The Five Lanes on a CEO’s AI Roadmap
Pragmatic CEOs and business leaders know how to 1. Initiate, 2. Insource, 3. Innovate (or wait), 4. Inspire, and/or 5. Insulate when it comes to incremental or transformative AI initiatives in their enterprises.
This is the first in a series of posts meant to help CEOs and business leaders adopt Artificial Intelligence at enterprises. The series tries to cut through the hype and focuses on ROI. These are the author’s personal views.
Most CEOs and business leaders understand that AI is not an end goal nor a strategy unto itself. It’s merely a tool that we must use in service of our business priorities. Any approach to AI must start with a clear prioritization of the overall business objectives, AI or no AI.
At the same time, AI can be a potentially transformative tool. If applied correctly, it can hyper-charge your business. As a simple thought experiment, let’s start by assuming that everything is possible with AI. We all know it’s not true, but if it were, what could your business priorities look like? In reality, this makes a good starting point for a bold AI vision. Ironically, it actually let’s you focus even deeper on your core competitive strengths.
AI is also evolving super fast. Open AI launched ChatGPT less than 2.5 years ago, and everyone is already experimenting with advanced concepts like Multimodality, Agentic AI with reasoning, and Large Action/ Concept Models. This pace of evolution makes the thought experiment less experimental. It’s reasonable to assume multiple breakthroughs in the next 3–5 years. Ergo, it’s reasonable to assume that any AI-fueled strategy can target 10x more growth/ change in top-level metrics than a strategy without AI.
The as-is Business Priorities and the AI-Fueled Business Priorities may not match 1:1, and that’s fine. However, once you have a list of AI Levers that can hyper-charge your business priorities, it’s time to pick a lane on your roadmap for each of them. You can jump ahead to a complete decision tree towards the end if you like.
Lane 1 — Initiate with 3rd Party Solutions
Key Question: Is the current state of the art reliable enough for your use case?
The AI ecosystem is innovating at two levels: First, the headline level — the things that work in the labs — and the second, the workhorse level — the things that perform reliably and at scale. There may be many vendors that are already providing the AI solutions that you need for your priorities. An easy win would be to start incorporating these products in your systems. In general buying is far quicker, more effective, more future proof and cheaper than building on your own in this category.
This lane is about super fast testing and rolling out. In this lane your team needs to worry about quality, governance, privacy, integration, and change management to drive higher adoption of these tools. In this lane you need to worry about articulating and enforcing a high bar around helpfulness, latency, compliance and measurement related to the solutions, including definitively measuring the ROI.
Lane 2 — Insource and Build your own Solutions
Key Question: Do you need a bespoke solution based on the state of the art AI technology?
There may be certain processes or insights that are core to your team’s competitive advantage. This is the most important reason you would consider insourcing the development of AI solutions — a vendor may never be able to understand all the nuances. This is perhaps the only acceptable reason as well. Most other reasons are typically made up and have more to do with team dynamics.
In this lane it may still be a good idea to initiate the solutions in Lane 1 with third party vendors to assess, to learn and to put in place the necessary quality, governance and privacy frameworks. In this lane your team needs to focus on the key differentiation for your business. In this lane, you must make sure that your team is spending majority of its time bolstering the differentiation rather than building commoditized products/ features that should really be in Lane 1 — Initiate.
Lane 3 — Innovate with the overall AI Ecosystem
Key Question: Is the current state of the art insufficient for your strategic needs?
A big part of your list of AI levers/ projects will be things that the AI is not yet ready for. Most likely you will find headlines on how this can be done, but no real testimonials from your peers. In a vast majority of these cases, it makes sense to just wait for the ecosystem to catch up. Given the pace of evolution of AI, it may be only six months away. However, there are situations where it may make sense to start innovating ahead of this ecosystem.
This lane has the potential to be the most meaningful for your business, and yet the most frustrating and/or expensive. Before you commit to any use case in this lane, put it through this four part test:
- If the state of the art was ready today, would this use case fall in the Lane 2 — Insource (as against the Lane 1 — Initiate)?
- Is the time to market for this use case critical for your team’s success? For example, is this a blocker for adoption of other AI initiatives? Or perhaps there is a key first-mover advantage?
- If it were feasible, is this use case the highest priority application of your resources?
- Is the innovation that the use case needs something that a future, better version of LLMs cannot do out of the box?
I would reemphasize the fourth test. All major players like Open AI and Gemini are continuously introducing new versions of the large language models, and each version is that much more powerful than the previous one. Then there are players like Deepseek who upend the status quo. More are likely to emerge in the future. The innovation that you need to invest in should be parallel to the LLMs themselves. For example, providing the right connectors, building the right memory or context, or investing in a unique, bespoke need that most LLMs are unlikely to cater to. A red flag in this lane would be if your team tells you that they need to improve the quality of an LLM to get the results. While there are exceptions, to me the word ‘fine tuning’ is also a major red flag.
If all four of these tests come back with a Yes, it’s a good sign that you must invest behind innovation in these use cases. Otherwise just wait a few months and reassess. In this lane you and your team need to unlearn and learn product development skills again, this time for AI.
Lane 4 — Inspire your Team to be more Open to AI
Key Question: Even if everything is good to go, does your team feel ready?
CEOs and business leaders know that the definition of a right-thing-to-do spans more than dollars and technology. Adoption of AI at scale can lead to unique human challenges with your team if not handled appropriately. For example, there may be anxieties around job security. Or there may be a sense of obsolescence because some people feel they are unable to use AI. There may be others who struggle with a machine-first approach.
Each of these, and every other human perspective, is valid even if seemingly unfounded. You may want to consider similar perspectives from other stakeholders as well, esp. your customers.
At the same time as more and more people use AI and realize its powers as well as limitations, they get more comfortable delegating to it. As a leader your job is to inspire no matter what. From a roadmapping perspective, you may find that some use cases are not the first ones you should take up. They need more discussion, higher familiarity with AI/ automation and a more supportive environment to be successful, even if every other indicator is green.
This is an often ignored, and yet super important, gestation lane for your bold ideas. In this lane you need to bring everyone together and make sure that your team (and other relevant stakeholders) are ready for your vision before you roll it out. An effective way to do this would be to focus on gateway use cases first, while emphasizing on human control at all times.
It’s also important to recognize that any approach to change management or inspiration should be comprehensive and not just for the AI projects in this lane.
Lane 5 — Insulate the Legal and Policy No-Nos
Key Question: Are your use cases limited by legal or policy compliance?
The legal and compliance boundaries that good CEOs and business leaders set for their scope of operations make for an overarching consideration.
There are multiple pieces of legislation related to AI that can be pertinent to your team. For example the EU AI Act or those being proposed by the California Civil Rights Council. In addition each team or company typically makes its own policy choices based on its own context. For example, a financial organization with fiduciary responsibility to consumers will think about this very differently than a B2B tech player. Over time, the regulatory burden is likely to increase while the policy burden, like human acceptance of AI, is likely to evolve favorably.
Irrespective, at any point it’s super important to insulate the use cases that are a no go from any kind of AI development. This is critical to keep your overall AI vision free from these types of distractions, which can bubble up unexpectedly. The larger the team you manage, the larger is the potential for legal/ compliance distractions to derail your AI agenda.
In this lane you and your team must focus on a good governance framework for AI, one that can proactively alert for any kind of non-compliant behavior. It can be a great way to push the legal/ compliance limits without actually inadvertently crossing the line. In this lane you should also think about pushing the envelope creatively, and use humans in the loop as a key lever to derisk.
There is simply too much in this playbook for a single article to even scratch its surface. However, framing your problem in these five parts will give you a strong head-start in adopting AI in an effective way for your enterprise. This will also ensure that your mistakes are small, and wins, even if hard-fought, transformational.
