AI Costs, Risks, and Pragmatism
Understanding artificial intelligence’s costs, risks, and practical considerations is crucial for businesses embarking on AI projects. The intricacies of AI development and its potential pitfalls make use of AI a matter best explored in depth well ahead of time. Fortunately, if a pragmatic approach for successful AI implementation is taken, the technology can prove very beneficial.
Calculating the Costs of AI
Measuring an AI project’s Return on Investment (ROI) involves very specific considerations unique to the complexities of AI development. This is certainly not a detailed guide on calculating total cost of ownership (TCO) or ROI, as those would be unique to your company’s situation. Still, you should consider these high-level insights into the primary factors that impact AI-specific projects.
Key Factors in Cost Calculation
- Business Understanding: Clearly defined expectations are critical for calculating ROI. A deep understanding of the business problem and its relevance to AI solutions is essential.
- Data Exploration and Preparation: The state and quality of available data significantly influence costs. Efforts to clean, structure, and prepare data for modeling, along with addressing data governance and security, are imperative.
- Modeling: AI models’ active design, processing, and training are very important. The iterative nature of AI systems makes accurate estimation challenging, especially in modeling and evaluation.
- Evaluation: Reviewing model results and assessing the need for further processing should be part of your evaluation. The dynamic nature of AI systems necessitates continuous evaluation and adjustment.
- Deployment: Transitioning a satisfactory model into production involves complex operations, considering changes to existing processes, technologies, and workflows.
- Operating and Maintenance: Ongoing infrastructure and resource costs, encompassing computing power, data storage, pipelines, monitoring, and reliability engineering, all need careful consideration.
- Continuous Improvement: AI systems are iterative, and planning for continuous improvement is vital for sustained value.
- Forced Improvement: Rapid evolution of third-party services or internal dependencies and new legislation can force updates and adjustments, adding to the overall cost.
Challenges in Modeling and Evaluation
Modeling and evaluation are often the most unpredictable and resource-intensive phases of implementing an AI plan. The iterative nature of AI systems, combined with the complexity of problem-solving these technologies, requires careful consideration when estimating the effort required.
Time to Obsolescence
Considering the rapid pace of AI advancements, the time-to-obsolescence (TTO) is a significant factor. While estimating TTO can be challenging, a pragmatic approach is to acknowledge the short lifecycle of cutting-edge AI technologies. A 12-month TTO, on average, underscores the need for continuous adaptation to evolving AI landscapes.
Mitigating Risks with AI
Mitigating risks is integral to any high-end investment, especially with cutting-edge technologies like AI. While specific risk mitigation strategies vary across projects, several general principles can guide organizations:
- Continuous Monitoring: Vigilance during the modeling and evaluation phases is crucial. Regular check-ins, readiness to call off or backtrack, and aiming for a proof of concept can enhance risk management.
- Business Continuity and Disaster Recovery: Assessing the impact of AI systems on business continuity and disaster recovery is critical. Considerations range from infrastructure DR to emergency re-training plans.
- Service Outages: Acknowledge the likelihood of service outages in the AI service realm. Plan for potential disruptions and assess the system’s survival during outages.
- Time to Obsolescence in Solution Design: Adopt a cautious approach to adopting the latest AI technologies. Stick to fundamental principles that are likely to withstand the test of time.
- Solid Development Methodologies: Implement robust development practices, from data management to infrastructure, automation pipelines, and integrations. The foundations of AI systems require meticulous attention to quality control.
A Healthy Dose of Pragmatism
As we navigate the AI landscape, several closing thoughts underscore the importance of a pragmatic mindset.
First, AI is a powerful tool that can’t do everything. It certainly cannot replace human staff. Before starting, research thoroughly, understand AI’s true limits and technical boundaries, and avoid complacency in the quest for efficiency.
Also, consider that AI systems need continuous oversight. Unlike traditional software, AI operates independently, making constant monitoring imperative to ensure alignment with intended objectives.
In particular, you’ll need to be vigilant about algorithmic inertia – the tendency for algorithms to persist in their learned behavior even when external factors change. Regular monitoring and intervention are essential.
Implementing robust AI solutions requires a strong framework for management. Organizations unfamiliar with disciplined data management and automation pipelines may struggle with AI solutions beyond basic levels.
Data security and organization are a big part of the risk with AI, too. After all, AI’s foundation is data. Diligence in handling data, considering ethical and proprietary concerns, and adhering to strict security protocols are paramount in AI system design.
In addition, data governance cannot be overstated. It forms the backbone of responsible AI implementation and ensures data’s ethical and secure use.
Given the prevalence of third-party services in AI implementations, prepare for service outages. Understand the implications for critical operations and assess risk factors.
Finally, you should exercise extreme diligence when considering AI service providers. Scrutinize startups for stability, ethics, service track record, and alignment with enterprise-grade standards.
AI Offers Many Possibilities
A comprehensive risk assessment, adherence to ethical practices, and a pragmatic mindset are essential for successfully implementing AI systems. As AI continues to evolve, a cautious approach, continuous monitoring, and adaptive strategies will ensure organizations harness the transformative power of AI while minimizing risks and pitfalls.
Dymeng can help you clean up your data and optimize your systems to prepare you for the future. We’re a full-service, custom technology consulting company, and we’d love to help you. Contact us today with questions or to discuss your project!