How Alan MacDonald Is Helping Diamond Shine Bright as Its New Chief AI Officer
Recently, creative agency Diamond dropped some big news, announcing that COO Alan MacDonald would also be taking on the title of the agency’s very first chief AI officer (CAIO).
Considering the buzz AI has been getting of late, both within the industry and beyond, this strategic move made a lot of sense, highlighting the agency’s commitment in the industry to ensuring ideas perform, while helping clients achieve an even greater MROI. However, it’s also not something many Canadian agencies have ever done before. While yes, there are absolutely AI enthusiasts within the walls of just about every Canadian shop, few - if any - had gone so far as to mint position like this, until Diamond did. As such, it represents not only the agency’s desire to usher in a new era of methodology, creativity and application, but to be on the cutting technological edge heading into 2024, gearing the entire team up for all new levels of effectiveness.
So, to learn more about why now was the right time for this, what the key focus and application points of AI are, and where the roadmap is leading going into the new year, LBB’s Josh Neufeldt sat down with Alan, as well as agency president Josh Diamond for a chat.
LBB> Josh, Alan, congratulations to you both on this big announcement! How’re you feeling right now with regards to this new leadership structure, and specifically, what made now the right time to make this step at Diamond?
Josh> I am feeling incredibly fortunate. Not many organisations have the privilege of having such a self-motivated and qualified AI enthusiast within their ranks. Alan's dedication and expertise in this domain promises a significant strategic advantage for Diamond. It's a unique opportunity to harness his passion and knowledge to drive innovation, not just for our team but also for the greater community we serve.
In terms of timing, we have been chatting about this for a while: the idea of an AI Lab called ‘Bionic Diamond’ and picking our spots where we can test and build AI-integrated solutions. And, the recent GPT update allowing anyone to create their own GPT accelerated the opportunity. So, the timing couldn’t be any better.
Alan> For me personally, the timing was perfect! I couldn’t have timed a master’s in AI better – no one could have predicted this incredible AI heat wave. And, with the obvious spike in AI usage and my passion and love for innovation, technology and data, we knew we wanted to do this for our people, clients and the community.
LBB> Leading up to the decision to create this position, what were the conversations and thought processes like? Was there any hesitation to officially mint chief AI officer as a role? And what are your visions for the future of this title?
Alan> We started Bionic Diamond in early spring as a means to signal our intent. We gained traction finding solutions to very specific client challenges and saw immense success with not only the process to build out proof of concept (POCs), but also the degree to which they resonated with our internal teams and clients.
Going forward, the vision is to elevate our internal knowledge/capabilities and product development. We envision products that are focused on using data in unique ways to increase the value we bring to our clients and their business.
Josh> We also engaged in extensive conversations surrounding AI's role and its impending impact on both our clients and organisations like ours. While the buzz around AI is constant in the news cycle, what often goes overlooked are the expansive use cases beyond the realm of typical ChatGPT applications. As we witnessed AI's tangible impact on real-world workflows and outputs, it became increasingly evident that embracing AI was not just a trend, but a necessity. Seeing firsthand the transformative potential against the backdrop of practical applications made the decision to integrate AI an obvious and essential step forward for us.
LBB> Building on this, tell us more about the day-to-day of a chief AI officer! What are you responsible for, and what are you looking to achieve, both on a personal and agency-wide level?
Alan> The role is to build AI evangelists across the agency, and fast. This can’t be done by one person, it needs a ‘village’. So, in short, running frequent training sessions internally that include GenAI, GenArt, GenAnalytics, and working to maximise our collective output. I’ve pledged to run 25 industry events alongside the VJAL Institute over the next 11 months (one month down). My responsibilities include building an AI-driven product development roadmap (and the infrastructure to support that), advancing our internal AI literacy, and identifying new ways we can integrate AI into our core offerings.
On a typical day, I may be meeting with our developers to discuss progress on AI prototypes, working with creative and strategy teams to brainstorm campaigns enhanced by data insights, analysing the success of recent AI implementations, planning an upcoming workshop on machine learning basics, or evangelising the promise of AI to new potential partners.
On an agency level, my goal is to have AI augment and elevate every stage of our workflow within the next two years. I want to arm all employees, from interns to executives, with the knowledge and tools to harness AI on their projects.
LBB> What do you expect the interactions to look like within the leadership team following this change? Will the workflows change at all? And how will you be balancing responsibilities as both COO and CAIO?
Alan> In terms of the first question, they already have. The senior leadership team has embraced the capabilities and related opportunities, and in parallel, our clients have been incredibly receptive to exploring ways in which AI can help in their business.
Beyond that, our operation runs on data. And one of my favourite lines (these days) is ‘No data, no AI’. We’ve always been obsessed with using data to inform many aspects of our business, so the two roles complement one another, almost perfectly. As COO, I'll continue directing company operations, seeking optimisation and efficiency gains. As CAIO, I'll lead the charge on integrating AI more deeply across all those operations - leveraging technology to take our data-driven approach to the next level. So, in the short term, yes, AI innovation may take priority, as it promises to transform our capabilities. But rather than a tradeoff between operations and AI, I see immense opportunity in blending them. With such a tight link already, my aim is for our AI advances to directly strengthen operational performance.
For example, automating more administrative tasks with AI could free up employee bandwidth for higher impact work. Meanwhile, applying AI prediction to resource planning could smooth out peaks and valleys in workloads. I have a long list of ideas where AI can directly bolster operations. And, by recognising the synergies between operational excellence and AI adoption, I feel well-positioned to balance both responsibilities. I see the CAIO role not as an addition to my COO duties, but as an amplification, allowing me to take our impressive operations even further. And externally, I plan to position our agency as an industry leader in ethical, creative AI adoption.
Finally, personally, beyond running workshops, I believe artificial intelligence can revolutionise our industry, but only if we build an inclusive culture focused on humans alongside technology.
LBB> Speaking of operations, you’ve mentioned using AI to ensure ideas perform and help clients achieve an even greater MROI. So, logistically, how will this work?
Alan> As part of our product roadmap, we are developing some game-changing predictive ROI tools to help drive campaign performance. Of course, I don't want to give away too much of our secret sauce! But at a high-level, these tools will leverage AI to analyse past data and identify what creative elements, messages, and formats tend to resonate best with target audiences. Rather than just guessing what might work, our teams will be equipped with data-backed insights into historical performance as they ideate concepts. We'll also have engines that can forecast how well a given campaign might perform pre-launch, allowing us to refine things on the front end.
LBB> Earlier, you mentioned charting a roadmap for the future at Diamond. What will this look like?
Alan> When we talk about Diamond's AI roadmap, it's useful to make the distinction between AI that optimises versus AI that innovates. On the optimisation side, we'll be deploying AI ubiquitously to enhance the ideation, production and analysis of our core creative offerings - finding efficiency gains and performance lift across services. But even more exciting is using AI to build entirely new products and capabilities that simply weren't possible before. We have several big bets in early prototyping that aim to leapfrog over incremental gains.
Suffice to say, my aim as CAIO is to not only amplify all existing agency services through optimised usage of AI, but more importantly, to open up innovative new offerings by proactively identifying and filling gaps in the market. If we realise both, we have a chance to completely redefine the category, rather than just playing catch up.
LBB> And tell us more about the workshops and educational sessions you’re hosting! What are the main teaching points you’ll be emphasising?
Alan> This hands-on AI training program is extremely exciting. In my commitment to run the 25 all-day workshops, I’m aiming to equip attendees of all backgrounds with practical AI skills.
Specifically, my highly interactive approach is ‘no one left behind’. We'll cover high-level machine learning concepts in an accessible, non-intimidating way, so everyone understands the fundamentals. But even more of the focus is hands-on-keyboard experimentation with real tools, guided by me. Whether building a chatbot, generating art/copy, or leveraging analytics, every participant will actively apply AI through prompts and coaching.
It's less a passive presentation and more an immersive sandbox for attendees to tinker with and unlock new ideas. We take an iterative approach to problem solving with these tools - relentlessly pursuing solutions. My goal is for people to walk away both inspired and empowered. If we can demystify AI while igniting curiosity and agency, people will naturally recognise ways to incorporate these ‘superpowers’ into their day-to-day work. So, my aim is for attendees to leave and immediately brainstorm on how to apply AI across every profession - from marketers to engineers to designers and beyond!
LBB> With this in mind, does your approach vary at all based on whether the target audience is internal or external?
Alan> The workshop structure has proven extremely effective, however, I always tailor aspects based on the target audience - whether internal teams or external clients. This customisation requires extensive upfront research on my end into each industry's unique challenges, use cases, and opportunities related to AI.
To start, I identify both relevant AI applications as well as real tools that attendees can immediately see practical value in for their roles. My aim is relevance that leads to immersion. The more industry-specific I can make the hands-on exploration, the quicker that ‘jaw-drop’ moment clicks, where they internalise where AI can augment their day-to-day work. For example, an upcoming workshop is focused on the hospitality sector. Therefore, we'll cover AI conversation bots for customer service, AI tools for demand forecasting to enable dynamic promotions pricing, machine learning for personalisation of guest offers, chatbots for reservations, and even algorithms to optimise kitchen production pipelines. I infuse the sessions with real and relevant hospitality use cases throughout.
Overall, it's crucial that attendees walk away dazzled by what's possible and armed with knowledge of specific AI solutions they can advocate for back at their jobs. My goal is sparking their internal innovation engines rather than just educating. By making the workshops directly relevant and immediately applicable, I inspire teams to drive transformative change.
LBB> Previously, you’ve been quoted as saying that ‘the real magic happens when we harness AI to interpret and utilise data in ways previously unimaginable’. So, how does one actually do this? And what new areas are you pushing into as a result of this?
Alan> I'm fond of leading with ‘No data, no AI’, but so many organisations sit on mountains of data where unfortunately, a lot of it ends up siloed or formatted in unusable ways across teams. The process has to start by wrangling those datasets, consolidating sources, and cleaning things up - not the most glamorous work, but foundational. It can be time and resource intensive depending on complexity and volume, but it’s absolutely crucial.
Once usable though, that's when things get fun. We suddenly have incredibly powerful tools now accessible to everyday users - SQL databases to pull insights from, or natural language interfaces allowing plain English data queries - making even junior analysts capable of things previously requiring data science degrees.
So, while data wrangling takes patience and elbow grease, the payoff enables completely new realms of analysis. There’s identifying correlations that teams never knew existed across their consumer base. Forecasting trends from subtle signals and at mass scale based on mountains of behavioural data. Or optimising resource allocations by modelling a thousand simulated scenarios.
In this way, the mantra pays off - with clean usable data, AI unlocks possibilities previously unimaginable. But without investing in the foundational data pipelines, even the most advanced algorithms have limited usefulness. So, step one for any business is consolidating and structuring information flows. Only then comes the fun part - letting AI loose to help interpret and model that data in transformative ways.
LBB> Finally, going into 2024, how will you be starting the year with AI in mind? Can you tease anything about the future?
Alan> Large language models are incredibly powerful tools but require real specificity to apply effectively. The future, at least in the very near term, is customised ‘twins’ - sort of assistants that can perform very niche tasks rather than general functions. Think less about building one gigantic model to handle everything, and more about tightly-scoped bots with specialised intelligence. While the big name chat apps grab headlines as generalists, we see more immediate potential creating focused AI alongside our different service lines.
For example, this could mean an AI tool that acts as a creative partner to the team, specialising in data-backed campaign ideation to complement our human creative directors. So, it becomes an additional ideation ‘team member’, providing data-driven concepts for consideration, but doesn't replace the human creative lead's judgement. Or, consider a custom analytics assistant tailored to a client's unique reporting needs, helping analysts parse volumes of data to surface key insights. It augments and streamlines the analytics workflow for human team members rather than operating autonomously.
The key is ensuring we don't lose sight of end user needs in embracing the shiny new tech. That's why our product roadmap focuses first on building AI solutions for specific organisational pain points, similar to the musings of small startups. The twins of sorts act as co-pilots for very targeted business challenges.
Only once we have things directly propelling core priorities will we scale up. But we think leading with sharper tools, even if narrow in scope, builds trust and adoption quicker across our partners. Then we can worry about generalisation. So, in 2024, we're less focused on bigger models, and more on purpose-built AI perfectly aligned to user workflows.
