Edition #1
If this is your first time reading, start here. Otherwise, jump right to the analysis.
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.
Table of Contents
- 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.
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tl;dr
AI is advancing faster than ever, return-to-office mandates are reshaping workplaces, and small language models are emerging as powerful tools—but how do you separate what's truly useful from what's just hype? In a landscape overflowing with bold promises, organizations must adopt a discerning approach, filtering noise from actionable insights. This edition of practical modernization breaks down the trends that matter, helping leaders navigate AI agentification, rethink rigid RTO policies, and explore the real potential of SLMs.
PROCESS —> AI Agentification Puffery
Overview: The current wave of AI agentification—enterprise-grade AI systems designed to automate or replace roles—is being rapidly championed by major tech players like Salesforce. While the potential for these tools is undeniable, the reality is that their present capabilities often lag behind the promises being made. Agentification is evolving at a breakneck pace, with changes occurring minute by minute. This creates a challenge: organizations must filter out hype and focus on what’s immediately actionable versus what may become transformative in the near future.
Analysis: The moment we’re in requires cautious optimism. The Stratechery essay highlights the uneven adoption of AI, illustrating how success hinges on readiness and the alignment of capabilities with organizational goals. Agentification excels in high-volume, repetitive tasks like manufacturing or live stock trading but struggles in contexts demanding nuanced human interaction. As emphasized in The Economist podcast, AI is advancing at an unprecedented pace, with promises of revolutionary change often outpacing current capabilities. This makes it difficult for organizations, especially smaller ones, to discern between hype and genuinely transformative tools. Leaders must focus on leveraging AI to complement human strengths rather than replacing them. This approach ensures immediate benefits while laying the groundwork for future advancements. Acting as a filter and guide, leaders can sift through the noise, prioritize actionable tools, and align their investments with both present and long-term strategic needs. By doing so, organizations position themselves to thrive as agentification continues to evolve and mature.
Impact: The rapid evolution of AI agents means they will likely become indispensable tools in the near future. However, premature or misaligned adoption can lead to wasted resources and strained relationships with stakeholders. For organizations, the key is to prepare for a future where agentification is essential, without overcommitting to tools that aren’t ready to deliver real ROI. Balancing short-term practicality with long-term readiness will be critical for success.
Recommendation: Position your organization as an early but discerning adopter of AI agents. Start by piloting tools that address immediate needs, such as using Canva’s re-size tool to generate social media-ready content Fundraising with AI (h/t George Irish) to write email appeals at scale, while remaining open to future advancements. Focus on building internal expertise to evaluate AI solutions critically. Act as a guide for your team, cutting through the noise of over-promised capabilities and steering investments toward tools that enhance mission alignment. By embracing this balanced approach, you’ll be better equipped to adapt as agentification matures and delivers on its immense potential.
PEOPLE —> 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.
TECHNOLOGY —> 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. Unlike Large Language Models (LLMs) that often rely on Retrieval-Augmented Generation (RAG) to dynamically pull information from external sources, SLMs are typically pre-trained on a narrower dataset, optimizing them for faster processing and specific task execution. This makes SLMs particularly useful in real-time applications where low latency is critical, whereas LLMs with RAG are better suited for broader, more dynamic knowledge retrieval and contextual adaptability.
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.