Wondering How to Support AI Adoption? A Guide for Leaders.
Not everyone is exploring how AI can help them improve productivity or enhance products or address entirely new market opportunities. How can an Executive help address barriers for adoption?
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If you’d like to support the growth of this resource, consider upgrading to paid and take advantage of the other ways I can help you.Over the past few months, several clients have posed similar questions regarding strategic decision-making concerning AI, so I thought it might be helpful for others if I wrote up some of what I’ve shared with them.
The key questions I’ve been asked (paraphrased):
Is AI a truly disruptive trend or a bubble?
Should I stop pursuing growth through hiring and instead focus on improving the efficiency of how we build software using AI?
What software development principles are subject to change in the AI-assisted software development era?
How do I encourage our teams to explore what is possible with new AI capabilities?
How should I think about spending on software developers and AI for the coming financial years?
These same questions were discussed at a few tech leader events I attended recently, and I’ve also seen some similar threads in the blogosphere. It appears these are questions in the Zeitgeist.
In this post, I will focus on the first, and I will address the subsequent question in a future post:
How do I encourage our teams to explore what is possible with new AI capabilities?
Should We Communicate an AI Mandate?
No doubt you’ve seen the mandate by Tobi Lutke, CEO of Shopify, on the use of AI:
https://x.com/tobi/status/1909252234455416902
As mandates go, it’s a well-structured, well-reasoned approach that reminds me of the Jeff Bezos API mandate, which appears to have delivered on its intent and more. The Jeff Bezos mandate is notable because it’s an exception regarding the success of mandates, which tend to introduce chaos, remove agency and ignore nuance that may be important.
Duolingo, a company whose product has successfully incorporated AI features, shared a similar company-wide memo this week.
This guide will provide ways to approach this without needing to resort to mandates, after all, AI coding mandates are driving developers to the brink
I wouldn’t rule it out completely, but it’s not where I would start. Given an adequate context of need and opportunity, the team will bring more energy to discovering value, retaining agency, and making choices.
Let’s look into where we should be starting.
How leaders can support the adoption of AI-assisted coding and the use of AI in product features
This brings me to the key question leaders have asked me recently. If AI is to become an essential component of efficient software development, how do leaders help their teams with this transition?
Here is where I suggest you start based on my experience leading other seismic changes, such as cloud adoption, DevOps practices, customer experience (CX) focus, and improving adaptability and responsiveness and various shifts over the past three decades:
Consider and address sources of friction for adoption.
To successfully foment adoption, we must first understand what contributes to friction and influences whether something is adopted. If we don’t do this, we are likely to put our teams in a position to overcome obstacles they may not have the agency or adequate support to overcome.
Here are some examples I’ve observed with productivity tools such as AI-assisted development tools:
Lack of clarity on where leadership sees the opportunity
Fear of burden from associated issues
Their Current commitments
Availability of collaborators
Lack of apparent opportunity to try and fail safely for learning
Consider and leverage the adoption curve.
This post provides details I have recently shared with CEOs and CTOs who have approached me on these issues. The majority of the content on this blog are free, with a small selection behind the paywall. For the price of a coffee a month, benefit from all the insights from my career experience and from working with top CTOs and support me helping technology leaders like you, globally. Lack of clarity on where leadership sees the opportunity
Many discussions about AI get bogged down quickly because AI can be applied to addressing many types of problems, so a great starting point is to get specific about which problem you believe it might help you address.
Making decrees about using AI without being particular about what types of opportunities you see and where they may be particularly relevant to your business can be frustrating or lead to excessive time-wasting.
Be specific about the use cases where you see an opportunity for AI
For example, distinguish between different applications of AI and how you see it benefiting your organisation. For example, here are some examples which highlight the aspect of AI, where it will be applied and the benefit:
We can use AI to solve our customers’ information problems for specific business processes where we have unique data for a competitive advantage.
AI product features for supporting additional pricing tiers.
AI-powered data cleansing for higher-quality data to improve the effectiveness of key business processes.
AI-assisted development for improving productivity.
AI tools for assisting research, automating reporting for shorter turnaround and reducing toil.
AI-assisted coding for improved efficiency with activities that distract us from value-adding work
Using “vibe-coding” for fast prototyping concepts for earlier validation with customers.
AI analysis and document formatting for better efficiency in reporting and research.
AI features for unique selling proposition (USP) - beware shallow moat!
Etc.
Of course, you could also replace all the mentions of AI with a more specific type of AI—is it generative AI, such as an LLM, Machine Learning, Deep Learning, Computer Vision, recommender algorithms, etc.?
You can leave space for your teams to assess which options best solve specific problems. The nature of the adoption curve and your business's existing demands can cause enough friction to delay adopting new technologies even when they may offer an advantage.
I am not a fan of starting with a solution and looking for a problem, but if you have a problem you know well and can see the opportunity for AI to provide a better solution than other options. It may be the only responsible thing for you to do: encourage and create the appropriate space for your team to explore these opportunities.
Fear of burden from associated issues
AIs are very good at solving problems they’ve seen before in their training. For example, if I ask a tool such as Lovable to build a brochure site to promote my coaching business, it would do a great job because it has seen thousands of examples. Similarly, with AI-assisted development tools such as Cursor, it will be strongest at problems it has seen examples of in its training.
The algorithms operate on probabilities, so sometimes the AIs will make suggestions that are flat-out wrong or, even worse, subtly wrong, such that they escape into production.
Your teams may hesitate to engage with tools that could result in unchecked interruptions to their time, due to potential out-of-hours production incidents and similar issues. Acknowledging these concerns can be beneficial, as it allows for collaboration in creating a plan to mitigate such problems, giving them a sense of agency rather than dealing with the consequences of leadership choices.
Their Current Commitments
Most people struggle to entertain new ideas when they are focused on the burden of their current commitments. Suppose they can’t see the current commitments changing, especially when they have a mix of new value creation and operational responsibilities. In that case, it may seem to them like there is never a time to invest in learning or experimentation.
Identifying opportunities within the current commitments can be powerful, where consideration of new tooling might be appropriate. By sharing with them, the leadership team is comfortable with the development team slowing down initially to operate at a higher pace later. The ideas in this post may help frame this concept with the team:
Availability of collaborators
Timing is a critical factor in successful collaboration. It’s underacknowledged and appreciated how often a group may be interested in a common goal. Still, at the point in time when each person is ready to engage, it passes like ships in the night because maybe other collaborators are busy with other commitments or still dealing with particular concerns that cause hesitation.
Using a combination of time and space can help people to be ready at the same time, and thus be more able to support each other and work together. I cover some tactics that can help with this in the following post:
Lack of apparent opportunity to try and fail safely for learning
When someone has deadlines, a backlog of quality issues to fix, or other factors that limit the margin for error, they are operating in a space where it can be difficult for them to prioritise far more speculative time investments. This is likely how investing time in AI can feel.
At the very least, they know they will need to slow down to learn how to benefit from AI. Secondly, having witnessed many other hype cycles, they will naturally wonder if they exert that effort and get no benefit. Worse, they may even feel they will bear the consequences of compromises on current priorities.
Look for opportunities that allow people to try out new tools safely with time to explore and low stakes for failure. This might be fun activities such as hackathons or innovation days, dedicated learning time for everyone during the week, or helping them make a plan that prioritises experimentation.
Consider and leverage the adoption curve.
You’ve likely seen the adoption curve as a concept and played out in real life. It’s often considered when finding markets or how markets evolve, but it can also be a helpful lens in change management.
As an alternative to painful top-down mandates, I suggest finding your company's innovators and early adopters and offering everyone an opportunity to join a coalition of the willing to explore AI-assisted development tools and share what they learn.
In this way, you are recruiting people into the opportunity and creating opportunities for social proof, which opens the door for people in the next phase of the adoption lifecycle. This provides significant agency for everyone and helps put maximum wood behind the arrow to find the value with the tools, working out the issues and mitigations for these, which builds up the safety that the late majority and laggards are looking for.
As negative as ‘laggards’ sounds, there is always a change that is the threshold for an individual that would cast them as the laggard. It may be because you can’t afford the risk or the disruption, or because you have been burned before. Your laggards can be an asset - your solutions have the resilience and robustness required when the laggards are signing up. Maintaining the respect and empathy for this group is essential - after all, you hired them, and as a leader, the responsibility stops with you!
Many dated change management materials refer to the concept of ‘mindset’, particularly fixed versus growth mindset. It’s poorly framed and doesn’t have strong efficacy in terms of science or effectiveness when it comes to bringing about change.
It’s been popular because it feels like it describes positive attributes in ourselves and explains drivers of change resistance. Note that this framing pushes responsibility away from leadership and onto individuals, which plays into our classic cognitive biases for optimising for our convenience. I cover this briefly in a section of this post:
https://www.greatcto.me/i/141841122/weaponised-growth-mindset
This doesn’t eliminate people's personal responsibility for participating in change that is good for the collective. Still, it can change how you engage with people and provide them the support and information they need to participate successfully.
Recap: How to encourage the use of AI-assisted coding
Understand and communicate why it’s valuable
Be specific: Communicate what aspect of AI will be used, where it will be applied, and what the benefit will be.
Avoid generalities, e.g. “we are shifting to AI!”
Engage the team on the means to help realise the organisation’s purpose.
Help identify and remove obstacles.
Create time and space for it.
Hackathons
Regular learning time - each week or budgeted per initiative
Part of the process of defining an initiative
Management Systems - Show that it’s valued
Rewards for innovative experimentation - not just features delivered
Celebrate the process.
Document and share new practices.
Establish champions and communities of practice.
Acknowledge and listen to the limitations.
There are very real challenges and limitations with this generation of AI tools.
Acknowledge the limitations and listen to the risks they introduce.
Don’t punish people for sharing risks. Work the problem together.
“It’s okay to share the many challenges we face, but I challenge you to share some ways for how it might work or how we might overcome these challenges.”Strong Adoption Requires a Solid Foundation
Some final thoughts…
The ideas in this post are based on experiences introducing other significant changes in work practices, such as agile and lean ideas, DevOps practices, using data for evidence-based decision-making, improving incident response practices, adjusting team structures dynamically, and many other disruptive changes.
It's also informed through my regular conversations with clients, peers, and coachees about their experiences supporting the adoption of this current generation of tooling and stimulating adoption within the consultancies I am a part of. This unique vantage point helps me cut past the hype and engage with people, finding real value using AI-assisted development. It’s my responsibility to help technology leaders have a clearer view of what is happening.
The practices described in this post are predicated on an assumed foundation of psychological safety and values that encourage learning and growth within your team. If these foundations are shaky, I recommend this post on the same topic, which spends some more time on these foundations:
Or browse my archive of posts across leadership, goal-setting, AI and more.
Has your team adopted AI tools to support their software development significantly? If not, what are the barriers that you see? What approaches are you trying to encourage an openness to explore when it comes to AI-assisted development tools? Share your perspectives in the comments.
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