Artificial intelligence (AI) has become integral to how companies operate, unlocking new efficiencies and capabilities across every function. While off-the-shelf AI solutions are widely available, they often fail to fully address the unique needs of each organization. Increasingly, businesses are turning to custom AI development to build tailored systems aligned with their specific goals, data, and processes.
In my decade as a data scientist and AI consultant, I‘ve helped numerous clients scope, build and implement custom AI solutions. In this comprehensive guide, I‘ll share my insights on:
- What exactly custom AI entails
- When to pursue custom vs. out-of-the-box AI
- Most common applications for custom AI
- Whether to build in-house or outsource
- Best practices for identifying the right custom AI vendor
Let‘s dive in.
What Are Custom AI Solutions?
Custom artificial intelligence refers to AI or machine learning systems built specifically for one company‘s individual requirements and use cases.
Unlike generalized off-the-shelf AI solutions designed for broad applicability across many companies, custom AI is tailored from the ground up to a business‘ unique:
- Data infrastructure and formats
- Business processes and workflows
- Strategic objectives and desired outcomes
- Operational constraints
This tight alignment to a company‘s singular needs allows custom AI to deliver maximum performance and business impact.
Examples of custom AI solutions include:
- Conversational chatbots trained on a specific company‘s customer support logs, language patterns and domain knowledge to deliver highly personalized customer service.
- Computer vision algorithms optimized for defect detection on a manufacturing plant‘s proprietary production line and parts.
- Predictive maintenance models that forecast equipment failures based on historical sensor data from an organization‘s machines.
- Dynamic pricing algorithms tailored to an ecommerce company‘s inventory data, supplier costs, and competitive landscape.
The key benefit of custom AI is its ability to maximize value and ROI by directly addressing an organization‘s precise requirements and challenges. This customization comes at the expense of additional time and investment compared to off-the-shelf solutions.
Common applications of custom AI solutions. Image credit: AIMultiple
When Does Custom AI Make Sense?
Custom developing AI solutions requires greater time, investment and effort compared to purchasing off-the-shelf solutions. But it can certainly pay dividends under certain circumstances:
Strategically Critical Applications
If an AI use case is strategically important or could deliver massive ROI if optimized, then custom development is often warranted to maximize performance.
For example, an AI virtual assistant that will automate key customer touchpoints across channels. In this case, the major business impact merits investing in custom conversational AI fine-tuned to unique customer data.
Performance Gaps with Off-the-Shelf Solutions
Sometimes pre-built AI solutions fail to deliver needed performance due to factors like:
- Suboptimal model training on more general vs. company-specific data
- Integration challenges with existing IT systems and workflows
- Market solutions not keeping pace with a company‘s evolving needs
When significant performance gaps persist after attempting to configure or enhance off-the-shelf solutions, custom AI development may be the best path forward.
Immature Market Solutions
Innovative companies on the cutting edge often find their specific AI needs are underserved by current market solutions. Especially for emerging applications of AI.
In such cases, pioneering custom development may be the only viable option to solve pressing business problems with AI.
Other Considerations
Companies may also elect to pursue custom AI when:
- Data privacy and security is paramount – Keeping sensitive data in-house rather than sharing with vendors
- Seeking competitive advantage – Exclusively benefitting from IP developed in-house
- Greater control is required – Over all aspects of the AI system, from data to algorithms to maintenance
These factors can compel companies to invest in tailored custom solutions versus commercial off-the-shelf offerings.
Most Common Areas for Custom AI Solutions
While custom AI can deliver value across virtually every function, some of today‘s most common applications include:
Computer Vision
Computer vision AI leverages cameras, sensors and deep learning algorithms to analyze visual data. Custom computer vision use cases include:
- Product defect detection in manufacturing
- Automated video surveillance analytics
- Medical imaging diagnosis
- Quality assurance on assembly lines
- Mining/resource exploration
Computer vision systems require extensive customization to each unique environment and use case. Tailored data labeling, model architecture and training are needed to maximize accuracy.
According to McKinsey, industries spending the most on computer vision include automotive, electronics, semiconductors, healthcare, pharmaceuticals, and consumer packaged goods.
Computer vision optimized for visual product inspection. Image credit: AVerMedia Technologies, Inc.
Conversational AI / Chatbots
Conversational AI and chatbots enable interactions between computers and humans through voice or text. These solutions are often built custom for:
- Specific industry/domain expertise
- Integration with internal company systems
- Brand-specific personalities and tones
According to Grand View Research, the global conversational AI market is expected to reach $15.7 billion by 2025. Top use cases include customer service and support.
Retail, banking, and healthcare are leading adopters. For example, Sephora‘s chatbot provides personalized skincare recommendations to customers.
Custom conversational AI chatbot. Image credit: Kasisto
Predictive Modeling
Predictive modeling uses historical data to forecast future events and trends. Custom predictive models are built to leverage:
- Company-specific data like sales records, inventory levels, website analytics
- Subject matter expertise related to the prediction problem
Use cases include:
- Demand forecasting and dynamic inventory optimization
- Churn prediction and customer targeting
- Predictive maintenance for industrial equipment
- Employee retention modeling
According to Mckinsey, machine learning-driven predictive modeling can improve demand forecasting accuracy by 10-20%.
Build In-House or Outsource Custom AI?
Once a company decides to pursue custom AI, a critical question is whether to build in-house or outsource development. Key considerations for each approach:
In-House Custom AI Development
Pros
- Better protection of proprietary data and IP
- Leverage expertise of internal data scientists
- Foster institutional knowledge and internal AI capabilities
Cons
- Significant resource requirements – data science, engineering, subject matter expertise
- Long development timelines – often 12+ months
- Opportunity costs versus other initiatives
- Challenging to build full in-house capabilities – hardware, tooling, ops
According to a survey by O‘Reilly, 51% of companies build custom AI solutions completely in-house.
Outsourced Custom AI Development
Companies can avoid large fixed investments by partnering with specialized AI consultancies and development firms to build custom solutions.
Pros of outsourcing
- Faster time to market – vendors move quickly
- Leverage outside AI and domain expertise
- Variable costs – only pay for what you need
- Focus internal teams on oversight vs implementation
Cons
- Loss of IP control and risks around data privacy
- Added coordination effort with external vendors
- Potential for higher long-term costs
According to the O‘Reilly survey above, only 15% of companies fully outsource custom AI development. But the majority use a hybrid model combining internal staff and external partners.
Which Route Is Best?
Choosing between insourcing and outsourcing custom AI depends on factors like:
- Available internal resources and capabilities
- Sensitivity and privacy of data
- Timelines for ROI
- Financial budgets
- Strategic importance of the application
Often a hybrid approach works best – partnering with specialists on parts of the project while keeping oversight and key IP in-house.
How To Identify The Right Problems To Solve With Custom AI
Since custom solutions require significant investment, it‘s critical to identify high-impact business problems where AI can drive step-change improvement.
Here is a step-by-step process for identifying the most valuable custom AI opportunities:
1. Catalog and prioritize business problems – Finance, operations, CX, risk, etc.
2. Estimate potential ROI – Which problems offer greatest financial benefits if solved?
3. Assess custom AI applicability – Where will AI make the greatest impact?
4. Analyze solution requirements – Data, infrastructure, timelines, costs, resources required.
5. Rank opportunities – By strategic value, ROI, and feasibility.
6. Select top projects – Choose opportunities offering maximum benefits given constraints.
This rigorous process surfaces the highest potential custom AI investments tailored to business objectives.
Best Practices For Selecting A Custom AI Vendor
For companies outsourcing part or all custom AI development, choosing the right vendor is critical.
Here is an 8-step process I guide clients through when evaluating partners:
1. Define Project Requirements
Detail project scope, objectives, data requirements, timelines, and constraints. This drives vendor selection.
2. Create Shortlist
Research the market to identify vendors with relevant AI and industry expertise. I leverage resources like expert communities to compile target lists.
3. Request Information
Issue RFIs/RFPs to collect info on vendor capabilities, experience, processes, and pricing models.
4. Hold Initial Discussions
Conduct calls with vendors to assess understanding of project goals and ability to deliver.
5. Issue POC Requests
Request vendors complete a limited proof-of-concept using sample data to demonstrate their approach.
6. Evaluate POCs
Assess POC deliverables for factors like solution design, code quality, model performance, and communication.
7. Select Finalists
Based on POC evaluations and overall due diligence, narrow the field to a few finalists.
8. Make Final Selection
Conduct onsite meetings and reference calls as needed. Negotiate project plan and pricing.
This rigorous 8-step process surfaces the vendor best equipped to deliver a successful custom AI solution tailored to the organization‘s specific needs and constraints.
Key Takeaways
The AI revolution continues rapidly transforming businesses. While off-the-shelf solutions abound, custom AI development is increasingly required to fully address unique company challenges.
As this guide covered, careful consideration of your strategic needs, implementation options, and partnership models can unlock tremendous value from custom AI investments.
With a thoughtful approach, custom solutions can provide unmatched capabilities, performance gains, and competitive differentiation powered by AI innovation. The business potential is profound.