Organizations ready to leap into AI must choose between out-of-the-box AI solutions or more tailored alternatives. According to recent research, 70% of companies use out-of-the-box AI solutions to power their productivity. So, how are those investments faring?

Off-the-shelf AI tools, such as ChatGPT for general use, Descript for podcast editing, or Eleven Labs for audio, can initially appear to be the perfect choice, offering clear cost structures, faster deployment, and a straightforward path to bringing use cases to market. When you're ready to move with AI or under leadership pressure to show results, out-of-the-box AI solutions can seem like a faster, less expensive choice than custom-built or in-house solutions. For some use cases and simple purposes, that might be true.

Yet, for many organizations that scale, other challenges quickly override convenience. Companies that use out-of-the-box AI tools can experience a variety of challenges, such as failing to integrate with your workflows or generate useful data outputs. Data is critical to enriching and optimizing AI performance, providing relevant context, and improving outputs. Without data strategy or training expertise, companies can struggle to get the best results from out-of-the-box solutions or keep pace as technologies evolve and updates and retraining are required.

So, does that make customization critical to creating actionable AI solutions? If so, what do business leaders need to know?

What's the appeal of out-of-the-box AI?

Imagine a small business eager to elevate its customer experience through AI. But a lack of experience, staff, or lower budgets can make AI innovation feel out of reach. Enter out-of-the-box AI solutions, which lower the barriers to entry in several key ways that make them enticing to businesses getting started with AI or investing in new areas. In this scenario, to give their customer service teams a boost, they invest in a ChatGPT model designed to streamline interactions and improve performance. But the story doesn’t end there. Let’s evaluate why organizations choose out-of-the-box AI solutions.

They're (theoretically) easy to use

Off-the-shelf AI solutions sell based on the idea that they can deliver results without requiring specialized knowledge. Businesses are counting on easy deployment features to integrate AI into their operations quickly and without disruption.

They (may) have lower upfront costs

Budget usually plays a key factor in this buying decision. Off-the-shelf AI solutions may have lower upfront costs than custom solutions. As a result, these tools seem like a good investment for companies on tight budgets or those exploring AI as a proof of concept.

They level the playing field with (possibly) greater accessibility

Plug-and-play AI solutions are designed to cater to a wide range of users, from small business owners without IT support to marketers looking for quick wins. The idea is to lower the bar for adoption or meet the needs of certain use cases.

A few examples come to mind: Jasper, which is AI for marketing copy, Midjourney for image creation and editing, and Scribe for process documentation. Each solution positions itself as a simple, effective tool for non-technical users and specific use cases.

Simplicity: Understand hidden costs

Initially, simple, off-the-shelf AI solutions offer important advantages. And they deliver on their promise for some use cases, such as using Jasper to create basic content. However, as usage increases and the tasks become more sophisticated and specific, companies often encounter a range of concerns with generic AI solutions. Evaluating these tools solely based on licensing fees often overlooks critical factors that impact ROI.

• Workflow inconsistencies: Pre-made AI tools often do not align well with a company's current processes. For instance, a copywriting AI might generate quality content but struggle to integrate seamlessly with your CMS or project management platforms, limiting impact and creating unnecessary work.

• Limited growth potential: Companies frequently discover these tools cannot expand alongside their requirements. To scale, they may need expensive upgrades or replacements, negating any initial savings.

• Low adoption: If AI tools don't provide obvious, immediate benefits, employees may hesitate to use them. A lack of customization options, like altering outputs or interfaces, can further discourage use, harming the organization's investment.

• Compatibility problems: Ready-to-use tools may not integrate smoothly with current systems, requiring additional custom solutions that lead to rising maintenance costs over time.

• Poor ROI: Ineffective tools lead to underused resources and do not enhance productivity, leaving businesses with disappointing returns and unfulfilled expectations.

How to select AI tools that work for you

When considering what tools to invest in, following a systematic and clear process can help. Don't immediately disregard out-of-the-box AI solutions, but consider them in the larger context of what's available, including solutions that can be refined and fully customized.

Create a plan to evaluate your options systematically. Start by developing a rubric or using a third-party partner to help determine which solutions best suit your business needs.

With your unique use case and other constraints in mind, explore the full range of options. What's available off-the-shelf, and how effectively would it meet your needs? Are there solutions that deliver part of what you need and allow you to train the model on your data? Finally, if these options don't exist—or they're cost-prohibitive—would a fully customized model best fit your needs?

Look at the potential costs and resource requirements. Go beyond initial licensing fees to account for integration, customization, and ongoing training costs. It's also important to factor in the talent you'll need onsite to provide technical management, data support, answer user questions, and so forth.

A lack of in-house knowledge doesn't have to prevent AI investments. For example, you could partner with consultants or data scientists to bridge gaps and help you make your most important investments.

A rubric to evaluate out-of-the-box AI

What would a potential rubric look like? Here are six factors that can help you better understand how a solution will perform and if it fits your needs best.

1. Customization capability: Can the tool be trained or fine-tuned with your data?

2. Workflow integration: Does it integrate seamlessly with your existing processes?

3. Long-term scalability: Will the tool adapt to future growth and changing needs?

4. ROI potential: Does the solution maximize value and productivity?

5. Developer adoption: Is the tool user-friendly and supported by your team?

6. Initial and ongoing costs: How do total costs compare with the value delivered?

The case for customization

If you're not finding the right off-the-shelf AI solution, consider customization. But what is customization, and how do you know if it's right for you? Customization typically involves tailoring AI solutions to align closely with specific business needs.

This might include fine-tuning existing models with additional training and your data to deliver better results. It could mean integrating multimodal abilities to deal with text, images, video, and sound or training the system to perform more advanced reasoning. It could even mean building an entirely new AI system from the ground up to meet your needs.

Choosing customization can help improve productivity through AI solutions that perform tasks to your standards more efficiently. It can also eliminate integration issues and ensure solutions align with your workflows and other technologies. Finally, by addressing very specific needs, custom AI tools can help you get to value more quickly, depending on your use case.

Deciding between AI solutions

Out-of-the-box AI solutions are often appealing for their fast deployments, low technical barriers, and low initial costs. However, to maximize the ROI of your investments, it's important to consider the hidden costs of inefficiencies, poor integration, and limited scalability. In some cases, out-of-the-box solutions perform well. In others, they can slow innovation and hinder performance.

To maximize your AI investments, strategically evaluate your needs, options, and potential solutions and consider partnering with a knowledgeable third party to help you navigate the fast-growing space. When aligned with your needs, AI can change how you do business.

Curious about how we can help accelerate your AI transformation? Talk to an expert today to explore a tailored solution that drives your business goals.

Nathan Israelsen

Nathan Israelsen is a seasoned Content Marketer with over a decade of experience in digital marketing, AI-driven campaigns, and content strategy. He combines storytelling with data-driven marketing to create high-impact assets, generating millions in SQLs and ACV pipelines. He has led AI-focused initiatives, managed editorial calendars, and developed content that enhances brand visibility. His expertise spans SEO, video production, social media, and integrated marketing campaigns, making him a dynamic force in the industry.