While generative AI promises enormous potential, communications companies seeking adoption face hurdles limiting impact including biases, legacy systems integration, skepticism and unclear governance. This article delves into specific pitfalls seen in early deployments and why carriers struggle scaling proofs-of-concepts across customer-impacting functions. Understanding challenges paves way to pragmatic mitigation strategies covered in later installments.
1. Mitigating Biases and Failures Through Continuous Oversight
Errors and biases creep into AI systems without governance.Meta's Galactica model showcased harmful biases when constraints slipped.Microsoft's viral Bing chatbot also later spiraled out of control during customer interactions. Such examples underscore consequences of uncontrolled failures in telecom’s customer-impacting functions.
Rigorous feedback loops and monitoring for issues before algorithms interact with subscribers become imperative. Adding such guardrails requires paradigm shifts even for technical teams used to moving fast and breaking things. With continuous tuning informed by human oversight (not just contained labs), carriers can mitigate uncontrolled failure risks that erode consumer trust.
2. Modernizing Antiquated Data Infrastructure for AI Integration
Disconnected legacy IT systems pose severe impediments to integrating cloud-based AI needing vast data. Cleaning and normalizing fragmented datasets constitutes 30-80% of effort before applying algorithms per surveys. For telecom’s antiquated systems, ratios likely worsen. Data wrangling and engineering skills also remain scarce.
These data realities make scaling proofs-of-concept requiring intelligence across functions near impossible. Modernizing individual line-of-business applications to cloud while unifying interfaces constitutes vital blocking and tackling to assimilate AI amidst legacy environments during transition periods.
3. Winning Over Skeptical Teams and Cultures
Beyond technical barriers, organizational culture issues frequently stall innovations like generative AI more severely. Frontline teams reliant on existing knowledge bases or monitoring systems push back on ceding decisions to “black box” algorithms.
Providers instead eased in friendly frontline groups into AI via clear improvements to daily jobs first before attempting bigger process changes. Using generative AI to populate knowledge bases and handle repetitive tasks built buy-in incrementally. Prioritizing cultural assimilation accelerates adoption.
4. Encouraging Responsible Innovation Through Governance
Ambiguous policies and compliance frameworks also slow generative AI adoption as algorithms grow more autonomous needing nuanced guardrails to encourage innovation while managing new risks.
Appointing cross-functional leadership councils to oversee enterprise AI projects with policy playbooks has proven effective. Equally vital are formalized AI ethics practices around inclusivity, transparency and fairness to uphold consumer trust amidst AI’s rise.
5. Growing Scarce Internal AI Talent Pipelines
The acute shortage of talent possessing both domain expertise alongside modern machine learning competencies constitutes the primary adoption barrier. Having appropriate skills in place proves more foundational than technical factors.
With demand massively outpacing supply, even tech-savvy carriers struggle to recruit and retain capable AI talent. Leading providers acknowledge realities by prioritizing extensive reskilling programs and competitive recruitment. Investing into internal people capabilities unlocks smoother scaling.
In summary, an array of technological, cultural and policy hurdles impedes rapid enterprise-wide scaling of generative AI. But pragmatic mitigation planning around data, skills, transparency and governance paves the way for smoothing adoption. The next installment covers specific recommendations on responsibly accelerating generative AI leveraging lessons from common pitfalls.
Sources:
1.https://hbr.org/2022/07/why-companies-struggle-to-adopt-ai-at-scale
2.https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/technical-debt-and-the-scarcity-of-ai-talent
3.https://www.brookings.edu/research/algorithms-and-bias-what-lenders-need-to-know/
4.https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/
5.https://www.technologyreview.com/2018/12/06/139313/when-ai-systems-break-bad/
