Lessons from the Frontlines of a Mid-Market Web3 Company
For me the past year has been nothing short of transformative! Let me rewind for a moment and set out how I got the gig. I’d been writing about a variety of subjects and had opined on the issues around generative AI when a contact reached out to find out if I could help with their strategy for implementing an AI solution within their customer services team. And so there it was… I found myself serving as a Fractional Chief AI Officer at a time when such roles were still relatively new. The role, with a mid-market Web3 company, genuinely allowed me to experience, and more importantly, influence the nexus of cutting-edge artificial intelligence and the rapidly evolving Web3 space. During the course of this year, I’ve encountered a number of challenges, experienced some thrilling successes, and suffered a few sobering lessons.
So… this short piece isn’t a mere reflection on the role of a part time CAIO; it also serves as a guide for companies that are looking to maximise the value of AI leadership (especially in the fast-paced world of Web3).
Understanding the Dynamics of a Fractional Role
Even now, the concept of a Fractional CAIO is still relatively new, but it’s gaining traction fast. For companies seeking the strategic oversight required to implement AI-driven initiatives they often have to dig deep. However, it can be done without the hefty price tag of a full-time executive. So, this is how I was engaged and in this capacity, I’ve spent much of the past year focusing on critical, high-level tasks, while ensuring that the project’s AI adoption aligns with the broader company vision.
A key lesson here is that while the role may be fractional in terms of hours, the commitment and impact are anything but. It’s essential to build trust and credibility quickly because, unlike full-time executives, a Fractional CAIO has less time to embed themselves into the culture. The success of the projects often hinges on forming tight-knit relationships with key stakeholders from day one.
Balancing Strategic Vision with Hands-On Implementation
Without doubt, one of the most significant challenges I faced in this role was balancing strategic leadership with the practical aspects of execution. Let’s face it, AI skills are not always abundant in companies for whom AI is still in its infancy, and while the company concerned recognised the power of machine learning, natural language processing and predictive analytics, they lacked the in-house expertise to deploy these technologies effectively.
Luckily I had some robust exposure to AI which dated back many years and with a renewed interest pre-pandemic (Thanks @Hispsto and @Sebastian Owen), I’d spent a serious chunk of time picking up hands on skills in the domain.
Back to the role, I found myself being asked to work closely with the board and leadership teams to devise a comprehensive AI strategy – one that didn’t just focus on the “cool” factor of AI but addressed specific and in some cases, significant pain points within the organisation. I’m not going to detail what that strategy looked like but some of the work that followed involved leveraging AI to enhance cybersecurity protocols, (this of course, is especially relevant in Web3, where security breaches can lead to catastrophic losses). Automating threat detection using machine learning algorithms meant that the company saw a 30% reduction in known security vulnerabilities within a six-month period.
The key takeaway? A CAIO must ensure that AI initiatives are not just driven by technology but are rooted in real business problems and measurable outcomes.
Example Projects: Driving AI Adoption Beyond the Boardroom
While supporting the board with AI strategy and high-level decision-making was a significant part of my year, there were also several hands-on projects I spearheaded that demonstrated the tangible value AI could bring to the company. These projects not only helped shape the company’s AI roadmap but also provided direct, measurable business outcomes.
Enhancing Customer Experience with NLP
One of the most interesting and sometimes exciting projects I led was the development of an AI-powered customer support system. The company’s existing support infrastructure and ’pager duty’ activities struggled to keep up with the demands of its fast-growing user base, and response times were lagging, leading to pretty vocal customer frustration.
We implemented a natural language processing system. Pretty straightforward but in essence, it could understand and respond to customer queries in real time. This was no ordinary chatbot; the AI was trained on a vast dataset of previous customer interactions and corresponding support responses. This enabled the AI system to provide more accurate and personalised responses. After a couple of months of development and some careful integration, we achieved a 40% reduction in customer service response times and a 25% increase in overall customer satisfaction scores.
Ultimately, this project alone highlighted significant potential for AI to enhance user experiences (a critical consideration for Web3 companies where users have an expectation of both innovation and seamless interactions).
Using Predictive Analytics for Business Growth
The implementation of a predictive analytics platform was another project that stands out to me. The company had vast amounts of data related to user behaviour, transaction patterns, and more general market trends. This wasn’t being fully utilised but I worked with the company’s data science team to develop several machine learning models that could predict user churn. This itself facilitated the company taking proactive steps to retain high-value customers with exceptional lifetime value.
Analysing user engagement data meant we were able to identify a number of patterns that suggested when a user might actually stop using the platform. This rapidly became key information meaning the marketing team were able to build and launch targeted retention campaigns that resulted in a 15% decrease in user churn within the first quarter after deployment.
This project illustrated the importance of data-driven decision-making and helped to demonstrate the immense value that Machine Learning with a healthy dose of AI can bring to the table in terms of driving business growth.
Streamlining Internal Processes with Robotic Process Automation
Ok… those who know me will know that I had spent a considerable amount of time working as the Executive Chair of a London consultancy specialising in RPA. Whilst my time there didn’t expose me to any RPA project work, it did give me an appetite to explore it further and after leaving the consultancy, I undertook several RPA projects cutting my teach on some very interesting use cases. What it taught me was that not all AI initiatives need to be customer-facing to make a real impact. During my time as a CAIO, one of the most significant improvements for the business came from automating several internal processes through RPA (UiPath). These included automating invoice processing using a legacy system, handling customer onboarding tasks and streamlining compliance reporting – all of which were previously manual, time-consuming processes that relied upon legacy technology.
The outcomes were that the automation not only freed up valuable staff time but also dramatically reduced human errors. This in turn led to more efficient and accurate operations. In some of these examples, tasks that had previously taken a number of days to complete could now be done in a matter of minutes. This allowed the company to focus its resources on more strategic initiatives or those where keeping a ‘human in the loop’ was critically important.
The lesson here is that AI isn’t just about cutting-edge tech; sometimes, its most significant value lies in improving the efficiency of day-to-day operations.
How Companies Can Get the Most from Their CAIO
As AI continues to become an essential component of business strategy, the role of a Chief AI Officer—fractional or full-time—will only grow in importance. Here are a few lessons I’ve learned from my time in this role that companies should consider when bringing on a CAIO:
Define Clear Objectives from the Start
My view here is that for a successful AI strategy, you must start with a pretty clear understanding of what you want to achieve and importantly, what your desired outcomes are. In my experience, many organisations simply jump into AI initiatives – often without fully considering their goals. This leads to wasted time and resources. Before engaging a CAIO, whether fractional or not, make sure you’ve identified specific business problems that AI can help solve and set measurable goals and outcomes for success.
Embrace Cross-Department Collaboration
One of the biggest challenges I faced as a CAIO was having to dismantle silos within the pockets of the organisation. Much like broader business change initiatives and like nearly all IT projects, AI initiatives often require input from various functions, from engineering to marketing to customer support and beyond. It’s essential to ensure a culture of collaboration is the starting point – a culture where different teams work together towards a common or shared goal. This is particularly important in mid-market companies where resources are more limited and the success of AI projects depends on cross-functional cooperation.
Build for the Long Term
For the avoidance of doubt, AI is not a quick fix! In my opinion, businesses must remain patient, stay resilient, and commit to building the right infrastructure, gathering quality data, and developing the necessary talent to support AI efforts for the long term. A key takeaway is that AI goes beyond being just a tool – it’s more like a way of thinking. The companies that achieve the most here are the ones that embed AI into the fabric of their core operations and consider its potential impact in every decision they make.
Keep Ethics at the Forefront
As businesses increasingly seek to integrate AI into their business operations, it is key to address ethical concerns, particularly where privacy and decentralisation are cornerstone principles. During my tenure as a Fractional CAIO, I sought to consistently prioritise ethical AI practices in decision-making and the best way of doing this was to promote transparency in AI models (and preventing bias in algorithms, whilst safeguarding trust, privacy and security).
For companies to build trust with their customers and steer clear of the risks associated with unethical AI practices, these considerations must always remain as a top priority.
Regularly Review and Iterate on AI Strategy
The field of AI is constantly evolving and strategies that are effective here and now might not be so successful in the future! It’s key here to continually evaluate and adjust your approach. Remember, improvise, overcome and adapt. In my role as a Fractional CAIO, one of the most impactful steps I took was the implementation of quarterly reviews of our AI projects. Beyond understanding challenges with the timetables itself, this process also helped us evaluate successes, make necessary adjustments and ensure we kept our AI initiatives aligned with the company’s overall objectives.
Conclusion: A Year of Growth and Transformation
Reflecting on the past year as a CAIO, I can honestly say with confidence that the journey has been both demanding abut at the same time, fulfilling. I’ve had the opportunity to contribute to projects that have delivered measurable business outcomes all empowered by AI. Whether focused on growth or operational effectiveness its key to set clear goals and outcomes, encourage teamwork and adopt a genuinely outcome focused perspective.
While AI isn’t a quick fix, when applied strategically, it holds the power to transform companies in ways once thought impossible.
In today’s fast-evolving landscape, organisations that embrace AI – and leverage experienced leadership to navigate its intricacies – will position themselves ahead of the curve, ready to face future challenges.
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