{"id":88642,"date":"2026-04-24T17:18:58","date_gmt":"2026-04-24T17:18:58","guid":{"rendered":"https:\/\/degreed.com\/experience\/?p=88642"},"modified":"2026-04-24T17:21:28","modified_gmt":"2026-04-24T17:21:28","slug":"your-guide-to-turning-ai-experiments-into-business-outcomes","status":"publish","type":"post","link":"https:\/\/degreed.com\/experience\/de\/blog\/your-guide-to-turning-ai-experiments-into-business-outcomes\/","title":{"rendered":"Your Guide to Turning AI Experiments Into Business Outcomes"},"content":{"rendered":"\n

Many businesses are testing AI tools, but only 25% of AI initiatives are delivering expected ROI, according to IBM<\/a>. Most AI transformations are failing during or right after experimentation. Long before adoption and long before outcomes. <\/p>\n\n\n\n

Those initiatives don\u2019t fail because of technology. They fail because:<\/p>\n\n\n\n

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  1. People can\u2019t keep up.<\/strong> Businesses focus on tech, but the missing link is human capability. Remember the 70:20:10 model of learning<\/a>? Forbes and Boston Consulting Group<\/a> introduced an updated version of that framework as it relates to AI. They noted that successful AI embedding relies on 10% algorithms, 20% technology, and 70% people and processes. Tech is only one piece of the puzzle. The far greater share is in people and processes.<\/li>\n\n\n\n
  2. The pilot couldn\u2019t scale. <\/strong>Testing on a small cohort is critical, but success in a pilot does not automatically equate to success across a global enterprise. To move beyond isolated proofs of concept, you need to transform your AI investments into repeatable processes and systems. They need to fit into your workflows and your tech stack. <\/li>\n\n\n\n
  3. The AI initiatives were never aligned with business and learner needs.<\/strong> Very few companies have made the leap to enterprise-level optimization where AI consistently delivers business outcomes. That\u2019s because there\u2019s missing buy-in and the initiatives weren\u2019t aligned to measurable, organization-wide goals or challenges. Any outcome would be abstract or qualitative at best.<\/li>\n<\/ol>\n\n\n\n

    So, what works in the real world to address these challenges?<\/p>\n\n\n\n

    At LENS 2026, a panel of leaders from Yara, HubSpot, Pearson, and GSK who have successfully made the move from experimentation to practice took the stage to share their insights. Explore their top in-practice lessons and what efforts ultimately made the biggest impact:<\/p>\n\n\n\n

    \"LENS<\/figure>\n\n\n\n

    Ground AI Initiatives in Business Friction, Not Idealism<\/strong><\/h2>\n\n\n\n

    It\u2019s easy to get swept up in what AI can<\/em> do, but successful implementation starts with what the business actually needs<\/em> it to do. If you start with the technology features and hype, you’ll likely apply it to insignificant problems. You might generate excitement, but you\u2019ll miss outcomes.<\/p>\n\n\n\n

    Leanne Jefferson, Global Head of Learning and Development at Yara, emphasized the importance of aligning AI initiatives with real business friction. For example, \u201cIn a global organization like Yara, scaling high quality leadership support like coaching is a challenge.\u201d<\/p>\n\n\n\n

    That made coaching a great opportunity to test AI capabilities\u2014in this case, through Degreed Maestro. Instead of just the „wow“ factor, they secured approval for the AI tool based on use cases that resonated for the business, which in this case, was leadership development. <\/p>\n\n\n\n

    The choice to keep the focus on resolving a clear pain point is what ultimately secured needed stakeholder buy-in and enthusiasm, despite ongoing company changes and a proclivity toward being risk-averse.<\/p>\n\n\n\n

    Transform L&D Into a Cohesive AI System<\/strong><\/h2>\n\n\n\n

    As AI matures, the role of L&D must also evolve, because learning is, if anything, more critical than ever for business success. According to McKinsey<\/a>, nearly 90% of leaders are seeking a significant change in how to develop employees.<\/p>\n\n\n\n

    \u201cAlthough we don\u2019t know what the future of an AI world might look like, there isn\u2019t a positive outcome that doesn\u2019t involve human development,\u201d Zoe Botterill, Head of Learning and Development at Pearson, said.<\/p>\n\n\n\n

    Forward-thinking companies are leaving static virtual learning behind and embracing self-architected, responsive systems. At HubSpot, this shift was so profound that they haven’t made a traditional e-learning course since April 2024, and it\u2019s even shifted traditional job roles.<\/p>\n\n\n\n

    „Many of us who were learning experience designers are now AI learning systems architects because they’re not creating e-learnings,“ Jackson said. This new role focuses on connecting systems and amplifying the work of early adopters within the business rather than trying to do everything alone.<\/p>\n\n\n\n

    As part of building a scalable system, the production and distribution of learning content also has to adapt to keep pace with ever-evolving change. The time to prepare quality learning experiences is shrinking because the information they are based on evolves so fast, and business timelines are moving at light speed. <\/p>\n\n\n\n

    To address this, Botterill suggested moving into a more iterative workflow that allows professionals to adjust content in flight: \u201cWe’re going to launch, we’re going to adopt a product mindset, we’re going to iterate and learn as we go.\u201d<\/p>\n\n\n\n

    \"Zoe<\/figure>\n\n\n\n

    Emerging AI capabilities are supporting this new way of working through responsive learning experiences<\/a> and content libraries<\/a> that update automatically.<\/p>\n\n\n\n

    Create a „Gym“ for Practicing Skills<\/strong><\/h2>\n\n\n\n

    One under-estimated benefit of AI capabilities, especially for talent development, is the psychological safety it can provide for practice. When employees need to practice applying skills, but aren\u2019t quite ready for the high stakes of a boots-on-the-ground scenario, tools like AI roleplays and one-to-one coaching can be a great option to bridge the gap. <\/p>\n\n\n\n

    „Nobody likes to practice roleplays in front of folks… This was a way that they could practice almost using it like a gym,“ said Antonia Jackson, Senior Manager of Learning Innovation and Technology at HubSpot. <\/p>\n\n\n\n

    She noted that when employees used Degreed Maestro<\/a> for difficult conversations, they felt more open to hearing feedback from AI than from a manager because it felt neutral and non-judgmental.<\/p>\n\n\n\n

    \u201cYou have to be able to fail because it’s in the failure that you find the goal, the treasure, and the lessons,\u201d Jackson said.<\/p>\n\n\n\n

    This safe practice environment allows people to fail comfortably or \u201cfail forward,\u201d and become more capable before feeling comfortable with the vulnerability of trying out a skill in front of a colleague or manager. Not only is this iterative and safe practice more effective, but it also takes less high-value manager coaching time to develop capabilities.<\/p>\n\n\n\n

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