From AI Productivity To Organizational Power: An AI Era Blueprint
Using a Large Language Model is More Like Playing This Organ Than a Spreadsheet
Wanamaker organ.
I interact with many organizations and five consistent questions arise:
This is the first of a three part series that will attempt to answer these questions for business leaders. The advice will be humble because things are moving so quickly, executives must act, but also remain curious and flexible. Globally there are billions of dollars flowing into AI. Every nation state sees that AI is central to national defense, and machines learn faster than humans. This flurry of factors means innovation in AI and the symbiosis of people and machines is going to accelerate at an accelerating rate.
Generative AI has rapidly moved from novelty to necessity, with over one third of workers willing to pay for the tools themselves. In a recent blog post Ethan Mollick noted that even thought the research shows individual level productivity of 10-40% or more, firms realize much lower levels of at the organizational level.
This gap isn't merely about technology readiness. It's about leadership, learning, and strategic integration. In the rush to adopt AI, many organizations are missing the deeper transformation required to turn individual speed into institutional acceleration. To bridge this divide, leaders need a new playbook for preparedness—centered on four critical imperatives: Content, Capability, Community, and Curation.
In the Genenerative AI age, leaders can no longer rely on their traditional content diet. AI changes too fast. The best ideas now emerge not just from Harvard Business Review or McKinsey reports but from research labs, GitHub repositories, YouTube, X, LinkedIn, expert newsletters, research papers, product announcements and many other sources. There is robust scientific evidence that people with more expertise in an area perceive the world in a new way. My former colleague Prof. Jim Cash used to call this more educated perspective, 'visual literacy'. Leaders who are not schooled in what is happening in AI just can't see the opportunities and possibilities. Therefore, if you want your organization to understand the potential for AI, you need to read and consume new information and inputs. The goal isn't technical mastery—it's strategic literacy.
Content Changes Minds
Leaders Need New Content sources
AI-literate leaders ask better questions. They spot emerging opportunities faster. And crucially, they model curiosity for the rest of the organization. You need to change the content you consume before you can change the context in which you lead.
Working with a large language model is not like implementing a technology, it's more like playing an enormously complex instrument. If you walked up to the Midmer-Losh organ at Boardwalk Hall in Atlantic City New Jersey has 33,112 pipes, 7 keyboards, hundreds of stops, and over a dozen foot pedals and play chopsticks on the middle keyboard, it's not that impressive. The reason it's not impressive is your lack of playing ability, not the quality of the instrument. The only solution for this is to have hands on experience for everyone in the firm – especially the senior management team and the board.
Leaders Build New Skills – To Win A New Race
Knowledge and Know How Are Critical For The Entire Leadership Team
Capability-building must be enterprise-wide. Some firms are launching 'AI centers of excellence.' Others pair junior 'AI natives' with senior managers to cross-train. All should ensure that learning is active, continuous, and contextual—focused not just on the tool, but also its use in the firm. As Generative AI reshapes job roles, the capability gap becomes a bottleneck. The future belongs to AI-complemented workers, not just AI-aware ones.
In fast-moving domains like AI, no single organization can learn fast enough alone. That's why executives must foster internal and external learning communities—spaces where employees, peers, and partners can share use cases, prompts, failures, and insights.
Internally, this might mean AI guilds, cross-functional prompt libraries, or weekly 'show-and-tell' sessions. Externally, it means plugging into peer forums, academic collaborations, conferences and vendor ecosystems.
AI Communities Amplify Learning
Leaders Learn From Leaders
Crucially, these communities should include both technical and non-technical voices. The best Generative AI ideas often come from unexpected places—an HR manager using GPT to rewrite job descriptions, or a field technician using vision models to pre-screen repairs. Communities not only accelerate learning—they amplify culture. They signal that AI is not a top-down mandate, but a shared frontier.
Most organizations are still using Genenerative AI tools as consumer products—ChatGPT for writing emails, Midjourney for visuals. But the future of enterprise AI lies in curated stacks of specialized tools, integrated with workflows, data, and business logic. Few organizations are happy with simply waiting for their main vendor, whether it is Microsoft, Google, Salesforce, or others, to serve all their AI needs. The innovation lanscape is just too varied and wide.
You Can't Just Wait For Big Suppliers To Move You Fast Enough
The World Is Abuzz With Innovation: Curate a Wider Network of Supply.
Executives must curate their own ecosystem of AI suppliers strategically. That means identifying best-in-class tools (from copilots to agents to orchestration layers), vetting vendors for security and compliance, and creating internal 'AI marketplaces' for easy access.
Equally important is curating the right partnerships. Collaborate with startups, integrate open-source models, engage with AI research hubs. The supply chain of intelligence is changing—don't wait for the big consultancies to catch up. Also, you will need to have a group of main stream big suppliers as well as a network of small innovators. Remember: every supplier choice you make either accelerates or inhibits transformation.
The first productivity revolution is personal. The second must be institutional.The AI era won't reward passive adopters. Organizations that treat Generative AI as a set of apps will see temporary productivity bumps—followed by competitive obsolescence. Those that lead strategically, through the 4 Cs (Content, Capability, Community, and Curation) of AI Preparedness, will not only boost productivity but reinvent how they operate. Leaders who embrace this moment with intentionality will shape the future of their organizations—not just survive it.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
The Verge
21 minutes ago
- The Verge
Here's what's inside Meta's experimental new smart glasses
Meta has revealed more information about Aria Gen 2, its experimental smart glasses designed to serve as a test platform for research about augmented reality, AI, and robotics. The glasses pack several improvements into their lightweight frame that could one day translate into consumer products, including an improved eye-tracking system that can track gaze per eye, detect blinks, and estimate the center of pupils. 'These advanced signals enable a deeper understanding of the wearer's visual attention and intentions, unlocking new possibilities for human-computer interaction,' Meta writes. Meta initially announced Aria Gen 2 in February, saying they will 'pave the way for future innovations that will shape the next computing platform.' They build upon Meta's first iteration of the glasses in 2020, which were similarly available for researchers only. Along with an improved eye-tracking system, Aria Gen 2 comes with four computer vision cameras that Meta says enable 3D hand and object tracking. Meta says researchers can use this information to enable highly precise tasks like 'dexterous robot hand manipulation.' The glasses also have a photoplethysmography sensor built into the nosepad, which allows the device to estimate a wearer's heart rate, along with a contact microphone that Meta says provides better audio in loud environments. There's a new ambient light sensor as well, allowing the glasses to differentiate between indoor and outdoor lighting. The Aria Gen 2 glasses include folding arms for the first time, weigh around 75 grams, and come in eight different sizes. Meta plans on opening applications for researchers to work with Aria Gen 2 later this year. The initiative builds on the successful development of Meta's Ray-Ban smart glasses, a form factor it aims to expand with its Orion augmented-reality glasses, a rumored partnership with Oakley, and a high-end pair of 'Hypernova' glasses with a built-in screen.
Business Insider
27 minutes ago
- Business Insider
How Digital Realty is upgrading its data centers for AI — and trying to stay green
This article is part of " Build IT: Connectivity," a series about tech powering better business. Digital Realty, a data center operator, is scaling up its infrastructure to keep up with AI's growth. But the tricky part is to do so in an environmentally friendly way. The International Energy Agency found that in 2024, data centers accounted for 1.5% of global electricity use. By 2030, that number could nearly double, reaching levels just above Japan's current annual energy consumption. In addition, AI infrastructure is cooled with enormous amounts of water. A report from the University of Tulsa found that a single facility can use up to 5 million gallons per day, enough to supply thousands of homes. With governments and companies pouring billions into AI infrastructure, those resource demands are only expected to grow. Through close collaboration between its sustainability, engineering, and design teams, Digital Realty, which operates more than 300 data centers worldwide, is working to reduce carbon emissions. That means sourcing more renewable energy, upgrading cooling systems, and rethinking where — and how — new sites are built from the ground up. To understand how Digital Realty is preparing for that future, Business Insider spoke with Aaron Binkley, vice president of sustainability, and Shea McKeon, global head of design and engineering, about the company's sustainability strategy. In a roundtable conversation, Binkley and McKeon shared how their teams are working together to cut emissions, balance business demands with sustainability goals, and stay ahead of AI trends in the data center world. The following has been edited for length and clarity. Business Insider: How do your roles and teams at Digital Realty contribute to the company's overall decarbonization strategy? Aaron Binkley: My role is global. I oversee sustainability efforts across the company, including work around renewables, decarbonization, development and construction, operations of in-service data centers, and collaboration on green finance, clean energy, energy performance, water, and more. A big part of my job is acting as a convener, bringing people together to ensure we're working collaboratively and surfacing the best ideas. Shea McKeon: I sit in the design, engineering, and construction department, which oversees all new developments and major renovation projects around the world. We're responsible for integrating sustainability into our new builds and working with operations and energy management to bring existing facilities up to the latest standards. Digital Realty has ambitious targets to cut direct and indirect emissions per square foot by 60% and supply chain emissions by 24% — each by 2030. How are your teams collaborating to hit those goals? Binkley: We think about how we build, power, and operate sustainable data centers. That starts with understanding emissions across the data center lifecycle. Design and construction impact embodied carbon, or the total amount of emissions associated with the data center lifecycle, from metal extraction to construction, all the way to equipment disposal. Operationally, it's all about electricity use. So we work closely with our energy procurement and strategy teams to decarbonize our electricity supply. For Scope 1 emissions, referring to direct emissions from sources owned by our company, we're switching from burning mainly diesel in backup generators to renewable fuels like hydrotreated vegetable oil, a diesel-like fuel which we've deployed across 17% of our operating portfolio. But 98% of our Scope 1 and 2 emissions, which include the indirect emissions from purchased energy, come from electricity, so that's the big nut we need to crack. We prioritize opportunities based on where we can make the biggest impact, designing efficient facilities and powering them with renewables. McKeon: I'd say on the Scope 3 emissions side, which are indirect emissions from the data center supply chain, that's where my team can really have an impact. We're constantly working with our design and construction partners to make sure we're specifying the right materials to help bring those emissions down. It's always at the forefront of our designs. We also partner with Aaron's team during our annual business reviews with key suppliers. We just wrapped those up recently, and Scope 3 emissions were one of the topics we discussed — how suppliers are performing and what we can do to improve. It's a never-ending, iterative process, but collaboration is key to making progress. AI models are creating a demand for computing power. How are you balancing this growth with your sustainability targets? Binkley: We've seen AI coming. It's front of mind now, and a significant portion of our bookings are AI-related. Even as our portfolio grows, we haven't pulled back on any sustainability commitments. We've made strong progress on sourcing renewables and decarbonization. We plan for that, and as AI pushes greater demand, we adjust our plans: rethink sourcing, get more integrated with acquisitions, and get involved earlier in planning and design. We're even part of early utility conversations when acquiring land, asking for clean energy solutions before we've started moving dirt. We're also using AI internally to improve energy and water efficiency. We developed an in-house program called Apollo AI to optimize building management systems across our portfolio. The platform helps our facility engineers find hidden anomalies like clogged filters and leaky valves and suggests improvements that can help drive energy savings. We also have AI tools focused on water systems, helping us fine-tune cooling performance and water chemistry to reduce waste. We really try to squeeze every last drop of productivity out of the energy and water we consume. McKeon: We're planning for 100% of our future buildings to have the capability to deploy liquid cooling directly to the chip, where coolant is circulated through metal plates attached to graphics processing units to remove heat. For those that don't, our engineering team is building roadmaps so we're ready if customers want to use energy-intensive technologies like generative AI that require high levels of compute power. Our modular design approach helps us learn and adapt quickly. And with liquid cooling, you don't need as much square footage per megawatt anymore. That's going to change how buildings are designed moving forward. What's the biggest challenge in aligning technical engineering demands and sustainability goals? Binkley: Our sustainability standards are part of our building codes, but the maximum amount of emissions that can be reduced in our facilities still varies based on customer usage. Some customers move into the data centers fully and operate at high intensity; others ramp up slowly. Modularity helps us handle those variations. The speed of growth is also a challenge — we need to stay ahead of customer demand, line up renewables, and anticipate equipment needs that take a long time to procure. We're building physical infrastructure, which takes years. You can't just flip a switch. McKeon: We're a multi-tenant facility. We lease out space to our customers, so while we control the infrastructure, the customer ultimately controls how they operate within that space. We can design proactively with energy efficiency in mind, and we encourage best practices like airflow containment and optimal temperature settings. But at the end of the day, we don't dictate how customers use their equipment. That creates a bit of a disconnect. Our engineering team can build in sustainability features, but our operations team has to be reactive depending on how each tenant deploys. Some customers come in and run at high utilization, which is great from an efficiency standpoint. Others move in slowly or use a mix of equipment that can affect how well the facility runs. So there's a line between what we can control and what we can influence. Luckily, our operations team is very sophisticated. They use automation, data, and AI to adapt in real time, dialing in temperature and managing airflow, all to run as efficiently as possible. Looking ahead, how are you evolving your decarbonization strategy over the next few years? Binkley: We're not pulling back on our commitments. We'll stay the course, and perhaps even get more aggressive. Clean energy is harder to source now, but still available. We've been able to secure renewables that offer real value and reduce costs. We're also going deeper into Scope 3 with our supplier engagement program, working with vendors to reduce the carbon footprint of the materials and products we buy. McKeon: I'd echo that. Sustainability is embedded in our design process. It's not just a benchmark — it's part of our culture. Our local teams are empowered to innovate project by project, and our global teams constantly share best practices. What works in France might be relevant in Chicago. It's a contagious, exciting environment to be in.
Forbes
27 minutes ago
- Forbes
Can AI Turn Workplace Chat Into Revenue? These Companies Think So
From Zoom to Slack, AI is turning workplace talk into strategic insight — and a competitive edge. When a multimillion-dollar deal falls apart, it's easy to begin a postmortem from the boardroom. But, in many cases, the warning signs often begin in chat threads that get ignored. It could be a missed message, or a client's tone shift, or even a subtle concern raised on WhatsApp that was never escalated. In most companies, these conversational breadcrumbs — often rich with insight — rarely make it into CRM systems or dashboards. And yet they carry valuable intelligence on critical business insights like why a deal soured, what a customer really felt, or how a competitor was named and shrugged off. That's the problem communication intelligence — a growing category of enterprise AI that interprets human dialogue in real time — wants to solve. It's part of a shift toward what industry analysts call agentic AI. According to Grand View Research, the global enterprise agentic AI market was valued at $2.59 billion in 2024 and is projected to grow at a compound annual growth rate of 46.2% through 2030. For more context, that translates to a projected market size of nearly $30 billion by 2030 — a big signal that demand for proactive, decision-making AI is accelerating across industries. Dima Gutzeit, founder and CEO of LeapXpert — the communications intelligence company that recently acquired messaging AI startup StartAdam — explained in an interview that valuable business insights often hide in plain sight within everyday conversations. 'A wealth advisor discussing deal details with a high-value client via WhatsApp typically had limited ability to share that context with their team. Communication intelligence changes that,' Gutzeit noted. For companies that communicate externally across channels like WhatsApp, iMessage and email — and internally on collaboration platforms like Teams or Slack — there's never been a more urgent need to harmonize the silos that dominate modern communication. It's no longer enough to simply store conversations; organizations must now better understand them, govern them and act on them. LeapXpert is not the only vendor tapping into this trend. Others include Moveworks, which uses generative AI to support workplace communication; which helps contact centers analyze agent performance; and Cognigy, which automates conversation flows across customer service teams. While their use cases differ, the underlying thesis is similar: Enterprise conversations are full of data — if only you can capture, interpret and govern it. Business leaders have long invested in business intelligence dashboards and CRMs — customer relationship management systems — to extract value from structured data. But the bulk of human communication happens elsewhere, in channels never built for analysis. That disconnect is creating so many blind spots, according to Gutzeit. 'If a company isn't governing consumer-grade tools like WhatsApp or Signal, it's leaving a huge risk in its knowledge and compliance management,' he told me. The cost, he continued, is more than regulatory exposure; it also makes teams duplicate work, lose situational awareness and miss high-value signals from clients. Communication intelligence aims to fix this by using AI to interpret conversation data as it happens — flagging risks, detailing key themes, or summarizing action items into CRM records. Done right, it turns daily chatter into strategic feedback. But done the wrong way, it could move an organization's communication channels from chaos to anarchy. LeapXpert's AI layer, Maxen, is one of a growing field of agentic AI tools that don't just respond to queries but proactively participate in workflows. That means suggesting next steps, reminding employees to follow up, or detecting compliance violations in real time. 'Agentic AI actively participates in conversations like a smart assistant,' explained Gutzeit. 'It summarizes key points, logs them into CRMs, and enables real-time collaboration — all without manual effort.' The broader enterprise AI ecosystem is headed in this direction. For example, Zoom AI companion now offers real-time meeting summaries and sentiment tracking. Microsoft's AI copilot is also embedding conversational intelligence into Teams. But while copilot and Zoom are great AI tools for internal use of one platform, Maxen takes a different approach, functioning like copilot but for all your channels in one place, whether internal or external. For many business leaders, the question is no longer whether AI can understand dialogue. It's now more about what to do with that understanding. In highly regulated industries like finance and healthcare, communication intelligence also offers a pathway to compliance. AI tools can flag language that violates policy, surface disclosures, or create audit trails across consumer messaging platforms. But there's also a fine line between governance and overreach. Many AI skeptics and critics have raised concerns that deep analysis of workplace conversations could blur ethical boundaries, especially when AI summaries misinterpret tone or context. To address this, vendors are building in transparency features. Maxen, for example, anchors every AI summary to source threads so that users can verify claims. 'Nuance is preserved through multiple layers of verification,' Gutzeit noted. 'Summaries quote directly from Slack, Teams, WhatsApp and CRM systems.' As vendors race to build agentic AI tools that scale, integration and context will be key differentiators. LeapXpert's acquisition of StartAdam, for instance, adds Slack-to-WhatsApp bridging, Discord support and native syncing with Salesforce, HubSpot and Microsoft Dynamics. The idea isn't just to govern communication but to also make it intelligent enough to drive decisions. As Gutzeit noted, that shift will eventually make communication intelligence as essential as CRMs or BI tools — a bold claim when you think about it, but one that may likely happen. 'Rather than just reporting sales numbers, leaders will increasingly say: 'Here's what our client conversations told us about what's coming next.'' It's not hard to imagine a near future where strategic planning doesn't begin with spreadsheets but with transcripts, where what was said, how it was said and who said it becomes a company's most valuable data.
