[AI New Business Creation] Qualiteg's Original Method for Evaluating and Selecting Business Ideas

Hello, and welcome to the Qualiteg blog. I'm Michele. Working on AI-driven new business development and marketing, I hear certain questions from clients again and again. Through this blog, I would like to offer my own answers to the challenges that anyone considering an AI-based business is likely to face.

Introduction

The rapid advance of AI technology is opening up new possibilities for businesses of every size, from startups to large enterprises. In conversations with clients, we are seeing more and more inquiries about innovative business ideas built on AI.

At the same time, many companies get excited about a brilliant idea, yet struggle with the methodology for turning that idea into a concrete, successful business. This is especially true for AI-based ventures, which require a different perspective from conventional business models.

In this article, we explain Qualiteg's original approach to evaluating ideas and selecting business ideas. In particular, we aim to provide answers to the challenges commonly faced by those considering AI-based business development.

Frequently Asked Questions from Clients: Pain Points in AI Businesses

Clients considering AI businesses often raise questions like the following.

We have plenty of ideas, but we don't know which one to commercialize. 

• The possibilities of AI are endless and ideas keep coming, but with limited resources it is hard to decide which idea to focus on.
• Management is telling us to launch some kind of AI service. An easy option would be a SaaS that simply wraps the ChatGPT API, but other companies are doing the same thing, and we worry we could never differentiate ourselves or build an advantage.

Isn't AI evolving so fast that our business plan will become obsolete?

• AI technology evolves every day. Even after investing time in a business plan, there is a real fear that it will quickly become outdated.
• We spent six months refining an internal plan for an AI chatbot, and during that time new LLMs kept being released one after another. In the end, most of the features we had envisioned became achievable with free tools. When is the right time to commit to an AI project plan?
• Last year we started exploring a new service using image-generation AI. But the technology evolved so quickly that the market advantage we initially envisioned kept eroding. Honestly, we still haven't been able to pull the trigger on the investment.

The PoC (proof of concept) succeeded, but we can't turn it into a business. 

• Our PoC for a customer-facing AI chatbot went well, but when we estimated the cost of rolling it out as an actual service, we concluded that recovering the investment would be difficult at our current revenue scale.
• Our demand-forecasting AI PoC for retail stores achieved excellent accuracy. But rolling it out to physical stores would require system integration, operational changes, and more cost and time than we expected... In the end, we couldn't reach internal consensus and the project has stalled.

Securing AI talent is difficult. 

• It has been six months since we posted a job opening for a data scientist, and we still haven't found the right person. Even after raising the salary range, we simply can't compete with the major tech companies.
• We finally managed to hire an excellent AI engineer, only to see them leave for another company within a year. How to improve retention of highly specialized talent is a real headache for us.

We face challenges in collecting and utilizing data. 

• Our company should have a wealth of customer data, but each department manages it separately. Consolidating it into a form usable for AI development is taking far more time and cost than expected.
• We have plenty of quality-control data. But because it contains personal and confidential information, we are struggling to coordinate with our legal department on how far we can use it for AI development.

Qualiteg's Original Method for Evaluating and Selecting Business Ideas

To address these challenges, Qualiteg evaluates and selects ideas—and supports their commercialization—through the following three steps.

Step 1: Evaluate Market Needs and Feasibility

First, we evaluate ideas along two axes: market needs and feasibility.

Market needs

• No matter how innovative the AI technology, a business cannot succeed without market demand. We define the target customers clearly and verify whether the idea can solve the problems they face. Market size, the competitive landscape, and customer acquisition costs also need to be considered.

Feasibility

• Beyond technical feasibility, you also need to consider the business model, revenue model, regulations, and ethical issues. Given the pace at which AI technology evolves, it is important to assess feasibility from a mid- to long-term perspective.

At this stage, frameworks such as the Lean Canvas and SWOT analysis can help you conduct an objective evaluation.

Step 2: Select and Validate AI Technologies

After evaluating market needs and feasibility, we select the most suitable AI technology.

Leveraging existing technology

• By leveraging existing AI services and APIs, you can keep development costs down and move quickly toward commercialization.

Developing proprietary technology

• Building a competitive advantage may require developing proprietary technology. In that case, development costs and timelines must be estimated carefully.

Using the selected AI technology, conduct a PoC (proof of concept) for technical validation. In the PoC, develop a prototype within a limited scope and evaluate its performance using real data.

Step 3: Formulate and Execute the Business Plan

Based on the PoC results, formulate a concrete business plan.

Building the business model

• Clarify how the AI technology will be incorporated into the business and how it will generate revenue. Consider a range of business models, such as subscription and pay-as-you-go models.

Marketing strategy

• Develop a marketing strategy to reach your target customers effectively.

Team composition

• Driving an AI business requires not only engineers but also people with diverse skills in business development, marketing, sales, and more. Assembling the right team is essential.

Agile development

• Because AI technology is constantly evolving, it is important to adopt agile development rather than a waterfall approach so you can respond flexibly to change.

Once the business plan is in place, move quickly to execution. It is important to adapt the plan flexibly based on market feedback. Set KPIs and monitor progress regularly to keep the PDCA cycle turning and grow the business.

"How do we achieve innovation beyond the boundaries of our own company?" — Qualiteg's Innovation-Cross offers a clear answer to this challenge faced by many large enterprises.

We thoroughly analyze your company's current state and draw up a blueprint for value creation through external collaboration. We cover the entire process of innovation co-creation, from strategy formulation to roadmap development, KPI setting, and execution support. Through idea workshops, hackathon planning, the use of cutting-edge AI technologies, and other diverse approaches, we support your company's innovation. Experienced consultants work alongside you, guiding you toward new value creation that blends internal and external expertise. From a lonely challenge to a journey of co-creation—Qualiteg's Innovation-Cross is that turning point.

The Importance of Data Strategy in AI Businesses

A data strategy is essential to the success of any AI business. Training AI models requires large amounts of data. But simply collecting data is not enough—what matters is gathering high-quality data and using it appropriately.

• Data collection
  • Clarify how you will collect the data you need. Beyond gathering data in-house, consider purchasing it from external data providers.
• Data preprocessing
  • Collected data must be processed into a format usable by AI models. This involves data cleansing, data transformation, feature engineering, and more.
• Data utilization
  • Use data not only for training AI models but also for business decision-making. Through data analysis, you can understand customer needs and discover new business opportunities.
• Data governance
  • Establish a framework to ensure data quality, security, and privacy. Compliance with regulations such as the GDPR is also required.

Qualiteg's Strengths

• Qualiteg is a strategic partner for achieving success in AI-driven business. Our team of consultants, combining deep expertise with hands-on experience, provides comprehensive support throughout our clients' AI adoption journey.

Advanced technical capabilities

• Drawing on a deep understanding of cutting-edge AI technologies—machine learning, deep learning, natural language processing, computer vision, and more—we advise on the AI solutions best suited to each client's business needs.
• We go beyond technology selection to solve technical challenges comprehensively, from algorithm selection and model development to performance optimization.

Commitment to creating business value

• We combine the expertise of a technical team with global experience spanning fundamental AI research to real-world deployment, and the practical strategy-building capabilities of consultants from global consulting firms and MBA holders. Together, these two strengths maximize the value of AI investments aligned with your corporate strategy.
• We closely analyze each company's management challenges and growth strategy, and contribute to tangible increases in corporate value through AI-centered business model innovation, new business development, and fundamental improvements in operational efficiency. We would be delighted to hear from you.

Practical hands-on support

• Our members, seasoned in both technology and business, provide thorough support from PoC planning through defining success criteria. We offer practical consulting across every phase of the project—from conducting the PoC (proof of concept) to rolling out pilot projects, full-scale system implementation, and operation and maintenance. (If you don't define PoC success criteria and align with the approver up front, you risk having the ladder pulled out from under you—so let's plan carefully together in advance.)

• We adopt agile development practices, supporting your adaptation to changing business conditions through rapid feedback and flexible responses. We also help build lasting AI capabilities through AI talent development and organizational support.

A powerful ecosystem

• We maintain a strong partnership network of leading AI vendors, cloud providers, data providers, research institutions, and investors. By drawing on this ecosystem to provide clients with the optimal resources, we accelerate the success of AI projects and support sustainable growth.
• Qualiteg is not just a consulting firm—we are a true partner in our clients' AI transformation journey. We unlock the full potential of AI and create the future of your business together.

Conclusion

Business development powered by AI holds great promise, but it also comes with many challenges. Through our original method for evaluating and selecting business ideas, Qualiteg helps clients overcome these challenges and lead their AI businesses to success. If you are considering an AI business, we would be glad to talk with you.

AI technology is constantly evolving. It is essential to keep gathering the latest information and to respond flexibly to change. As a partner that learns and grows together with our clients, Qualiteg will continue to create the future of AI business.

Thank you very much for reading this column to the end. At Qualiteg, we provide training and consulting on AI technologies and new business planning. If you are interested, or if you have any specific needs, please feel free to contact us through the inquiry form here.

We also offer a popular workshop for those who want to master the steps of new business creation. The training focuses on having each participant think about the business from an executive's perspective, and the content is designed not only for planning staff but also for their counterpart engineers, designers, and marketers.