Common Hurdles in AMS Adoption

AI Adoption: Reality Bites After speaking with customers across various industries yesterday, one thing became crystal clear: there's a significant gap between AI hype and implementation reality. While pundits on X buzz about autonomous agents and sweeping automation, business leaders I spoke with are struggling with fundamentals: getting legal approval, navigating procurement processes, and addressing privacy, security, and governance concerns. What's more revealing is the counterintuitive truth emerging: organizations with the most robust digital transformation experience are often facing greater AI adoption friction. Their established governance structures—originally designed to protect—now create labyrinthine approval processes that nimbler competitors can sidestep. For product leaders, the opportunity lies not in selling technical capability, but in designing for organizational adoption pathways. Consider: - Prioritize modular implementations that can pass through governance checkpoints incrementally rather than requiring all-or-nothing approvals - Create "governance-as-code" frameworks that embed compliance requirements directly into product architecture - Develop value metrics that measure time-to-implementation, not just end-state ROI - Lean into understanability and transparency as part of your value prop - Build solutions that address the career risk stakeholders face when championing AI initiatives For business leaders, it's critical to internalize that the most successful AI implementations will come not from the organizations with the most advanced technology, but those who reinvent adoption processes themselves. Those who recognize AI requires governance innovation—not just technical innovation—will unlock sustainable value while others remain trapped in endless proof-of-concept cycles. What unexpected adoption hurdles are you encountering in your organization? I'd love to hear perspectives beyond the usual technical challenges.

Most people look at 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 and assume it is just 𝘁𝗼𝗼𝗹𝘀 𝘄𝗶𝗿𝗲𝗱 𝘁𝗼 𝗮 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹. It’s easy to build a demo - maybe even POC - that way. It’s much harder to build something that lasts and scales. The real work begins beyond that and below the surface where systems need to coordinate, adapt, and operate in production environments - safely. That’s where most the friction is and the biggest hurdles: 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, and 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆. And it’s where 𝘮𝘰𝘴𝘵 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯𝘴 𝘧𝘢𝘭𝘭 𝘴𝘩𝘰𝘳𝘵 - until this day. And it is not just a technical challenge. It is about designing systems that let people - with or without deep AI background - turn their idea to an Agentic solution, without needing to assemble the whole necessary stack themselves. To do that well, we believe five areas matter most: • Technology – Agents must evolve, stay efficient, and meet enterprise requirements. That requires deep infrastructure, not surface-level wrappers. • Tooling – Teams need tools that abstract complexity, reduce time-to-value, and work across levels of technical fluency. • Governance – Trust, explainability, and compliance should be defaults, not afterthoughts. • Infrastructure – Control matters. Systems should run where teams need them to, not just where a vendor dictates. • Enablement – Adoption only happens when people feel confident building. Training, documentation, and real support are non-negotiable. These are the areas we’ve chosen to invest in. At aiXplain instead of chasing trends, we decided to build 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗻𝗲𝗲𝗱𝗲𝗱 𝘁𝗼 𝘁𝗮𝗸𝗲 𝗔𝗜 𝗯𝗲𝘆𝗼𝗻𝗱 𝗱𝗲𝗺𝗼𝘀 𝗮𝗻𝗱 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁.

𝐃𝐫𝐢𝐯𝐢𝐧𝐠 𝐀𝐝𝐝𝐢𝐭𝐢𝐯𝐞 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 (𝐀𝐌) 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧: 𝗔 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 I spent more than a decade driving AM adoption at Siemens Energy. It was rewarding. But it wasn’t always easy. This technology reshapes processes, perspectives, and productivity. Yet overcoming hurdles internally can be challenging. Here’s how to accelerate AM adoption and create impactful change. 💡 1. 𝗦𝗵𝗮𝗿𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗦𝘁𝗼𝗿𝗶𝗲𝘀 𝗳𝗿𝗼𝗺 𝗦𝗺𝗮𝗹𝗹 𝗪𝗶𝗻𝘀 Success breeds confidence. Start with accessible, low-risk applications of AM to generate quick wins and demonstrate real, tangible value. You build credibility and enthusiasm across the board by showcasing these easy-to-realize first applications. 🏢 2. 𝗘𝗻𝗴𝗮𝗴𝗲 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 Gaining buy-in at the executive level is a game-changer. A high-level executive sponsor brings influence, credibility, and the necessary resources to accelerate adoption. They champion the value of AM, helping to break down silos and shift AM from a niche technology to a strategic priority. I’ve found that having executive support aligns the entire organization and gives AM initiatives the momentum they need. 🧩 3. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀’ 𝗖𝗼𝗻𝗰𝗲𝗿𝗻𝘀 Stakeholder concerns are often grounded in practical realities rather than resistance to change. Addressing mechanical performance, cost-effectiveness, and reliability upfront can ease worries and build trust. Involving stakeholders early fosters a collaborative environment where everyone feels invested in the journey. 🌐 4. 𝗣𝗿𝗼𝗺𝗼𝘁𝗲 𝗮 𝗖𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗦𝗵𝗶𝗳𝘁, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗮 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗦𝗵𝗶𝗳𝘁 Successful AM adoption requires more than just technical expertise. Encourage a mindset where teams see challenges as opportunities for learning and experimentation. By promoting cross-functional collaboration and a “learn-as-you-go” approach, you’re investing in a new manufacturing process and a culture that embraces continuous improvement. 🌍 𝗔𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗔𝗠 𝗶𝘀 𝗔𝗯𝗼𝘂𝘁 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 & 𝗠𝗶𝗻𝗱𝘀𝗲𝘁 Adopting additive manufacturing means mastering both the technology and the mindset it requires. By focusing on building small successes, securing executive sponsorship, listening to stakeholder concerns, and fostering a culture of innovation, organizations can leverage AM to its full potential. Have you introduced new technologies and processes to your company? What worked for you?

One major hurdle we faced was our initial focus on an on-premises architecture. While this provided granular control, it complicated deployment, management, and maintenance. Ensuring 100% uptime and data security in a self-hosted environment required meticulous attention to detail. 1. Technical Hurdles: Implementing robust security protocols was essential. We utilized advanced encryption algorithms like AES-256 and TLS 1.3, along with a comprehensive key management system to protect sensitive data. Additionally, we designed a scalable, fault-tolerant architecture to handle increasing data volumes without sacrificing performance. 2. Compliance: Adhering to regulations such as GDPR and HIPAA posed another challenge. We established strict data retention policies, access controls, and audit trails, necessitating extensive research and regular audits to maintain compliance. 3. User Experience: Balancing security with user experience was crucial for adoption. We focused on creating intuitive interfaces and streamlined workflows, incorporating user feedback through extensive testing. These challenges taught us the importance of fostering a security-first culture within our organization. Regular training and a commitment to data protection became integral to our development process. Collaboration with industry experts and participation in security communities also helped us stay informed about emerging threats and best practices. Looking ahead, We are exploring technologies like homomorphic encryption and secure multi-party computation to enhance data protection. Additionally, we aim to expand our platform's capabilities to support specialized features for sectors such as healthcare and finance.

Last week at AFR CFO live summit and #6DegreesAI, there were similar questions around AI adoption often stalls and how organisations can balance risks and benefits. AI adoption tends to become patchy when organisations aren’t ready for AI, facing these common challenges: ➡️AI Literacy Gaps: Leadership and employees may not fully understand AI’s capabilities or lack the skills to work with it. ➡️Data Challenges: Poor data quality, silos, and privacy concerns hinder progress. Reliable, accessible data is critical. ➡️Legacy Systems: Outdated infrastructure increases costs and complexity. ➡️Ethical and Regulatory Concerns: Bias, transparency, and ethical risks create hesitation. ➡️Cultural Resistance: Fear of disruption and siloed efforts weaken collaboration and alignment. ➡️Lack of Use Cases: AI for AI’s sake doesn’t work. Without clear goals, adoption stalls. So, how can you help getting your organisation ready for AI? I have some advice. 1️⃣ Establish a Clear AI Strategy: Align AI initiatives with business goals like growth, cost optimisation, or innovation. Prioritise measurable use cases and ensure executive buy-in. 2️⃣ Build Strong Foundations: Invest in data management, governance, and cloud infrastructure.Ensure data quality and accessibility across the organisation. 3️⃣ Start Small, Then Scale: Begin with low-risk, high-impact pilots (e.g., customer service automation). Measure success, learn, and create a roadmap for scaling. 4️⃣ Upskill Employees and Foster Collaboration: Provide AI training across all levels. Encourage cross-functional collaboration between technical and business teams. This is super important. Develop a change management plan to address resistance. 5️⃣ Ensure Ethical and Responsible AI Deployment: Develop and audit AI ethics frameworks. Address bias, transparency, and compliance proactively. Simulate high-risk scenarios to guide decision-making. What are you missing today? AI success begins with readiness, and the willingness to learns something new. Read the article from CFO live in comments. The Australian Financial Review 6 Degrees Media SAP Lainie Morison Kym Boyle Mike Vorias Matt Wilkinson Varun Taneja, CA Ching Ooi Alexia Bayyouk Matt Wilkinson Paul Thompson #businesAI #changemanagement #AIready

The top challenges for financial advisors in adopting alternatives include high administrative burdens (48%), lack of liquidity (36%), and compliance concerns (29%). Fees, client understanding, and asset allocation also pose significant issues. These barriers highlight the operational complexity advisors face when incorporating alts. Streamlining back-office processes, improving product liquidity, and strengthening compliance frameworks could help overcome the administrative hurdles. Better client education may also boost understanding and adoption. Chart from CAIS & Mercer State of Alternative Investments in Wealth Management.

Are you a small to medium-sized assisted living community? You’ll want to hear this! Adopting new technology can come with its fair share of hurdles.  So here’s a quick rundown of these common challenges and how you can turn them into opportunities: → Financial constraints Tight budgets making tech upgrades tough? Solution: Seek out tech solution providers with proven results and return-on-investment (ROI). Check out grants, subsidies, and cost-effective solutions that grow with you. → Limited IT support and infrastructure Not enough IT support or infrastructure to back up new tech? Solution: Team up with tech providers for support and roll out changes gradually. → Resistance to change Feeling the pushback from staff or residents? Solution: Offer clear, step-by-step training and create a supportive environment to ease the transition. → Regulatory compliance Are complex regulations feeling overwhelming? Solution: Partner with experts to stay compliant and up-to-date on regulatory changes. → Interoperability issues New tech struggling to mesh with what you already have? Solution: Opt for tech that’s known for simple and easy integration with your current systems. → Training and support needs Lacking resources for proper training? Solution: Lean on tech partners for ongoing training and support. Embracing new technology doesn’t have to be intimidating.  Face these challenges head-on and you'll find the right solutions to level up your care, improve your financial outcomes, and efficiency. How are you handling new tech in your facility?

Challenges and remedies putting AI agents into production. tldr - nontechnical user adoption for ai agents is really hard right now. I spoke with a team recently who has gone all in on agents. I wanted to understand the rough edges. Here are some: **Evaluating Outputs (i.e function calling, action planning, etc)** - This was one of the main headaches. the team didn’t want to reinvent the wheel but ended up creating their own LLM benchmark leaderboard that can evaluate an LLM capability across 10 different test sets. (My favorite of course is the function calling eval set) **Having a scalable tool orchestration framework** - After much deliberation and experimentation they chose Go as their framework of preference (moving away from Python), for scalability and developer velocity. **Personalization** - Sometimes tools need to behave differently based on the user that uses it. A simple one is Knowledge bases. I just want to see MY files, not others! Propagating this from input to tool is a tricky one. **Sharing tool output in a meaningful way** - They haven’t fully figured this one out and are constantly experimenting with different thresholds for when to put outputs directly into the LLM call and when to attach files. One way to solve it is using configuration IDs and loading on demand Credentials and Personal data when a user requests it. **End user adoption** - This may be the biggest hurdle because: 1. Users don’t (always) understand how the agent is different from LLMs/chatGPT. Meaning, they don’t understand what/how they can utilize the agent’s tools for their ends - tools are chosen by the agent and often hidden from the user. 2. Users sometimes uncomfortable with or intimidated by an iterative way of working and they give up quickly on prompt engineering 3. Users are ‘too busy’ to train themselves and experiment on an agent properly. The current learning curve for agents is steep 4. Biggest hurdle yet is finding use cases that are suitable for the tasks in their role specifically. How can you make the experience more intuitive for the user? (see learning curve above) 5. Feature requests don’t always align with usage. Requested features don’t end up getting used because another part of the flow does not work and they dont spend the time to try and make it work. (See numbers 2 and 3) Lots to talk about at the upcoming conference. And don't tell anyone but we will raffle off a MacBook to one lucky live attendant 🤫🤫

74% of companies struggle to scale AI. Here’s what’s holding them back: 1. Slow Corporate Processes Even when the opportunity is clear, adoption takes time. Companies spend months, sometimes over a year, to: • Decide budgets • Organize teams • Determine who’s in charge Corporate processes move slowly. Painful, I know. Speeding them up often requires strong leadership. Sometimes directly from the CEO. 2. Regulatory Concerns in Europe In Europe, regulations add another layer of complexity. For example: • Models like OpenAI’s voice tools aren’t entering the market. • Businesses hesitate due to compliance uncertainty. This mirrors what happened with GDPR. Back then, corporate lawyers often said "no" to new ideas because it felt safer. Today, regulations on AI feel just as overwhelming. 3. Technical and Organizational Complexity Adoption is technically and organizationally complex. Companies need to: • Integrate AI into legacy systems • Address data governance and manage cloud platforms • Work with multiple vendors They also face: • Hiring challenges • Budget adjustments • Legal teams unfamiliar with regulations 4. A Perception Problem Many leaders view regulations as overly complex. But often: • They haven’t read the rules. • They can’t pinpoint specific issues. Experts argue compliance is straightforward, but in practice, companies stumble over the details. 5. Survey Insights Surveys highlight regulatory concerns as a major barrier in Europe: • 70% of companies see it as a top issue. • Perceived difficulty adds friction, even when compliance is possible. These barriers slow down adoption. Companies are held back by many hurdles. What's your take? — Enjoy no BS AI content? Repost this and follow me for daily insights. My niche is AI in business - tap the 🔔 on my profile to never miss a post.