Artificial Intelligence’s relationship with technical debt and it’s avoidance

Today we are heavily invested into adoption of Artificial Intelligence (including ML, DL, Rule Engines, RPA, IA etc. etc.). We are overwhelmed with information from social media, vendors, users and competitors. In this journey of adoption of Artificial Intelligence we often forget the major implication of following our FOMO.

In this article I will be highlighting some of the factors which impact potential technical debt while adoption of AI journey and how can it be mitigated and avoiding AI into a legacy system baggage of tomorrow.

Don’t forget what we call bad legacy systems, non agile and non scalable systems of today, they were pathbreaking and revolutionary at the time of their peak :) just like Generative AI is today.

Navigating Technical Debt in AI Adoption: Factors, Influences, and Mitigation Strategies

In the rapidly evolving landscape of Artificial Intelligence (AI) adoption, organizations often find themselves grappling with the inadvertent creation of technical debt. This phenomenon arises from various factors, and understanding them is crucial for devising effective strategies to avoid and mitigate potential challenges

Factors Influencing Technical Debt in AI Adoption

Rapid Technological Advancements: The swift pace of AI evolution can lead organizations to adopt cutting-edge solutions without sufficient time for thorough evaluation and integration. This means lack of interdependence mapping, long term adoption strategy and existing ecosystem onboarding

Data Quality and Integration: Incomplete or poor-quality data can result in suboptimal AI models, creating technical debt as organizations struggle to refine models and address data-related issues post-implementation. This is majorly when data is forced manipulated or mixed with synthetic data to meet the requirements of required models. This can also arise because of limited wrong data sampling which is not a true representation of the entire population

Lack of Talent and Expertise: The scarcity of skilled AI professionals may compel organizations to deploy AI applications without the necessary expertise, leading to suboptimal solutions and long-term maintenance challenges. Corner cutting due to budget constraints on learning and development can result in these challenges, lack on focus on continuous improvement and documentation can addon to this challenge. Afterall documentation is the most boring thing to do :) but we can definitely be innovative in doing so effectively

Regulatory Compliance: Adherence to evolving data privacy and regulatory standards poses a constant challenge. Neglecting compliance during AI implementation can result in technical debt through subsequent adjustments to meet legal requirements. Responsible AI is the major focus as adopters of AI solutions, we need to be responsible, committed and knowledgeable about the threats, weaknesses and our responsibilities and lack of this can resulting in heavy technical debts for future

Overlooking Ethical Considerations: Ethical concerns related to AI can result in backlash and necessitate modifications to align with societal expectations, contributing to technical debt. Hence initial investments in looking the entire value chain, it’s impact and response from customer and communities (internal and external) is important however is missed out in the race of adopting AI

Garbage in Garbage out : Last but not the least incase we apply / adopt AI on a non linear process with challenges with data and process controls it will result in Garbage in and Garbage out resulting in building technical debt in long term limiting the benefits we can reap of the technological advancement  

Strategies to Avoid and Mitigate Technical Debt in AI Adoption

Comprehensive Planning: Prioritize a thorough planning phase, encompassing feasibility studies, impact assessments based on responsible AI adoption principles and realistic timelines. This upfront investment can prevent rushed decisions and reduce technical debt. It’s advisable to have regular proactive validation based on these principles and flexibility to improvise principles based on new developments.

Data Quality Assurance: Establish robust data governance practices to ensure data quality and integrity. Regularly audit and cleanse datasets to mitigate potential issues before they become embedded in AI models. AI models and data sanitization should be practiced in consideration with regulations keeping customer’s interest in center for long term adoption

Invest in Talent Development: Foster a culture of continuous learning and invest in training programs to equip teams with the skills needed for AI adoption. This reduces reliance on external consultants and enhances internal capabilities. Internal capability building is a great asset for long term benefits with huge initial investments however if you look at the holistic view of AI adoptions, it’s long term scaling requirements and risk mitigation the initial investment is valid. Along side investments in development of resources it will be great to invest in documentation as per standard principles for long term reference and knowledge management and avoiding knowledge drain

Continuous Monitoring and Evaluation: Implement a systematic monitoring framework to track AI model performance, detect deviations, and proactively address issues. Regular evaluations can uncover potential technical debt and guide timely interventions. AIOps plays a major role here where the entire value chain and it’s relationship and interdependencies can be helpful. Definitely this needs to be developed leveraging AI and automated tools to help in making it more effective and efficient

Ethics and Compliance Integration: Embed ethical considerations and regulatory compliance checks throughout the AI development lifecycle. This proactive approach minimizes the need for retroactive adjustments and reduces the risk of technical debt. Responsible AI with customer’s interest in center should be the focus. Outside In approach should be considered while establishing policies and controls for AI adoption

Apply 5S before applying AI – Sort, Set in Order, Simplify, Standardize & Sustain, it should be adopted in a process optimization approach before adopting AI to enhance the benefits we reap from AI adoption in a process. Sort the process and processing steps to the right order, to simplify the process and eliminate what is not required and have a continuous validation at regular intervals pre and post AI adoption to avoid technical debt because of change in process

Open Ecosystem: We should go ahead with a Open Ecosystem / Open infrastructure in mind which is open to multiple vendors and has the ability to work on principles of microservices, APIs and connectors in a lego / modular approach. This gives us a lot flexibility in adoption of AI and embracing the benefits from multi vendor approach : further can be read in my earlier article : (3) De-risking Artificial Intelligence ventures - Transforming Insurance Mindset to Insure-tech Mindset | LinkedIn

Avoid Vendor Lockin: Vendor Lockin can be avoided with an open vendor strategy, looking at factors which can lockin for long term increasing the technical debt of an organization and can pull back in legacy baggage zone building it faster then ever. More can be read on my older article : How to avoid RPA vendor lock-in — Boye & Company (boye-co.com)

AI Sandbox: Trying small, failing faster and making it secure and responsible should be focus of adopting AI and this can help in reducing long term technical debt. Further about this can be read on the article Introducing an AI Sandbox — Boye & Company (boye-co.com)

AI Security threat resilient: In the age of AI a lot of security threats and navigating it keeping the potential threats in future can help in building a strong, responsible and security AI adopting organization: Further can be read in the article (4) Cybersecurity Risks in the Age of AI: Navigating the Complex Landscape | LinkedIn

Conclusion: Adopting AI is no longer a good to have, it’s a must have in long run and a competitive advantage, having said that it’s expensive and if we don’t watch out towards the technical debt it can have a huge cost direct / indirect.

Long term planning and investment in open architecture and ecosystem with a more microservices / modular approach will help in reaping the benefits in short term and sustainability in long term keeping in mind some of the recommendations suggested in the article.