Digital Transformation Steps

Everyone's freaking out about GEO, LLMO, and AEO. After 7 months of running tests across tons of sites… I can tell you this: It's all built on SEO fundamentals. The same principles that rank you on Google also get you cited in ChatGPT, Claude, and Perplexity. So before you buy into shiny new tactics that promise “AI visibility”…here's what actually moves the needle: 1. Trust Signals AI tools pull from review platforms to assess business credibility and expertise. Build trust signals in the right places: - Local businesses: prioritize Google Business Profile reviews and responses - SaaS companies: maintain strong G2 and Capterra profiles  - Ecommerce: focus on Trustpilot or industry-specific review platforms - Respond to reviews professionally and keep profiles updated 2. Document Structure LLMs love well-structured documents. Instead of optimizing just for human readers, structure content for AI platforms too: - Add company context throughout documents. Instead of "our latest update," write "Acme Corp's Q4 2024 update" - Use clear headings and comprehensive sections that can stand alone - Include key facts in multiple formats (inline text, bulleted lists, data tables) 3. Link Building for Relevance Quality and topical relevance matter more than quantity for AI visibility. Focus your link building efforts: - Target industry-relevant sites where your brand mention makes logical sense - Pursue guest posts and collaborations within your industry - Don't ignore nofollow links from high-authority sites in your niche - Seek brand mentions even without direct links. (the mention itself carries weight) Avoid completely unrelated sites. 4. Topical Authority Still Rules LLMs are trained on the same web content that Google indexes. The more deep, high-quality content you publish around your niche, the more AI systems recognize you as the go-to source, the more you get mentioned. Take out the trash. Delete random blog posts about topics unrelated to your business. They're actually hurting your AI visibility. 5. Be everywhere LLMs crawl Repurpose your content across Reddit, Medium, LinkedIn, and YouTube. These platforms get crawled heavily by AI, and showing up on them regularly builds brand visibility. LLMs love patterns. The more places they see you, the more they assume you’re an authority. 6. Technical setup - Use HTML-driven pages - Add schema markup - Clean site architecture (no page more than 3 clicks from homepage) - Ensure your critical content loads server-side (most AI crawlers don't render JavaScript) 7. Traditional Search Feeds AI Most AI tools use Bing or Google's index for real-time data. Better search rankings directly improve AI visibility.

Last week, a customer said something that stopped me in my tracks: “Our data is what makes us unique. If we share it with an AI model, it may play against us.” This customer recognizes the transformative power of AI. They understand that their data holds the key to unlocking that potential. But they also see risks alongside the opportunities—and those risks can’t be ignored. The truth is, technology is advancing faster than many businesses feel ready to adopt it. Bridging that gap between innovation and trust will be critical for unlocking AI’s full potential. So, how do we do that? It comes down understanding, acknowledging and addressing the barriers to AI adoption facing SMBs today: 1. Inflated expectations Companies are promised that AI will revolutionize their business. But when they adopt new AI tools, the reality falls short. Many use cases feel novel, not necessary. And that leads to low repeat usage and high skepticism. For scaling companies with limited resources and big ambitions, AI needs to deliver real value – not just hype. 2. Complex setups Many AI solutions are too complex, requiring armies of consultants to build and train custom tools. That might be ok if you’re a large enterprise. But for everyone else it’s a barrier to getting started, let alone driving adoption. SMBs need AI that works out of the box and integrates seamlessly into the flow of work – from the start. 3. Data privacy concerns Remember the quote I shared earlier? SMBs worry their proprietary data could be exposed and even used against them by competitors. Sharing data with AI tools feels too risky (especially tools that rely on third-party platforms). And that’s a barrier to usage. AI adoption starts with trust, and SMBs need absolute confidence that their data is secure – no exceptions. If 2024 was the year when SMBs saw AI’s potential from afar, 2025 will be the year when they unlock that potential for themselves. That starts by tackling barriers to AI adoption with products that provide immediate value, not inflated hype. Products that offer simplicity, not complexity (or consultants!). Products with security that’s rigorous, not risky. That’s what we’re building at HubSpot, and I’m excited to see what scaling companies do with the full potential of AI at their fingertips this year!

74% of business executives trust AI advice more than their colleagues, friends, or even family. Yes, you read that right. AI has officially become the most trusted voice in the room, according to recent research by SAP. That’s not just a tech trend — that’s a human trust shift. And we should be paying attention. What can we learn from this? 🔹 AI is no longer a sidekick. It’s a decision-maker, an advisor, and in some cases… the new gut instinct. 🔹 But trust in AI is only good if the AI is worth trusting. Blind trust in black-box systems is as dangerous as blind trust in bad leaders. So here’s what we should do next: ✅ Question the AI you trust Would you take strategic advice from someone you’ve never questioned? Then don’t do it with AI. Check its data, test its reasoning, and simulate failure. Trust must be earned — even by algorithms. ✅ Make AI explain itself Trust grows with transparency. Build “trust dashboards” that show confidence scores, data sources, and risk levels. No more “just because it said so.” ✅ Use AI to enhance leadership, not replace it Smart executives will use AI as a mirror — for self-awareness, productivity, communication. Imagine an AI coach that preps your meetings, flags bias in decisions, or tracks leadership tone. That’s where we’re headed. ✅ Rebuild human trust, too This stat isn’t just about AI. It’s a signal that many execs don’t feel heard, supported, or challenged by those around them. Let’s fix that. 💬 And finally — trust in AI should look a lot like trust in people: Consistency, Transparency, Context, Integrity, and Feedback. If your AI doesn’t act like a good teammate, it doesn’t deserve to be trusted like one. What do you think? 👇 Are we trusting AI too much… or not enough? #SAPAmbassador #AI #Leadership #Trust #DigitalTransformation #AgenticAI #FutureOfWork #ArtificialIntelligence #EnterpriseAI #AIethics #DecisionMaking

In a recent discussion with Priscilla Ng, Prudential plc’s Group Chief Customer and Marketing Officer, we delved into Prudential’s shift towards customer-centricity. This conversation underscored the seamless integration of digital innovation and the essential human touch in the insurance sector.   Here are five key insights from our discussion applicable across industries:   🔹Strategic Integration of AI and Human Insight: Prudential is not just using AI to streamline processes; they are using it to significantly enhance personalization and customer service. From simplifying underwriting to transforming service at customer touchpoints like call centers, AI is proving to be transformative. How can other industries use AI not merely for efficiency but as a catalyst for customer connection?   🔹Empowering Employees: In the journey of digital transformation, the role of technology is as crucial as the people behind it. Priscilla emphasized the importance of equipping over 15,000 employees with the necessary mindset, skills, and tools to excel in a digitally evolving landscape. What strategies can companies implement to ensure their teams thrive amidst technological change?   🔹Balanced Approach to Digital and Human Interaction: Despite extensive technological integration, the human element remains critical at Prudential. Their approach ensures that digital enhancements support rather than replace human interactions, thereby strengthening customer relationships. How can businesses maintain this balance to enhance, not undermine, human connections?   🔹Navigating Challenges in Transformation: Adapting to digital transformation comes with challenges, from aligning large teams with new strategies to continuously adapting to emerging technologies. Priscilla shared that a steadfast focus on customer-centricity is essential for navigating these challenges. How can other organizations keep their focus on customer needs while managing transformation complexities?   🔹Continuous Learning and Adaptation: A crucial aspect of Prudential’s transformation is fostering an environment of continuous learning and adaptation. This involves training in new technologies and developing a deeper understanding of customer needs and behaviors. How can continuous learning be structured to keep pace with rapid technological advancements and evolving customer expectations?   This dialogue is part of McKinsey’s ongoing series exploring how leaders steer their companies through transformations. Stay tuned for more insights shaping today’s business landscape. Full interview: https://lnkd.in/gtjphW2s   #Leadership #DigitalTransformation #CustomerCentricity #InsuranceIndustry #AI

In an unprecedented and concerted effort to shape the legal and ethical landscape of AI, a tsunami of AI standards are currently in various stages of development. These standards, spearheaded by different ISO/IEC Joint Technical Committee working groups, are set to clarify key terminologies, shape system requirements, and guide users in implementing AI technologies effectively and responsibly. Some notable standards include ISO/IEC CD 5339 and ISO/IEC 25059:2023, which respectively offer guidelines for AI applications and a quality model for AI systems. ISO/IEC DTS 25058 and ISO/IEC CD TR 24030 provide crucial guidance for evaluating the quality of AI systems and present a diverse range of AI use cases. Data quality is a major focus, with a family of ISO/IEC DIS 5259 standards dealing with addressing data quality for analytics and machine learning, including data quality management requirements, the data quality process framework, and data quality governance. AI transparency is tackled by ISO/IEC AWI 12792, while issues of unwanted bias in machine learning tasks are addressed by ISO/IEC CD TS 12791. ISO/IEC CD TS 8200 further ensures the controllability of automated AI systems. In the domain of ethical and societal concerns, ISO/IEC TR 24368:2022 and ISO/IEC TR 24030:2021 provide overviews of ethical and societal considerations and use cases for AI respectively. Meanwhile, standards like ISO/IEC 23894:2023 offer guidelines for risk management of AI applications. Compliance with these ISO/IEC standards could play a role in assisting companies to align with the new AI regulations like the forthcoming EU AI Act. The AI Act will regulate high-risk AI systems, mandate transparency, fairness, robustness, and human oversight, among other requirements. Standards such as ISO/IEC AWI 12792 and ISO/IEC CD TS 12791, which cover AI transparency and unwanted bias, could provide companies with guidelines on meeting the EU's requirement for transparency and non-discrimination. Demonstrable compliance could then serve as evidence in the case of any legal disputes relating to these aspects. Likewise, the ISO/IEC 23894:2023 standard, which offers guidelines for risk management of AI applications, aligns with the AI Act's emphasis on safety and risk management. Adherence to this standard could potentially provide a framework for demonstrating compliance of these regulatory obligations. Data quality management is another area where the ISO/IEC DIS 5259 standards could assist in ensuring adherence to the AI Act. The Act requires that high-risk AI systems are trained, validated, and tested with good quality datasets, and compliance with these ISO/IEC standards could help companies fulfill this requirement. However, it's important to stress that while these standards can guide and support compliance, they are not a replacement for comprehensive legal advice tailored to the specifics of a company's situation and jurisdiction.

Many Digital Twin projects fail. Why? The #1 killer of DT projects is: Data Preprocessing. A true Digital Twin isn't a model. It's an engine. And the fuel for that engine is data. But how do you build the plumbing? How do you get data from your physical asset into your virtual model and then get valuable insights back out? Here’s the 5-step breakdown of the engine you actually need to build: Step 1: Data Acquisition Your engine is useless without fuel. This starts at the source. - IIoT Sensors: These are the nerves of your asset. They measure pressure, temperature, vibration, location—whatever matters. If you can't sense it, you can't twin it 😂 - Real-time Transmission: The data can't be a day old. You need a high-speed data bus (like MQTT, OPC-UA) to transmit that sensor data now. - Data Preprocessing: Again, this is the #1 killer of DT projects. Raw sensor data is dirty. It's noisy, full of gaps, and in the wrong format. You MUST clean, normalize, and filter it before it goes anywhere else. Step 2: The Modeling Now your clean data has somewhere to go. - Digital Twin Construction: You map the data streams to the virtual asset. "Sensor 1A" is now officially the "vibration reading for Pump 7." - Virtual Model: This isn't just a 3D drawing. This is a physics-based or ML model. It understands thermodynamics, material fatigue, or fluid dynamics. This is where the data gets context. Step 3: Analytics This is where the ROI lives. The engine is running. Now, what does it do? Predictive Analytics: Your model takes the data and simulates "what if?" What happens if I increase the load by 20%? When will this specific component fail? - High-Performance Computing (HPC): These complex simulations can't run on a laptop. You need the horsepower to process massive data streams and run complex algorithms instantly. Your data is no longer just describing the past. It's actively predicting and optimizing the future. Step 4 & 5: Security & Standards Your high-performance engine needs a chassis to hold it together. Amateurs forget this. Pros build it first. - Cybersecurity & Privacy: You just connected your most critical physical assets to the cloud. Securing this isn't an afterthought; it's priority #1. - Interoperability Standards: Your sensors, software, and platforms must speak the same language. If you build a proprietary, closed system, you're building technical debt. Plan for an open architecture, always. -------- Follow me for #digitaltwins Links in my profile Florian Huemer

Most companies don't have an API problem. They have an API discovery problem. How to address it? Your APIs already run on AWS, Azure, or other gateways. They work fine. The real challenge? Nobody can find them, understand them, or adopt them easily. Every API integration requires multiple calls and months of dev work. Here's what typically happens: • APIs scattered across Postman, GitHub, and multiple gateways • Documentation is outdated or buried in Confluence • Internal teams asking, "Wait, do we have an API for that?" • Potential partners are unable to onboard themselves • Compliance and governance nightmares    Sound familiar? This is where a proper developer portal changes everything. Not another gateway. Not more infrastructure. Just one unified portal where all your APIs live, are documented, and ready to use. This is exactly what Digitalapi.ai, partner of this post, does: 1) Auto-discovery across your entire stack Connect your AWS gateways, Postman workspaces, and GitHub repos. AI automatically finds, catalogs, and documents every API. No manual work needed. 2) AI-powered documentation that never gets stale Every endpoint update is instantly reflected in your docs. Internal teams and external partners always see the current state, eliminating the number 1 reason integrations fail. 3) Built-in governance and compliance Automatic checks ensure your APIs meet security standards and compliance requirements. No more manual audits or spreadsheet tracking. You know something is wrong the moment an issue is introduced. 4) Branded portal for 3rd party adoption Open your APIs to external developers through a professional, branded portal. They can discover, test, and integrate, all self-service. That means so many fewer calls! 5) Monetization built in Turn API access into revenue with subscription tiers, usage-based pricing, and automated billing. Your APIs become a business channel, not just a technical feature. Just like it always should have been. The result? • Internal teams find and use existing APIs instead of rebuilding them • Partners onboard themselves without bothering your engineering team • New revenue streams from API subscriptions • Faster integrations = faster partnerships = faster growth Your API already exists. Make it discoverable, governable, and monetizable. Check out http://www.DigitalAPI.ai and see how a proper dev portal transforms scattered APIs into a growth engine. Did you ever struggle with an API integration? Let me know in the comments :) #productmanagement #api #apistrategy

Let skepticism shape your innovation, not stall you. Most rooms I’m in are brimming with Al-assisted development demos and genuine optimism about how quickly software teams can now move. That energy is real and valuable. AI is no longer just helping developers write a few lines of code faster. It increasingly helps teams refactor across files and repos, produce tests, explain unfamiliar code, and advance work through the SDLC workflows. Yet, I sometimes notice the quiet pauses before the tough questions. People worry about sounding negative, or slowing momentum, or being the only one who is uneasy. Those instincts are not only okay, but they are also just as valuable. The skepticism matters more now, not less, because the question is no longer whether AI can generate code. For me, bringing the hard questions supports progress: • What business or engineering outcome is this improving, beyond developer velocity? • Where can this fail: logic, resiliency, security, privacy, or maintainability? • What is the smallest production-relevant test that proves value? • What review, monitoring, and rollback mechanisms need to exist before we scale it? • How do we preserve human judgment where it matters most? I invite challenges to my ideas because that is how we build better ones. A few principles I’ve found useful, especially in the context of mission-critical platforms: • Challenge constructively. Do not just identify the risk and admire the problem, help design the safer path forward. • Trade “no” with “how.” If this approach is not ready, what is the fastest responsible way to learn? • Pair excitement with evidence. Instrument outcomes, test rigorously, and keep a clean rollback path. • Treat trust as a deliverable. In AI-assisted development, control is not friction. It makes speed sustainable. Our best outcomes happen when excitement fuels ambition while skepticism sharpens it. Because in this new environment, skepticism is not the enemy of innovation but is part of the engineering discipline that keeps innovation real and production worthy.

Let me explain... ▶️ Attackers Are Weaponizing Trust Itself Cyber criminals are increasingly focusing on getting better at hijacking trust signals that fool users into taking harmful actions, developers into downloading harmful packages, etc. Worse off, we've spent years, training users to rely on and look out for the very trust signals that attackers are getting better at convincingly mimicking. Consequently, traditional security tools are being bypassed ever more often. Trust is broken! ▶️ Trust Transcends Perimeters In modern architectures, trust lives in identities, tokens, APIs, supply chains, and even human relationships. When we grant an application, partner, or employee a high level of trust, we're effectively enlarging our “attack surface” to WHEREVER that trust extends. A compromised cloud credential or an abused API token can bypass traditional defenses undetected, because the system assumes “trusted” traffic is not harmful. ▶️Supply-Chain Dependencies Each third-party library, managed service, or vendor relationship is a trust link; a vulnerability or breach in any link immediately widens the attacker’s reach into your environment. ▶️The Zero Trust Paradox The rise of “zero trust” architectures means every request must be authenticated, every session evaluated, every transaction authorized. Ironically, the constant negotiation of trust doubles as an attack surface. Here's why; if your policy engine or identity provider is misconfigured, overloaded, or compromised, attackers can gain unfettered access. So here's my prognosis: - Expect adversaries to increasingly target IAM systems, API gateways, and CI/CD pipelines, exploiting the very mechanisms organizations rely on to grant access and permissions. - Personalized deep fake attacks will surpass mass phishing by 2027. - Discerning leaders will deploy tools that operationalize context at scale. CONTEXT IS NOW KING!!! Organizations will shift to context-aware trust assessments; monitoring behavioral anomalies, device posture, and risk signals at every transaction to detect misuse of “trusted” assets. - As orchestration tools become universal, attackers will shift to poisoning CI/CD pipelines. A malicious change to a shared workflow or action could inject backdoors into every deployment, turning your “automation trust” into a systemic vulnerability. In fact, Gartner predicts a 50% rise in breaches traceable to vendor software flaws or misconfigurations. - By 2026, both defenders and attackers will leverage AI for behavior modeling. Attackers will focus on “data poisoning”, through faux-legitimate actions making anomaly detection. Building Trust Is The Only Future That Matters!

My last couple of posts have been heavy on this theme: The website isn’t the marketing hub anymore. AI is rewriting the rules. Now, I've gotten some pushback, and I get it. This idea is still cutting-edge in many industries and will be for a while—think the local remodeling contractor, who will remain dependent on Google map packs for some time, but . . . Shout out to John Andrews and Sam Harding for this idea to help make this point. Remember the Browser Wars of the ’90s? That battle wasn’t about browsers; it was about who controls distribution. The winner decided how users experienced the web. Sound familiar? We’re back in a similar fight. But this time, it’s not about browsers or websites. It’s about AI. And the harsh truth? Your website is no longer the center of your marketing universe. It’s just another client in an AI-driven ecosystem. If you're still building everything around your website, you’re missing where the customer journey actually starts today. AI is the new gatekeeper. Large language models are the new homepage. What to do: ~ Structure your content for clarity: headers, bullets, short paragraphs. ~ Feed your own materials into tools like ChatGPT custom GPTs. ~ Submit links to AI-friendly aggregators like Perplexity or Bing. Prompt-ready content is the new SEO. Search engines indexed keywords. AI indexes answers. You’re not writing for spiders anymore, you’re writing for synthetic assistants. What to do: ~ Write in question/answer format. ~ Use schema markup and structured data to help AI extract the right info. ~ Test your content through ChatGPT—does it summarize your message well? AI-native collateral is the new lead magnet. PDFs and landing pages are fine, but imagine giving your audience a branded GPT or app that solves their exact problem. What to do: ~ Create AI-guided chat tools (ManyChat, Intercom, GPT bots). ~ Turn key insights into prompt libraries for your audience. ~ Train a micro-assistant that delivers value in your voice and framework. The ecosystem is greater than the standalone domain. Your website is part of the story, not the whole book. You need to meet your audience inside other platforms, other feeds, other tools. What to do: ~ Cross-publish with intent: blogs become emails, videos, Shorts, GPT inputs. ~ Format content for syndication: highlights, quote blocks, stat cards. ~ Embed value inside tools—calculators, diagnostics, AI-guided quizzes. ~ Ask better questions: Can this be summarized by AI in under 60 words? Would AI recommend this if asked? Are we surfacing this in multiple ecosystems? Is this referenceable—not just linkable? We’re not in a web-first world anymore. We’re in an AI-first marketing environment. The tools have changed, but the principle hasn’t: Be where your customers are, and make it ridiculously easy for them to know, like, and trust you. Don’t just optimize your website. Optimize your ecosystem.