Practical modernization is your guide to equipping teams and organizations with the strategies needed to navigate and thrive in the Age of AI, ensuring sustainable success. In today’s world, the sheer volume of information can make it challenging to identify what truly matters. This analysis explores trends and patterns that impact the People, Processes, and Technologies shaping modern organizations. As Scott Belsky aptly puts it in Implications: "We don’t cover news; we explore the implications of what’s happening." This approach is particularly valuable for leaders seeking to move beyond surface-level trends to uncover deeper impacts, paradigm shifts, and actionable insights for their organizations.
Our goal is to inspire action and thoughtful organizational modernization that aligns with your mission and values.
If this is your first time reading, here's how this analysis is structured:
- Analysis, Impact, and Recommendations: A deep dive into key trends and their implications for your organization.
- What we listened to, watched, or read: Short summaries of resources that informed our insights.
- Applications of AI we think are worth a look: Practical examples of how AI can support organizations.
- Your recommendations for each other: Highlights of peer suggestions within the community.
- Something that restored a bit of our faith in humanity: Inspiring stories to close with optimism.
Analysis, Impact, & Recommendations
The RTO Mandate Trap
Overview: Return-to-office (RTO) mandates have become a trend among major corporations, particularly in Big Tech and Finance. However, what works for these giants may not work for organizations of all sizes. Blanket mandates risk alienating talent, especially younger workers who value flexibility and autonomy.
Analysis: In industries where RTO is seen as an easy solution for post-pandemic workforce adjustments, organizations must take a more nuanced and thoughtful approach. The pandemic showcased the efficacy of flexible work models, with many teams not only maintaining but enhancing productivity while working remotely. Rigid RTO mandates risk alienating talent, particularly younger employees who prioritize autonomy and flexibility. Moreover, enforcing such policies without understanding employee preferences sends a message of mistrust, potentially eroding organizational culture. Research from Administrative Science Quarterly further illustrates how decentralized workplace models can foster innovation and collaboration when effectively managed. Organizations that did not over-hire during the pandemic are in a unique position to innovate instead of revert, exploring hybrid or roaming policies tailored to both individual and team needs. These alternatives can sustain morale, encourage autonomy, and ensure collaboration thrives where it truly matters. Ultimately, aligning workplace strategies with organizational goals and employee satisfaction is essential to navigating this transition successfully.
Impact: Talent retention and organizational culture are on the line. Insights from Administrative Science Quarterly show that rigid RTO policies often exacerbate turnover as skilled employees seek more flexible opportunities elsewhere. This is particularly true for younger workers who value autonomy. Mandatory in-office policies can erode morale and trust, creating a less collaborative environment. Conversely, flexible workplace models have been shown to foster innovation and adaptability, making organizations more resilient. Ignoring employee preferences risks not only losing top talent but also weakening the cohesion and productivity necessary for long-term success.
Recommendation: Engage your team to understand their preferences through surveys and open discussions. Use these insights to craft a policy that aligns organizational goals with employee needs. Rather than defaulting to a one-size-fits-all RTO mandate, consider innovative approaches like hybrid or roaming work models, which encourage autonomy while supporting collaboration. Drawing on research from Administrative Science Quarterly, clearly communicate the purpose and expected benefits of any new policies, and create channels for ongoing feedback. This iterative approach ensures adaptability and strengthens trust, leading to a more cohesive and resilient organization.
AI Agentification Puffery
Overview: The push for AI agentification — enterprise-grade AI systems replacing lower-skill roles — is heavily promoted by major tech players like Salesforce. While these systems promise significant ROI, their utility for all organizations is not guaranteed. The narrative often ignores the specific needs and nuances of smaller or mission-driven organizations.
Analysis: Many organizations adopt AI tools without a clear understanding of their goals or the trade-offs involved. The Stratechery essay on AI’s uneven adoption underscores that industries implementing these tools often see mixed results based on their readiness and alignment with AI capabilities. While subscription-based chatbots and off-the-shelf AI tools offer scalability, they rarely deliver transformative outcomes. The Economist podcast further highlights a growing divide: large-scale agentification works well in sectors with high-volume repetitive tasks but falters in areas requiring nuanced human interaction. Enterprise solutions like agentification, marketed as replacements for low-skill roles, may inadvertently undermine the personalized touch many organizations depend on for success. Organizations must also grapple with ethical implications — a theme raised in the podcast — particularly around job displacement and maintaining trust with stakeholders. For mission-driven teams, there is a critical need to balance efficiency with empathy, ensuring that technological adoption enhances rather than detracts from their core values. By starting small with pilot projects, organizations can refine their approach, assess real-world impacts, and ensure any solutions adopted resonate with their long-term mission and strategic objectives.
Impact: Misguided adoption of AI tools can lead to wasted resources, unmet expectations, and a disconnect from organizational objectives. Insights from the Stratechery essay highlight how poorly tailored AI can strain resources without delivering promised efficiencies, especially in sectors requiring human interaction. The Economist podcast underscores that automation often prioritizes cost-cutting over meaningful engagement, potentially eroding trust with donors and staff. Organizations prioritizing automation over personalized impact risk diminishing relationships and weakening team dynamics, particularly when human touchpoints are core to their value proposition. Striking the right balance between efficiency and empathy is crucial to sustaining long-term success and mission alignment.
Recommendation: Reassess AI solutions with a strategic lens focused on mission alignment. Prioritize tools that improve team productivity and supporter engagement, leveraging them as support systems rather than replacements for human interaction. Start by piloting smaller, mission-specific AI tools to validate their effectiveness. Building on insights from Stratechery’s essay and The Economist’s podcast, organizations should integrate ethical considerations into their AI strategy to balance efficiency with empathy. Develop a roadmap for adoption that aligns with broader organizational objectives, ensuring the tools enhance rather than detract from your mission.
Useful Small Language Models (SLMs)
Overview: Unlike large-scale AI systems, Small Language Models (SLMs) are designed for specific datasets, making them a versatile tool for internal organizational use. These models offer organizations the opportunity to customize AI solutions to their unique needs, enabling teams to work more efficiently and effectively.
Analysis: SLMs provide a focused approach to AI adoption by tailoring functionality to the specific datasets and tasks relevant to an organization. For instance, a nonprofit could train an SLM to assist with grant applications, while a corporate team might use one to streamline internal communications. IBM's exploration of small language models emphasizes their ability to operate effectively with reduced computational resources, making them highly accessible even for organizations without extensive infrastructure. This modular nature of SLMs allows for incremental implementation, reducing the risk of large-scale disruptions and fostering adaptability. Furthermore, SLMs can be fine-tuned to handle specific linguistic or operational challenges unique to an organization, ensuring their output aligns with institutional goals. The cost-effectiveness and scalability of SLMs make them particularly appealing to organizations with limited budgets, allowing them to integrate advanced AI solutions into workflows with minimal friction while maximizing their impact.
Impact: Properly implemented SLMs can dramatically improve productivity by tailoring AI capabilities to meet specific organizational needs. IBM's research highlights how SLMs’ reduced computational requirements make them accessible even for smaller teams, leveling the AI playing field. By automating repetitive or data-heavy tasks, these models free staff to focus on strategic and creative initiatives, enhancing organizational impact. The cost-effectiveness of SLMs, compared to larger and less-focused AI systems, ensures that even budget-conscious organizations can harness the power of AI. Beyond operational efficiency, SLMs can foster collaboration by centralizing knowledge and improving internal workflows, paving the way for more innovative and impactful outcomes.
Recommendation: Leverage SLMs as a strategic tool to improve team efficiency and streamline workflows. Collaborate with tech providers to develop models finely tuned to your organizational data and specific needs. Initial implementations could focus on areas such as automating content curation, enhancing internal knowledge sharing, or optimizing donor communications. Start with a proof-of-concept project to validate the model’s value and demonstrate its practical impact. Drawing on IBM’s findings, emphasize scalable and cost-effective deployments that integrate seamlessly into existing operations. Prioritize solutions that empower your teams and amplify their capacity to focus on strategic priorities.
What we listened to, watched, or read
Here are the articles and resources that informed this edition's analysis:
- Five Hybrid Work Trends to Watch in 2025 from MIT Sloan Review: Explores evolving hybrid work dynamics and their implications for organizational policies. (Link)
- Administrative Science Quarterly's article on decentralized workplaces: Discusses how decentralized workforces reshape team collaboration and leadership approaches. (Link)
- Building High-Performance Teams in 2025: Beyond the Location Debate from IT Revolution: Highlights psychological safety, role clarity, and feedback loops as cornerstones of effective teams. (Link)
- AI’s Uneven Arrival from Stratechery: Examines the varied adoption of AI across industries and the challenges organizations face in implementing AI solutions effectively. (Link)
- Does AI Need a Reality Check? from The Economist podcast: A conversation with Gary Marcus exploring the limitations of AI technologies and the need for responsible adoption strategies. (Link)
- IBM on Small Language Models: Explores how SLMs can empower organizations by offering tailored, resource-efficient AI solutions. (Link)
- 2025 AI & Data Leadership Executive Benchmark Survey by Randy Bean: Provides key insights into how executives are leveraging AI and data for strategic decision-making in 2025, highlighting the importance of aligning AI strategies with organizational goals. (Link)
Applications of AI we think are worth a look
These are not paid placements. They are simply tools we have found valuable through our own experience.
ChorusAI is a fit-for-purpose tool designed for nonprofits to save them time and harmonize their content creation workflows.
https://www.chorusai.co/
Napkin AI turns your text into visuals so sharing your ideas is quick and effective.
https://www.napkin.ai/
NotebookLM is your personalized AI research assistant.
https://notebooklm.google.com/
Your recommendations for each other
Nick Scott's breakdown of the new free AI offer from Google via Carmen Barlow (https://www.linkedin.com/feed/update/urn:li:activity:7285542342466412544/)
Raycast Focus can block specific apps or websites — and uses a countdown timer to help keep you focused via The Verge's Installer
https://www.raycast.com/core-features/focus
Something that restored a bit of our faith in humanity
#sticknation: https://www.wbur.org/endlessthread/2024/12/06/stick-nation