Leading the AI Revolution: Critical Challenges and Solutions for CIOs and IT Procurement Teams

Generative AI is revolutionizing industries by automating tasks, generating content, and enhancing decision-making. As these technologies rapidly advance, they are set to become even more industry-specific and efficient, leveraging synthetic data and AI-augmented development. According to McKinsey, AI could potentially deliver additional global economic activity of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. This accelerated evolution demands immediate action from CIOs and CPOs to proactively navigate these advancements and the resulting AI challenges, ensuring their organizations remain competitive and innovative.

To thrive in this evolving landscape, companies must address several critical challenges in the procurement and integration of AI technologies. This includes developing clear strategies for engaging with AI-powered vendors who are increasingly leveraging generative AI in their sales and marketing processes. By addressing these challenges head-on, organizations can harness the full potential of generative AI, driving innovation and maintaining a competitive edge in the digital era.

Here are seven challenges CIOs and CPOs must proactively navigate and ways to mitigate those challenges as you deploy AI-driven technology.

1. Enabling Client Procurement Processes with Generative AI Technology

Generative AI technology promises to enhance procurement processes by improving data analysis and decision-making capabilities. However, successful implementation requires effective change management, high-quality data acquisition, and seamless integration with existing business systems. Developing flexible and resilient procurement processes is essential to adapt to market changes and disruptions. Embracing AI can drive efficiency and innovation, ultimately leading to more informed and strategic procurement decisions.

2. Procurement of Generative AI-Enabling Technology

Procuring generative AI technology involves selecting the right tools and platforms to ensure they integrate well with existing systems. Organizations must address contractual clarity regarding data use and intellectual property while managing the high costs associated with AI technologies. Effective data management and robust security measures are crucial, alongside building internal AI expertise and incorporating sustainability practices. Negotiating indemnification clauses and securing performance guarantees are essential to mitigate legal risks and ensure the reliability of AI outputs.

3. Procuring Services with Embedded Generative AI Technology

Evaluating services embedded with generative AI involves assessing their potential cost savings and impact on efficiency. For instance, our research indicates that systems integrator productivity in support of SAP ECC to S4 migrations can be improved by 33% in both greenfield and brownfield scenarios.

However, organizations must be cautious of overbuying generative AI technology from multiple vendors because this could lead to redundant capabilities and inflated costs. This parallels the ERP systems boom, where many companies unknowingly purchased overlapping functionalities from different vendors, resulting in wasted resources and integration challenges.

Organizations must also address risks related to reliability, quality control, and data security while adapting to new pricing models that reflect reduced labor components. Building collaborative relationships with vendors is crucial for co-developing tailored AI solutions that meet specific business needs. This ensures both parties benefit from the advanced capabilities of generative AI.

4. Dealing with Vendors Using Generative AI in Sales and Marketing Processes

Organizations face significant challenges as vendors increasingly leverage advanced generative AI in their sales and marketing processes. These next-generation AI tools enable vendors to access broader datasets and deploy superior models compared to their clients, leading to a marked information asymmetry. Vendors with these advanced AI capabilities can craft highly personalized sales pitches, predict client needs more accurately, and propose optimized solutions that might be beyond the client’s current technological understanding.

This disparity places clients at a disadvantage, as they may lack the comprehensive data and advanced AI models that vendors possess. To mitigate this challenge, organizations should consider partnering with third-party advisors who can provide critical insights, benchmarks, and advanced technology assessments, helping level the playing field with better information and strategies.

5. Managing Risks in the Generative AI-Enabled IT Supply Base

Managing risks in the AI-enabled IT supply base involves ensuring vendor compliance with security and ethical standards and auditing AI systems for fairness and bias. Promoting redundancy and resilience in AI deployments is crucial to avoid single points of failure. Additionally, it’s crucial to be aware of potential legal risks, such as the pending Mobley vs. Workday lawsuit concerning data security breaches and discriminatory impacts of AI screening tools. Establishing comprehensive legal frameworks helps manage risks associated with AI in the supply chain, ensuring that all AI systems operate within defined ethical and legal boundaries.

6. Ensuring Regulatory and Privacy Compliance for Clients and Vendors

Ensuring regulatory and privacy compliance involves not only adhering to current and evolving laws but also upholding responsibilities to customers by providing transparency about AI practices. The newly enacted EU Artificial Intelligence Act, effective from August 2024, imposes stringent regulations on high-risk AI systems. This legislation mandates detailed risk assessments, transparency measures, and compliance with fundamental rights to ensure AI systems are safe and trustworthy. Organizations must integrate procurement data into business planning for enhanced compliance and ensure that AI-related procurements meet stringent data privacy standards. Both clients and vendors have a duty to maintain clear communication about data usage and regulatory adherence, building trust through transparency and accountability in AI deployments.

7. Ensuring Pass-Through of Savings from AI Advancements

Monitoring AI advancements to capture cost savings and negotiating terms that ensure clients benefit from technological progress is essential. The next 18 months will see significant improvements in the quality and size of AI models, with frontier models becoming more sophisticated and efficient. Advances in model optimization techniques, such as quantization and low-rank adaptation (LoRA), will enable the development of more compact yet highly effective models, reducing computational costs while improving performance.

Developing metrics to measure the impact of AI tools and investing in training to build internal AI expertise are critical steps. Managing compute costs associated with AI operations and utilizing advanced analytics to drive superior sourcing decisions will help organizations optimize their investments in AI. Ensuring that cost savings and efficiency gains from AI advancements are transparently passed through to clients enhances the overall value and trust clients put in AI.

For instance, one of our Canadian clients has been working under a fixed-price agreement with a large systems integrator for the past two years to deploy SAP. Recognizing the advancements in AI, we are facilitating discussions between the client and the vendor to incorporate these new technologies into the engagement. By leveraging AI to optimize various aspects of the deployment, both parties are looking to collaboratively create a win-win scenario. This will not only reduce the overall project costs but also significantly improve implementation efficiency, demonstrating the tangible benefits of staying at the forefront of AI advancements.

How UpperEdge Can Assist with Generative AI Challenges

At UpperEdge, we recognize the transformative potential of generative AI and the complexities it brings. As your trusted advisor, we offer a comprehensive suite of services to help you navigate these challenges effectively. Our expertise in IT sourcing and negotiation ensures that you make high quality decisions regarding AI tools and secure favorable contractual terms, addressing critical issues such as data use, intellectual property, and cost management. We tailor our strategies to support risk management, compliance, and vendor collaboration, providing the necessary guidance to optimize your AI investments and maintain a competitive edge.

Our IT Cost Optimization Services leverage proprietary benchmarks and data-driven methodologies to deliver commercial terms that ensure long-term value, flexibility, and predictability. Additionally, our Project Execution Advisory Services maximize project success through diagnostics, risk mitigation strategies, and supplier engagement tactics, ensuring accountability and value realization from AI investments.

By partnering with UpperEdge, you can confidently address the challenges of generative AI, ensuring successful implementation and strategic advantage in the rapidly evolving digital landscape. Contact us today to learn how we can help.