Year after year, organizations across industries have continued to push the limits of their technology capabilities through the increased adoption of AI. To put this into perspective, global investments in AI more than doubled in 2023, reaching $200 billion, and the market is now expected to reach a valuation of nearly $2 trillion within the next five years, predominantly driven by the advent of generative AI.

However, increased investment in AI does not guarantee positive outcomes or a faster ROI. In fact, many AI projects are falling short of expectations due to challenges rooted in data privacy and security.

Data Privacy and Security as Top Barriers to Successful Implementation

While AI adoption has its inherent challenges, the research shows that most organizations believe issues around data privacy and security are the most prominent barriers to success.

Overall, this shouldn't come as much of a surprise. After all, 63% of global consumers remain concerned about AI's potential to compromise their privacy and expose their personal data. Given that generative AI adoption is still in its early stages, organizations must also contend with the inevitable introduction and evolution of regulations regarding the secure and ethical use of these technologies.

Moreover, it appears that many organizations are prioritizing a rapid AI adoption strategy to keep pace with their competitors. This approach, while potentially driven by the desire to quickly leverage AI's capabilities, may lead to rushed investments and adoption campaigns. Striking a balance between gaining new capabilities and proactively building trust with consumers and regulatory institutions is crucial for long-term success.

Despite the feeling of being caught between a rock and a hard place, treating privacy and security as an afterthought will inevitably hurt an organization's AI efforts in the long term. Sure, you could be the fastest to introduce the latest innovation, but would it really be worth putting your compliance posture and brand reputation at serious risk?

Can Spending on AI Result in Overcoming These Challenges?

When faced with challenges, organizations often default to allocating more resources, especially financial, to find solutions and improve outcomes. However, when it comes to critical issues like privacy and security in AI, simply increasing investment doesn't guarantee better results. Our recent examination of AI spending trends confirms that AI adoption is a top priority for organizations, with 25% planning to increase AI spending by over 50% and 8% planning to increase it by up to 100% or more in the coming years.

Yet while these planned increases in spending may point to the fact that organizations have seen increased funding result in greater success, in actuality, this isn't the case. The truth is, only slightly more than half (51%) of businesses have described existing projects as being “very successful,” suggesting a notable disconnect between AI investment and outcomes particularly in relation to privacy-centric goals.

Why Foundational, Outcome-Centric Approaches to AI Adoption Will Win the Day

To be clear, organizations aren't necessarily choosing to ignore privacy and security when investing in AI. Rather, they have often rushed into AI adoption without a clear vision of what they hope to accomplish. And therein lies the ultimate paradox: Everyone's goal is to maximize the ROI associated with their investments in AI, but few have established concrete goals and a distinct set of processes for achieving them.

Again, the most reasonable explanation is that many organizations are simply more focused on time-to-market than actual outcomes. While there's no denying that recent advancements in AI have caused an increase in competition and subsequent sense of urgency, it's crucial for business leaders to remember that the most successful strategies are those that are outcome-centric, prioritizing proper governance, measurable frameworks, and adaptive change management processes over rapid implementation and industry hype.

To ensure AI success, organizations should prioritize a privacy-first approach from the outset. This involves incorporating data privacy and security measures into the development process, aligning with the DevSecOps methodology. By proactively addressing compliance and fostering public trust, organizations can fully capitalize on the potential of AI while mitigating risks.

But what would such an approach look like in practice? While there's no official playbook and strategies will inevitably vary depending on the industry and desired outcome, there are at least a few areas that organizations can focus on to ensure privacy and security are integrated at the earliest stages of adoption and maintained throughout the entire AI lifecycle:

• Proactive compliance: Although the regulatory landscape around AI is still in its developmental stages, businesses can and should be proactive by ensuring any AI systems and/or products they create are in compliance with existing data protection regulations and frameworks, such as the General Data Protection Regulation and the California Consumer Privacy Act.

• Privacy-centric data collection processes: Consumers today are increasingly protective over their personal information and have come to value trust more than ever when it comes to the brands they engage and interact with. As such, organizations looking to implement AI should focus on creating products that respect consumer privacy and only collect, process, and analyze data that is needed to support the functionality and evolution of their underlying systems.

• Continuous risk assessments and threat modeling: Finally, leveraging advanced cybersecurity tools to perform regular threat modeling and risk assessments on new AI-based solutions is crucial for successful adoption. Not only will this better position organizations to mitigate emergent threats, but it will also help establish processes for ensuring compliance with both existing and evolving privacy regulations.

Ultimately, the AI race is a marathon, not a sprint. Organizations that prioritize privacy and security from the outset, building trust with consumers and regulators, will be the ones who truly thrive in the long run. Short-term gains achieved at the expense of trust will ultimately prove unsustainable in this evolving landscape.