Enormous potential is attainable with AI, but so is an increased possibility of risk.
Artificial Intelligence (AI) has rapidly transformed commerce to the point where there’s really no area in modern business that hasn’t been touched by it. That means that if you have not started your AI journey, now is the time to consider how to maximize the value AI can bring to your organization. However, despite the significant benefits that may come with AI, this technology can also introduce risk. As such, it’s important to implement guardrails at the outset of your journey to protect, monitor and control your AI implementation.
AI refers to the science of simulating human intelligence in machines, allowing them to perform tasks that previously only human beings could handle. Many AI machines attempt to find the best way to solve problems by analyzing data and finding patterns to inform their output. While AI may feel like a new idea, the concept behind it dates to the 1950s — and the AI journey has been evolving ever since.
BPM’s core principles for a successful AI journey
Three core principles are at the heart of BPM’s AI strategy:
- AI is not new, and it is here to stay. It is not a question of if you should consider implementing AI into your operations. It is a matter of how and where you can best leverage it. AI is not a recent phenomenon; it has been around for more than two decades. However, generative AI has recently taken operational use cases into new territory as a growing number of companies explore its potential, causing a fundamental shift — on par with the internet and the cloud—that is reshaping the future of commerce.
- AI is a positive force. AI is disrupting and transforming certain professions. We view this as a net positive development — increasing value and enabling workers to spend time on more complex projects and developing future innovations. AI can also optimize learning and training to provide more effective programs tailored to each individual’s needs and supporting improved performance outcomes.
- Security and controls are key. Great potential is attainable through an effective AI strategy, but there is also an increased possibility of risk. It is critical for businesses to consider how they will protect, monitor and control their AI implementation — and specifically, what guardrails they will put around it.
The importance of controls
In the current economy, many companies are thinking about how to optimize current processes as much as possible. AI has a critical role to play in that scenario. The priority for most middle-market organizations today is pinpointing how they are currently using AI (i.e., identifying any “shadow AI” scenarios). They are working to figure out how best to embed AI into their operations (via various use cases) to bolster productivity. And the teams responsible for risk may not be a part of that conversation.
We’ve seen this story play out before with prior digital transformations, where in the rush to innovate, security and controls were often an afterthought. And then afterwards, the vulnerabilities that could have been prevented suddenly emerge. This same trend is happening once again, except only this time the spotlight is much bigger and brighter — and the pace of innovation is much faster.
Given how quickly AI is fueling change, companies will need to move faster around controls to keep up with risks they may introduce with AI integrations. These challenges can be mitigated by including the risk function in the AI journey from the beginning.
The risks that come with AI are significant and evolving. For example, generative AI tools operate on large amounts of data. These systems are not necessarily set up to comply with General Data Protection Regulation (GDPR) and other laws. That’s why it’s critical to monitor their use. Additional factors to apply guardrails around include protection against intellectual property and copyright infringements, potential fraud and cybersecurity attacks and sustainability concerns, among others.
Changing regulatory and risk landscape
It’s important to get ahead of this now, because soon controls around AI will likely be mandatory. Currently the regulatory landscape in this area is in flux, but there has been recent progress. For example, the U.S. Government has published Executive Order 13960 which established rules around the safe and secure use of AI in the public sector — and more legislation is expected on the horizon.
In the EU, they’ve voted on the EU AI Act which seeks to govern the use of artificial intelligence in what is set to be the world’s first comprehensive AI law. Legislation is also in pending in Canada, Singapore and other jurisdictions. Stanford University’s 2023 AI Index shows that 37 AI-related bills were passed into law globally in 2022 alone.
Building your AI security controls framework now will lay the groundwork for addressing upcoming regulatory changes coming down the line, as well as help your organization get ahead of emerging risks such as model inversion, data poisoning and more.
How BPM and Cranium can help you navigate the AI journey
There’s pressure on businesses right now to implement AI as fast as possible, and leadership teams often don’t want to wait for security and risk. It’s widely viewed as an impediment. But we see it differently. We think that controls don’t slow you down, in fact they enable you to hit the gas even harder because you now have the proper guardrails in place.
Our seasoned professionals across Advisory, Technology, Cyber Risk and more are leading the charge to bring Artificial Intelligence and process automation to middle market companies. As a middle market firm ourselves, we know firsthand about the opportunities, risks and challenges that AI offers as we work to implement these principles into our own operations. Our team can help you perform an AI governance assessment and implementation, or a related service that can help set you up for success on your AI journey. Contact us to today to learn more.
Cranium and BPM have joined forces to help middle market companies think through risk mitigation in their AI implementations.