Why 80% of Enterprise AI/ML Adoption Fails

Author(s): Srijay Gupta

Originally published on Towards AI.

The fear of missing out on the AI gold rush is leading many businesses to take proactive steps. Why is it that over 80% of AI/ML projects fail, while only 40% of traditional IT projects share the same fate? The technology’s nascency is a major contributor. AI is a bleeding edge domain advancing rapidly, meaning solutions are less robust and projects hit more technical snags compared to leveraging established frameworks.

Exacerbating these platform growing pains is a widespread talent shortage, lack of quality data, poor scoping, overpromising vendors, and difficulty defining requirements for constantly moving targets. Together, these factors converge into a perfect storm of failed expectations. Users resistant to imperfect systems further reduce realized value. For AI/ML solutions to succeed, businesses need strategies fitting of such a disruptive technology.

One such strategy to beat the troubling odds of a high failure rate is the appointment of AI Committees – specialized task forces drawn from cross-functional teams to assess and implement AI technologies, with a core focus on building employee trust and adoption. In this article, you’ll receive an insider’s blueprint for how effective governance and collaboration within these committees can help extract real business value from AI.

Managing Expectations

The hype around AI leads to unrealistic expectations about fully automating processes with AI. Yet, the most adept AI committees establish realistic expectations, communicating openly about potential risks and limitations. This prevents hasty, unchecked AI deployments.

For example, instead of immediately automating all content creation, a focused start might use AI to generate first drafts of certain localized marketing assets. This targeted approach allows testing before a broader rollout, building trust in AI’s capabilities.

Managing expectations also means setting achievable milestones for AI adoption suited to the organization’s capacity. Committees should steer leaders away from overnight transformations towards incremental enhancements that augment human capabilities. This balanced perspective ensures AI serves as an enhancing tool rather than a replacement.

Gaining Leadership Buy-In

The success of any AI initiative depends on leadership support across the C-suite. When executives actively participate in AI governance, they signal that AI is a strategic priority, not just an experimental technology. Their involvement enables the required investments in talent, data, infrastructure, and change management.

Ongoing collaboration between leadership and the AI committee provides a feedback loop for sharing results, challenges, and ideas. This promotes experimentation and ensures alignment with business objectives. Furthermore, leadership commitment to ethical AI adoption exemplifies crucial principles for organization-wide integration.

Starting Small for Quick Wins

The most effective AI committees advocate starting with small, tightly scoped projects before pursuing wide-scale adoption. This focused approach allows testing the waters without overextending limited resources too early.

Starting small also provides quick wins that demonstrate AI’s capabilities and concrete benefits. Take, for instance, a SAAS company that employed AI to analyze sales call transcripts, automatically logging data such as follow-ups and objections into their CRM. This provided instant insights to sales managers while eliminating manual note-taking.

Furthermore, a targeted start enables the AI committee to establish best practices and guidelines for future initiatives. By tackling smaller tasks first, the committee can evaluate AI effectiveness and mitigate risks before scaling further. This pragmatic approach aligns the adoption pace with organizational capacity and readiness.

Enabling Employees to Innovate

Empowering employees to experiment with AI is a cornerstone of effective adoption. The best AI committees create an environment where employees feel safe to test AI applications and provide feedback. This collaborative approach ensures AI solutions meet users’ needs rather than being top-down directives.

Enablement goes beyond just providing tools. It means fostering a culture of innovation and continuous learning around AI. Employees should have the freedom to explore AI’s potential within secure guardrails without fear of failure. This bottom-up innovation allows organic growth of AI capabilities.

Moreover, enablement requires providing adequate resources so financial constraints don’t hinder experimentation. The organization must supply access to data, platforms, and guidance to power AI exploration.

Measuring the Impact of AI Initiatives

Robust evaluation of AI initiatives provides the data-driven insights necessary for continuous improvement. Key performance indicators (KPIs) combined with qualitative feedback highlight what’s working and what can be improved.

Quantitative metrics evaluate factors like the number of customer inquiries processed, sales leads generated, and support tickets handled by AI systems. These showcase productivity gains.

Qualitative data assesses the subjective experience of employees and customers interacting with AI. Surveys, interviews, and focus groups gauge satisfaction, trust, and ease of use.

Ongoing impact reviews also enable benchmarking of AI performance over time as the technology advances. Regular assessment based on real-world data powers an agile, iterative approach to calibrating AI strategies.

Reducing Vendor Dependence

While external AI partnerships can offer valuable capabilities, committees should take care to mitigate risks. Many AI startups are new and financially unstable. Over-relying on unproven vendors for critical functions is dangerous.

Committees should champion a step-by-step collaboration approach, starting perhaps with pilot projects. Conduct in-depth due diligence to assess partners’ ethics, culture, expertise, and track record. Have contingency plans to bring capabilities in-house or switch vendors if partnerships under-deliver or dissolve. Own your data to prevent lock-in.

The most effective committees view partnerships as a supplemental source of AI capabilities, not a complete outsourcing of core functions. With pragmatic vendor management, organizations can tap into the AI ecosystem while controlling their own destiny.

Establishing Ethical AI Practices

Implementing cutting-edge technologies carries inherent risks, especially when not approached with precision and foresight. AI committees champion a culture where employees are encouraged to innovate but always within well-defined ethical boundaries.

Comprehensive training programs are essential to achieving this, ensuring that employees grasp the intricate nuances and potential repercussions of AI-driven solutions. Regular AI system audits, bias detection mechanisms, and transparency protocols are imperative to uphold fairness and accountability.

In domains such as consumer lending, a dedicated Fair Lending Analytics team is crucial for scrutinizing AI/ML-generated credit decisioning and pricing outputs. Moreover, when considering collaborations with third-party AI vendors, a meticulous assessment of their ethical standards and past performance is non-negotiable.

Conclusion

In my previous blog, “The Future of Work: Upskill or Be Left Behind”, I discussed the imperative for employees to upskill in the face of technological advancements. The role of AI committees is a natural extension of this narrative.

From managing expectations to gaining leadership buy-in, and employee enablement to impact measurement, the best practices we’ve explored offer a roadmap for organizations to integrate AI as a core capability.

While AI will not replace human roles, its thoughtful application promises to boost productivity, efficiency, and innovation. With sound AI governance, we can overcome the fear of the unknown and create an exciting future powered by the symbiosis of human and artificial intelligence.

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Published via Towards AI