Role of Technology in Cross-Functional Productivity

Data and Business teams operate in two different domains that speak different languages. For example, data analysts speak in terms of datasets, keys, joins, columns, filters, and aggregations, while data engineers speak in terms of pipelines, dependencies, and lineage. On the other hand, business domains use metrics, drivers, levers, segments, goals, tactics, strategy, initiatives, and features. Being data-driven or data-informed requires effectively translating between these two domains. This involves cross-pollinating data and business teams to align them with the same end goals, which is easier said than done. However, frameworks and tools can serve as powerful collaborative agents, much like engineering teams and how they interface with the business. One such framework that can help is metric trees, which maps the processes by which the business operates and serves its customers - including the inputs, outputs, relationships, entities involved, and their states. By building tools that help data and business teams collaboratively design and operate on metric trees, the translation friction can be significantly reduced. Or, even practically eliminated depending on the sophistication of the tooling. This incentivizes more people to participate leading to dramatic improvements in the quality of conversations and decision-making.

Last quarter, I sat down with a dozen organizations to understand how they're empowering their blended teams to succeed. A fascinating pattern emerged in our discussions about technology. One of the most striking success stories came from a financial services firm that cut their project coordination time by 50%. Their approach wasn’t about using more tools—it was about selecting the right ones and ensuring they were integrated into their workflow effectively. What stood out across industries is the critical role that the right technology plays in team success. Some of the most effective tools include: - Project management platforms (like Monday.com or Trello) that give everyone instant visibility - Communication tools (Slack, MS Teams) that bridge the physical/virtual gap - Secure document sharing systems (O365/Sharepoint, Dropbox, Google Workspace) that balance collaboration with data protection - Virtual workspace tools (Zoom, MS Teams) that empower distributed teams collaborate effectively   What truly sets successful teams apart is how they use these tools. For example, one team standardized MS Teams for all communication and collaboration, creating a unified space for real-time work. They also used AI for automated note taking, generating concise meeting summaries and highlighting key moments in video recordings, ensuring that team members who couldn’t attend could quickly catch up on the most critical parts and stay aligned.   The key takeaway here? Technology isn’t just about having the latest tools—it’s about making the right tools work for your team and using them in a way that enhances productivity and collaboration.   What tools have you found most effective for your blended teams? How do you ensure you're using them to their fullest potential?   #WorkforceTech #DigitalTransformation #FutureOfWork

In today’s fast-evolving world, technology like artificial intelligence offers tremendous potential to boost productivity. However, technology by itself cannot deliver the full transformation businesses need. To achieve real gains, organizations must rethink their processes, reskill their workforce, and align technology with business strategies. The most powerful impact of AI lies in its ability to augment human capabilities, not replace them. When used effectively, AI can handle routine tasks, freeing employees to focus on higher-value work such as creativity, problem-solving, and strategic decision-making. This results in a more dynamic, innovative, and efficient workplace. Reskilling employees becomes critical in this equation. As AI automates repetitive tasks, workers must be trained to take on more complex roles that leverage their unique human skills. Companies that fail to invest in employee development may find themselves unable to fully capitalize on AI’s potential. Additionally, organizations need to reassess their operational models. Successful AI implementation often requires process redesign, where workflows are restructured to maximize the technology’s capabilities. Integrating AI seamlessly into business operations helps ensure that it supports productivity rather than simply adding more complexity. Finally, leadership plays a crucial role. Embracing AI and other technologies requires a cultural shift, where innovation is embedded at every level of the organization. Leaders must create an environment that encourages experimentation, fosters learning, and celebrates innovation to thrive in this new era of productivity. In conclusion, while technology offers a foundation for enhanced productivity, it is the thoughtful integration of human skills, process innovation, and leadership that truly unlocks its potential. Organizations that prioritize these elements will lead the way in this technological transformation, achieving sustained growth and success. #ثامر_شاكر #leadership

Transforming Operations: The COO's Guide to Gen AI Mastery Gen AI and agentic AI offer opportunities to boost operational efficiency. This article by McKinsey & Company reveals how COOs can unlock real value from AI by optimizing operating structures, data governance, and change management. Key Highlights 🔸 Gen AI’s Potential Across Operations →$4.4T productivity potential over time. →Real gains across R&D, manufacturing, procurement, and production. • 20–30% dev time saved in code work. • 40% reduced maintenance workload + 3% OEE boost. • $15M in contract savings found. • Autonomous shift handovers powered by agents. 🔸 Why Many Still Struggle →Only 19% report >5% revenue impact from gen AI. →1% of companies have achieved AI maturity. →Fragmented development and slow scaling remain major issues. 🔸 3 Core Roles for COOs in Gen AI Rewiring → Define Operating Structure • Centralize early using COEs reporting to CEO. • Enable scale while minimizing redundancy. • €300M EBITDA improvement from one roadmap. → Shape Data Governance • Centralize data sources to eliminate conflict. • Emphasize human oversight and robust validation. → Lead Change Management • Integrate gen AI into daily work, not just tech upgrades. • Empower users with change champions and new workflows 🔸 Rethinking Operating Structures →Involve C-suite and cross-functional leaders early. →Start centralized, then distribute capabilities. →Design for impact, not indefinite pilot phases. 🔸 Identifying Domains → Balance quick wins with scalable transformation. → Assess if gen AI is the right solution or if traditional analytics would suffice. 🔸 Data Governance in Practice →Harmonized, centralized data = consistent, accurate inputs. →Data conflicts ended by aligning R&D, engineering, sales, and support 🔸 Change Management →Gen AI = new way of working, not just automation. →Centralized workflows cut SOW time from days to hours →Thousands of hours saved, employees focus on high-value tasks. 🔸 Strengthening COO–CIO Collaboration →When COOs define and CIOs scale the tech, the enterprise benefits. →Agility + shared vision=timely returns on AI investment. Conclusion By fostering cross-department collaboration, ensuring robust data governance, and championing change management, COOs can lead their organizations into an AI-driven future. Insightful article by Curt Mueller, Darryl Piasecki, Marie El Hoyek, and Oana Cheta Ali Arsanjani, PhD Alex Wang  Aishwarya Naresh Reganti  Adam Biddlecombe Chiara Baston, PhD  Cobus Greyling  Evgeny Krapivin  Elvis S.  Enzo Wälchli  Itamar Golan  Hui Yang  David Sauerwein  Jan Beger Jaideep Kewalramani Louis-Clément Schiltz Lewis Tunstall  Martin Roberts, Marcin Gwóźdź  Michael Spencer   Pau Labarta Bajo  Pascal BORNET Pramodith B. Pavan Belagatti Rafah Knight  Saba Hesaraki Stijn Spanhove Vincent Granville  Vijay Morampudi Vikram Pandya Prasanna Lohar #AI #ArtificialIntelligence #GenAI #AgenticAI #COO

Cross-functional influence is something assistants have already been doing for years. So I was excited to see this powerful insight from the new 2026 Global Learning & Skills Trends Report. “As AI weaves through every line of business, organisations must break down silos and encourage learning that spans traditional functional borders.” In other words, departments can’t afford to work in isolation anymore. With AI touching every part of business, we need to share knowledge and learn across teams. This is great news for assistants because whilst most roles sit neatly in a box - finance, HR, IT, operations. Assistants never have. You move between all of them, often in the same day. You understand how decisions in one department ripple across another. You see where projects overlap, where communications break down, and where risks are quietly building. In the AI era, this vantage point becomes critical. AI has the power to accelerate transformation, but it also magnifies silos. One department experiments here, another automates there, and suddenly the organisation is duplicating effort, exposing itself to risk, and missing opportunities for alignment. Here’s how to own your influence. 📌 Ensure conversations happen between teams who would otherwise never sit at the same table. 📌 Turn technical detail into strategic insight for your executives, and executive vision into concrete actions for your teams. 📌 If you spot duplication, flag risk. 📌 Smooth out communication flow and coordination, to make transformation faster, not slower. The report is clear. The organisations that thrive will be those that integrate knowledge and scale learning across every function. That requires people who can influence without authority, who can build trust across silos, and who can make the invisible connections visible. Which is exactly what assistants have always done. The challenge now is to own it, name it, and demonstrate it. Stop framing this as “just admin.” Start positioning it as the cross-functional influence that protects organisations from risk, drives adoption, and unlocks ROI. Because the thing is, too many executives still underestimate just how much of this work you’re already doing. If you don’t claim it, they won’t see it. Cross-functional influence is suddenly a business-critical capability. If organisations overlook the assistant’s role in delivering it, they’ll pay for it in silos, stalled projects, and lost ROI. 🔁 Repost to share 👉 Follow me Lucy Brazier OBE for daily administrative profession related content and inspiration.

Personal Reflections on Using 3D tool in Practice as a Patternmaker 1. Work Development Process ✏️ 2D patternmaking → DXF conversion → 3D draping simulation. 2. An efficiency-focused view from the standpoint of a patternmaker ✏️ Personal Productivity in Patternmaking Tasks When comparing 3D draping results with physical samples in mind, the predictions are quite accurate. However, certain aspects—such as the natural stretch that occurs after sewing, and the way draping changes depending on cross cut, bias cut, or straight cut—are expressed less naturally than in physical samples. That said, I believe that a patternmaker with extensive practical experience can recognize these subtle differences and resolve them accordingly. The ability to freely apply materials, colors, and design details is certainly an advantage, but I consider this to be a benefit primarily for the design team. In conclusion, as an experienced patternmaker, I find it challenging to pinpoint areas where 3D draping meaningfully improves the efficiency of a personal patternmaking workflow. 3. A view from the standpoint of cross-functional teamwork. ✏️ Communication with Cross-Functional Teams It’s a transformative shift—like moving from handwritten letters to email. Or from written explanations to video calls. In short: WYSIWYG. What you see is what you get. This visual clarity dramatically improves alignment across teams. 4. A perspective on the entire production process ✏️ In the context of end-to-end garment production, I believe leveraging 3D tools facilitates visual and precise cross-functional communication. This minimizes miscommunication and sampling errors, accelerating production timelines and improving overall efficiency.