AI’s potential as a force for positive transformation is immense. It’s no longer a question of whether you should or if you will tap into the power of AI for your enterprise, but how.
Juniper recently hosted over 40 CTOs, CIOs, AI luminaries, and global thought leaders for two days in Las Vegas to get to the heart of this question. Packed with insights, learning, and engaging conversations, the summit exceeded all my expectations. I’ve always been an AI optimist, and after this event, my belief is stronger than ever.
The primary goal of this summit was to help define how organizations can best seize the AI moment. The net result, as detailed in this ebook, was that, to succeed with AI, organizations must consider four key objectives:
1. Define clear AI goals and intentions
What do you need AI to do for you? What are you hoping to achieve? How will you measure success?
The first step with any implementation is to define what you need AI to help you achieve. Real-world use cases highlighted at the summit included:
- Transcribing and analyzing customer service interactions to improve customer satisfaction and operational efficiency
- Generating action items based on employee engagement materials to enhance productivity and employee well-being
- Optimizing enterprise search to provide faster and more accurate information retrieval
- Translating, automating, and performing quality assurance on code to streamline software development processes
- Detecting network incidents and cyberfraud to bolster cybersecurity measures
Interestingly, one statistic that was shared is 82% of organizations feel significant pressure to adopt AI, but only 36% actually feel prepared.
When it comes to measuring the success of AI initiatives, attendees agreed it can prove to be a challenge. Quantitative metrics are often difficult to establish unless AI tools are widely embedded and used consistently in repeatable processes. And qualitative results are broadly open to interpretation and can be highly subjective. Of the companies surveyed, half were not measuring AI initiatives at all, 18% were relying on qualitative metrics, and 32% were using subjective feedback from users to gauge the impact of AI on productivity. As AI adoption grows, more standardized and quantitative measurement methods will emerge and must be applied in order to clearly define ROI and organizational impact.
2. Establish effective AI governance
How will your organization work cross-functionally and balance speed and risk to make it happen?
Moving too fast on your highest priority AI initiatives can cause chaos, such as fragmented adoption, multiple teams purchasing the same or similar products, or private data exposed to public models. A governance framework sets up guardrails for how your organization can use AI safely and responsibly. It spells out where and how AI is applied, which data is used where and how it’s secured, and who is responsible to ensure effective governance (often a combination of the CIO team, product, legal, and other teams).
One takeaway from the event that really resonated with me is that the technical issues will sort themselves out, but having the right guardrails in place and having alignment in your organization around those things as you go through this journey are just incredibly important.
It was suggested that one effective approach to scaling AI deployments is to start with a pilot project in one team. Once the product proves successful, similar use cases can be identified across the organization, allowing the solution to be extended to other areas. This method helps in managing risks and ensuring a controlled and measured adoption of AI.
3. Implement an intelligent AI data strategy
What’s your plan for collecting, securing, and organizing data and putting it into action for your AI?
AI models require large amounts of high-quality data that is well organized and easily accessed. Many organizations today struggle with the reality of data being scattered across different departments and platforms, inevitably leading to data silos. These silos make it difficult to consolidate and access the necessary data; and issues with inconsistent, incomplete, or inaccurate data can introduce bias into AI models. Ensuring compliance and privacy across various systems is also critical to success.
To proactively address these challenges, it’s important to establish a robust data architecture that dictates how data is collected, stored, secured, formatted, organized, and labeled. This architecture should also manage how data is transformed, distributed, and consumed by both human users and AI models.
4. Integrate AI security and policies
Does your organization have the security protocols in place to ensure your AI isn’t exposing your organization (and your users and data) to risk?
By proactively addressing security challenges, you can leverage AI’s benefits while minimizing potential threats. This includes protecting against unauthorized access and data corruption, implementing comprehensive governance policies, ensuring transparency with AI vendors, and monitoring AI activities to prevent inappropriate responses.
Another issue posing significant compliance and data security risks is the unsanctioned use of AI tools by employees and vendors, a.k.a. “shadow IT.” To mitigate these risks, your organization should regularly educate employees, provide training on approved AI tools, and monitor network activity.
I loved the advice Diana Kelley, CISO with Protect AI, gave to our audience on the topic. She said, “I would absolutely start with an acceptable use policy for employees. Do one for development and make sure you talk to the CIO and your team about what you need to look at as you’re adopting new AI.”
Effective governance frameworks, such as the NIST Risk Management Framework, OWASP, and MITRE’s ATT&CK framework, can help your organization manage AI-related risks and ensure the safe and responsible use of AI technologies.
It’s time to seize the AI moment
By addressing these four key areas, your organization will be well on its way to navigating the complexities of AI and seizing the opportunities it presents—today and tomorrow.
Ready? Go deeper into the topic by downloading our How to Navigate Complexity and Embrace Opportunity in the AI Era ebook today.
