Evaluating Workflows for Efficiency

Deloitte 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗻𝗲𝘄 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝘂𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀. Not every workflow needs an agent. Some are perfect. Some are a waste of time. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝘀𝗼𝗹𝗶𝗱 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝗶𝗳 𝗮 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮𝗴𝗲𝗻𝘁𝗶𝗰: 1. Reasoning & context → Best when tasks require logic and adaptation (customer service, supply chain). → Not ideal for static analytics (segmentation, forecast models). 2. Autonomy & escalation → Works if an agent can act first, escalate if needed (incident mgmt, compliance). → Not useful for one-off tasks like code generation. 3. Clear process end → Agents should own a workflow with a defined outcome (expense verification). → Not ongoing states (digital twins). 4. Goal-oriented workflows → Focus on achieving outcomes, not just steps (resolve customer issue, procurement cycle). → Not basic automation (pre-drafted emails, CRM entry). 5. Multistep & interconnected → Strong fit if the process spans tools/systems (onboarding, claims). → Weak fit if it’s a point solution (doc comparison). 6. Cyclic & repetitive → Best when tasks repeat with learning (CV screening). → Not irregular, ad hoc analysis (attrition causes). 7. Non-explanatory → Great if no “why” explanation is needed (change request mgmt). → Poor fit where leaders demand causality (dashboards). 8. Learning potential → Ideal when feedback improves results (marketing campaigns, fraud detection). → Not static rules (email segmentation). Agentic AI isn’t about sprinkling agents everywhere. It’s about identifying workflows where: → Reasoning creates value → Autonomy reduces human bottlenecks → Multistep orchestration drives outcomes → Feedback loops improve performance over time That’s where the ROI is. 𝗣.𝗦. 𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝘄𝗿𝗶𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝘄𝗵𝗶𝗹𝗲 𝗼𝘁𝗵𝗲𝗿𝘀 𝘄𝗮𝘁𝗰𝗵 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀. 𝗜𝘁’𝘀 𝗳𝗿𝗲𝗲, 𝗮𝗻𝗱 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗿𝗲𝗮𝗱 𝗯𝘆 𝟮𝟬𝗸+ 𝗽𝗲𝗼𝗽𝗹𝗲: https://lnkd.in/dbf74Y9E

Most change initiatives don't fail because of the change that's happening, they fail because of how the change is communicated. I've watched brilliant restructurings collapse and transformative acquisitions unravel… Not because the plan was flawed, but because leaders were more focused on explaining the "what" and "why" than on how they were addressing the fears and concerns of the people on their team. People don't resist change because they don't understand it. They resist because they haven't been given a compelling story about their role in it. This is where the Venture Scape framework becomes invaluable. The framework maps your team's journey through five distinct stages of change: The Dream - When you envision something better and need to spark belief The Leap - When you commit to action and need to build confidence The Fight - When you face resistance and need to inspire bravery The Climb - When progress feels slow and you need to fuel endurance The Arrival - When you achieve success and need to honor the journey The key is knowing exactly where your team is in this journey and tailoring your communication accordingly. If you're announcing a merger during the Leap stage, don't deliver a message about endurance. Your team needs a moment of commitment–stories and symbols that anchor them in the decision and clarify the values that remain unchanged. You can’t know where your team is on this spectrum without talking to them. Don’t just guess. Have real conversations. Listen to their specific concerns. Then craft messages that speak directly to those fears while calling on their courage. Your job isn't just to announce change, but to walk beside your team and help your team understand what role they play in the story at each stage. #LeadershipCommunication #Illuminate

Most people evaluate LLMs by just benchmarks. But in production, the real question is- how well do they perform? When you’re running inference at scale, these are the 3 performance metrics that matter most: 1️⃣ Latency How fast does the model respond after receiving a prompt? There are two kinds to care about: → First-token latency: Time to start generating a response → End-to-end latency: Time to generate the full response Latency directly impacts UX for chat, speed for agentic workflows, and runtime cost for batch jobs. Even small delays add up fast at scale. 2️⃣ Context Window How much information can the model remember- both from the prompt and prior turns? This affects long-form summarization, RAG, and agent memory. Models range from: → GPT-3.5 / LLaMA 2: 4k–8k tokens → GPT-4 / Claude 2: 32k–200k tokens → GPT-OSS-120B: 131k tokens Larger context enables richer workflows but comes with tradeoffs: slower inference and higher compute cost. Use compression techniques like attention sink or sliding windows to get more out of your context window. 3️⃣ Throughput How many tokens or requests can the model handle per second? This is key when you’re serving thousands of requests or processing large document batches. Higher throughput = faster completion and lower cost. How to optimize based on your use case: → Real-time chat or tool use → prioritize low latency → Long documents or RAG → prioritize large context window → Agentic workflows → find a balance between latency and context → Async or high-volume processing → prioritize high throughput My 2 cents 🤌 → Choose in-region, lightweight models for lower latency → Use 32k+ context models only when necessary → Mix long-context models with fast first-token latency for agents → Optimize batch size and decoding strategy to maximize throughput Don’t just pick a model based on benchmarks. Pick the right tradeoffs for your workload. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Six months ago, a client almost pulled the plug on an AI implementation we were running. Three weeks in. Leadership was aligned. The use case was clear. The tools were live. And yet adoption had started to stall. Usage dropped. Teams quietly slipped back into old workflows. Moments like this define whether an AI project succeeds or dies. At ALTRD, our instinct isn’t to defend the system we built. Our instinct is to investigate the system we missed. So we paused the rollout and audited what was actually happening inside the workflow. What we found was instructive. The training had landed well. But the implementation had been designed around how leadership thought the team worked. Not how they actually worked. Two things were quietly breaking adoption. First, we had optimized the visible workflow but missed an invisible step. There was a key handoff happening informally between two people over WhatsApp. It wasn’t documented anywhere. It never showed up in process charts. But it was where the real decision-making happened. Our redesigned workflow skipped that moment completely. Second, there was a quiet skeptic in the system. The team lead everyone naturally looked to before trying something new had concerns she hadn’t voiced in any meeting. Not because she was resistant, but because she wasn’t convinced the workflow would hold up under real pressure. Once the team sensed that hesitation, adoption slowed down. So we fixed the system. We remapped the actual workflow, not the documented one. Then we worked directly with the team lead. Not to sell the tool, but to understand the operational concerns and redesign parts of the system around them. The engagement expanded. And that project ended up becoming one of the most valuable learning moments for how we implement AI today. Two lessons we now carry into every engagement at ALTRD: Document the informal workflow, not just the official one. And find the quiet skeptic in the room early. They’re rarely the blocker. They’re usually the signal that something important hasn’t been designed properly yet. AI implementation isn’t just a technical system. It’s a human system. And if you want adoption to stick, you have to understand both.

There’s a huge difference between ‘I got AI to do this amazing thing for social media points’ and ‘I got AI to do this thing that generates a lot of revenue for my business or our clients.’ Real-world AI is very different. Most agents require small language models. Large context windows and multiple rounds of model calls turn the unit economics of foundational models negative for many use cases. Everything we build for clients starts with local AI. We spend no more than 2 days trying to get the workflow running on the Dell Pro Max T2 in my office. If it won’t run locally, using a frontier model rarely changes that. We scale the agent to support a small set of early adopters. This phase is critical. An early adopter cohort has been trained to use agents at their earliest maturity phase. Most users would reject the agent in this raw form. But this phase is intended to rapidly improve the agent’s workflow integration, orchestration, and reliability. Human feedback from trained early adopters improves agent performance faster than any other approach I have found. We iterate on more than just the LLMs. This phase fills in the knowledge graph, improves tool usage, adds guardrails, and informs the usage of more traditional machine learning models to augment the agent. When improvements plateau, we assess the agent. It is only promoted if its impact on outcomes meets user or customer expectations. Is it valuable? How does it reorchestrate workflows? Can the business monetize it? We roll the agent out to an alpha release cohort to scale the feedback flywheel. At this point, we know we have something valuable. We’re trying to improve its reliability and handle more workflow variations before a wider launch. We only evaluate frontier model usage at this phase. We finally know enough to make targeted decisions about where in the workflow frontier model performance could make a big enough difference to be worth considering. The alpha release also reveals adoption barriers for the agent and reorchestrated workflow. Most agents require us to craft an adoption journey for users and customers. That typically includes training for internal users and a phased rollout for customers. When improvement plateaus again, the agent is ready for general release. The process takes 2-3 months, and only about 30% of the workflows we try in my office end up going the distance. Data and information architecture make a huge difference. One client with a very mature knowledge graph is seeing a workflow success rate of over 50%. Small models perform significantly better for their use cases. #DellProMax

The idea of increasing speed in business by 10% can be very tempting, especially for entrepreneurs who often feel huge pressure to capitalize on opportunities before they vanish. However, it's crucial to distinguish between speed and urgency. Speed focuses on how quickly tasks are completed, often prioritizing rapid execution over other important considerations. This leads to: 1. Compromised Quality: Rushing through tasks can result in mistakes, lower quality, and ultimately damage to your brand. 2. Burnout: Constantly working at a high speed can lead to burnout, reducing overall productivity and company morale in the long run. 3. Shallow Work: Fast work often means less time for deep, strategic thinking, which is essential for innovation and problem-solving. Urgency on the other hand emphasizes working with purpose, clear goals, and timelines, without necessarily rushing. This approach can: 1. Enhance Quality; Focusing on doing things right ensures that the output is reliable and of high quality. 2. Sustain Momentum: A steady, deliberate pace can be more sustainable, helping maintain team energy and engagement. 3. Encourage Strategic Thinking: Working with intent allows for more thorough analysis, planning, and execution. Of course it depends on the task we are talking about because let’s face it some things just have to be rushed. But, while increasing speed by 10% overall might seem beneficial in the near term, it’s often much more effective to prioritize doing things correctly and with purpose. For businesses to win in the long run, entrepreneurs should try to balance urgency and quality, moving forward steadily and smartly rather than hastily and recklessly.

Hyperautomation has emerged as a game-changer in the technological landscape, changing how businesses streamline operations, reduce costs, and enhance efficiency. By combining AI, ML, and robotic process automation (RPA), it transformed industries. Gone are the days when automation was limited to assembly lines or customer service bots. Hyperautomation transforms everything — from crunching financial data to streamlining inventory management — into a unified, efficient digital ecosystem. For instance: ▶️ In warehouses, IoT devices monitor inventory and trigger restocking before shelves go empty ▶️ Financial tools like RPA bots process invoices while AI forecasts cash flow trends ▶️ ML algorithms pinpoint supply chain inefficiencies and suggest actionable fixes The result? A seamless, real-time operational flow that saves time, money, and resources. Gartner projects that by 2026, 30% of enterprises will automate more than half of their network activities- up from under 10% in 2023. In finance, AI algorithms detect fraudulent transactions faster than human analysts, while RPA tools manage expenses and generate reports in seconds. Customer service chatbots powered by natural language processing (NLP) handle routine queries, leaving human agents free to focus on high-stakes issues. In manufacturing, predictive maintenance minimizes costly machine downtime by identifying potential issues before they arise. AI-powered quality control systems catch product defects that human eyes might miss, while workflow automation optimizes resource allocation. In the ever-complex supply chain, hyperautomation ensures real-time responsiveness. AI systems analyze traffic and weather to optimize delivery routes, while IoT devices keep stock levels in check. The result? Faster deliveries, fewer errors, and significant cost savings. While the potential of hyperautomation is undeniable, it raises questions about its impact on human labor. Repetitive, low-skill jobs are at the highest risk of being replaced. But, this shift also opens doors for workers to upskill to manage and optimize these systems, focusing on creative and strategic tasks instead of mundane ones. The narrative shouldn’t be “man versus machine” but “man with machine.”  Valued at $45 billion in 2024, the hyperautomation market is projected to exceed $307 billion by 2037. Its future lies in driving sustainability, enabling hyper-personalized experiences, and achieving seamless end-to-end automation. As businesses continue to embrace this technology, it’s vital to maintain a human-centric approach: prioritizing ethical considerations, data privacy, and workforce training. The real question is: How will we harness its potential? #technology #AI #automation #innovation #business

These days, many CROs and sales leaders reflect upon the past 12 months and come up with resolutions for the new year. I personally find one task particularly helpful - reflecting about projects, tasks, or activities to stop immediately. In other words, to back up our strategies as leaders we need to apply focus by giving our teams permission to stop irrelevant activities. We demonstrate authentic #leadership by reinforcing words with actions, and stop projects and initiatives that do no longer align with our priorities. The benefits? Focus. Distractions and excuses are removed. Employee engagement is improved, and life should become more fun for people who see friction removed from their role. Here are some specific tactics I have found most helpful to make this happen: 💡Describe the vision with clear supporting goals. It’s impossible to align the team against specific tasks and tactics if there’s no line of sight to the overall objectives of the business. CROs need a vision that their sales teams can rally around which is inspirational, simple and clear. It needs to demonstrate believable impacts on the customer experience which link to measurable economic outcomes. In other words, “if we behave in this way, we create value for our customers and value for us”. 💡Formally audit current initiatives and activities. businesses are a chamber of ‘great ideas’, many of which sprout arms and legs in the form of informal or formal projects. And, often, these projects have loose (if any) goals, lack project management and dilute critical resources. 💡 Review KPIs: is every KPIs you set and review aligned to the goals and activities you said were important? If not, cease their existence immediately! there is no point painting a compelling vision for the business, ceasing initiatives and activities, but still reporting KPIs that reflect deprioritised topics. 💡Walk the talk: it’s critical for CROs to be acting and communicating in ways that are aligned with desired changes. As role models, and in a similar vein to KPI setting, CROs should act as a reinforcing mechanism by personally ceasing activities and aligning to the overall agenda. More ideas and practical tips for Spring Cleaning in the Forbes expert collection attached in the comments below.

Most people think every task deserves them. They're wrong I met an entrepreneur who was always overloaded. He said: "I never have enough time to get everything done." The reason he felt this way was: He had no clear plan or strategy to manage his time effectively. Even though he was highly skilled and motivated. He couldn’t figure out how to balance his workload, but knew he needed to find a solution fast. Sadly, I wasn’t surprised. But I told him with the right tactics, he could master his time. It didn’t matter how many tasks he had. Here’s what he did: Plan to Win, Every Day He ended each day by setting three top priorities for the next day.   This way, he avoided decision fatigue and started each morning with a clear focus. Automate the Everyday   He used AI tools to handle scheduling, routine emails, and admin tasks. Automation worked while he slept, freeing up his brain for more important work. Build an Ironclad Focus Fortress He blocked out “deep work” hours with no interruptions. His team and clients respected these windows, boosting his productivity. Optimize Your Energy, Not Just Your Time He aligned his tasks with his natural energy levels. Creative work during peak times, repetitive tasks during low-energy periods. This helped him achieve more without burning out. The Snap Decision Rule He handled small tasks immediately if they took two minutes or less. This kept his mind clear and maintained momentum on bigger goals. Decide What Deserves You He filtered his to-do list daily:   - Does this contribute to my growth?   - Can it be delegated or dropped?   By eliminating low-impact tasks, he focused on what truly mattered. The Distraction-Free Zone   He unplugged for at least an hour each day.   No emails, no calls, no scrolling.   This time was for creative thinking and strategic planning.   Silence became his tool for clarity and innovation. Months went by: And he transformed his business. He mastered his time and achieved remarkable results. So here’s my take: Every overloaded entrepreneur can find time mastery. With the right tactics, you can focus on what truly matters. And achieve more than you ever thought possible.

How do you align an entire company around the same goals? It’s something we consider very important at Thinkific especially as the team has grown. Recently, we started rolling out V2MOM to help bring more structure and clarity to that process. For anyone unfamiliar, V2MOM is a goal-setting framework created by Marc Benioff at Salesforce. It stands for Vision, Values, Methods, Obstacles and Measures — a simple but powerful way to clarify what you’re trying to achieve, how you’ll get there and what might stand in your way. We’ve used a few goal setting frameworks over the years (OKRs, Rockefeller Habits) but something always felt like it was missing. I felt we had room for improvement in how we identified obstacles and anchored goals in guided principles. What I like about V2MOM is the structure. It’s not just about setting a vision and defining success, it also forces you to think through the values that guide your work, the potential obstacles and the specific methods you'll use to get there. Another shift for us is in how we cascade goals. My V2MOM connects directly to my direct reports’, and theirs to their teams. There’s still room for team-level priorities, but everything ties back to the company’s broader vision. That level of alignment brings a lot more clarity: on what we’re doing, what we’re not and how each person contributes to the big picture. So far, I’m a fan and I’ve also heard positive feedback from our team who’ve said V2MOM is helping reinforce a stronger sense of unity, shared goals and collective impact. It’s not a silver bullet, but it’s helping us be more intentional about both what we’re working toward and how we get there. Always curious — what frameworks or tools have you found most effective for aligning goals across your team or company?