Enterprises worldwide are racing to integrate AI into their operations. Businesses want to swiftly harness AI’s potential to improve efficiency and fuel innovation. However, many of those same businesses find themselves staring at a huge gap that separates ambition from readiness.

A survey from Gartner found that only 54% of AI projects make it from pilot to production, and this was attributed to challenges in scaling, business alignment, and governance complexity. Results from Cisco’s AI Readiness Index report agree. Ninety-seven percent of companies are experiencing an increased urgency to deploy AI-powered technologies, but only 14% of them are actually ready to integrate AI into their businesses. 

According to these numbers, it’s likely that your enterprise falls in the “not ready” camp.

However, with purposeful reflection on what it means to be ready for AI, your enterprise can take concrete steps toward fully and successfully integrating AI into your operations.  

What is AI readiness? 

AI readiness goes far beyond enthusiasm for innovative technology. AI readiness requires a critical assessment of your organization in the following areas: 

• Infrastructure: As you consider your technology landscape, including hardware, software, and network capabilities, evaluate whether they support AI applications. 
• Data management practices: Effective AI systems require strong practices in the areas of data quality, accessibility, and governance. Determine whether your organization has these practices in place. 
• Workforce AI literacy: Evaluate the current skill levels around AI, identifying skillset gaps that training and hiring can address. Take heart; the AI skillset gap is a common challenge among all enterprises today. 

These areas are interconnected. A robust infrastructure supports advanced data practices while a knowledgeable workforce is required to leverage infrastructure and data to harness AI’s full potential. 

How to conduct an AI readiness assessment 

Your first step toward AI readiness is to organize your internal processes. Determine where your enterprise stands for each area and create a roadmap to put the necessary pieces in place.  

Step 1: Begin with low-cost-of-failure use cases 

To kickstart AI integration and adoption, focus first on use cases with high potential impact but low risk of failure. This strategy allows you to explore AI's capabilities without the danger of significant setbacks. 

Identifying such use cases begins with understanding your business processes and pinpointing areas where AI can deliver quick wins, such as automating routine tasks, enhancing customer service, or optimizing operations. These initial projects serve as practical learning experiences. From this learning, you can gauge your readiness and refine your approach. As a result, your enterprise will build confidence in its ability to leverage AI technologies. 

Starting with this cautious yet strategic approach will lay a solid foundation for more ambitious AI endeavors.  

Vijoy Pandey, SVP and Head of Outshift by Cisco, spoke in an interview about determining initial use cases for AI in your business. Listen in below. 

Step 2: Employ initial use cases as diagnostic tools 

Your initial AI use case endeavors will reveal a lot about your organization. They’ll serve as diagnostic tools, uncovering your gaps in skills, data management, and technological infrastructure. Initial AI projects may validate or refute your assessment of your organization.  

The outcomes and challenges of these pilot projects will help your organization identify specific areas for improvement and development. It may feel like a slow and arduous start, but your adjustments at this stage will pave the way for more complex and impactful AI applications in the future. This approach ensures a strategic, informed path to scaling your AI efforts. 

Step 3: Secure data and AI pipelines 

Securing your data and AI pipelines is crucial to maintaining the trustworthiness of your AI solutions. Two main concerns drive the need for robust AI security measures. First, your organization must safeguard itself against the threat of data breaches. The effectiveness of AI solutions hinges on the quantity, quality, and diversity of your data. This makes your data an incredibly lucrative target for cyber threats. To secure your AI and data pipelines, implement comprehensive cybersecurity procedures, such as: 

• Encrypting data both at rest and in transit 
• Using secure authentication mechanisms 
• Updating and patching your systems and software tools regularly 
• Employing role-based access control (RBAC) to ensure only authorized individuals have access to sensitive data 
• Implementing regular security audits 
• Monitoring for unusual activity or signs of data exfiltration 

Your second AI pipeline concern involves ensuring your AI models are free from data bias, toxicity, and inaccuracies. These issues can compromise the integrity and reliability of your AI models, leading to flawed decision-making that may unfairly impact individuals or groups. Some ways you can safeguard your models from these threats include: 

• Using only trusted and vetted data vendors that follow strict data quality and ethical standards. 
• Employing diverse datasets that accurately reflect the real-world scenarios that AI is meant to navigate. 
• Auditing and testing models regularly for bias and accuracy. 
• Implementing transparent model governance practices 

By prioritizing security and integrity in your AI initiatives, you can build AI solutions that are not only effective but also trusted by your users and stakeholders.  

Step 4: Prepare your workforce for AI 

Develop a comprehensive training program to nurture AI literacy across your organization. Provide resources and opportunities for employees to learn about AI technologies, their applications, and ethical considerations. Encourage cross-disciplinary collaboration to foster innovation and facilitate the smooth integration of AI into various business processes. 

Fostering cultural readiness for AI is also essential. This involves cultivating an environment that encourages curiosity, innovation, and adaptability toward AI technologies. Encouraging open dialogue about the benefits and challenges of AI, while actively involving employees in AI initiatives, can help build a culture that embraces AI-driven transformation. 

Continuous learning and professional development in AI equip your entire team to leverage AI effectively, driving enterprise success. 

Step 5: Prioritize robust data management 

High-quality data will be critical to the success of your AI projects. Your enterprise needs to prioritize robust data management, which involves the following: 

• Establishing clear policies for data collection, storage, processing, and analysis. 
• Implementing strong data governance frameworks and ethical guidelines to address potential biases and privacy concerns. 

When your AI projects have these foundational practices in place, you ensure the accuracy, consistency, and accessibility of your data. In addition to bolstering the effectiveness of your AI solutions, this foundation also builds trust in your AI initiatives, ensuring they deliver valuable insights and outcomes for your enterprise. 

Step 6: Iterate for continuous improvement 

Adopting an iterative approach to AI integrations means embracing continuous learning and improvement. By analyzing the insights and challenges of initial AI projects, your enterprise can refine its strategies and address any identified gaps. This ongoing process allows for the scaling of AI efforts, as each step builds on the previous ones and clears the path for more complex and impactful AI applications. 

Equipping enterprises for AI readiness 

Integrating AI into your enterprise operations demands readiness across your infrastructure, data management practices, and workforce capability. Starting with low-risk, high-impact projects helps identify and bridge gaps while securing your data and AI pipelines ensures trust. Each step needs to be in place before you begin to see tangible results from your AI integrations. And they must be in place before you can scale. 

Outshift is leading the global conversation around trustworthy and responsible AI, equipping enterprises with the necessary tools and expertise to navigate the complexities of AI integration. Learn more about Outshift AI here.