Breaking Down the AI Mental Block

Here we are in the last month of 2024, with the AI revolution in full swing. Isn’t it amusing that businesses globally are still intimidated by AI investments? We are still playing catch-up, endlessly pitching the benefits of chatbots and automation. Why Are business leaders still hesitant to dive headfirst into this AI-powered future? What’s holding us back?

I believe it boils down to two primary mental blocks, the two Cs: Cost and Cybersecurity. It’s high time we tackle each one of them head-on!

Is the price tag of AI worth the potential benefits? With not enough business use case success stories to answer these questions, CEOs struggle to validate and budget the cost of AI implementation. To break this mental block, let’s first try to understand what contributes to it:

1. Assumption of High Costs: Business leaders assume that AI is very expensive, but we don’t have any direct AI benchmarks to validate that.
2. Compute/Platform Costs: Large data volumes can make basic data processing costly. Going by this, AI reading and interpretation of large-scale datasets might be perceived as even more expensive.
3. Engineering Costs: Since AI is a relatively new arena, a new skill set needs to be developed within the team. This involves lots of experiments and the development of best practices. Supportive infrastructure, such as semantic layers, may need to be developed and matured to ensure that AI is built on the best data foundation.

While it’s true that AI projects can be resource-intensive, it’s crucial to shift our focus to return on investment (ROI). Organizations can make informed decisions about AI adoption by clearly defining the business value and understanding both short-term and long-term benefits.

Here are three mindset changes to consider:

1. Making Informed Decisions Around AI Adoption – It’s called an ‘informed decision’ for a reason: Start with the ‘informed’ before proceeding with the ‘decision.’ We need to make ourselves as AI-informed as possible. Explore the art of the possible: review case studies, evaluate emerging applications, assess pitfalls and cautions, measure risks, experiment, dream, and conduct ‘what-if’ scenarios. Inspiration and ideas will follow.
2. Define an AI-native future-state vision that will guide a long-term journey, but then carve out a Minimum Viable Product (MVP) focus area as a starting place. The MVP will provide much-needed experience, enable the creation of best practices and reusable designs, inform of things NOT to do, and help guide the rest of the AI revolution in other parts of the overall enterprise.
3. Pursuit of the unknown - Without an articulated business use case, there can be no ROI analysis. That’s not to say that the use cases for AI don’t exist; it’s just that the paradigm shift to an ‘AI-enabled’ world hasn’t occurred yet with product and technology leaders, or they aren’t able to articulate them. There is a common belief that more and more of those “aha” moments are occurring, but achieving those moments takes an active and deliberate pursuit of the unknown (absence of ROI).   We will eventually reach a tipping point where AI will be a pervasive part of our solution architecture, and we’re starting to see those moments happen now. More ROI stories will follow.

For now, CEOs must remember that the cost of inaction in the face of AI disruption can be far greater than the cost of adoption.

The more I talk to business leaders, the clearer it becomes that cybersecurity is seen as a major barrier to AI adoption! With millions of dollars at stake, you can’t blame anyone for being a bit intimidated. But remember, Cyberattacks aren’t new, and neither are the defences.

To break this mental barrier, let’s break down the key concerns out there:

1. Data Privacy Violations: The fear of sensitive data being compromised is paramount. AI models often process vast amounts of personal and confidential information, making them potential targets for malicious actors.
2. Business-Critical Data Theft: The loss of proprietary information can have devastating consequences for businesses. If not adequately secured, AI systems could become a gateway for hackers to steal valuable data.
3. Regulatory Noncompliance: With increasing data privacy regulations like GDPR and CCPA, businesses must ensure that their AI implementations adhere to these standards. Failure to comply can result in hefty fines and reputational damage.

These are valid concerns, but they can all be worked out. Business leaders must approach cybersecurity strategically when investing in AI!

Here are some key factors to consider:

1. Formalize Acceptable Use Policies: Establish clear guidelines for the use of GenAI solutions within the organization to enable CISOs to implement tailored security controls.
2. Manage and Control Usage: Implement measures to monitor and control the use of hosted and embedded GenAI applications.
3. Update Application Security Practices: Perform static analysis (SAST) and software composition analysis (SCA) on code generated by AI to identify and mitigate vulnerabilities.
4. Train Employees: Educate the information security team and end-users on cybersecurity best practices for GenAI to prevent accidental breaches.
5. Ensure Vendor Accountability: Require GenAI service providers to address privacy, copyright, traceability, and explainability challenges.
6. Implement Content Anomaly Detection: Use tools to mitigate input and output risks and enforce acceptable use policies.
7. Establish Responsible AI Principles: Develop clear guidelines for AI’s ethical and responsible use.

Cybersecurity should not be an afterthought in AI implementation. It must be a foundational pillar from the very beginning.

Also, remember that artificial intelligence is a game-changer in cybersecurity. AI is democratizing cybersecurity by making advanced security solutions more accessible and affordable for businesses of all sizes. From reducing costs to enhancing threat detection and response, AI offers numerous benefits that help companies protect their digital assets. Investing in AI-driven security solutions can yield substantial financial returns, with businesses reporting an average cost savings of $2.22 million in 2024 due to reduced data breach impacts!

The ‘Cs’ are merely myths. We truly need a robust AI adoption strategy to address both cost and cybersecurity concerns.

Join the AI race today—whether you choose to crawl, walk, or run, the choice is yours! Reach out to us to start transforming your business with AI.