7 best practices for product teams to consider when building with AI

This guide examines the best practices for product teams to consider when building with AI and to accelerate the path to deployment.

7 best practices for product teams to consider when building with AI

AI research is powering some of today’s most innovative technologies. Not surprisingly, AI-first companies (where AI is integral to the company’s product or platform), are increasingly coming to market and outstripping their competition.

Product-led growth companies are also taking advantage of this enormous opportunity by embedding AI into powerful features that increase adoption and drive growth. ​

More than 1 in 4 dollars invested in U.S.-based startups in 2023, for example, has gone to an AI-related company, more than doubling from the previous year. AI avatar startup Synthesia raised a $90M series C at a $1.0B valuation while AI research company OpenAI raised $300M at a $29.0B valuation.

As companies race to take advantage of these new opportunities for AI-powered products, you may be thinking about how to best embed the right AI into your product roadmap.

How to embed and integrate AI into your product roadmap

The following guide examines seven best practices to follow when integrating AI into a product roadmap so you can accelerate the path to deployment.

1. Focus on user value

The first step is to look at the value AI will bring to your customers. Taking time to examine and clearly define the problem you'd like to address—in the context of your business—will help you set clear, actionable goals. ​

Here is a primary question to consider:
• What customer problems do you hope to solve with the help of AI? For example, will embedding an AI model into a new feature increase process efficiency for your customers? Will you be able to use an AI model to create a feature that helps your customers meet stringent compliance and regulatory requirements?

Identify user value, so you can choose the right AI model for your use case and optimize an effective work stream to incorporate the right features.

2. Set a measurable goal

Once you’ve defined user value, it’s time to set a measurable goal to ensure the intended outcome is met. For example, you may want the AI model to reduce customer service call time by 10%. Or to increase the prediction accuracy of your conversation intelligence feature by 20%. 

If you’re having trouble defining the goal clearly, consider these questions:

  • What outcome do you hope your AI-powered product will deliver? Will it provide more accurate data? Will it save time?
  • Will the main benefit be internal or external-facing?
  • What benefit will your customers gain?

3. Create an action plan to manage data

If you are planning to develop an AI solution from scratch or customize a pre-existing one, collecting and cleaning up data is an important step in the process toward integration and deployment. The data you collect needs to closely align with your goal and be able to efficiently scale with the use of your AI-powered product.​

You also need to consider how much data you’ll need and where to source that data from.

Questions to consider include:
• Will adding more data make the model stronger or will a lesser amount of data be sufficient to meet your project objective?
• What are the costs associated with data sourcing?
• If you choose to scrape data from the product itself, make sure you also investigate any potential regulatory or compliance issues.

Finally, decide if managing this data internally or externally makes more sense for your company and use case. Sourcing and managing data can be time and cost-intensive, so unless you have the dedicated internal resources, it may make sense to outsource the work.

When thinking about data, keep these key considerations in mind:

  • Data type
  • Data size
  • Data source
  • Data Frequency
  • Data costs
  • Privacy or compliance concerns

4. Identify the best AI model for your use case

Not all AI models will make sense for your use case. Consider your user value, defined previously, and your goals to guide you here. The list at the end of this section describes some common tasks, which will work best with different architectures or models. Make sure to choose the model that works best for your task.​

When choosing an AI model, consider if you want to build your model in-house or source it from a third-party partner. While building in-house may seem like it offers more opportunities for customization, the process can be labor and cost-intensive and require a lot of specialized expertise most teams don’t have on hand. If you do have the internal expertise, do you need to pull developers and engineers off of other high-priority projects? If you want to hire an AI expert for your team, sourcing this talent can be difficult given the small pool of highly qualified talent.

You will also need to consider ongoing maintenance if you choose to build in-house. Can you support ongoing maintenance internally once deployed? Can you ensure the model will be continuously state-of-the-art? How complex will the infrastructure need to be to successfully support all of these components?

If you decide that collaborating with a third-party AI partner is the best approach, make sure to read the Choosing the right AI partner section to guide you through the process.

Popular AI models for business applications:

  • Sentiment Analysis
  • Face Recognition
  • Summarization
  • Generative AI
  • Content Moderation
  • Fraud Detection
  • Entity Detection
  • PII Redaction
  • Diagnosis Prediction
  • Topic Detection
  • Intent Recognition

5. Identify the KPIs

Now, it’s time to identify the appropriate Key Performance Indicators (KPIs) you will need to track to measure success. KPIs are dependent on both your measurable goals and specific AI models, so make sure both of these are solidified prior to defining the KPIs.​

6. Create cross-functional teams

Building AI-powered products always requires a cross-functional effort. You’ll need to secure support from key players (including Engineering, Marketing, Product, and Support), and define the responsibilities for each team. 

Often, these cross-functional teams are invaluable in providing feedback for revisions that will directly translate into a better customer experience and adoption.

7. Ship, maintain, and iterate

You’ve successfully shipped your new AI-powered product! Now what? To be successful in the long term, consider how you’ll manage user feedback, incorporate new feature requests and/or updates, monitor performance, and apply new cutting-edge research. Ensure your product is continually using state-of-the-art AI models to stay competitive. Consider whether or not you can manage this with internal research. If not, should you rely on a third-party partner with dedicated AI research teams? ​

Completing this final step will ensure your product retains its value and utility in the long term.

Potential blockers and risks

While there are many reasons companies are choosing to integrate AI into their business, it's important to note the potential blockers and barriers to success.

Recently, we surveyed 10 AI-first companies that shared the primary reasons they initially hesitated to build with AI.

They cited the following:

  • Difficult to monetize (30%)
  • Limited developer and/or engineering resources (30%)
  • Internal buy-in across multiple stakeholders (20%)
  • Lack of AI expertise (20%)

Let’s look at each of these potential challenges more closely.

Difficult to monetize 

Some product teams cited confusion about a clear path to monetization as a main blocker to building an AI-first product. While this can initially seem like a hard stop to implementation, it’s important to consider the first two best practices before pulling the plug: focusing on the user value and setting a measurable goal.

How many new users could you stand to capture by implementing an AI-powered product? How much would this new product increase revenue? Or would the new feature mitigate risks around infrastructure, data privacy, or compliance? ​

By mapping all of this out in advance, and taking temperature checks and making adjustments along the way, companies can ensure end profitability.

Limited developer and/or engineering resources 

Other product teams raised concerns about having limited internal developer and/or engineering resources. While building a new AI model in-house would certainly require a large in-house team of researchers and developers, there are third-party partners that offer state-of-the-art AI model integration via APIs and can work as a partner with you during the build process.

Look for a provider with a strong emphasis on continual research and development, as well as on-demand support, to ensure best-of-class models and integration.

Additional Reading: Do I need a custom AI model?

Internal buy-in across multiple stakeholders 

A few product teams were concerned about gaining internal buy-in across multiple stakeholders and teams, which is necessary during the build process.

It helps to make sure your measurable goals are well-defined and align with your company’s overall internal goals. This alignment will help secure buy-in internally by making it easy for each team to see the long-term value of AI integration.

Lack of AI expertise 

Finally, some product teams were also concerned about a lack of internal AI expertise. While a basic understanding of AI models and applications is helpful, there is a wealth of guides and resources available today to help. It may also be helpful to partner here with a third-party AI partner, as discussed above, to lean into the expertise of others in the field as your foundation. 

Choosing the right AI partner

It's important to choose an AI partner for your use case, an industry-leading expert who can help you meet your goals and stay competitive.

If you choose to look for a third-party partner to help with your build, keep these five considerations in mind.

1. Does the partner have state-of-the-art AI models that are continuously updated?

Prioritize partners that continuously update and improve their AI models with new data and iterate based on the latest AI research and breakthroughs. This ensures that the models you use in your product or feature are competitive and not outdated.

2. Does the partner have a dedicated in-house research team?

Look for AI partners who have a strong in-house AI research team, which demonstrates competency and a commitment to excellence. Research teams often have a changelog, which is updated on a regular and frequent basis. This level of transparency ensures you are leveraging models that are cutting-edge.

3. Does the partner have AI models that are ready to use right away?

Consider an AI partner that offers models that are ready to use right now—with minimal internal maintenance needed. There are many AI models on the market today, but some require more internal work than others. If you have a small engineering or developer team, or are simply resource-conscious, look for a partner that offers this option.

4. Can the partner offer on-demand support?

Find a partner who offers 24/7 end-to-end support, including health checks, personalized monitoring, and more. These advanced services are also especially helpful for teams with limited in-house resources. A 24/7 support option indicates that the AI partner is dedicated to serving you and helping you achieve your goals.

5. Is the partner transparent and do they have advanced security provisions in place?

Select a partner who is honest and transparent about how they store the data they process. Check to make sure they openly share the methodologies for their research, particularly widely-distributed content such as benchmark reports. Also, look out for troubling signs like deceptive or misleading tactics or communications.

6. Does the pricing model fit your business strategy?

Consider how well the partner’s pricing model fits your business strategy. Will you be sending data in monthly batches? Daily? How much data will you need processed today and when the product scales? Understanding these metrics will help ensure long-term profitability.

7. Does your AI partner offer additional partnership capabilities? 

Finally, look for an AI partner that provides additional partnership opportunities. This could include AI roundtables with their research team, early access to new AI models or updates, or even co-marketing and event collaborations, such as a webinar or conference discussion. 

By searching for companies that offer a true partnership, you can rely on their expertise and development efforts and accelerate your growth through additional strategic and collaborative efforts.

Need help understanding how to leverage AI for your products? Learn how other companies (listed below) are integrating AI into their business to better serve their customers.

Real AI products and features in action

Conversational Intelligence

CallRail embedded AI-powered transcription and AI-powered summaries to improve its call transcription accuracy by up to 23% and to double the number of customers using its product.

Read the entire use case.

Contact Center SaaS

Leveraging cutting-edge AI models, Aloware was able to ship Smart Transcription to automate most QA tasks for its customers in just six weeks from build to deployment. 

Read the entire use case.

Hiring Intelligence

With the AI-powered transcription data, Screenloop’s team built a highly competitive platform that helps its customers spend 90% less time on manual hiring and interview tasks and realize a 20% reduced time to hire for open roles.

Read the entire use case.