Navigating the AI Journey – Volume 1: Advisory
In the last decade of technological advances, Artificial Intelligence (AI) and Machine Learning (ML) stand out as a transformative force that holds immense potential for businesses across all industries. However, the path to harnessing this potential is far from clear for most organizations. At the same time, leaders are bombarded with headlines on AI-related topics like generative artificial intelligence (genAI), artificial general intelligence (AGI), massive fundraises for early stage startups, big tech partnerships, copyright issues, a writers strike, a slew of new companies that claim to have “the AI for…” or “the copilot for…”, just to name a few from our inboxes this morning. Honestly it is not surprising that leaders have either struggled to get started with or create real value from AI.
Enter “The AI Journey” – a meticulously designed framework that offers guidance and direction to companies wanting to generate tangible value from AI. This journey is not just a theoretical construct; it's a proven framework drawn from our experience delivering 200+ AI projects. It is by no means the only way to adopt AI technology, but we can confidently say that those who embark on this journey find themselves on a smoother path to AI success, delivering more value and encountering fewer roadblocks. It has become an irreplaceable part of all of our engagements as it helps us to chart the best course for our clients.
The Three Phases of the AI Journey
The AI Journey has three distinct phases, each designed to maximize the value that can be generated from AI and the probability that this value will be realized. These phases are Advisory, Exploration, and Launch & Scale. This first volume will focus on Advisory. Volume 2 will cover Exploration, and our third and final volume will discuss Launch & Scale.
Advisory: Setting the Foundation for Success
The first phase of the journey is about building a solid foundation for your AI program. It's akin to mountaineers preparing their packs before embarking on an adventure. You do not generate value by completing this phase in isolation, but you will certainly handicap your future AI initiatives if you overlook it. In this phase, companies appoint an AI leader who will spearhead the AI program, engage internal stakeholders and external advisors, and provide education resources to ensure that everyone involved understands the fundamentals of how to apply AI to your business.
Exploration: Rapid ideation and iterative development, for AI
Most businesses currently find themselves stalling somewhere in the Exploration phase — a juncture where innovation takes center stage, and real value starts to be delivered. However, this phase is also shrouded in complexity, and at times it can feel like navigating uncharted waters. This happens for a number of reasons:
AI use case discovery is hard – Finding the right use cases to tackle with AI requires a deep understanding of your business and experience with AI technologies; a combination that rarely lies within one individual or team.
There is noise surrounding AI – The current AI landscape is filled with too many voices, each vying for attention. The sheer volume of information, trends, and opinions circulating around AI can leave organizations uncertain about where to begin their AI Journey.
The daunting upfront investment – The investment required to build AI technologies can feel like a steep first hill to climb. Historically, AI solution development has required months of investment before value can be assessed. Not to mention the types of resources required are expensive and few businesses actually have a pipeline of AI projects large enough to justify hiring an entire team. Especially during a time when leaders are being told to do more with less, AI initiatives can seem like a risky way to invest valuable capital.
We have designed the Exploration phase to overcome these hurdles by building it around three core principles:
Ideation is quick and done in an environment where business, technology, and AI specialists can work together without being interrupted.
Delivery teams are as lean as possible and the only software that is built is that which contributes directly to generating and / or proving the value of the AI use case.
AI software is delivered iteratively, the same way that agile methodologies are used to deliver value via traditional software.
After overcoming the creative and innovation challenges in the Exploration phase, businesses find that it propels them forward on the AI Journey. It is a phase where ideas take shape, teams collaborate, and value emerges.
Launch & Scale: Sustained value with AI
The final stage of the AI Journey is the Launch & Scale phase. It focuses on taking the successful pilots from the Exploration phase and launching them to a larger audience. It starts by making pilots “ready for production” after some additional development efforts. It continues by monitoring those solutions to understand their usage, performance, and value creation. And it concludes with businesses leveraging these technologies to drastically enhance their offerings, operations, and in some cases, their business models.
The AI Journey
Every conversation that we have with clients today starts by showing them the visual below. This is The AI Journey and the first order of business is to understand where on this journey your business is. After you pinpoint the stage that you are at and the stage(s) that you might have skipped, it becomes much easier to determine where to get started.
Phase 1: Advisory
In the journey to tangible valuable, the Advisory phase is to the AI Journey what training camp is to professional sports teams. You certainly will not win a championship in training camp, but you can lose one. This phase lays the essential groundwork for your AI program. Its overarching goal? To align your teams around the value AI can generate and provide them with the necessary resources to educate themselves effectively. By executing this phase properly, you will set your teams up for success later on in the journey.
Step 1: Assign a Leader
The first step in the Advisory phase is the appointment of an AI program leader—a strategic move that sets the tone for all subsequent initiatives. All too often, the leader of the AI program is simply given to the person who is the most technical, the most vocal when it comes to AI technologies, and in rare cases, someone specifically hired to run this program. If you are selecting your AI program leader like this, you are setting yourself up for a rough trip. This leader is not just a figurehead; they are the driving force behind your AI program and are responsible for its success.
Ideally, this leader should be someone who embodies curiosity, is willing to challenge established norms, and has an action-oriented mindset. They should have a track record of delivering value iteratively rather than someone who can only work in long, waterfall-style projects. The need to be humble and willing to learn because they cannot be the expert in every part of the AI program. But they also need to be decisive when push comes to shove and a decision needs to be made to either change directions or forge ahead. Lastly, they need to be collaborative because they will be paired up with advisors who can provide the business, technical, or AI expertise that the program leader lacks. In small to medium sized enterprises (SMEs) we have seen this role played by a number of different members of the leadership team including CEOs, CTOs, CDOs (Chief Data Officer), CPOs (Chief Product Officer), or CIOs (Chief Information Officer). From our experience though, the title matters much less than the qualities of the leader themself.
The leader should also be given a mandate. In the AI Journey methodology, a mandate means something very specific. A mandate contains an objective tied to a tangible business outcome (e.g. increase gross margin, reduce operating costs, etc.) and the resources to accomplish it (e.g. budget to hire / contract, time from an existing team, etc.). Without both of these elements, you do not have a mandate and your AI program sputter shortly after getting started.
Step 2: Engage Other Stakeholders
The success of your AI program hinges on much more than just one leader. Even more than other technology projects, AI initiatives are cross-functional efforts where you have to bring stakeholders from across your business together. We have observed that teams who are aligned on the goals of their AI program move through the AI Journey with much less friction than those who only involve other stakeholders on a “need to know” basis.
At this stage, team members beyond the AI program lead do not need to spend too much time preparing for AI initiatives but at the very least, your company’s CEO should announce the AI program. The company should know who the AI program lead is and the mandate should be shared with the management team, if not the entire company. Such a public announcement may feel like overkill, but radical transparency on your AI initiatives helps build buy-in and squashes any uneducated rumors before they start.
Once the program has been announced, it does not hurt to have 1-1s with the stakeholders from across the business who are going to contribute in one way or another. In these meetings it is critical for the AI program lead to genuinely seek input and guidance, rather than tell others how the program is going to be run. Ask general questions, not just ones that pertain to an individual’s day-to-day job. You would be surprised where some of the best AI use cases come from.
To further bolster your efforts, find a part-time AI advisor to pair with your AI program lead, especially if you have very little AI expertise within your company. At this step in your journey a full-time expert is not needed but having someone to bounce ideas off of as you get started will help to ensure that you start moving resources in the right direction. This advisor should have significant AI expertise and be technology-agnostic. They need to be able to communicate the trade-offs associated with AI initiatives to you in an unbiased way so that you can make the most informed decision for the AI program you are running.
If properly executed, this step will create buzz and anticipation for your AI program, fostering enthusiasm even before the first AI use case has hit the canvas.
Step 3: Provide AI Education Resources
While it is true that not everyone involved in your AI program needs to be a seasoned AI expert, projects run much smoother if everyone has a basic understanding of what AI is (and is not) and what types of AI are useful in your industry. From our experience, AI education is best accomplished through two mediums.
The first is what we call an “AI 101” workshop. These full-day workshops are delivered by an AI agency and they bring together a large group of internal stakeholders (~15 – 20 people). At first, they are focused on teaching everyone the basics about AI:
What is AI?
What is the difference between AI and ML?
What are the different types of AI?
What is generative AI?
After the group understands some of the basic concepts, the workshop dives into industry-specific use cases for AI. The facilitators talk through how the technology was implemented by different companies, what the results were, and what the challenges were. Before wrapping up, the group is usually led through a collaborative exercise that gets them thinking about how AI can be applied to their own teams.
The second are self-guided resources that allow individuals to learn in a way that best suits themselves. Popular resources include books on AI, online courses, subscriptions to AI-focused publications, events, and conferences. Allowing people to educate themselves in their own way also helps to develop diversity in the perspectives and knowledge that everyone brings to the ideation sessions that happen later on in the journey.
While the exact execution of the Advisory phase will vary for every team its core principles remain constant. If you've communicated your intent to harness AI as a driver of value creation, appointed a capable AI program leader, and equipped your team with the necessary resources, you're primed to move forward to the Exploration phase where value is first created.