Omnichannel Experience Strategy

Having spent over a decade in the Salesforce Ecosystem, here is why I am betting on Hubspot.... I’ve closely followed the evolution of CRM & MAP tools—not just as a user, but as an investor. Ive consumed Industry Reports, Gartner/Forrester Analysis, & any other reports I can about trends in this space for many years. (I did my MBA Capstone project on Salesforce back in 2014!!) Here is how I see it... Salesforce, with its impressive market share and revenue, is undeniably still the dominant player. But HubSpot’s rapid growth, is hard to ignore. HubSpot’s approach to growth is fundamentally different—while Salesforce has built its offerings through acquisitions, resulting in a somewhat fragmented user experience, HubSpot has grown organically. They’re building a truly integrated solution from the ground up, capitalizing on the GTM Ops trend rather than sticking to the old silos of Sales Ops, Marketing Ops, and CS Ops. Unlike Salesforce, which has acquired numerous companies and stitched them together, HubSpot feels like one cohesive platform. As a RevOps leader—or even as an end user—you can feel the difference. Salesforce’s platform often feels like separate tools trying to work together, whereas HubSpot delivers a seamless experience. Why does this matter so much? Because integrated solutions are the future. When your tools are tightly integrated, they can leverage AI and data far more effectively without relying on middleware or additional integration tech. This integration is crucial for scaling AI capabilities across your operations, allowing for more sophisticated insights and automation. I’ve also seen firsthand how HubSpot is innovating at a rapid pace, consistently rolling out features that are setting new industry standards. Their creative approach, of allowing users to opt into betas combined with the thriving community of partners and users, further sets them apart. HubSpot’s evolution from an SMB tool to an enterprise-level solution is an evident focus as seen through their introduction of Ops Hub, HIPAA compliance features, and other tools that enhance scalability while maintaining user-friendliness. HubSpot’s trajectory is one that excites me, not just as a tech enthusiast but as someone who understands the value of a well-integrated, forward-thinking platform. With a 33% year-over-year revenue increase, HubSpot is expected to reach $5 billion in annual revenue by 2026. The future growth potential, especially as HubSpot expands its product offerings and international reach, makes it a smart bet, especially in a market projected to grow to $145.79 billion by 2029. As we move into 2025 and beyond, I believe HubSpot is poised to challenge Salesforce’s dominance, especially as more companies seek the benefits of a unified, AI-driven CRM solution. #Hubspot #revOps

I just taught Claude to directly query my CRM. Complex workflows became single prompts: A month ago my network kept talking about something called Model Context Protocol (MCP). Initially abstract, I understood it simply as: MCP lets AI models directly access your existing tools and databases. Think of it like the invention of USB: → Before USB: Multiple incompatible ports → After USB: One universal connection → Before MCP: Custom data integrations → After MCP: Universal plug-and-play AI connectivity Then a week ago I got an email from my personal CRM provider Clay that they had support for MCP. Historically, CRMs have acted as passive databases, requiring manual interactions to deliver insights. Here is what I used to do when I wanted to know who within my network had changed roles recently: OLD PROCESS: → Log into Clay CRM, export contacts as CSV → Clean and format data in a spreadsheet → Copy-paste formatted data into Claude → Manually instruct Claude to analyze job changes → Copy Claude’s insights back to Clay → Update contact records individually → Manually set follow-up tasks for each contact NEW PROCESS: → Simply instruct Claude: “Identify contacts in my network who recently changed jobs, showing their old and new positions and when I last interacted with them.” → Claude directly accesses Clay via MCP → Finds contacts who’ve recently changed jobs → Instantly provides a detailed, actionable list The results aren't perfect, but they turned a previously tedious process into an effortless query. The technical setup took 5 minutes: → Generated a Clay API key → Connected through Clay’s Smithery page → Installed Node.js locally → Ran one terminal command → Restarted Claude, confirming integration MCP's power comes from three shifts: → From isolated silos to interconnected intelligence → From sequential tasks to seamless orchestration → From human middleware to direct and automated interactions While it is early days, I believe we are scratching the surface with what is possible. I'm now working with several of our portfolio companies to explore how we can do deeper AI integrations. In an age where everyone has access to similar AI tools, the real competitive advantage isn't the tool itself. It's how deeply you embed it into your workflows.

In my 20+ years of implementing HR tech solutions, one thing is clear: success hinges on understanding the last person in the value chain. Whether it's an end-user or an admin, their experience makes or breaks adoption. We must spend time on the ground, understanding their real issues - from manual uploads to circuitous processes. HR tech founders & leaders, ask yourself: --> Are you spending enough time with your end-users? --> Do you truly understand their pain points? Only then can you build a system that truly serves its purpose. #HRtech #Implementation #UserExperience #Adoption

Are you considering implementing a new ERP system? Lately, I've engaged in a number of discussions regarding the selection of ERPs, their capabilities, and the intricacies of their implementation process. For any business embarking on this journey, it's a significant decision, but one that holds the potential to transform operations. Drawing from my experience as a CFO, I've witnessed the impact that new ERP implementations can have on businesses. It can present remarkable possibilities to streamline operations, enhance decision-making, and stimulate growth. However, it can also come with its own set of challenges and complexities. So, what exactly does it take to ensure a successful ERP implementation? 1️⃣ Process-Oriented Strategy   - Prioritise Processes: Instead of getting lost in features, focus on your business workflows. Identify areas for enhancement, pinpoint bottlenecks, and imagine how the ERP can boost agility.   - Thorough Mapping: Take stock of current processes and spot any gaps. Consider factors like mobile accessibility, real-time alerts, and data analytics as you modernise. 2️⃣ Harnessing Team Potential   - Team Dynamics: The team driving any ERP implementation is of great importance. You will need to gather a diverse group of executives, project managers, end users, and IT specialists. Their collective insights and dedication will be key to a successful implementation.   - Skills and Expertise: Look beyond job titles. Recruit team members with relevant expertise, industry knowledge, and a knowledge of your chosen ERP platform. 3️⃣ Selecting the Right Implementation Partner   - Industry Understanding: Your chosen partner should be able to grasp the fundamentals of your industry. Seek referrals and validate their track record.   - Methodology: What is their implementation approach? It should reflect their own learning and not just be a generic template. 4️⃣ Avoiding Common Pitfalls   - Robust Governance: Establish strong project governance from the outset.   - Clear Scope Definition: Set precise objectives and requirements - avoid scope creep!   - Data Integrity: Ensure your data is clean and reliable.   - Training: Invest in comprehensive user training, during implementation and after.   - Executive Support: Secure backing from leadership. 5️⃣ People-Centric Strategies   - Inclusive Teams: Engage stakeholders at all levels. Everyone should feel accountable for success.   - Promote Collaboration: Foster open dialogue and teamwork.   - Risk Awareness: Acknowledge potential risks and address them early. Oh, and finally, as the CFO ensure the budget is appropriate and costs controlled! Remember, a successful ERP implementation hinges not only on technology but also on people, processes, and collaboration. I would love to hear about your implementation stories and the key to success. 👇 #ERPImplementation #DigitalTransformation #BusinessGrowth #CFOInsights 

The rush to implement AI solutions can lead to significant pitfalls. Here's a provocative thought: the greatest risk in AI isn't just inaction. It's implementing without understanding. Let’s unravel why AI implementation demands careful thought and expertise. The promise of AI is undeniable. But when businesses leap without looking, the consequences can be dire. → Mismanaged data leads to flawed predictions. ↳ Garbage in, garbage out—AI doesn't magically fix bad data. → Overreliance can breed complacency. ↳ AI is a tool, not a crutch. → Lack of understanding can result in ethical oversights. ↳ Algorithms must be checked for bias and fairness. → Insufficient expertise can stall projects. ↳ Proper training and a clear strategy are essential. AI implementation isn't just about tech. It's about aligning with business goals and ethics. So, how do we get it right? Prioritize data quality → Clean, accurate data is nonnegotiable. Invest in education → Equip your team with the knowledge to leverage AI effectively. Engage multidisciplinary teams → Combine tech expertise with business acumen. Embed ethical considerations → Regularly audit models for bias and fairness. Iterate and refine → Continuous learning and adaptation are key. Remember, AI isn't a onesizefitsall solution. It's a journey that requires thoughtful planning and execution. Done right, AI can transform businesses, enabling them to act with foresight and agility. Yet, it's the careful, calculated steps that ensure this transformation is both successful and sustainable. What steps have you taken to ensure AI success in your organization? Share your thoughts below.

Organizations talk about customer journey mapping. Singapore Changi Airport Group has been implementing it for over 30 years. Most airports manage infrastructure. Changi manages experience at scale. You’ve seen the alternative. You land after a long flight. There’s an online arrival form you didn’t know about. No Wi-Fi. No guidance. Long queues. Friction at every step. At Changi, the passenger journey is engineered end-to-end. If you haven’t completed your SG Arrival Card, you’re notified multiple times before passport control. If you still need to complete it, terminals are provided immediately. Clear. Simple. Fast. No chaos. Security? Not a central bottleneck. Changi uses biometric clearance at immigration, and security screening is conducted at each individual gate. The result: smaller volumes per checkpoint, shorter queues, faster throughput. Its operational design is aligned to customer experience. I fly close to 180 days a year. The difference is not cosmetic. It’s structural. Changi focuses relentlessly on the critical passenger touchpoints: ▶️ Arrival preparation ▶️Immigration flow ▶️Security design ▶️Digital integration It consistently ranks among the world’s best airports. Not because of waterfalls or shopping — but because it understands "experience is strategy implemented." Its awards don’t come from vision statements. They come from designing and implementing the journey that customers actually experience. That’s why Changi isn’t just an airport. It’s a masterclass in implementation.

CRM is not dead. The old way of using CRM is. There is a growing narrative in the market that CRM is fading. That narrative misses the real shift that is happening. CRM as infrastructure is not going anywhere. What is going away is the outdated operating model that turned CRM into an administrative system instead of a growth engine. For years, legacy CRM environments have looked the same: • Highly customized • Expensive to maintain • Burdened with technical debt • Low adoption from frontline teams • AI added as a layer, not embedded in work That model created systems of record. It did not create systems of impact. What is emerging now is very different. CRM is becoming the revenue operating system for the enterprise. The conversation should not be about product names or feature lists. Customers do not buy technology catalogs. They buy outcomes: • Faster revenue execution • Higher seller productivity • Better customer engagement • Shorter time to value • Lower total cost to operate The modern model is built around a unified platform where humans and AI work together in the flow of work across Sales, Service, Marketing, and Contact Center. Key differences in this new approach: • AI is embedded directly into workflows, not bolted on • Adoption is a design principle, not an afterthought • Value is delivered in the first 90 days, not after a year • Implementations are measured in months, not multi-year programs • The focus is measurable business outcomes, not system configuration Companies still need secure customer data, governance, identity, and core business process infrastructure. That foundation does not disappear. CRM remains the backbone. What changes is how that backbone is used. Enterprises cannot afford to keep layering new agents and tools on top of already expensive, fragmented CRM stacks. The future model is: Simpler platform + lower cost + AI in the flow of work = better business outcomes. CRM is not dying. CRM is evolving from a passive database into the active operating system for customer engagement and revenue execution. That is a very different story.

Here’s how I revamped our buyer's journey with HubSpot CRM, and why it might just be what your business needs too. Admittedly, I was skeptical about CRM hype. Yet, once I gave HubSpot a shot, the results spoke for themselves, and here’s a quick rundown: 1/ Centralized data: All our customer information is now in one spot. This clarity has revolutionized our follow-up process. 2/ Automated workflows: Reducing manual tasks has allowed our team to redirect their focus towards strategic thinking and creativity. 3/ Real-time analytics: Immediate insights into campaign performances enable us to adjust tactics swiftly, enhancing effectiveness. 4/ Seamless integration: Linking marketing and sales efforts has helped us eliminate operational silos, fostering a more unified approach. The impact? A more fluid journey for our customers and a faster sales cycle for us, scalable as we expand. How has integrating a CRM system reshaped your approach to customer interaction? Have the results met your expectations, or have you encountered unforeseen challenges? Let’s share insights! #hubspot #crm #data

This paper delves into the challenges and intricacies of implementing machine learning applications in healthcare. While ML has shown impressive results in testing phases, its transition to practical application often faces hurdles. The study uses action research to explore the final stages of a project aimed at implementing an ML algorithm to predict patient no-shows at a Danish hospital. The study emphasizes that implementing ML solutions is an intricate innovation process. Success hinges on blending technical and sociotechnical expertise. IT departments, while tech-savvy, might not possess the skills for sociotechnical change, just as users understand local practices but might not be tech experts. The challenge lies in merging these competencies. The "last mile" discourse often overlooks the depth of innovation needed for ML implementation. There are additional nuances to this primary insight: 1️⃣ ML solutions cannot be finalized while they are decoupled from the practice level. Their training and predictive performance depend on operational data that result from the work practices affected by the algorithm predictions. Work practices, data quality, and algorithm predictions interact in emergent ways. Development activities, or innovation, continue after ML solutions are recoupled with practice. 2️⃣ The stakeholders in ML implementations may not always be in alignment. For instance, implementers might have top-management support but still face local hesitation. In the absence of pre-implementation alignment, achieving alignment becomes a primary objective for the implementation process. However, misalignment can increase the work required to obtain positive reinforcement between the hiatuses of machine experience and human trust. 3️⃣ The main implication of this study is that the implementation of ML solutions is an innovation process. To succeed in this process, implementers must devise and employ a rich set of innovation tactics to develop the innovation, create interest among stakeholders, and navigate any resistance that may arise. The paper offers a deep dive into the real-world challenges of implementing ML in healthcare, a sector where the stakes are high. By understanding the tactics and strategies used in the face of these challenges, readers can gain insights into the complexities of bridging the gap between theoretical potential and practical application of ML in a critical domain. 🌐⇢ https://lnkd.in/eyM2XcPQ ✍🏻 Christopher Gyldenkærne, Jens Ulrik Hansen, Morten Hertzum, Troels Mønsted. "Innovation tactics for implementing an ML application in healthcare: A long and winding road." International Journal of Human-Computer Studies 181 (2024). DOI: 10.1016/j.ijhcs.2023.103162 ✅ Sign up for my newsletter to stay updated on the most fascinating studies related to digital health and innovation: https://lnkd.in/eR7qichj

“Knowledge vs Experience" In most organizations, we consistently see two distinct profiles: 1. Individuals with strong knowledge (certifications, frameworks, theory) 2. Individuals with deep experience (high exposure, fast execution, pattern recognition) The real question is no longer “which one is better?” but: which one delivers faster, more consistent, and measurable results? From a technical perspective, the differences are clear: Knowledge-driven - Strong in analysis and structured planning - Leverages frameworks (OKR, Agile, Lean, etc.) - Risks are identified early - Often slower in execution due to over-analysis Experience-driven - Fast decision-making and execution - Relies on pattern recognition from past cases - Highly adaptive to real-world dynamics - Prone to bias and difficult to scale without systems The problem arises when one dominates: Without experience: → Strategies look perfect on paper but fail in execution → Too much discussion, not enough output Without knowledge: → Fast execution, but inefficient → Repeated mistakes due to lack of structured learning A results-driven approach looks like this: 1. Start with knowledge (baseline) Use data, frameworks, and best practices to define direction. 2. Execute fast (experience loop) Run small-scale pilots or MVPs to validate assumptions. 3. Measure objectively Focus on clear metrics: - Output (what was delivered) - Outcome (business impact) - Efficiency (time and cost) 4. Iterate continuously Combine real-world feedback with updated knowledge. The operating formula: Knowledge → Action → Feedback → Improvement → Repeat Top performers—both individuals and organizations—are not those who know the most or have worked the longest. They are the ones who learn the fastest through disciplined execution loops. Because in the end: Knowledge defines the “right way” Experience proves the “working way” And real results come from combining both—executed with discipline and measured with clarity. #Execution #Performance #Leadership #ContinuousImprovement #ResultsDriven #SulthoniAmri