95% of AI Software Gets Zero ROI – Why Bland Is the Exception
A recent report from MIT revealed that despite the ~$35 billion invested into AI, only 5% of businesses are seeing a profitable return. For the disproportionate 95%, AI integration is providing absolutely no return.
According to the report, the main reason why AI fails to contribute to company profit is its misalignment with company operations. Many enterprises have incorporated AI tools — often at exuberant costs — without actually having a need for them. While AI promises to expedite tasks and streamline workflows, a lack of contextual understanding has left it as an expensive, unnecessary add-on in many cases.
The majority of businesses have tried two pathways to AI integration: in-house solutions or building with tools like OpenAI’s ChatGPT. Although both promise digital transformation, these services have yet to consistently deliver the ROI they promise.
Let’s find out why.
Why In-House AI Solutions Don’t Make the Cut
Building an AI solution in-house seems, on paper, to solve some of the potential issues that have been circulating in the AI space. Doubts about data storage and privacy with leading models have caused many businesses to set out to develop their own AI models. This approach promises to create custom-built models and deliver real-world value to a business.
However, the process of creating an AI tool that actually solves issues in business workflows is more complex than it first appears. Here is a selection of reasons why businesses struggle when looking to an in-house AI solution:
- Cost: Developing an in-house AI solution is extremely costly. Businesses have to recruit machine learning engineers and AI infrastructure experts while also funding the ongoing training costs of their custom-built model. These costs can quickly outweigh the ROI for AI tools, especially if companies are integrating AI out of desire rather than necessity.
- Complexity: Integrating AI systems into existing technology stacks could lead to a clash between modern and legacy architecture. Without a highly specialized team to manage the integration, growth, and sustaining of an AI model, it can quickly become ineffective in an enterprise environment. That’s not to mention the implementation of AI without a clear goal, where a large-scale tool fails to deliver specific use case problem-solving.
- Lack of ROI: Continually investing into the training and upkeep of AI models rapidly becomes a capital drain in organizations. Even if an AI tool were able to streamline aspects of an employee’s workflow, this pales in comparison to the investment costs. The MIT report suggests that leaders are currently facing a case of sunk cost fallacy rather than addressing the lack of feasibility of their in-house AI solution.
While these potential factors are major considerations, they’re only scratching the surface of why in-house AI software is challenging to create and implement. Even amongst AI adopters that see success from AI, a staggering 99% do not believe they’ve reached maturity, according to McKinsey.
The Limits of Building on OpenAI
For companies that are looking for a pre-built AI system, leaders in the space like OpenAI are often a top choice. This pathway seems to avoid the huge start-up costs of in-house development and provides access to a pre-trained model right out of the box.
However, the MIT report reveals that companies still run into similar problems with these alternative solutions; leading providers fail to offer companies ROI on their investments. Here are a few of the main limits of building on OpenAI:
- Generic Deployment: ChatGPT is built for mass adoption, making it a generalist model that lacks specific knowledge of industry practices or the ability to perform meticulous tasks. The true ROI of AI lies in being able to automate processes that are value-producing for businesses, with ChatGPT and other models’ generalisms not having this capability.
- Misalignment with Company Workflows: As ChatGPT isn’t specifically made for a company and its internal workflows, employees can struggle getting it to produce effective outputs. Businesses may have to repeatedly prompt ChatGPT to perform a task in a certain way, spending more time correcting than they would have spent actually completing a task.
- Lack of Adaptability: The MIT report outlines that general AI tools like ChatGPT are ineffective at adapting to feedback and aligning with prompt expectations. When businesses use these tools that don’t evolve with them, they fail to deliver the precise utility they need.
While tools like OpenAI are enormously powerful, their general capabilities lack precision. It’s within this precise, targeted use of AI that measurable ROI lies.
Why Bland Cuts Represents the Successful 5%
The MIT study is a major reality check for the AI space: both in-house and OpenAI deployments fail to deliver ROI. Third-party solutions are often too generic, built as catch-all LLMs that are unable to produce specific problem-solving abilities. In-house solutions are more effective, but can be an impossible resource drain for businesses, especially small and medium companies.
Bland solves the core problems that the MIT report finds with AI systems: it is custom-built, serves a specific purpose, and solves a real-world problem for existing workflows. Unlike in-house builds, Bland eliminates up-front costs and development drains, providing a fully realized AI system to improve customer service workflows with AI voice agents.
Unlike OpenAI, Bland is a custom-built and highly specific tool that’s specifically created to deliver utility to companies that need AI voice agents or expert AI customer support. Bland integrates directly into existing workflows to enhance them, continuously adapts to feedback, and can receive instruction and guidance in natural language to make the process as easy as possible.
If you’re looking for an AI tool in the 5%, reach out to Bland today to see how our team can help your business thrive.