparency and interpretability across your business. Transparency in AI is not just for risk management. It also supports Environmental, Social, and Governance (ESG) initiatives, especially when addressing community concerns about the fair and ethical use of AI.

Why is transparency a challenge?

There’s a reason transparency is a challenge. Machine learning and AI algorithms and models aren’t exactly easy to understand. The number of parameters – settings for the conditions of their operation – they utilise and how exactly these parameters interact with one another can become downright complex.

This complexity is exacerbated by the way these models interact with one another, and how they integrate with data. I always say that with complexity comes opacity. It makes it difficult for people to interpret how the model makes decisions and identify if these decisions contain bias.

When ModelOps is applied in this process, an organisation can marry technologies, people, processes, and data while better managing transparency in model development. Where models are often managed through a collection of poorly integrated tools, ModelOps can ensure transparency of the oversight of model testing, versioning, model stores, and model rollback. A unified and interoperable approach to ModelOps supports governance and transparency that reduces regulatory risk and bias.

So, when do you deploy a ModelOps approach? When you want to scale AI projects, decrease complexity in managing a net of poorly integrated development tools, and prepare for regulatory compliance.

What are the benefits of ModelOps? Derisking AI-infused processes, creating a governable AI framework, and ensuring transparency. And it achieves this through streamlined management of AI and decision models from their inception to their development and, ultimately, their productive and ethical use.

A derisked AI reality

We know ModelOps approaches work. One specific success story is the National University Health System (NUHS) in Singapore. To gain a full view of the patient journey and garner an understanding of a growing ageing population, NUHS developed its ENDEAVOUR AI platform using ModelOps management. The platform has enabled the organisation to derive a complete view of patient records, uncover real-time diagnostic data, and make diagnostic predictions.

Dr Ngiam Kee Yuan, group chief technology officer at NUHS, said, “Our state-of-the-art ENDEAVOUR AI platform drives smarter, better, and more effective healthcare in Singapore. We expect [ModelOps] will accelerate the deployment of safe and effective AI-informed processes in a more scalable, containerised way.”

Those organisations using ModelOps quickly benefit from a more straightforward path to ML and AI model deployment. They have also democratised the development of AI by ensuring that data engineers and data analysts can enter the process without risk of failure because they have a qualified and effective framework to work with.

Risk-free AI unlocked by ModelOps

Don’t let talent shortages or AI legal and reputation risk from deploying “black box” solutions limit your AI value realisation. Scalable, robust ModelOps lays the paving for a road to AI that lets you “derisk with benefits”. It ensures that the AI models you develop and deploy adapt to your organisation’s changing needs. It also creates the governance agility required to keep you one step ahead of the ever-changing regulatory environment.

TibcoTIBCO Software Inc. unlocks the potential of real-time data for making faster, smarter decisions. Its Connected Intelligence Platform seamlessly connects any application or data source; intelligently unifies data for greater access, trust, and control; and confidently predicts outcomes in real time and at scale.


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