Why AI Resilience Requires Foresight, Infrastructure, Continuous Learning

Helping companies become AI resilient is a complicated but essential process for what comes next in the channel.

So claims Josh Santiago, chief strategy officer at Dynamic Consultants Group. AI resilience requires a multifaceted approach to manage the specific challenges that companies face in consulting, workforce preparation, responsible AI development and other practices.

Santiago will lead a session titled, "Building AI Resilient Organizations" at the upcoming MSP Summit, Sept. 16-19, in Atlanta.

Channel Futures caught up with Santiago and asked him about his upcoming session.

DCG's Josh Santiago

Channel Futures: What are your general thoughts on what it takes to build AI resilient organizations? What's necessary to do so? Why is it important?

Josh Santiago: Building AI resilience is crucial in the next phase of this technological jump. It requires a multifaceted approach encompassing strategic foresight, adaptable infrastructure, and a culture of continuous learning. Organizations must invest in developing AI literacy across all levels, from leadership to frontline employees, to make informed decisions about AI integration. Simultaneously, they must establish robust data governance frameworks and ethical guidelines to ensure responsible AI use. Flexibility in business processes and technology stacks is essential to adapt to AI advancements swiftly. Organizations that fail to build AI resilience risk obsolescence, while those that succeed will be better positioned and take massive market share. What are the largest barriers to AI resilience?

Related:Register Now for The MSP Summit, Sept. 16-19, Atlanta

CF: What is required to ensure ethical AI development?

JS: Teams of developers, ethicists and domain experts from varied backgrounds should collaborate to identify and address potential biases and ethical concerns. This team, coupled with robust ethical frameworks, will help craft clear guidelines and principles that prioritize fairness, transparency, accountability and human rights. Another factor is transparency and explainability: AI systems should be designed with interpretability, allowing end users to understand how decisions are made and enabling meaningful human oversight.

The last two things are privacy protection and regular audits. Teams must focus on maintaining strong data governance practices, and privacy-preserving techniques must be implemented to safeguard individuals' personal information. Assessments for continuous evaluation of potential biases, unintended consequences and ethical implications are crucial. These things must be underpinned by accountability mechanisms that drive clear lines of responsibility and liability for AI-driven decisions, with processes in place for redress when errors or harms occur.

Channel Futures: How should channel partners transition into AI-enhanced consulting paradigms?

JS: Microsoft partners' transitioning begins by investing heavily in skill development, both upskilling existing staff (like Power Learn Academy) and hiring AI experts. Simultaneously, they must expand their service portfolios to include AI-driven solutions, focusing on specific industries or business functions where Microsoft's AI tools, like Copilot and Azure OpenAI, can provide significant value. These partners forge strategic alliances, enhancing their capabilities. They start with small-scale proof-of-concept projects, gradually building expertise and client trust. Throughout this transition, they emphasize robust change management practices. As they evolve, these partners become not just service providers but also educators, helping clients understand and leverage AI's potential.

CF: What needs to be done to ensure employees are appropriately reskilled in AI maintenance and creation?

JS: First, partners must begin with comprehensive skills assessments to identify gaps in workforce capabilities. From there, they can work with another partner or use an internal team to develop tailored training programs that blend theoretical knowledge with hands-on experience, often partnering with educational institutions like Power Learn Academy to access cutting-edge curricula. These companies foster a culture of continuous learning, encouraging employees to experiment with AI in real-world projects and learn from successes and failures. They implement mentorship programs, pairing AI experts with newcomers, and create cross-functional teams to spread literacy throughout the organization. Leadership plays a crucial role, championing the importance of these new skills and leading by example in embracing new technologies. Regular skill audits help these organizations stay agile, adjusting their training focus as AI rapidly evolves.