6 Reasons Why Your Organization’s Generative AI Strategy Might Be Struggling

Here at Portal26 (formerly Titaniam), we stand as a driving force in enabling enterprises around the world to harness one of the most significant technological advancements of the past decade; generative AI. 

While we’re an award-winning cybersecurity company, we have received extensive recognition over the last year from analysts, experts and the wider industry for our generative AI visibility, insight, security and GenAI governance platform.

The platform we’ve developed empowers businesses that have adopted (or are yet to adopt) this emerging intelligence, by providing an array of foundational capabilities to help them both scale and monitor their generative AI applications. Generative AI is supporting unprecedented productivity and business transformation – not to mention the competitive edge that it boasts – and we’re proud to help enterprises to capitalize on these advances. 

In fact, we’ve noticed that many enterprises that have already invested in generative AI are coming up against some common challenges, and these issues tend to be caused by a few key culprits. If you’re facing any of the following, then it’s time to make your generative AI investment work harder.

1. You’ve not set clear Gen AI KPIs

It’s common knowledge that any enterprise strategy ought to begin with clear KPI’s, but not all teams are able to apply the metrics they’re most familiar with to their newly adopted generative AI platforms. If you haven’t established specific KPIs tailored to your organization’s objectives, such as accuracy in threat detection, response times, and overall system visibility, then you won’t have an accurate understanding of how you’re benefiting (or will benefit) from your investment. 

By implementing measurable benchmarks, you gain the ability to track progress, identify areas for improvement, and ensure that your GAI solution is aligned with the bigger picture – for example, your overriding growth, security and or governance goals.

2. You’ve not invested in Gen AI employee training

The effectiveness of any Gen AI solution is closely tied to the proficiency of the team using it. If your workforce is not adequately educated about the features and capabilities of your generative AI tool, its full potential remains untapped. Investing in comprehensive training programs ensures that your team can harness the tool to its fullest extent.

3. You’re seeing data anomalies in your Gen AI analysis

Anomalies in data analysis ought to be seen as red flags, as they are the clearest signifier of gaps in your generative AI usage. If you observe inconsistencies or irregular patterns in the data processed by the generative AI tool, treat them as an opportunity to enhance its response mechanisms (i.e, the prompts in use).

4. You’re not aware of the impact generative AI has on your organization (good or bad)

When generative AI isn’t being measured, monitored, or managed, your enterprise could be hit with a nasty consequence in terms of your ROI. Organizations should avoid seeing their entire generative AI budget invested in the tools themselves – rather, a significant portion of this expense should be spent supporting ways to ensure full visibility over the technology. Once your organization has understood the importance of visibility in generative AI applications, you’ll be able to review the bigger picture of your investment.

This might look like nurturing accountability in the C-suite of your organization, or setting periodic adoption management checks, and both actions serve to enhance the way generative AI is currently being utilized. Having clear insights means having clarity over real time data, and many organizations sideline this value as they fixate on the initial benefits of the technology itself.

5. There are clear discrepancies in your Gen AI threat detection and response times

Another sign that your generative AI platforms may not be operating at its full potential is noticing discrepancies in threat detection, or delays in response times. This oversight exposes your business to potential infiltration, compromising data safety, and a clear loss on your overall investment in AI.

6. Your enterprise doesn’t have any form of Gen AI governance

Rolling out a generative AI system without an established governance framework will most likely lead to misuse – whether intentional or unintentional. If your employees are currently using generative AI platforms on their own autonomy, rather than being guided by an ethics-led, corporate code of conduct, then you won’t see the best of your solution.

Key areas organizations should consider before investing in generative AI

In order to unleash the full potential of generative AI for business purposes, organizations must account for the following considerations in regards to employees: 

• Where are employees using generative AI?
• How are employees using generative AI?
• What type of generative AI tools are being used?
• Is all usage respectful of applicable risk factors?

Businesses also need to review their own stance in regards to the following, overarching pointers: 

• Are there internal policies for acceptable use of generative AI? Are they clear and concise for staff at levels to understand and work in line with?
• Are organizations monitoring whether these policies are being followed?
• Is there a defined governance standard for employees to follow?
• Are there any generative AI knowledge gaps within the workforce?

This is all underlined by the business’ core purpose for using generative AI, and how all of the aspects we’ve highlighted can be monitored and measured over time. Accounting for these factors will inform internal decision-making processes, outlining the most impactful ways that an enterprise can support adoption and acceleration of generative AI platforms.