Issue #2

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Dear Reader,

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If the tech decisions HR leaders have to make aren’t complex enough already, Generative AI adds another layer of choice. AI in HR guru Antony Onesto shares a simple framework to combat a ‘paradox of choice.’

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For the first issue of this newsletter, I thought it would be good to tackle one of the hairiest topics most of us HR leaders are running into: how to select AI tools.

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Because looking at the worktech landscape, you will find hundreds of tools. You will find core HR, recruiting/applicant tracking, performance systems, and even tools that connect employees on Slack.
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My current company has systems that cover core HR, recruiting/applicant tracking, performance, automated workflows, and a system that brings all the data together and provides dashboards and reporting.

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We even have a system that allows us to access all these different systems, somewhat of a system fourth wall break. If there are so many tools inside companies, you can imagine how many actually exist.
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All these tools don’t include the tools you use for email, real-time messaging, and things with your company. Now add new AI tools, and it’s very understandable why so many of us are facing something that’s known as ‘the paradox of choice.’

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The Paradox of Choice

If you’ve ever walked down the shampoo aisle at a supermarket, you likely have been paralyzed by too many choices, so which do you choose? You choose the familiar shampoo you have always used because there are too many choices, and each shampoo's differences are minor.
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This is what Barry Schwartz calls “The Paradox of Choice.” In one of my favorite TED talks, Schwarz laments that:

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In other words, boundaries are essential for meaningful and satisfying choices.
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If, like me, you’ve been receiving limitless pitches of “miracle AI tools,” you’re likely facing an overwhelming array of options that can lead to the same decision paralysis and stress you experience in that shampoo aisle of the supermarket.

The Antidote for Too Much Choice: a Decision Framework

In this week’s edition of “AI in HR Today,” I introduce a framework inspired by Barry Schwartz's concept that offers a structured approach to simplifying AI tool selection while acknowledging current market dynamics and technological trends, allowing you to make the right choices about AI tools.
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And just for a bit of context: spending on generative AI surged to $13.8 billion in 2024—a sixfold increase from the $2.3 billion spent in 2023. This explosive growth reflects a fundamental shift from experimentation to implementation, with nearly three-quarters of decision-makers anticipating broader adoption of generative AI tools.
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However, the market remains in its early stages, with only a third of use cases reaching production while many others continue in evaluation phases. The data supports this, as a recent report found that 100 AI for Work tools already have over a million monthly users. That’s a lot of choices.

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Here’s how you make sense of it all:

1. Understanding Your Needs

Before diving into the vast sea of AI options, clearly understand your company’s specific needs and friction points. This self-awareness is crucial in distinguishing between merely choosing and choosing well.

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The process begins with thoroughly assessing current challenges, desired outcomes, and available resources. Consider not just immediate requirements but also future scalability needs and growth projections.

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This foundation of self-knowledge helps filter out irrelevant options and significantly reduces decision fatigue. This year, the emergence of customizable AI models has added another layer to consider—the ability to personalize services while maintaining privacy and security.

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Create a clear evaluation framework that weighs these factors according to your organization's needs and constraints. Focus on evaluating tools against key criteria such as ease of use, cost structure, technical compatibility, and specific task requirements.

2. Prioritizing Simplicity

The trend toward user-friendly AI applications has gained significant momentum in 2024, particularly with the rise of small language models (SLMs). These models have democratized AI access by offering effective solutions with reduced computing requirements.

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When evaluating generative AI tools, focus on those that provide intuitive interfaces and streamlined workflows. The ideal solution should integrate smoothly with existing systems while minimizing the learning curve for users.

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Remember that the most sophisticated solution isn't always the most effective—sometimes, the simpler option delivers better results.

3. Embracing "Good Enough"

The pursuit of perfection often becomes the enemy of progress in AI adoption. Instead, embrace a 'satisficing' approach—selecting solutions that adequately meet your needs rather than endlessly searching for the perfect tool.
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This approach proves particularly relevant in 2024's rapidly evolving landscape, where waiting for the perfect solution might mean missing out on significant competitive advantages.

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Define your minimum viable requirements and accept that improvements will come through iteration rather than immediate perfection.

4. Understanding Trade-offs

Every AI solution involves trade-offs, and acknowledging this reality early in decision-making reduces anxiety and improves outcomes. The rise of multimodal AI in 2024 offers more comprehensive solutions but often introduces additional complexity.
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Consider carefully the balance between functionality and simplicity, cost and capabilities, speed and accuracy, and control versus convenience. Document these trade-offs explicitly to ensure stakeholder alignment and set realistic expectations.

5. Expert Validation

As the AI landscape grows more complex, the value of expert guidance increases proportionally. In 2024's environment, with its growing emphasis on ethical AI practices, red teaming, and model evaluation, seeking expert insights becomes crucial.
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Leverage industry expertise, user reviews, and detailed case studies to validate your decisions. Pay particular attention to vendors' track records in ethical AI practices and their commitment to transparency in model evaluation.

6. Implementation Strategy

Start your AI journey with carefully selected pilot projects that can demonstrate quick wins while minimizing risk. Document lessons learned and gather systematic feedback to inform future scaling decisions.
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As you expand your AI implementations, maintain flexibility in your approach, adjusting strategies based on performance metrics and emerging opportunities.

AI for HR: The Future

As someone navigating this ‘paradox of choice’ in HR tech generally, and AI HR tech more specifically, I find it clear that the AI landscape will continue to evolve rapidly.

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Additional trends like data-driven AI, customization through fine-tuning, and the growth of open-source and genetic AI will all reshape what’s possible. Stay informed about emerging technologies and trends (I will certainly support you here with this weekly newsletter), but keep the pace of change from paralyzing your decision-making.

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Remember that the goal isn't to find the perfect AI solution but to select and implement tools that effectively meet your organization's needs while positioning for future growth.

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The paradox of choice in AI selection doesn’t need to be overwhelming. By following this framework, you can transform what might seem like a daunting challenge into a structured opportunity for meaningful technological advancement.

Your Next Move

The most successful implementations often come not from choosing the most advanced solution, but from selecting tools that align well with organizational capabilities, culture, and strategic objectives.