Tailored AI Best Next Steps
Determining the best next step in your team’s AI journey can be tough. AI is not just a technical puzzle; it requires teams to draw on curiosity, problem-solving, and creativity.
That’s why we created the AI Best Next Steps Assessment: your responses to 12 short questions can help you prioritize efforts to make the most of AI. We’ve included a walk-through of the assessment below, or you can use this printable overview.
Maturity Levels
For each question, we’ve provided three possible responses. Don’t overthink it: choose the response that most closely matches your team’s experience.
Your team’s maturity level in each topic area drives tailored coaching on your best next steps with AI. Based on your responses, find the maturity level that best describes your team’s readiness for AI, and see specific recommendations for next steps.
Laying Groundwork | Scores of 1
You’re paving the way for effective use of AI. You have multiple areas of opportunity to progress. An initial challenge will be in choosing one or two focus areas, generating quick wins, and moving up to the next maturity level.
Building Speed | Scores of 2
You’re investing in key components to deliver AI. At this stage, you’ve successfully covered several aspects that lead to effective artificial intelligence initiatives. Your best next step is to lean into strengths and address those opportunities that are holding back meaningful progress.
Reaping Benefits | Scores of 3
You’re well-positioned to experience real advantages from AI. You’ve mastered many of the critical elements that contribute to success with AI and automation. Your main opportunities are around translating these strengths into tangible AI benefits for stakeholders.
For each area, average your scores together — and round down if you’re between levels.
Team Readiness
AI can seem like a technical puzzle to solve, but the human elements of success are equally critical. We explore three aspects of team readiness in the following questions.
Culture and Risk Tolerance
AI initiatives will inevitably include surprises, setbacks, and learning. The better your team is able to plan for and respond to dynamic efforts, the more you’re set up for success with artificial intelligence.
How does your team feel about experimentation, where the expected results — and even how the work will get done — may be unclear?
1: We follow the plan
2: We’re open to surprises
3: We embed experiments
How does your team’s leadership respond to surprises and setbacks?
1: Not very productively
2: Moderately well
3: Curiously and kindly
If your responses average close to 1, you’re Laying Groundwork.
AI-related initiatives may feel quite uncomfortable for your team, since oftentimes both the outcomes of AI efforts and how we’ll pursue them are ambiguous at the outset.
Be clear that part of success in early AI efforts is learning: we don’t typically anticipate financial returns early on, so we need to identify ways of gauging what went well and what we want to change for next time.
Clearly stating the ambiguity at the beginning of the effort, and defining success in a realistic manner, can prevent surprises and bad news later, too.
Consider taking a light-hearted approach to defining success criteria, and make sure to include measures that will feed lessons learned. For instance, as part of the initiative retrospective, you may give awards for pointing out issues during testing, bringing down the dev system with a resource-intensive request, etc. The goal is to highlight that we’re learning together, and very early stages are often more about curiosity and innovation than financial results.
Of course, we want to progress to those financial results, so take care to identify success criteria that make it clear what worked well and what we want to change for next time.
If your responses are around 2, you’re Building Speed.
If your responses get close to 3, you’re Reaping Benefits.
Your team is well-suited to the age of AI: you’re comfortable experimenting and embedding learning into initiatives. Even more importantly, your team feels safe taking surprises or bad news to leadership — this is huge!
You’ll need to draw on these strengths to excel in AI-related initiatives. Expect the ambiguity levels to be higher than normal. Break down what strategic adaptability means to your team and include progress toward it in your AI-related initiatives’ success criteria.
Set expectations throughout the team and with leadership that AI-related initiatives will have surprises. Draw on your strong team culture to keep conversations constructive and focused on taking the best next step you can.
AI Experience and Comfort Level
How your team feels about AI in general and how much you all have used AI (professionally and personally) shapes your collective best next step. Familiarity with AI, curiosity about how it can be of benefit, and some level of support for using it effectively are key components to success in this area.
How does your team feel about AI in general?
1: Averse, against it
2: Neutral
3: Enthusiastic, excited
How much experience and support does your team have in using AI?
1: Not much
2: Some
3: A good amount
If your responses average close to 1, you’re Laying Groundwork.
Sounds like you have the opportunity to shape the AI frameworks for your team! If you can find even one teammate to work with on this, that will increase your chances of success, so it’s worth some effort looking for a partner on this journey.
Identify the functional and technical leaders who hold the keys to signing off on AI-related initiatives or preventing them. How do these leaders feel about AI? What keeps them up at night? How does AI fit within that?
For your larger team, what holds their focus? How does AI relate to that? What conceptual bridges could you build to connect what’s important to them to the opportunities AI provides?
With an understanding of key players, concepts, and workflows related to AI, sketch out how your team can build AI awareness, comfort, and competency over time. Expect to start small, build momentum, and add interested team members to your AI advocacy cohort.
If your responses are around 2, you’re Building Speed.
First order of business: find a few other folks excited about using AI well at work (bonus points if they’re familiar with at least one AI tool from personal use).
Work together to evaluate your team’s current AI posture and identify where you all see an opportunity to improve AI comfort levels and competencies on your team. Make sure to build on what's working well, so you can get some momentum and learnings before addressing stickier items.
Consider setting up casual conversations to take turns sharing an AI experiment you’ve each done recently, what you learned, and what you’re taking into your next AI-related effort. This can be focused on personal use, if professional use is still discouraged, but it will still build enthusiasm and iterative practices of doing, learning, and growing with respect to AI capabilities.
If your responses get close to 3, you’re Reaping Benefits.
Kudos on contributing to a team that is comfortable with AI, eager to use it for professional excellence, and equipped to use it safely.
Make sure to do regular environmental scans of AI advancements and compare them to your training, enablement, and guardrails. It can work well to create working groups to share experiments, lessons learned, and best practices — and to keep updating them as toolsets, techniques, and priorities evolve.
Readiness to Act
If you know who you’ll work with on the next AI initiative, you’re that much closer to success. In the same vein, you’ll want to have the inputs for AI initiatives at the ready, potentially data to be analyzed, processes to automate, and more.
Do you know who you’d work with to design, propose, and implement an AI initiative within your team?
1: Not really
2: I have some ideas
3: Yes, they’re ready!
Do you have a sense for where you’ll turn for the inputs needed for an AI initiative you have in mind?
1: Not a clear sense
2: I have a general sense
3: Definitely, ready to go
If your responses average close to 1, you’re Laying Groundwork.
Find a fellow AI enthusiast, preferably with a different skill set or perspective, and plan out next steps together. What seems like an exciting and achievable place for you to start?
For an information-related effort: what information do you need, and where you can reliably find it? For a process-related effort: what manual process or point of friction could you address, and who do you need to coordinate with?
If your responses are around 2, you’re Building Speed.
Jot down your initial thoughts on an AI initiative and take it to someone who’s trustworthy and can help you take at least one productive step forward. Share your vision, focusing more on what’s clear and points toward success than what’s fuzzy and troublesome, and define actionable next steps for each of you to get closer to making this AI initiative (or something even better you think up) a reality.
If your responses get close to 3, you’re Reaping Benefits.
Great work! Make sure to represent the information you have in mind clearly, with enough context for someone who hasn’t been percolating on it for awhile. Think about the first person you’ll work with: what’s in it for them? how will this potentially help them, and at what cost? Aim to address potential concerns proactively.
Technical Readiness
Of course, our teams can be fully ready to benefit from AI and our technology still hold us back. We consider three dimensions of technical readiness in the following topics.
AI Roadmap Stage
Every team – and individual – has their own specific stops on the AI journey, but we can bucket them into a roadmap of sorts, from curiosity through targeted exploration all the way to having targeted AI use cases. The speed we move along the roadmap relates to how urgent and important it seems for your team to adopt AI, and how clearly your goals connect to AI opportunities.
Which stage of the AI roadmap best represents your team’s current state?
1: Curiosity about AI
2: Targeted AI exploration
3: Defined AI use cases
Does your team have a sense for how AI can contribute to accomplishing your near-term goals?
1: Not really
2: Generally, yes
3: Yes, with specifics!
If your responses average close to 1, you’re Laying Groundwork.
You have the chance to define what success looks like for your next (perhaps your first?) AI-related initiative — exciting! Take a step back and identify a few goals for your team over the next several months.
With those goals in mind, how can automation, machine learning, generative AI, etc. address real challenges or opportunities? What can success look like, describing as vividly as you can? And then, build a pitch for that initiative that connects in with overarching goals, addresses a specific challenge or opportunity, and includes exciting yet achievable success measures.
If your responses are around 2, you’re Building Speed.
Build on the clearest aspects of your team’s AI roadmap stage to maximize your chances of success.
For instance, you could build an AI-related initiative that directly furthers your team’s goals. Alternately, if you see some AI initiatives on a wishlist that seem promising, do the work to connect them to existing objectives and success criteria.
If the desire to use AI in general is the most compelling piece to build on, break down options that can make an impact in the next six months, evaluate them against the best success measures you have, and build a pitch that clearly shows the analysis you’ve done and what leadership can expect to achieve.
If your responses get close to 3, you’re Reaping Benefits.
Bravo, your team is set up to excel in this evolving environment. Make sure to connect your next AI-related initiative to your ongoing objectives for the greatest impact. Also, include success criteria around specific learning objectives in addition to more typical progress metrics.
Technical Foundation
The success of an AI initiative is greatly reliant on inputs. Often this includes cleansed, organized data or defined, repeatable processes. If your technical inputs for AI and automation are ready to go, you’re at a major advantage. Many of us realize we have considerable work to do cleansing and cataloging data to be used with a large language model or mapping our processes to be automated.
How reliable and trusted are your team’s technical inputs to a potential AI initiative?
1: Untested
2: Somewhat reliable
3: Very trustworthy
How accessible are your team’s technical inputs to a potential AI initiative?
1: Quite difficult to access
2: Somewhat accessible
3: Ready to use
If your responses average close to 1, you’re Laying Groundwork.
You have several critical steps to work through before you can benefit from AI. You can start with the lowest-hanging fruit from a technical perspective, or from a compelling AI use case. Let’s consider both.
If you opt to start from the technical side: which data set is most accessible and organized? Which manual processes are repetitive and relatively easy to predict? Consider shaping an AI initiative around deriving insights from a targeted data set or automating some manual efforts.
If you prefer to start from the functional side: what AI use case has generated buzz on your team? What is leadership sending podcasts and blog posts about? Figure out how to connect that specific type of AI with your business needs and technical access, and make the most of the team’s enthusiasm.
If your responses are around 2, you’re Building Speed.
You have some critical steps to work through before you can benefit from AI. Build on areas of strength: think about how to design an AI initiative around the technical components that are most trusted and easiest to access. This may include data or processes that you have some level of influence over, to smooth out coordination on your AI efforts.
If your responses get close to 3, you’re Reaping Benefits.
This is great news. It’ll be considerably easier to roll out an AI initiative with trustworthy and accessible inputs. You can focus on identifying, designing, and rolling out a compelling AI effort, confident that the foundation under it is strong. (If you can, share the kudos from your AI-related success with those who built the strong foundation before you!)
Budget and Investment Valuation
If you’re familiar with the evolution from waterfall to Agile project approaches, funding AI initiatives takes this journey a step further. Not only do we not know the outcome of an AI initiative (and the return it will bring), we may not even know all of the steps. If your budgeting and planning process allows for the ambiguity and R&D-like nature of AI initiatives, it will serve you very well.
Do you have budget identified for AI-related efforts?
1: No
2: We could access funds
3: Yes
Are you able to approach AI-related efforts more with an R&D approach than expecting a near-term pay-off?
1: No, need a solid ROI
2: We have some flexibility
3: Yes, this is expected
If your responses average close to 1, you’re Laying Groundwork.
Understand what it would take to represent AI more clearly within your team’s budget, preferably with some portion of the funds allocated to experimentation.
If your responses are around 2, you’re Building Speed.
Work with finance, program management, and/or executive stakeholders to access funds for a specific AI initiative, being clear that expected success criteria include learning, identifying clear next steps in your AI journey, and increasing strategic adaptability.
If your responses get close to 3, you’re Reaping Benefits.
Awesome. Be very clear in your request for the AI initiative you have in mind what success will look like, including learning, identifying actionable next steps in your AI journey, and increasing strategic adaptability.
Connect with the FlexPoint Team
If you want to discuss your responses or how to interpret this coaching, we’d love to connect! Please use this scheduling tool to set up time with a FlexPoint leader who can help you navigate next steps.
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