Navigating the future of Tech. Best Practices in AI Product Development
Unlock the potential of AI product development with best practices that drive innovation and profitability. Learn how to navigate market conditions, choose the right neural networks, and optimize costs for maximum ROI.
Intro. Brief Overview of AI Product Development
- The development of AI products involves creating software or systems that incorporate artificial intelligence capabilities to provide specific functionalities or solve complex problems. The development of AI products requires collaboration among data scientists, AI engineers, software developers, designers, domain experts, and potentially legal and regulatory professionals. Successful AI product development delivers innovative solutions that leverage AI capabilities to solve real-world challenges and deliver value to users and businesses.
- AI is far from a mystical force; instead, it's a tool with tangible capabilities. From initially having high hopes and inflated expectations, the utilization of AI has evolved into a practical business opportunity. This transformation reflects a shift from the initial excitement about AI's potential to a more grounded and realistic perspective in the business world.
- The process of creating an AI product should commence with a well-defined identification of a business challenge or a problem that needs solving. It's crucial to thoroughly comprehend how AI can provide a solution tailored to that specific business context. Once this objective is established, the subsequent phases of AI product development and integration will progress with greater ease and effectiveness. This approach ensures that AI is aligned with and serves the specific needs of the business.
Importance of AI Product Development In the Current Business Landscape
Market conditions are evolving in a manner that prompts companies to focus on cost savings. Businesses employ diverse strategies for this purpose. Some reduce their workforce, while others seek process optimizations to cut costs. Some even prioritize essentials over higher-order needs to achieve savings.
Companies are currently reevaluating their understanding of value in response to changing economic conditions. The financial situation is also starting to impact positions related to AI.
There is ample research demonstrating how AI investments are yielding significant returns, and companies are now generating substantial profits through AI.
In the 2022 survey of senior data and technology executives by NewVantage Partners, 92% of large companies reported that they are achieving returns on their data and AI Product Development investments. That’s up markedly from 48% in 2017 (the research was comparable).
Before we dive into explaining the best way to choose and develop AI products for maximum value, let's first highlight why AI products hold immense importance in today's business world.
Innovativeness. AI is transforming the business world, creating new opportunities for innovation and growth. AI is a powerful tool for improving operational efficiency, enhancing customer experiences, and generating valuable insights. Companies that successfully leverage AI technology can gain a significant competitive advantage, streamlining their operations and driving growth.
Efficiency. A major advantage of AI in business lies in enhancing operational efficiency. AI-driven automation simplifies processes and cuts costs, giving employees more time for important tasks. For instance, chatbots manage common customer questions, and machine learning enhances supply chain efficiency by reducing waste. Moreover, AI offers real-time monitoring and analysis of business processes, rapidly spotting and solving problems.
Enhancing creativity. The influence of Artificial Intelligence on content marketing is experiencing rapid expansion. Using algorithms, AI assesses the existing data to formulate optimal content marketing strategies. For instance, if the analysis reveals that a certain content type outperforms others, a team can tailor the approach accordingly. AI aids in determining writing topics, selecting keywords, determining publishing frequency, and more. In essence, AI empowers to craft top-notch content that ranks prominently.
Forecasting & planning. Precise forecasts empower enterprises to gauge the likelihood of purchases, anticipate both immediate and prolonged outcomes, and distribute resources efficiently. Product-market fit predictions are much more accurate with the help of AI. Research comparing AI projections with human expert predictions consistently portrays artificial intelligence as the prevailing contender. While algorithms and AI won't supplant human intelligence down the line, their proficiency in data analysis will invariably serve as a valuable tool for business.
Profitability. AI products can drive businesses to profitability in several ways by leveraging advanced technologies and data-driven insights. For example, AI product development can uncover opportunities for product and service innovation. Businesses can develop new revenue streams by creating AI-powered products or adding AI-enhanced features to existing offerings.
Mitigating risks. AI can assess risks by analyzing historical and real-time data, helping businesses make better-informed decisions about investments, financial strategies, and risk mitigation. Moreover, AI algorithms can detect patterns associated with fraudulent activities, reducing financial losses due to fraud in areas such as financial transactions and cybersecurity.
In the pursuit of economic survival and success, companies embark on a journey of experimenting with technology to unlock maximum business benefits. This journey often commences with the AI Models.
AI Model vs AI Product Development
At the core of every AI solution lies a 'model' designed to facilitate automation, assistance, or personalization. This model leverages the available understanding of the user's context and requirements to forecast essential aspects directly associated with user advantages. The AI model is something that lacks alignment with practical use cases. It operates by ingesting data and generating some predictions. Upon integration into various contexts or the creation of a dedicated product, like a chatbot or integration into a route planning system, it transforms into an AI product - something that brings real practical value and meets business needs. This signifies the concept of the AI product development process.
An AI product is a tangible manifestation rooted in neural network technology. It performs specific tasks with precision, amplifying value, and operational efficiency. In essence, an AI product development results in a solution primed to deliver substantial benefits. The model is like a step before reaching that point. It can predict things using the neural network, but a company is not exactly sure how to use those predictions yet.
First Bridge is versatile in how we work with different methods and approaches. For example, we can easily integrate our model into a client's system, which speeds up the process of creating AI products. We're also capable of starting fresh or improving existing clients systems etc.
Our primary aim here is to show what an ideal process looks like, what guides start from the very beginning and go all the way to the end. We begin consulting by understanding where to start, like identifying where our AI solutions can be applied. This process leads to building a strong AI product. This way of working shows that we're not only focused on immediate needs but also on predicting and meeting future demands. By doing this, we make sure our AI solutions are always at the forefront of the industry.
Meeting a Business Problem
Before diving into AI product development, it's crucial to ask yourself a few key questions: Where will AI source its information, and what specific information holds importance for your business? This leads us to defining the issues and objectives for implementing AI within your organization. What will influence the final decisions? This whole process is inherently intricate and intellectual. A business challenge or goal can be stated as follows:
- Solve the problem. Let’s see an example of a logistic company, the challenge at hand that it possesses actual data indicating delays in deliveries. Our objective is to comprehensively analyze the factors contributing to these delays and devise strategies to curtail them. These delays have a direct impact on both profits and the satisfaction of customers. Additionally, we aim to establish a system for real-time monitoring of latency data in the foreseeable future.
- To enhance the current operations or products. For example, when a company is focusing on tasks that don't involve urgent issues. Instead, it is driven by the desire to refine and amplify various processes with a clear vision of the outcomes aim to attain. This approach aligns with our intention to scale the business and achieve improved efficiency and effectiveness across different facets of our company.
A specific theoretical model is chosen for each stage, and we possess a clear understanding of the entry and exit points of AI product development. In the case of a growing business with a substantial scale and a non-trivial format, the company typically possesses a clear understanding of the reasons it requires AI.
Custom Solution vs. Ready-Made
In our experience, we frequently encounter situations where companies struggle to determine whether they should opt for a custom solution or a ready-made one.
Choosing between custom AI solutions and ready-made products depends on factors like budget, timeline, complexity of the problem, and the level of customization needed. Custom solutions offer tailored precision but demand significant investment, while ready-made products provide quicker deployment and cost savings, albeit with some limitations on customization. The decision ultimately hinges on the specific goals and resources of the business.
We suggest keeping in mind that every business, even those in the same industry, can have unique requirements. A one-size-fits-all solution won't address the diverse needs of these businesses. For instance, if a product is designed for one logistics company, it may not work seamlessly for another. Integration into existing systems, budget variations, and other factors must be considered. So, complete customization is often necessary when developing AI products for businesses.
Some worldwide famous companies demonstrate successful stories with onboarding custom AI products:
For example, Walmart has developed a custom AI-based store monitoring system. The initial benefit of this equipment will be its ability to assist the team in managing product inventory and availability. Essentially, the team will utilize real-time data to identify opportunities for increased efficiency, enabling associates to determine more accurately when to replenish products, ensuring that items are consistently in stock when required.
Uber is deeply rooted in their operations. Uber harnesses AI for diverse purposes, including fraud detection, risk identification, rider-driver matching, route tracking, and numerous other functionalities. Sometimes it is also called an AI-first company. Michelangelo, Uber’s machine learning (ML) platform, supports the training and serving of thousands of models in production across the company. Michelangelo’s architecture must handle the complexities that arise from different functional needs and maintain consistency between training and serving environments.
Hilton. This luxury and leading hotel chain leverages check-in systems that seamlessly integrate AI technologies, such as facial recognition, to enhance the check-in experience for guests. Instead of queuing up, guests can utilize the Hilton app and facial recognition to complete the check-in process. In addition to these advanced check-in systems, Hilton has recently introduced its AI-driven robot, Connie. Connie is proficient in responding to guests' inquiries about the hotel, offering recommendations for local attractions and dining establishments, and even aiding guests in securing restaurant reservations.
Many large companies also hire contractors to develop an ATS, commonly referred to as an Applicant Tracking System, such as Lactalis or Metinvest. It is AI-powered software designed to oversee the entire recruitment and hiring process, encompassing tasks like job postings and job applications. It efficiently organizes data related to job seekers, making it easily searchable. As the name suggests, an ATS keeps tabs on candidates throughout the hiring journey. It aids in scheduling interviews, sends out notifications and alerts, and automates email communications to candidates and stakeholders, including recruiters and hiring managers. ATS helps parse hundreds and thousands of candidates' resumes, saving significant amounts of manual work and enabling managers to optimize budgets.
Unlocking the Data Maze for AI Products
To begin with AI product development, it is necessary to establish a task and systematically organize the data-gathering process. We mentioned before the sources from which AI will gather data for analysis and determine which ones are pertinent to the business task. There are several fundamental types of data:
- Substantial volume of accumulated information, including internal data that can be sourced from within the organization. This encompasses data from systems like CRM and more:
- Information about sales transactions, customer orders, and revenue figures can provide insights into buying patterns, customer preferences, and market trends.
- Financial statements, profit and loss records, and budget data can be analyzed to gain a better understanding of the company's financial health and performance.
- Data on website traffic, user interactions, and app usage can inform website and app optimization strategies.
- Information about marketing campaigns, ad performance, and customer engagement can help refine marketing strategies.
- Data collected from customer surveys, feedback forms, and market research can provide valuable insights into customer satisfaction and preferences.
- Data on equipment maintenance, downtime, and operational processes can lead to improved operational efficiency.
- Data related to compliance requirements, legal issues, and regulatory changes can support risk management and legal compliance efforts.
These various types of internal data, when collected and analyzed with AI, can uncover actionable insights and drive informed decision-making across different areas of the business.
- If our current internal data is insufficient or lacks the quality needed for analysis, considering synthetic data could be a viable solution. Yet, it's important to note that this approach is more useful for predicting scenarios and generating simulated future outcomes. Furthermore, every synthetic dataset originates from a “seed”, and it's crucial for this source to align with the desired quality standards. Or consider a scenario where a client requires image operations with their logo, say about 3 thousand variations. In practical terms, delivering such a comprehensive set to technical consultants is often unfeasible. This is where dedicated tools come into play, enabling the synthesis of various types of data — text, images, even audio.
- Open data sources, public data. Let's consider a proptech industry example: a system that evaluates housing desirability using data from public crime registries, local crime rates, and weather conditions.
In summary, it cannot be overstated how critical the process of data selection is when it comes to harnessing the potential of AI. The quality, relevance, and diversity of the data chosen lay the foundation for the effectiveness and accuracy of AI-driven solutions. Making informed decisions about data selection ensures that AI systems can uncover meaningful insights, make accurate predictions, and deliver tangible value to businesses. As data fuels the AI engine, meticulous attention to this stage can ultimately define the success and impact of AI initiatives.
Choosing a Neural Network
The matter of choosing neural networks holds significant weight. While we won't delve deep into intricate technical jargon in this article, it's crucial to note that neural networks come in various forms, and making the right choice matters. Selecting the appropriate neural network is intricately tied to hardware setup and expenses. The effectiveness of this selection, along with its task performance, directly validates the associated costs. Taking a deep dive into this stage with contractors is really crucial. We recommend not to leave it to luck. Even if your business is medium-sized and not a big corporation, this step deserves a good talk. Chatting with your tech consultants about things like setting up the right hardware or using cloud tech for the neural network, and how much it will cost, is super important to succeed in future.
Once all the factors are accounted for and the relevant data is selected, we reach the final stage: integrating the neural network itself. Throughout this process, the developer's role remains unchanged.
Cost-cutting Problem in AI Product Development
Relevance comes into play here due to a pressing concern shared by all: cost reduction. Optimizing processes and human sources, meticulously evaluating what to trim and what to allocate resources to, while considering the gains from these savings, becomes a pivotal aspect of product development.
The business objective should be quantifiable — a clear target like achieving a 20% faster delivery speed, leading to fuel and time savings that translate into specific monetary gains. As a business interacts with AI tech consultants, it's prudent to also assess the current cost of the product development process and how it might expand as data accumulates, tailored to the business's nuances. This involves examining whether an increased volume of data yields equivalent outcomes.
Choosing Training Technique
Algorithms used to build the model fall into three Machine Learning techniques:
- Supervised Learning: This technique involves learning from examples where humans understand the input-output relationship. The output is annotated for inputs, solely based on the input data. A model is then built to mimic human judgment, generating outputs similar to the examples it's trained on.
- Unsupervised Learning: In this technique, the output varies with the data. It's used for tasks like clustering input data into groups, even if you don't know the exact nature of those groups initially. For instance, finding customer segments or identifying significant features. These groups change as your data evolves.
- Reinforcement Learning: This technique involves self-learning through 'gamification.' The model improves its performance based on interactions with its environment.
The Cost of Training AI Models and the Cost of Operating Them
Considering the costs of training the AI model and getting it up and running are really important for any company. If a company carefully followed the steps we mentioned earlier, then using the AI product effectively is like reaching the top. This is when we start to see the real costs of keeping the model working and launching it for actual use. Doing this efficiently is super important, and it works best when we follow a clear order of steps.
Sometimes, companies go for the usual ways of making products instead of using AI-focused methods. Or they might try out ideas or look for solutions that are already made, but those might not fit exactly what the business needs are. Because of this, getting real advantages from using AI becomes tricky, and the whole process of making AI products can turn into a long journey full of mistakes, things that don't work, and costs that keep going up.
In recent years, the integration of Artificial Intelligence into various aspects of business operations has been nothing short of transformative. AI product development has played a pivotal role in this evolution. Companies across industries have recognized the potential of AI to drive efficiency, enhance decision-making, and provide more personalized customer experiences.
In summary, AI product development is at the forefront of the ongoing digital transformation of business operations. It empowers companies to leverage AI's capabilities to solve problems, make data-driven decisions, enhance customer experiences, and ultimately thrive in an increasingly competitive marketplace. As AI technology continues to advance, its role in shaping the business landscape will only become more significant.
In today's fast-paced and data-driven business landscape, staying competitive and achieving long-term success requires more than just adapting to technological trends—it demands proactive innovation. One area that has emerged as a game-changer for businesses across industries is AI product development. The role of AI in reshaping business operations and customer interactions cannot be overstated, and investing in AI product development has become an imperative for companies aiming to thrive in the 21st century.
1. Driving Innovation: AI product development represents a cornerstone of innovation in the business world. It offers a means to harness the potential of cutting-edge technologies, enabling companies to pioneer new solutions and services that weren't possible just a few years ago.
2. Competitive Edge: In a global marketplace, businesses that invest in AI gain a competitive edge. AI-powered products can provide unique features, streamline processes, and improve customer experiences, setting a company apart from its competitors.
3. Enhanced Decision-Making: AI equips decision-makers with powerful tools for data analysis, predictive insights, and trend identification. This empowers organizations to make informed, data-driven decisions, reducing uncertainty and risk.
4. Cost Savings: While AI investments require an upfront commitment, they often lead to significant cost savings in the long run. Automation of repetitive tasks, improved efficiency, and better resource allocation can all contribute to a healthier bottom line.
5. Personalized Customer Experiences: AI-driven products enable businesses to understand their customers on a deeper level. This personalization enhances customer experiences, fosters loyalty, and increases customer lifetime value.
6. Scalability: AI products can be scaled to accommodate growing business needs, making them an adaptable and sustainable investment. As a company expands, its AI solutions can grow alongside it.
7. Future-Proofing: Investing in AI product development is a strategic move to future-proof a business. AI technology continues to advance rapidly, and organizations that invest wisely in this field position themselves to adapt to evolving market dynamics.
8. Compliance and Security: AI can assist in maintaining regulatory compliance and enhancing security measures. AI-driven risk assessment and cybersecurity solutions are becoming indispensable in today's digital landscape.
In conclusion, the importance of investing in AI Product Development for business success is not just a matter of staying current; it's about shaping the future. Companies that recognize AI's transformative potential and commit to developing AI-powered products are more likely to thrive in an increasingly competitive and data-driven business environment. The dividends of such investments include innovation, competitiveness, efficiency, and the ability to meet the evolving needs of customers and markets. Embracing AI is no longer an option—it's a strategic imperative for long-term business success.