10 Questions Companies Ask Before Starting an AI Project in 2025

Artificial intelligence is no longer just a futuristic concept; it’s a practical tool that businesses of all sizes are incorporating into their workflows. In fact, 77% of companies are currently using or exploring AI solutions, and over 80% consider AI a top strategic priority. However, before launching a large-scale AI project, business leaders often have important questions. No matter what industry you're in: retail, finance, healthcare, logistics, or SaaS. In this blog you can discover the top 10 questions companies ask before starting an AI initiative. Knowing the answers can save you time, money, and headaches, and help you build an AI solution that delivers real value.

1. What business problem are we trying to solve with AI?

Before jumping into algorithms and tools, you need absolute clarity on the problem you want AI to address. Successful AI projects start with a clearly defined business goal or pain point. Are you aiming to automate repetitive tasks, improve prediction accuracy, reduce customer churn, or increase efficiency in a workflow? Be as specific as possible. For example, instead of a vague goal like “use AI in customer service,” define success in business terms: e.g. “reduce customer support response time by 50%” or “cut churn by 20% in the next year.” This ensures the AI project stays aligned with real business needs.

Why it matters: Many AI initiatives fail because they weren’t solving a meaningful problem or had no clear success criteria. Leaders must clearly define the business problem the AI project is expected to solve and the metrics that will measure success. This early step sets the direction for the entire project and prevents wasted effort on “cool” AI tech that doesn’t move the needle.

2. What budget should we allocate for an AI project?

Costs for AI projects can vary dramatically depending on scope and approach. Generally, there are two broad paths:

• Off-the-shelf AI tools: These are ready-made solutions or APIs you can license or subscribe to. They tend to have lower upfront costs (some even have free tiers) but recurring subscription fees. Many off-the-shelf AI platforms cost from a few hundred to a few thousand dollars per year for basic usage, and up to tens of thousands annually for enterprise plans. This makes them budget-friendly for starting out, though costs can scale up with heavy usage (e.g. API calls or user seats).
• Custom AI development: Building a custom AI solution tailored to your needs requires a higher initial investment. Depending on complexity, custom AI projects might range anywhere from tens of thousands to a few hundred thousand dollars (or more) in development costs. One analysis found custom AI development can easily cost $20,000 to $500,000+ for complex solutions. There are also ongoing expenses like maintenance, cloud infrastructure, and expert support. A rule of thumb is to budget an extra 10–20% of the project cost per year for maintenance and updates on a custom system.

Having a realistic budget in mind will guide your strategy. A smaller budget might mean starting with off-the-shelf tools or a limited-scope pilot. A larger budget could justify a custom build if the use case demands it. Remember: budget isn’t just the upfront coding cost – include data preparation, cloud computing costs, vendor fees, and training for your team. It’s wise to get quotes or estimates for different approaches before deciding.

3. Do we have internal talent to manage the project?

AI projects need the right people to drive them. Ask yourself if your team has the expertise and bandwidth to manage an AI initiative. Key roles include a product/project owner to keep things on track, data scientists or ML engineers to build models, data engineers to handle data prep, and IT/DevOps folks to integrate and deploy systems. If you have a CTO or data science team, they might lead the effort. But if not, you may need outside help.

Lack of in-house AI talent is a common challenge – in one Gartner survey, 64% of IT leaders said a talent shortage was the biggest barrier to AI adoption in their organization. Similarly, many companies report difficulty finding people with the right AI skillsets. If you don’t have experienced AI engineers or data scientists on staff, consider partnering with a consulting firm or hiring an AI agency to guide the project. They can provide the specialized expertise (and even project management) to ensure the AI development stays on course.

Tip: Even if you outsource development, assign an internal point person or team to work closely with the vendor. Having internal “product owners” who understand the business goals ensures the project solves the right problem and will help with adoption later. Over time, you can also invest in training your staff or hiring new talent to build up internal AI capabilities for the future.

4. Should we build custom AI or use pre-built tools?

This is a classic question with no one-size-fits-all answer – it depends on your needs, budget, and timeline. Custom AI solutions are developed from scratch (or heavily tailored) to your specific requirements, whereas off-the-shelf AI refers to existing platforms, services, or software that you configure to your needs.

• Custom AI offers tailored results and full control. You can address unique business challenges, integrate deeply with proprietary systems, and differentiate from competitors. However, custom builds come with higher cost and longer development time. They often require significant expertise and can take months to deploy. Choose custom when the use-case is core to your business or highly specialized – e.g. a healthcare diagnostic algorithm or a proprietary recommendation engine – where off-the-shelf tools fall short.
• Off-the-shelf AI offers speed and lower initial cost. These ready-made solutions (from cloud AI APIs to packaged software) can often be deployed in days or weeks, and many have pay-as-you-go or subscription pricing. They are great for standard tasks like generic chatbots, image recognition, or analytics, especially if you want quick wins. The trade-off is limited flexibility – you can only do what the tool allows – and potential vendor lock-in or data sharing with a third-party. Off-the-shelf is ideal to start small or when your need is common (e.g. using a pre-trained vision API to classify images).

In practice, many companies use a hybrid approach: start with off-the-shelf tools to pilot the idea and learn, then consider a custom build later if needed for scale or competitive differentiation. For example, you might use a SaaS AI tool for basic analytics initially, but invest in a custom AI model once you identify a high-value use case that off-the-shelf solutions can’t handle or to own the IP. Always weigh the long-term costs too: what’s cheap now (off-the-shelf) might become expensive at scale (due to usage fees), whereas custom AI has upfront costs but could be more cost-effective over time for heavy workloads. The key is to align the choice with your project’s goals and constraints.

5. What data do we need and is it ready?

AI is only as good as the data behind it. A common saying is “garbage in, garbage out” – if your data is poor quality, your AI’s outputs will be too. So, take a hard look at your data before starting the project. Ask:

• Do we have the right data to solve the problem? For example, if you want an AI to predict customer churn, do you have historical customer interaction and churn data? Identify what data sources are needed (databases, logs, images, etc.).
• Is our data clean and well-prepared? Real-world data is often messy – full of errors, duplicates, missing values, or inconsistent formats. Plan for a data cleaning and labeling phase. Data preparation (cleaning, labeling, standardizing) typically consumes a large portion of effort in AI projects. It’s not glamorous, but it’s absolutely essential for good results.
• Do we need external or additional data? Sometimes your internal data isn’t enough. You might augment it with external datasets or open data (for instance, using public weather data if building a sales forecast model influenced by weather). Ensure you have a way to access any external data or APIs needed, and account for any licensing costs if applicable.
• Do we need to anonymize or secure data? Especially if dealing with personal or sensitive information, consider steps like removing identifiers, aggregating data, or encrypting fields so that you protect privacy while still enabling the analysis.

In summary, audit your data. If it’s not ready, budget time to fix it. Many AI failures trace back to data issues – either not having enough data or using “bad” data. Invest early in data quality; it will pay off in model performance. If you discover gaps (e.g. you don’t have data on a key factor), you might need to collect more data or adjust project scope. It can also be useful to run a quick feasibility test: can a simple prototype model on existing data give signal? This can validate that your data can actually drive the AI to learn the intended pattern.

6. Are there any compliance or data privacy risks?

In many industries, you can’t just move fast and break things when it comes to data: compliance and privacy regulations loom large. Before starting an AI project, identify any legal or policy constraints on the data or the model’s use. Consider:

• Privacy laws: If you operate in regions under data protection laws like the EU’s GDPR or California’s CCPA, using personal data for AI requires strict adherence to those rules. GDPR, for example, mandates consent and purpose limitation for personal data and can impose fines up to 4% of global revenue for violations. Ensure your AI project has a lawful basis to use the data and that you’ve addressed rights like user consent, data anonymization, and the ability to delete data if requested.
• Industry regulations: If you’re in a regulated sector, there may be specific rules. Healthcare projects involving patient data must follow HIPAA in the U.S. for medical privacy. Financial services have regulations around customer financial data and model transparency. HR data may be subject to employment laws and fair hiring practices. Verify any industry-specific compliance needs – e.g. FDA rules for AI in medical devices, or auditing requirements for AI that makes decisions on loans or employment.
• Data governance and security: Even if not externally regulated, it’s wise to follow best practices. Who will have access to the data and model? How will you secure data against breaches? Plan for strong security controls (encryption, access control, monitoring) when handling sensitive data. Also consider if your AI model itself poses any risk (for example, if it could inadvertently reveal sensitive info it was trained on).

Many companies overlook these issues until late in the project, which can lead to costly delays or even project shutdown. Don’t let that be your case. It’s much better to involve your compliance or legal team early to spot red flags. For instance, if using cloud AI services, ensure the cloud provider meets your data residency and security requirements. If your AI will make decisions that impact individuals (like credit approvals), plan for transparency and bias testing to satisfy regulators or ethical guidelines.

Bottom line: Treat compliance as a first-class citizen in AI projects. Mishandling personal data can lead to legal penalties, lost customer trust, and security breaches. Regulations like GDPR aren’t optional – build privacy by design into your AI workflow. It’s cheaper to do it right from the start than to retrofit compliance later or pay fines for mistakes.

7. How long does it take to implement an AI project?

The timeline can range widely – anywhere from a few weeks to over a year, depending on the project’s complexity and scope. Setting realistic expectations up front is important to avoid frustration later. Here’s a rough breakdown:

• Small pilot or proof-of-concept (PoC): Approximately 2–6 weeks. Pilots are limited experiments to validate an idea. For example, training a quick model on a sample of data or integrating a pre-built AI API into a demo app. These can often be done in under 2 months, especially using existing tools or minimal integration. The goal of a pilot is speed – test viability and learn, rather than build a full solution.
• Mid-scale project (MVP or departmental solution): Around 2–4 months. This might involve developing a custom model with some complexity, or integrating an AI tool into one business unit’s workflow. It includes phases like data preparation, model development, testing, and initial deployment. Many moderate projects (say an AI dashboard or a chatbot for one team) fall in the 8–16 week range for an initial usable version.
• Full-scale production system: On the order of 6–12+ months. A large-scale AI implementation (enterprise-wide or mission-critical system) can easily take half a year to a year or more. This accounts for robust development, extensive testing (including edge cases), integration with multiple existing systems, user training, and iterative tuning. For example, rolling out a company-wide predictive maintenance AI or a personalized recommendation engine across a huge e-commerce platform could be a year-long endeavor. Don’t underestimate the time needed for iteration and user acceptance: often the AI model itself might be built in a few months, but integrating it, validating it against business metrics, and getting users comfortable can add extra months.

Keep in mind these timelines are general. Simpler uses of AI (like using a vendor’s API) lean toward the lower end. Pioneering projects or those involving cutting-edge research could take longer than a year. Also, many AI projects start with a pilot phase then scale up – e.g. 1-2 month pilot, then 6-month project to fully implement if pilot is successful. It’s wise to include buffer time for unexpected hurdles (data issues, model re-training, etc.).

The key is: plan a phased approach with milestones. Define what you can deliver in 1 month, 3 months, 6 months, etc. This helps manage stakeholder expectations. In a survey, over 60% of companies reported AI projects took longer than expected, often because they underestimated the time required for data prep or integration. By mapping out the stages (as described in earlier questions) and their timeframes, you can give leadership a realistic roadmap.

8. How do we measure success?

Before you start building, define how you’ll know if the AI project is successful. What key performance indicators (KPIs) or outcomes matter most to your business? Common success metrics include:

• Cost reduction: e.g. “AI automation will save $X per year in operating costs” or “reduce manual work by Y hours per week.” If you’re using AI to automate tasks or improve efficiency, track the time or money saved compared to the old process.
• Revenue or conversion uplift: e.g. “increase conversion rate on our website by 5% through personalized recommendations” or “boost cross-sell/up-sell revenue by $N via AI insights.” For customer-facing AI (marketing, sales, e-commerce), measure its impact on sales or customer value.
• Accuracy/quality improvements: e.g. “achieve at least 90% accuracy in defect detection (versus 70% human accuracy)” or metrics like precision/recall for predictive models. If the AI’s purpose is to make predictions or classifications, track how well it performs (preferably against a baseline of what you had before).
• Customer experience metrics: e.g. “improve customer satisfaction (CSAT) scores by 0.5 points” or “decrease average support response time to under 1 minute via an AI chatbot.” If the AI interacts with users, measure its effect on user satisfaction, retention or NPS.
• Process speed or throughput: e.g. “the AI scheduling system should schedule appointments 3x faster than the manual process,” or “handle 1,000 queries per minute.” For operational improvements, measure changes in speed, volume, or scalability.

Importantly, choose measurable, concrete KPIs and set target values before you start the project. For example, rather than “improve forecasting,” say “reduce error in demand forecasts by 20%.” Having clear success criteria focuses your team and avoids scope creep. It also provides a basis to evaluate the AI’s ROI after deployment.

During the project, track progress against these metrics. If the AI isn’t meeting them, you may need to iterate or even reconsider the approach. In fact, defining success early can also help kill a project if it’s not hitting the mark – which is better than sinking more cost into an idea that isn’t delivering. On the flip side, if you do achieve the targeted KPIs, you’ll have a great case to celebrate and possibly scale the AI solution further.

Pro tip: Include both technical metrics (like model accuracy, latency) and business metrics (like revenue lift, cost savings). An AI model could be 99% accurate but if it doesn’t move a business needle or nobody uses it, it’s not a success. Conversely, even a less-accurate model might be successful if it’s still better than the status quo and drives business value. Define what success looks like in business terms and use that as your north star.

9. What kind of support or training will our team need?

Introducing AI into a business isn’t just a technical deployment – it’s a change management exercise. A top reason AI projects fail is lack of user adoption and understanding. One study found 67% of marketers saw lack of AI education and training as their biggest adoption challenge. In a broader sense, successful AI projects are 70% about people and process, and only 30% about technology, according to experts. So, you need to plan for how your team will embrace and effectively use the new AI tools.

Consider these support elements:

• Training sessions: Provide hands-on training for the end-users of the AI system. This could be as simple as instructing customer service reps on how to interpret and trust AI-driven recommendations, or teaching analysts how to use a new AI-powered analytics dashboard. Don’t assume people will just “figure it out.” Target training to the audience – some may need basic AI concept education (to build trust and fluency), others need step-by-step on using the software.
• Documentation & easy UI: Ensure there are user guides, FAQs, or tooltips in the interface to help users. If the AI tool is internal, consider a simple cheat-sheet or intranet page about it. Also invest in a user-friendly interface; complex AI tech should be hidden behind a clean, intuitive UI so that employees can use it without frustration.
• Internal champions: Identify power users or stakeholders who are enthusiastic about the AI project and can champion it among their peers. These champions can provide peer-to-peer support, gather feedback, and demonstrate the value of the tool. Having a respected colleague vouch for the AI’s usefulness can overcome skepticism on the front lines.
• Iterative feedback and improvement: Treat the deployment phase as the beginning of another cycle. Provide channels for users to give feedback or report issues. Maybe set up a regular check-in meeting or an online form where users can suggest improvements. This not only helps refine the AI system, but also makes users feel heard and invested in its success.

Remember, the best AI solution is useless if your team doesn’t actually use it. Many AI projects flop because the employees find it too confusing, don’t trust its outputs, or feel threatened by it. To address AI hesitancy, communicate clearly why the AI is being introduced (e.g. to assist them, not replace them, in their job) and how it will make their work easier. Provide reassurance about any job security concerns if relevant (for instance, emphasize that automating tedious tasks frees them to do higher-value work).

A RAND Corporation report noted that 80% of AI projects fail, largely due to human and organizational factors rather than the tech itself. Investing in training and change management is how you beat those odds. So plan and budget for the “people side” of AI adoption – it’s not an optional nice-to-have, but a critical success factor.

10. Who can help us do this right?

Given the complexity of AI and the thousands of AI vendors and consultants out there, companies often wonder how to find the right partner for their specific needs. The reality is that choosing the right AI partner or solution provider is crucial – the wrong one can lead to wasted time and money, while the right one will accelerate your project’s success.

Here’s how to approach it:

• Do your research: Look for vendors or agencies that have experience both in the AI technology and in your industry or use-case. If you’re a retailer wanting a recommendation engine, an AI firm with e-commerce experience might be ideal. Check case studies or client references. Don’t be afraid to ask potential vendors for examples of similar projects they’ve done or for success metrics they’ve achieved.
• Evaluate multiple options: Don’t just go with the first AI consultant that appears. It’s wise to evaluate a few. This could include big-name consulting firms, specialty AI boutiques, or even independent freelancers, depending on your project’s scale. Compare their proposals on approach, timeline, and costs. Ensure they understand your business problem (red flag if they jump to proposing a solution without listening deeply first).
• Consider match-making services: A newer approach is using an AI project matchmaking service. For example, AiTopMatch (AiTopMatch.com) is a platform that connects companies with vetted AI agencies/experts based on your specific goals, industry, budget, etc. Instead of you spending weeks searching and vetting, such services do the legwork to find a shortlist of ideal partners. They often can save you time and ensure you get a provider that’s a good fit. (As always, still do your own due diligence on any match recommended).
• Look for a collaborative partner: The best AI vendors act like partners, not just contractors. They should be asking as many questions of you as you of them. A good partner will help refine your requirements, be realistic about what’s feasible, and maybe even save you money by pointing you to simpler solutions if appropriate. They should also be transparent about how they work (methodologies, tools, IP ownership, post-project support, etc.). Chemistry matters too – you’ll be working closely together, so choose a team that you feel “gets it” and communicates well with yours.

In summary, you don’t have to go it alone. If you’re not sure how to start or lack certain capabilities, finding the right external help can make all the difference. As one AI startup founder put it, businesses know AI is the next big shift but many find it confusing and out-of-reach – which is why services like AiTopMatch aim to make it easier by matching companies to the right experts quickly. Whether you use a match service or not, take the selection of your AI partner seriously. The goal is to bring in the expertise you need while ensuring the solution aligns with your business and is delivered on time and on budget.

Ready to bring AI into your business? By answering these ten questions, you’ll be well-prepared to embark on an AI project with eyes wide open. Starting an AI initiative is indeed a significant undertaking – but with a clear problem definition, realistic budget and timeline, strong data foundation, compliance checks, success metrics, team readiness, and the right partners, you dramatically increase your odds of a successful outcome. Instead of wandering the AI landscape alone or risking weeks trying to find the perfect agency, you can leverage the insights above (and platforms like AiTopMatch for vendor selection) to jumpstart your AI journey. With the right planning and support, you can build an AI solution that genuinely delivers on its promise and drives your business forward in 2025 and beyond.

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