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AI Roadmap 101
A step-by-step guide to creating a winning AI strategy
Peter Benei & Torsten Sandor
February 19, 2025
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Table of Contents
Introduction
Why AI strategy is mandatory for scaleups
AI is no longer a luxury reserved for tech giants – it’s becoming a must-have strategy for small and medium-sized businesses (scaleups). Recent advances (like the emergence of tools such as ChatGPT) have made AI more accessible, enabling even lean teams to leverage capabilities that once required large R&D budgets.*
In fact, 88% of scaleups have implemented at least one AI system in their operations.* This rapid adoption is driven by a clear message from the market: ignore AI at your peril. More than 80% of scaleup leaders worry that neglecting AI could hurt their competitive position* and nearly 49% admit that falling behind on AI will leave them at a disadvantage compared to rivals.*
Why is AI so critical for scaleups? In a word, opportunity. AI can automate repetitive tasks, uncover insights in data, personalize customer experiences, and drive efficiencies that directly impact the bottom line. 87% of scaleups believe AI offers long-term benefits, and 74% say AI investments help meet their business objectives in areas like operational efficiency and customer engagement.*
In other words, when done right, AI can be a great equalizer, allowing nimble scaleups to punch above their weight and compete with larger players. Unsurprisingly, 72% of scaleups are confident AI can help achieve their business goals within a year.*
However, adopting AI is not as simple as installing new software. The real challenge is strategic: many small businesses dabble in AI without a clear roadmap, leading to siloed projects, wasted budget, or even security risks. A solid AI strategy ensures that each AI initiative aligns with your business goals, that your team is prepared, and that you can maximize ROI rather than chasing hype.
This introduction sets the stage for why having an AI roadmap matters. In the following sections, we’ll explore the common pitfalls when there’s an “AI strategy gap” and then provide a step-by-step approach to build a winning AI roadmap.
We’ll also review recent data and trends (with up-to-date statistics from the past year) to ground our insights in reality and highlight practical tools and frameworks to help you implement AI successfully. By the end, you should understand how to navigate AI adoption strategically – turning uncertainty into a plan for AI-powered success for your scaleup.
The AI strategy gap
Common pitfalls and their consequences
Many scaleups recognize AI’s potential but struggle with a strategy gap – the difference between experimenting with AI and strategically implementing it.
Let’s first identify what often goes wrong when there isn’t a clear AI roadmap:
Lack of clear strategic planning
Over half of small and medium-sized businesses have no formal discussion or plan for AI.
According to a mid-2024 survey, 55% of scaleups are not formally discussing AI’s strategic implications at the leadership level.*
AI initiatives (if any) are ad-hoc, driven by trend-chasing or individual departments, rather than a cohesive strategy.
The consequence? Disconnected projects that don’t tie into core business objectives. For example, a company might deploy a chatbot on their website because “everyone is doing it.” Still, it provides little value without integrating it into their customer service workflow or CRM system.
Skills and knowledge gaps
A successful AI program requires the right expertise – data scientists, AI engineers, or at least staff trained in using AI tools.
Yet, 62% of business owners admit to a shortage of in-house AI expertise, and 69% of scaleups respondents had no AI-related training or education in the past year.*
This skills gap can lead to stalled projects or misuse of AI. Employees might not understand how to interpret AI outputs or maintain AI systems. In practice, we see many small firms piloting an AI tool, only to abandon it because the team wasn’t trained to use it fully. Without addressing this, even the best AI tech can underperform.
Fear, uncertainty, and FOMO
Leaders are often caught between fear and FOMO (fear of missing out) when it comes to AI.
On one hand, there’s pressure not to fall behind – research in late 2024 found 40% of SMBs worry they must adopt AI quickly or miss out on opportunities.*
On the other hand, nearly 20% are anxious that they might be adopting AI too early – before the technology is stable or proven.*
Over half (54%) of SMB respondents are cautious about early AI adoption* and concerned about AI’s “unknown future.” This divide can paralyze decision-making: some companies rush into AI projects without proper planning (just to “keep up”), while others delay beneficial projects due to analysis paralysis.
Both extremes are risky. Rushing in without a strategy can lead to wasted investment; excessive delay means missed opportunities and competitive lag.
Uncoordinated, bottom-up adoption
In many organizations, AI tools are creeping in from the bottom up – employees sign up for free AI tools or use ChatGPT on their own to boost productivity.
A recent 2024 Microsoft/LinkedIn study found 75% of knowledge workers are now using generative AI tools like ChatGPT at work, and strikingly 78% of those folks brought these AI tools into the workplace on their own (so-called “BYOAI”).*
This organic adoption shows employees see value in AI, but if management isn’t aware or involved, it creates governance problems.
In fact, most workers who use AI admitted they’re reluctant to tell their bosses– often due to a lack of guidance or fear of how it’ll be perceived. The consequence is that company data might go into unmanaged tools, posing potential security or privacy risks.
Without a clear strategy and IT oversight, sensitive information could be exposed, and the organization misses the chance to capture and scale these AI-driven productivity gains officially.
Misaligned projects and wasted effort
Without an overarching strategy, AI projects can easily target the wrong problems. One common pitfall is implementing AI where it’s flashy rather than where it’s truly impactful for the business. For example, a scaleup might invest in an advanced predictive analytics tool but fail to have the data quality or volume needed to make it work – a mismatch that a strategic assessment would have caught. It’s no surprise that integration woes are common.
In North America, 38% of SMBs cite integration with existing systems as a top AI challenge, and 33% cite a lack of in-house expertise as a hurdle.*
These issues often surface late if no upfront strategy is addressed. Industry analysts have noted that many AI projects never move past the pilot stage due to misalignments. (One global study from S&P even pointed out that many AI projects fail to scale beyond proof-of-concept due to legacy data systems and unclear strategy.)
The cost of these stalled projects – in time, money, and morale – is high for a resource-constrained scaleup.
Security, privacy, and ethical risks
An AI strategy gap also means important policies can be overlooked. AI systems often need a lot of data – some of it sensitive (customer information, personal data, etc.). If SMBs jump in without a data governance plan, they could violate privacy laws or their own customers’ trust.
Notably, 40% of scaleups are concerned about data privacy and security when integrating AI, which indicates many know it’s an issue but may not know how to address it.*
Similarly, using AI without understanding it can lead to biased outcomes or compliance issues (for example, an AI making hiring recommendations that inadvertently discriminate). Without a strategy that includes ethical guidelines and risk management, scaleups expose themselves to legal and reputational consequences.
What are the consequences of these pitfalls?
In short: underwhelming results. Businesses may invest in AI subscriptions or software and see little ROI because it wasn’t aligned to a real need or because staff won’t use it.
They may get excited by initial AI pilots but fail to scale them (the phenomenon of “pilot purgatory”). Even worse, they might face backlash if an AI initiative causes an error or controversy – for instance, an automated marketing email that goes awry due to improper AI handling of customer data.
This can make leadership more skeptical of AI, further stalling innovation.
The good news is that these pitfalls are avoidable.
The very awareness of an “AI strategy gap” is the first step to closing it. Next, we’ll dive into how to proactively build a clear AI roadmap – one that ensures your AI investments hit the mark and drive real business value.
Your AI roadmap, step-by-step
STEP 1 - Start with your business goals
Scaleup leaders need a clear roadmap for AI adoption to overcome the strategy gap. Think of this as a strategic plan that aligns AI initiatives with your business goals, resources, and timeline.
Below is a step-by-step tactical approach to building an AI roadmap that can guide your organization from initial idea to successful implementation. Each step is designed to be practical and actionable.
Start with the business goals, not the technology.
Ask: What do we want to achieve with AI? Identify 2-3 key objectives where AI could make a big impact. For example, your goals might be to improve customer acquisition, reduce operational costs, or increase production capacity.
Ground this in pain points or opportunities you’ve observed. If customer service is slow, maybe AI (like chatbots or automated routing) can improve responsiveness; if sales leads are slipping through the cracks, maybe AI can help prioritize or follow up.
The idea is to ensure any AI project directly supports a business outcome (revenue growth, efficiency, customer satisfaction, etc.). Clarity here will keep your roadmap focused.
It’s worth noting that scaleups see a variety of potential wins from AI – common aims include adapting faster to market changes, cited by 53% of SMBs, cutting operational costs (51%), and expanding into new markets (41%).*
You don’t have to chase every benefit at once; pick what matters most for your strategy. Establish measurable targets for these objectives (e.g. “reduce customer support response time by 50%” or “increase sales leads by 20%”).
These will become your North Star for AI initiatives.
STEP 2 - Assess your AI readiness
Before jumping into solutions, take stock of where you stand.
This readiness assessment covers:
Data: Inventory the data you have – customer data, sales data, operational data, etc. Is it collected and stored in a usable format? Is it clean and consistent? Data is the fuel for AI. If you find gaps (for instance, you have website traffic data but no sales conversion data linked to it), you might need to improve data collection or quality first. Also consider data volume and privacy – do you have enough data to train an AI model, and do you have the rights/consent to use it? Data can be siloed or incomplete. It’s telling that inadequate infrastructure or data environment is a top obstacle – 42% of SMBs say their infrastructure isn’t sufficient to fully leverage AI.* As part of readiness, evaluate your IT infrastructure. Can your current systems integrate AI tools?
People: Gauge the skill level and capacity of your team. Do you have anyone on staff with data science or machine learning experience? Even if not, do you have tech-savvy team members who can be trained? Identify internal champions – people excited about AI who can help drive projects. Skills gap is a common problem – recall that one-quarter of scaleups report lack of skilled staff as a major barrier to AI.* If that’s your case, your roadmap might include a plan to hire or partner for these skills. Upskilling existing employees is another avenue.
Processes and Culture: Consider how ready your organization’s culture is for AI-driven change. Are your teams open to adopting new tools and workflows? Is there fear that “AI might replace jobs” that you must proactively address? Also, review your current processes to see where AI could fit in smoothly. If a process is chaotic or not well-defined, fixing the process might be a prerequisite before automating parts of it with AI.
Budget and Resources: Finally, assess your budget for AI initiatives. AI solutions range from very affordable (even free trials) to significant investments. Knowing your budget range will help prioritize solutions. Remember to budget for software, training, potential new hires or consulting, and any necessary data infrastructure.
By the end of the readiness assessment, you should have a clear picture of strengths (e.g. “we have lots of customer data to leverage”) and gaps (e.g. “we need cloud storage” or “no one on our team knows about machine learning”).
This will inform you of the next steps in your roadmap. It’s better to address gaps early – for example, if you lack expertise, plan to bring in an expert or consultant at least for initial guidance.
STEP 3 - Identify high-impact use cases
With your objectives defined and a clear view of your capabilities, brainstorm specific use cases for AI in your business.
A use case is a specific problem or task that AI could improve. For instance: “Use AI to automatically qualify leads from our website” or “Use image recognition AI to manage inventory from product photos”.
Engage your tech team and your business units in this brainstorming. Often, the best ideas come from the pains your team experiences daily. Your marketing team might suggest “AI to personalize email campaigns” while your customer support might suggest “a chatbot to answer common FAQs.”
List out these ideas, then prioritize them based on impact and feasibility. A practical method is to score each use case on two dimensions: Business impact (e.g. revenue potential, cost savings, customer satisfaction) and Implementation feasibility (e.g. data availability, technical complexity, cost/time required).
Plotting use cases on an impact-feasibility matrix can highlight “quick wins” – high impact, relatively easy projects – which are great starting points. For example, automating an internal manual task with AI might be highly feasible and save a lot of employee hours (impact), whereas building a sophisticated AI-driven product recommendation engine might have high impact on sales but also high complexity if you lack data on customer behavior.
Start with one or two use cases that score well on both impact and ease. These will form the first projects on your roadmap, allowing you to build momentum. Keep the more ambitious ideas in the backlog for later once you have more experience and possibly more data/resources.
This step ensures you focus on AI projects that matter most to your business and are doable with your current resources. It also helps communicate to stakeholders why you’re choosing these projects – because they promise real value.
STEP 4 - Secure buy-in and allocate resources
Any strategic initiative needs support from the top. As a leader, you might already be the sponsor of this AI roadmap, but ensure that all key stakeholders are on board. This includes other executives/owners, and managers of departments that will be affected. Present the vision, the opportunities, and the prioritized use cases you identified.
When leadership and teams understand the why, they are more likely to support the how.
Next, clear ownership of the AI initiative will be assigned. Decide who will lead the project (e.g., an “AI project manager” or a committee). In a small business, this might be an existing IT manager or an enthusiastic department head who is tech-savvy.
Define roles:
Who will handle data preparation?
Who will interact with any vendors or consultants?
Who will oversee training employees on the new AI tool?
It’s also important at this stage to allocate budget and time. Even quick-win projects require some investment of hours and, possibly, software fees. Treat it like any other project with a dedicated (even if small) budget and timeline.
Also plan for external help if identified in the readiness step – this could mean earmarking funds for a part-time data science consultant or partnering with a vendor.
Remember, seeking outside help is not a sign of weakness. It’s often a smart shortcut to getting things right the first time. SMB survey data shows a trend of leveraging external experts: e.g., North American SMBs often work with providers for technical integration (68%) and training support (53%) in their AI journeys.*
Finally, establish governance at this point. Set some ground rules or a framework for evaluating and using AI. For instance, you might create a small steering committee to review progress, address ethical considerations, and ensure the project stays aligned with business goals. Governance might include deciding on policies (like “we will not use customer personal data in AI without consent” or “we will have a human review AI-driven decisions in hiring”).
Laying this groundwork now will pay off as you execute the roadmap.
STEP 5 - Pilot a project and prototype
With a priority use case selected, it’s time to start small and learn. Develop a pilot project around that use case. The goal of a pilot (or proof-of-concept) is to implement the AI solution on a limited scale to validate that it works and delivers value before rolling it out company-wide.
For example, if your use case is “AI to qualify sales leads,” your pilot might use an AI tool on a subset of incoming leads for one quarter, while the rest are handled typically to compare outcomes.
When building your pilot, leverage existing tools and services to move faster. You generally have three options: build, buy, or tailor.
Building an AI solution from scratch (coding your own machine learning models) is the most complex and usually unnecessary for most use cases, given the many AI services available. Unless you have strong technical talent in-house, consider using pre-built solutions or platforms: for instance, use a chatbot platform to create a customer service bot rather than developing NLP algorithms in-house, or use your CRM’s AI features (Salesforce’s Einstein, HubSpot’s AI, etc.) to score leads instead of coding a custom model.
The good news is that many business software tools now have AI capabilities baked in – and two-thirds of scaleups say it’s important that vendors provide AI in their solutions. This means you might already have access to some AI features in your software.
For the pilot, define what success looks like (your KPIs from step 1). Maybe it’s reducing the time to handle a support ticket by 30% or increasing sales conversions from AI-qualified leads by 15%.
These metrics will guide you. Implement the solution on a small scale and monitor results closely. Keep the duration short and focused – pilots are often 4-12 weeks. Importantly, gather feedback from the team using it. If it’s an internal AI tool, do employees find it helpful? If it’s customer-facing, are customers reacting positively?
Pilots are as much about organizational learning as technical validation. Expect some hiccups – maybe the AI isn’t as accurate initially, or integration with your database needs tweaking. That’s normal. Use this phase to iterate: adjust parameters, provide more training data, or refine the process around the AI tool.
For example, if a marketing content AI is generating ok results, you might learn it needs more guidance or a narrower scope – that insight is gold before a wider rollout. Also, at the pilot stage, keep communication open. Let the broader team know this is a test, manage expectations (AI is not magic; it’s a tool to improve things gradually), and celebrate early wins (like “AI helped us answer customer chats 2x faster this month!”).
Quick wins build buy-in. By the end of the pilot, you should have evidence of whether the use case delivers the expected value and if any adjustments are needed.
STEP 6 - Evaluate results and plan scaling
After the pilot period, step back and measure the outcomes against the success criteria you set. Did the AI solution meet or exceed the key performance indicators (KPIs)?
For instance, how many hours of manual work did the AI automate per week if the goal was to save time? Calculate the impact. Perhaps your pilot chatbot handled 500 customer queries, resolving 400 of them without human help – that might equate to, say, 100 hours of support staff time saved in a month. (This can be translated into cost savings too; more on measuring ROI in the next section.) Also, assess qualitative feedback:
Are users comfortable with the tool?
Did it integrate well into workflows?
Identify any shortcomings: maybe accuracy needs to be higher, or employees need more training to use the AI effectively. The pilot may reveal that the use case isn’t as beneficial as hoped – that’s okay. Better to know now. You might tweak the approach or even decide not to pursue that project further. On the other hand, if the pilot shows strong results, it’s time to formulate a scaling plan.
Scaling could mean rolling the AI tool out to all users (e.g., all customer support reps will use the chatbot or all incoming leads will go through the AI scoring system). Ensure you have the infrastructure to support a larger scale (if you were on a trial account, you might need a full subscription; if you used sample data, now connect to live full databases).
Also, address any issues found in the pilot before full deployment. For example, if the pilot showed the AI struggled with a certain category of data, consider retraining the model with more data of that type or put guardrails in place (like routing those special cases to humans). Update your internal processes and documentation to include the AI system: e.g., create a standard operating procedure for sales reps to review the AI-generated lead scores each morning or guidelines for support agents on when to let the bot handle an inquiry versus intervene.
Essentially, integrate the AI solution into business as usual. This might involve minor reorg or role adjustments (maybe your data analyst now spends time monitoring the AI’s performance each week). In your roadmap timeline, this stage is where the pilot graduates to an official project. Schedule training sessions for a wider group of users if needed so everyone is up to speed.
Communicate the results of the pilot to the broader company – showing the ROI will bolster support as you scale. For instance, sharing that “Our AI pilot improved upsell rates by 20%* or “We saved 10 hours/week on invoice processing” helps reinforce why scaling up is valuable.
STEP 7 - Ongoing training and change management
Adopting AI is not a one-off event. It’s an ongoing journey.
As you roll out AI solutions at scale, invest in training and change management to ensure long-term success.
First, training: make sure current and new employees know how to use the AI tools properly. This could be as simple as a one-hour workshop or an online tutorial for a software tool. Remember that comfort with AI varies – some team members may be power users, and others might be intimidated. Foster an environment where people can ask questions and share tips. You might establish internal “AI champions” or super-users who can help their peers in each department.
Also, educate staff on basic AI concepts relevant to the tools (for example, if using a machine learning forecasting tool, explain in simple terms how it works and its limitations, so users have the right expectations). Training isn’t just technical; it also means setting guidelines for using AI responsibly. If employees use generative AI (like content creation tools), you might train them on your company’s quality standards and review processes to vet AI-generated outputs.
Next, change management: any new technology can face resistance. Communicate clearly how the AI fits into roles. Emphasize that these tools assist, not replace – for example, “The AI system will take care of the data entry, allowing you to focus on engaging with customers.”
Acknowledge fears and address them. It can be helpful to share success stories or testimonials from team members who have benefited (“This new AI-powered report used to take me 3 hours to compile, now I get it with one click – I can use that time to analyze the insights instead,” says your Marketing Manager).
Celebrate milestones as the AI roadmap progresses – treat the first big win as a company win (e.g., when data shows the AI initiative increased revenue or saved costs). This positive reinforcement encourages adoption. Additionally, update your performance metrics or incentives if needed to align with AI-enhanced workflows (for instance, if AI lead scores now aid sales reps, you might start tracking how quickly they follow up on high-score leads as a performance metric).
The key is integrating AI into the fabric of work and keeping the team engaged and on board. Plan for periodic refresher trainings or onboarding sessions for new hires so that knowledge stays current.
STEP 8 - Monitor, measure and evolve
An AI roadmap isn’t static. The final step is about establishing a cycle of continuous improvement. Once your AI solutions are in place, set up a process to monitor their performance regularly. This includes technical performance (Is the model accuracy holding up? Are response times fast enough?) and business performance (Are we still seeing the cost savings or growth initially observed? Is usage of the tool by staff consistent?).
Many AI models can drift over time – for example, if the data pattern changes (say, consumer behavior shifts), a predictive model may need re-training to stay accurate. Schedule periodic reviews of the AI models and track your ROI metrics over time. If the AI isn’t delivering as expected, investigate why: maybe users found a workaround or stopped using it (a sign of a UX issue or lack of trust), or external conditions changed. Also stay alert to new AI trends and opportunities.
The AI field is evolving rapidly – what wasn’t possible or affordable for scaleups a year ago might be now. For instance, a year ago, large language models were just emerging; now they are widely accessible and can be fine-tuned for custom tasks relatively cheaply. Make it part of someone’s role (perhaps the AI project lead or an innovation committee) to scan the landscape for relevant new AI tools or updates that could enhance your deployed solutions or enable new use cases. At the same time, watch out for new risks (new regulations on AI usage, for example, or new competitive moves – if a competitor rolls out an AI-powered service, you might need to respond).
Based on your observations, update your AI roadmap annually (if not more often). This could mean adding new projects (maybe after successfully implementing two use cases, you’re ready to tackle a third, more ambitious one) or adjusting course. Perhaps you planned to build a custom AI solution in year 2, but now a SaaS product offers that capability at a lower cost – you might pivot to using the off-the-shelf product. Keep involving stakeholders in these evolutions so that everyone remains aligned. Essentially, treat your AI strategy as a living document. Many scaleups that are successful with AI adopt an agile mindset – implement, learn, tweak, and scale up successes.
By following these steps, you develop a strategic yet flexible AI roadmap. It ensures you start with a strong foundation (clear goals and readiness), achieve some early wins (through focused pilots of high-impact use cases), and build on those wins in a controlled, value-driven way. It’s a journey of incremental adoption guided by a big-picture plan.
Always remember: the roadmap is there to serve your business strategy. Regularly ask, are our AI efforts still aligned with what our business needs? If yes, you’re on the course to AI success. If not, use the roadmap process to recalibrate.
With this approach, even smaller organizations can navigate AI adoption confidently, avoiding common pitfalls and capitalizing on AI’s transformative potential.
Practical tools and frameworks
The people-process-technology framework
One simple way to ensure you cover all bases is to plan around People, Process, and Technology. This framework is often used in digital transformation projects and applies well to AI.
PEOPLE
Plan for the human side of AI. Identify roles and responsibilities. Map out a training plan for employees. Plan for any change management – how you communicate changes to the team and get buy-in. For example, if introducing an AI assistant for your sales team, decide how you’ll train the sales reps to use it and how you’ll gather their feedback. Employee training is crucial: more than half of employees using AI received no training on its risks or proper use, a gap you can proactively close with this framework. Make sure to include an AI usage policy so people know the guidelines.
PROCESS
Integrate AI into your business processes. Redesigning workflows, if necessary, to incorporate the AI tool. Document the new workflow step-by-step. For instance, if you add an AI-driven lead scoring system, your new sales process might be: Lead comes in —> AI scores the lead —>
If score > 80, send to sales rep, if < 80, nurture via marketing. Consider process metrics to monitor. Many scaleups use a pilot process framework like “Crawl-Walk-Run” for AI: Crawl (manual + AI assist) – initially, run the AI in parallel with human oversight to validate; Walk (automate partially) – let AI handle parts of the process autonomously, with humans managing exceptions; Run (automate fully) – integrate AI deeply once it’s proven. This phased approach is a process framework to increase AI’s role gradually.
TECHNOLOGY
Choose the right tools and ensure they fit your tech stack. This includes deciding on build vs. buy, selecting vendors, and planning integration. Make use of practical AI tools that don’t require you to reinvent the wheel. If you need conversational AI, consider using an existing platform rather than building a chatbot from scratch. If you need predictive analytics, look at AutoML tools from Google or Microsoft that allow you to feed data and get a model without heavy coding. There are also many no-code or low-code AI tools emerging that let you create AI-driven apps with drag-and-drop interfaces. Ensure that whatever tool you choose can connect with your existing systems. Plan for data storage and computing needs: if you’ll be processing large datasets or running machine learning models, consider using cloud infrastructure, which can scale with your needs.
Use-case prioritization matrix
We mentioned prioritizing use cases in the roadmap section. Here’s a tool to do it.
Create a simple matrix (even on a whiteboard or spreadsheet) plotting Impact on one axis and Feasibility on the other. List your potential AI projects as dots on this grid.
To determine impact, ask questions like:
How much could this save us or increase revenue?
Will it improve customer experience significantly?
To determine feasibility, consider:
Do we have the data for this?
Is there an off-the-shelf solution we can use?
How complex would it be to implement, given our skills?
Once plotted, focus on the High-Impact, High-Feasibility quadrant – those projects are your low-hanging fruit.
High-impact but low-feasible projects might go on a longer-term plan (perhaps you’ll tackle those once you’ve built up more capability or if technology becomes more accessible).
This matrix brings objectivity to deciding where to start and is a great visual to share with stakeholders about why you’re choosing specific projects first. It prevents the trap of going after something just because it’s flashy; you want measurable impact.
AI project charter
It’s helpful to create a one-page project charter for each AI initiative. This is a framework borrowed from project management.
It should concisely state:
Objective - What are we trying to achieve, e.g., “reduce customer churn by predicting and intervening with at-risk customers using AI”
Scope - What’s included or excluded, e.g., “focus on online customers, excluding in-store for now”)
Timeline & Milestones - E.g., pilot by Q2, full rollout by Q3)
Team & Responsibilities - Who’s the project lead, supporting members, any external partners)
Success Metrics - KPIs like churn rate reduction or ROI targets
Risks/Assumptions - E.g., assumes we have 2 years of customer data to train a model; risk that data may be incomplete)
Writing this down for each project forces clarity. You can use it internally to keep everyone aligned and ensure you think through the core aspects before diving in. It’s much easier (and cheaper) to tweak a plan on paper than during development.
If you engage a consultant or vendor, sharing this kind of charter with them will help them understand your needs quickly.
AI implementation tips
Start small, then scale (crawl-walk-run approach)
We can’t emphasize enough the value of this approach. Frame every AI adoption in phases:
Crawl – implement in one area or a limited way;
Walk – expand usage and integrate more;
Run – scale up and optimize.
If introducing AI for customer support, crawl might be a bot that answers only after hours for a few common questions; walk might be a 24/7 bot for all FAQs with handoff to humans; run might be a bot handling complex inquiries with an AI analyzing sentiment and guiding the support rep. By breaking it up, you reduce risk and learn along the way. Plus, this makes budgeting easier – you’re not committing a considerable sum upfront, instead investing gradually as results prove themselves.
Documentation and knowledge sharing
Treat your AI tools like any other necessary business process – document how to use them. Create simple user guides or SOPs for tasks involving AI. For instance, if you implemented an AI forecasting tool for inventory, write a short guide: “How to run the forecast, what the outputs mean, what decisions to make, or who to inform with the results.” This helps if you have new hires – they have reference material. Encourage knowledge-sharing where employees share tips or use cases of AI. Some companies have an internal newsletter or Slack channel for “AI tips” – e.g., someone discovers a great prompt to use with ChatGPT to summarize weekly reports and shares it with the team. This grassroots sharing can significantly improve overall AI literacy and usage in the company.
Regular review & refinement workshops
Set a cadence (perhaps quarterly or biannually) to formally review your AI initiatives’ performance and brainstorm improvements or next steps. Think of it as an AI strategy retro & planning session. Include cross-functional leaders: review KPIs from AI projects, ROI achieved, issues encountered, and new AI developments in your industry. This is where you update your roadmap. It’s also an opportunity to consider new ideas: e.g., “Now that we have achieved X with AI, what if we applied AI to Y area of the business next?” If feasible, you can also invite employee input via an anonymous suggestion form or in the meeting. Some of the most innovative ideas can come from frontline employees who see daily tasks that could be optimized. That’s how many AI use cases in scaleups surface – the staff doing the work think “there must be a better way,” and nowadays, often, the better way could involve AI.
Next steps
If you’re excited about the possibilities of AI but unsure where to begin or how to accelerate your progress, you don’t have to figure it all out alone.
This is where expert guidance can make a difference. At Horizon 01, we specialize in helping scaleups like yours develop and execute winning AI strategies.
From initial readiness assessments and roadmap development to selecting the right tools and managing implementation, we have the experience to guide you at each stage of your journey. We understand your business's unique constraints and opportunities, and we’re passionate about making AI an accessible, game-changing tool for your growth.
Interested in taking the next step? Reach out to Horizon 01 for a consultation. We can discuss your business goals and challenges and provide tailored insights on how AI can drive success in your context. Whether you need a full end-to-end strategy or just a second opinion on a pilot plan, we’re here to help you chart the optimal course.
Contact Horizon 01 at [email protected] to kickstart your AI journey. Let’s turn the ideas and frameworks you’ve read about into action for your business.
Appendix
Charts, figures, and sources
Pax8 & Channelnomics: AI buying trends report - source
WSI: 2024 AI business insights survey - source
BuilderAI: AI integration is SMBs study - source
Microsoft: Work trend index - source
Salesforce: SMB trends report - source