The most compelling challenge facing businesses of all sizes today is maximizing the value of AI investments while keeping up with evolving risks and steady stream of regulatory obligations, all within the limits of available resources and funds.
According to McKinsey’s ‘the state of AI report in 2022‘, AI adoption has doubled; however, the report underlines the need to bolster trust. Clearly, AI solutions offer many benefits but also carry downside risks, like two sides of the same coin. The EU and US have introduced guidelines, but these will continue remain works in progress as the risks associated with newer models continue to evolve. Leaders responsible for AI adoption must consider effective risk management to maximize its benefits.
Today’s most pressing challenges facing businesses concerning AI investments:
- Provability of AI business case
- Effective AI governance to ensure performance and forge human-machine alliances
- Striking the right balance between AI performance and risk management
- Preserving trust in AI performance
- Facing steady stream of regulatory uncertainties
You can read more about each below to gain deeper understanding:
Provability of AI Business Case
Businesses are often challenged with translating what worked well during the innovation phase to a scale-up environment.
The well-intentioned business case for AI aligned to the company’s vision and its potential for monetization lacks quantifiable numbers for establishing benefits and associated upside and downside risks. Further, talent and internal expertise are often the barrier.
Due to considerable visibility and awareness on risks surrounding AI adoption, investors are taking a hard stance in the message of ‘reduce risks’ before investing on scale-ups in the matters of trust and legal compliance. Trust encompasses many dimensions, including ethical decisions machines are being asked to make in the absence of what belonged to humans before, associated bias, safety, and the privacy of humans now being transferred to machines as well as ensuring defensive postures for human-AI interactions.
Through lessons learned, we understand it is imperative for a viable AI business case to clearly articulate and quantify its benefits, associated risks, talent requirements, and resources needed to realize them, all within the context of its nature and purpose. The business case should take ethical and legal considerations into account within the boundaries and its scope, as well as at adaptive scale.
Effective AI Governance to secure AI Performance, Value, Equity and Human-Machine Alliance
AI governance vastly differs from a traditional governance framework most organizations are familiar with, and the set of nuances this pose is where most organizations struggle in aligning with its key governing differentiators.
A robust AI governance should not bring strong bias to accountability and transparency but enable comprehensive risk management practices suitable for managing risks and vulnerabilities that are unique to AI systems (some this explained in the Provability of Business Case section).
Furthermore, if AI solutions are built on an open-model or platform-based business model, characteristics specific to such model and its governance are unique and must be taken into consideration. Beyond compliance and risk management, the governing controls necessary to manage partner-based platform ecosystems involve extending trust controls (see Provability of AI Business Case for trust dimensions) and assuring oversight for effective decision-making acknowledging advancements in AI outcomes is a function of data sharing and risk management.
We recognize, an effective AI governance should have deep understanding of ethical and legal implications of AI risks along with sound conceptual knowledge and impact of its adaptive nature. An empowered AI governance should take accountability and oversee human to AI value mapping, its alignment and maintain documentation necessary for building and sustaining reliable trust and compliance.
Finding the sweet-spot: Striking the right balance between AI Performance and Risk Management
The advent of GPT4 has ascertained that the newer advancements in AI and data analytics are a double-edged sword for business.
There is clear excitement of new opportunities and ability for businesses stay competitive and by the same token, have introduced emerging risks and vulnerabilities that need to be proactively and effectively managed and monitored. The Board and shareholders are demanding that businesses find pragmatic solutions to balancing the value waiting to be unlocked with effective risk management strategies all the while staying out of legal complications of emerging legislations.
Preserving Trust in AI Performance
We see this business challenge an extension of ‘Risk Management’. Enterprises have started to recognize that building trust is one of the critical components in driving competitive advantage.
As technology evolves, as seen with GPT4 and other significant advancements in the areas of generative AI, enabling and sustaining trust (see Provability of AI Business Case for trust dimensions) is more complex, evolving and requires domain expertise and cross-disciplinary rigor. Businesses need to find ways to communicate clearly and transparently with customers about how their data is being legally and ethically used and how it is being protected.
Facing Steady Stream of Regulatory Uncertainties
Each advancement of AI model comes with its own set of unique risks and challenges. The compounding effect of this have generated a flurry of regulations mostly awaiting to be enacted.
As always, regulations is a step (or several steps) behind risks posed. Businesses, particularly those operating in a highly regulated industry find this daunting to stay current with relevant laws.
If any of this describes your current challenge, find out how we can help you
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