It’s clear that the future is in AI but why then not every company has adopted AI capabilities yet? One of the reasons that stops companies from adopting AI is the time and costs associated with AI development. Surely, developing AI products from scratch is extremely time and resource-draining. The good news is you can make a shortcut and adopt already existing tools instead of investing in AI development. This is a strategy that smart companies use to achieve their business goals and you can do it as well.
Further, we’ll examine the 5 best practices for adopting AI on the example of companies that are already using AI capabilities to increase revenue and customer satisfaction. But before we dive into real-life case studies, let’s see what makes this strategy work.
Many companies mistakenly believe that integrating AI requires building it from scratch. Logically, it means they need extensive technological expertise and resources to start using AI. But the truth is that gaining a competitive edge through AI doesn't require building a brand-new technology. By embracing pre-existing AI solutions, companies can mitigate risk and accelerate time-to-market without building solutions from scratch.
But how do you set yourself apart when everyone is using the same foundational models? While AI tools are accessible to all, your business data is what truly sets you apart and can become your competitive edge. By training AI models with your own datasets, you can build a unique product that meets your business objectives and offers value to users.
Succeeding with AI isn't necessarily about starting from square one. It's about smartly integrating existing AI solutions and training them on unique datasets. This approach allows companies to stand out and maximize the value they get from AI technology.
Here is how you can adopt this strategy step-by-step.
The first step in leveraging AI effectively is to understand how your business could benefit from it. Identify the specific pain points, inefficiencies, or opportunities where AI can make the most impact and optimize your business processes. This way, you can ensure that your AI investments deliver tangible value.
Don’t rush investing all your resources in AI adoption right away. Instead, begin with targeted AI implementations that address specific use cases or processes. By starting small, you can minimize risks and gain valuable insights into how AI can be adopted on a broader scale. As these initial projects prove successful, you can gradually expand AI adoption based on achieved results and with your business goals in mind.
Experimenting is key to avoiding the waste of time and money when it comes to AI adoption. Explore and test AI solutions, even if it means your AI projects can occasionally fall short of expectations. Remember, failure is often a stepping stone to success, providing valuable lessons that can inform your future AI initiatives.
Every AI project should have a clear goal and measurable outcomes. Take Walmart's AI adoption as a cautionary tale. In 2017, the company introduced AI-powered robots for inventory management aiming to speed up the process. But in 2022, Walmart realized that robots weren't adding value, because humans could handle the job with much less time required. This is a perfect example of why businesses should only use AI when it solves a real problem, and not just for the sake of innovation.
To avoid ending up in the same situation, establish clear business metrics upfront to evaluate the success of your AI project objectively and early on. For instance, instead of setting a vague goal like "improve inventory management," a clear business metric could be "reduce inventory holding costs by 15% within one year" or "increase inventory turnover rate by 20%." These specific, quantifiable metrics provide a clear benchmark to measure the effectiveness of the AI solution and ensure it aligns with the company's strategic goals.
The AI project doesn't end once a solution is implemented, it's always a work in progress. Regularly evaluate AI solutions based on users’ feedback and evolving business requirements. Iterate on design, functionality, and performance to ensure your solution stays competitive and relevant on the market.
By following these steps, your company can adopt AI efficiently, getting the maximum benefit at minimal investments. Now let’s see how smart companies are adopting AI and why it’s essential to ignore widespread beliefs that don’t prove to be true.
There are several common beliefs about AI that often lead companies down the wrong path. Let's break down these misconceptions and highlight the better approaches that smart companies are taking to embrace AI.
Usually, companies believe they need to go big right away, aiming for a full-scale AI project and implementing the final product in one go. This "big bang" approach might sound impressive, but it's often risky and comes with a hefty price tag. Instead, smart companies think like startups: they start small.
Rather than trying to build something almost as big as a rocket ship, smart companies focus on specific, manageable projects. Whether it's improving customer onboarding or enhancing early engagement, the key is to pick an area where AI can make a real difference. By picking achievable goals and proving success with a Minimum Viable Product (MVP), companies can test whether AI adoption is worth the effort and gradually expand the adoption across other business operations. This approach minimizes risks and allows companies to see tangible results quickly, setting the stage for broader AI adoption in the future.
Mercury Financial offers a compelling example of the Think Big, Start Small strategy in action. Founded in 2014 to help customers restore their credit profiles, Mercury understood early on that AI was key to delivering personalized customer experiences fast. Instead of following a big-bang approach, Mercury took incremental steps to integrate AI into its operations, making smarter use of resources and reducing risks.
The task was to help customers improve their credit profile, whether by nudging them to manage multiple credit card payments or warning them about nearing credit limits.
Mercury didn't try to build its own AI technology from scratch. Instead, they began searching for an AI-driven engine that could give every customer the nudge through the right channel and in the right sequence. By customizing available AI tools with their own code, they learned what strategies work the best for each customer, and created a personalized system that efficiently uses hundreds of variables, delivering payment-related messages at the right time to each customer. This approach allowed them to experiment without wasting time and money on building new technology from scratch, and continually improve their customer engagement strategies.
By drawing on AI potential to personalize their customer experience, Mercury’s pilot program led to a 10% improvement in customer satisfaction within six months. By starting small, learning along the way, and gradually scaling their AI project, Mercury enhanced personalization and obtained meaningful results.
In a rush to be the first on the market, many companies try implementing AI without having a clear business goal in mind. Simply adding an AI-powered text rewriting feature to your product without a well-defined purpose will not lead to a meaningful impact unless it's tied to measurable business goals. Adopting AI 'for the looks' is a risky bet that can’t promise great results or impressive ROI.
A smarter approach to AI integration begins with defining your business needs and goals. Start by identifying specific challenges and opportunities your business faces. This involves evaluating existing processes, customer interactions, and market demands that AI can address. By aligning AI implementation strategy with your business needs, you can ensure that it adds genuine value and drives meaningful outcomes.
Harvey is one of the examples of how you can adopt an existing solution to your specific business needs. Founded by an attorney and an AI researcher with experience in large language models (LLMs), the startup wanted to solve a growing complexity in legal work. Traditional methods of sifting through thousands of contracts and emails were inefficient and time-consuming so they decided to streamline these tasks with the help of AI.
Instead of rushing to implement a generic AI solution, Harvey opted for a thoughtful approach. They partnered with OpenAI to create a custom-trained case law model tailored to the specific needs of legal professionals. This custom model was designed to handle tasks requiring complex reasoning, extensive domain knowledge, and capabilities beyond open-source solutions. By focusing on the unique challenges of lawyers, such as drafting documents, answering complex litigation questions, and identifying discrepancies between contracts, Harvey ensured that their AI solution was both relevant and impactful.
The approach taken by Harvey paid off significantly. They managed to create a case law model where every sentence is supported with the specific case it’s citing which can’t be done with open-source options. As a result, their revenue increased by 10x in 2023, and the company raised $80M in Series B funding at a $715M valuation. By focusing on specific business challenges, the company invested in a tailored solution that was perfectly aligned with the pain points of their customers.
Quite often, project managers tend to lean on traditional methods like Waterfall or Scrum when handling AI projects. Although these methods are popular and effective in the software development industry, they aren’t well-suited for AI projects that pose more risks and demand a lot of flexibility. The linear, sequential nature of Waterfall and the fixed iterations of Scrum can’t accommodate the iterative, data-driven, and often unpredictable nature of AI development.
This is why smart companies are moving away from traditional methods and exploring alternative methodologies tailored for AI projects. One such innovative approach gaining traction is Cognitive Project Management for AI (CPMAI). CPMAI integrates Agile principles with AI-specific and data-centric focus, offering a more flexible, adaptive, and collaborative framework for managing AI projects.
Here are the 5 main characteristics of CPMAI:
By adopting CPMAI, companies can ensure that AI projects are delivered on time, within budget, and meet business goals. Let’s see how Salesforce adopted CPMAI to deliver their AI-powered platform.
Salesforce's Einstein AI platform embodies the iterative and incremental nature of CPMAI. Unlike traditional software projects that follow a development cycle with distinct phases (requirements, design, development, testing), Einstein AI algorithms are continuously trained and refined thanks to customer data and feedback fed to them 24/7.
The success of Einstein AI relies heavily on data-centric decision-making, a core characteristic of CPMAI. Salesforce uses vast amounts of customer data to adapt and enhance AI predictions, recommendations, and insights in real time, ensuring they remain relevant and effective. By analyzing this data, the platform can generate more accurate predictions, personalized recommendations, and actionable insights that help businesses drive sales, enhance customer engagement, and optimize business processes.
CPMAI enabled Salesforce to integrate AI seamlessly into its existing business processes and workflows. This allowed for a more streamlined and efficient adoption of AI capabilities across various departments more effectively than traditional methods would allow.
Usually, businesses prioritize technical metrics measuring the success of their AI solutions. While model accuracy, training time, and computational efficiency are important for assessing the performance and efficiency of AI algorithms, they don't guarantee successful AI implementation or optimal results.
Smart companies take a more holistic approach and consider metrics that align with their business needs. They don't only include direct financial outcomes, but also key performance indicators (KPIs) that reflect customer satisfaction, employee productivity, operational efficiency, and other strategic goals.
Let's dive into why focusing on business metrics over technical metrics such as accuracy is crucial, using fraud detection and ways to manage it as an example.
Accuracy is an important technical metric that measures the performance of an AI model. But it doesn’t distinguish between different kinds of errors, treating false positive and false negative cases as equally bad. In fraud management, the difference between these two errors is significant in terms of costs. When a bank’s model wrongly blocks a legitimate credit card transaction as if it were fraudulent, that’s a false positive and could cost the bank $100, given that the user might use another card. But when the bank’s model wrongly allows a fraudulent credit card charge to go through (false negative), that could cost the bank $500 on average.
Imagine a medium-sized regional bank, serving 100,000 credit cardholders. Each cardholder typically makes around 1,000 transactions per year, totaling 10 million transactions annually.
With 10 million transactions and a fraud rate of 0.1%, the bank could be looking at annual losses of $50 million. However, by implementing a fraud detection model that is aimed at minimizing overall costs rather than just maximizing accuracy, the bank can mitigate these risks. The goal is to create a fraud detection model that balances between catching less costly false positives and avoiding more costly false negatives.
By making these trade-offs, the bank could potentially save around $16 million annually, significantly reducing its fraud-related losses. While accuracy is important, it's just one piece of the puzzle. In the end, it's the business metrics that truly matter, as they reflect the model's impact on the company and its customers.
Many companies consider hiring AI specialists in-house to integrate AI capabilities and have better control over their projects. But in reality, hiring full-time AI developers can lead to unnecessary costs and time losses.
First of all, hiring full-time employees comes with a hefty price tag because of recruitment costs and overheads. Additionally, after all the efforts put into team assembling, the members can quit your company and you’ll have to start all over. And what if your AI project doesn’t succeed? With AI, risks are much higher than with any traditional software project and there’s always a possibility that you will invest time and money in a team that doesn't pay off.
A smarter approach is to start by hiring external experts for a pilot project. This way, you avoid the high overhead costs associated with hiring and maintaining an in-house team. Second, you gain access to a broader range of expertise and experience, as software development vendors often have experience across multiple industries and use cases. Lastly, if the project doesn't yield the expected results, you can easily pivot or terminate the engagement – and it's not quite the case if you have a full-time in-house team.
By beginning with a pilot, you can validate the concept and get results early on, immediately getting the user feedback. Once the pilot proves successful and aligns with your business goals, transitioning to an in-house team becomes a viable option, allowing for product refinement and updates.
At Modeso, we're all about delivering quick, lasting value. Our Lean product development approach means we get your ideas off the ground faster with fewer risks. Here’s our step-by-step process that has helped launch dozens of software products quickly and successfully.
We kick off by syncing with your product vision and project scope, ensuring clarity from the get-go. We pinpoint the problem your product aims to solve, outline the key features for your MVP, define functional requirements, and provide a detailed estimate of the project's cost to align expectations and goals.
Before diving into coding, Modeso ensures your product's viability through prototype validation with real users. We design the UI/UX, develop a clickable prototype for hands-on interactions, test it with potential users, and continuously iterate based on their feedback to refine the user experience.
At this step, we bring your MVP to life, integrating cutting-edge AI capabilities. We rigorously test the functionality, ensuring stability and performance, and implement DevOps practices to streamline deployment.
At Modeso, we don't just launch and leave; we're committed to the ongoing success of your product. We provide application maintenance services and Service Level Agreements (SLA), and continually enhance UI/UX and functionality. We ensure your product evolves by upgrading the technology and responding to user needs with new feature development.
At the growth stage, you can transition to a dedicated team model for ongoing product development and even partner with us through Modeso Ventures to ensure your product's sustained growth in the long run.
Accurate, relevant, and comprehensive data is the foundation for any AI model. Whether you're looking to integrate AI to streamline business operations or improve customer satisfaction, the quality of data will significantly impact the success of your AI product.
Here are several tips on how to ensure optimal data quality:
Gather a variety of data that can impact the quality of your solution. For instance, if you're using AI to automate email sequences for lead nurturing, you can integrate data from numerous sources: email marketing software, CRM system, LinkedIn or other social media, Google Analytics, purchase history, and such. Every data source plays a vital role in training your AI model.
Quality beats quantity. Invest in robust data cleaning and validation processes to eliminate errors, inconsistencies, and biases within your dataset.
Business needs and trends evolve over time, and so should your data. Regularly update your dataset with new information to keep your AI models relevant and effective.
Break down departmental silos and give access to your data model to employees from various departments. Their insights can provide valuable perspectives on updating and refining the dataset with new features and information.
As your business grows, so will your data needs. Invest in scalable and secure data infrastructure capable of handling the volume, variety, and velocity of data required for AI applications. Make sure your data infrastructure can support future growth and evolving AI requirements.
Data isn’t the only challenge when it comes to AI adoption. Here are some other key technical considerations when planning to integrate AI.
While AI holds great promise to transform businesses, there are several challenges that companies often face when trying to incorporate AI into their operations. Take a look at the most common challenges of AI implementation:
In industries where data is predominantly unstructured or offline, such as retail shops, capturing relevant data can be a significant challenge. Without comprehensive and structured data, AI models may lack the insights needed to make accurate predictions or recommendations.
To allow for refining and updating your AI solution, it's crucial to have a scalable infrastructure. Systems should be built on loosely connected microservices to allow for easy scaling. Without a scalable architecture, AI solutions may be limited in their scope and effectiveness.
AI systems require ongoing data input, monitoring, and adjustments to adapt to changing conditions and improve performance over time. Companies need to continuously train and optimize their AI models to ensure they remain effective and relevant.
With the increasing reliance on data for AI applications, companies face growing concerns about data privacy and security. Ensuring compliance with regulations like GDPR or CCPA, safeguarding customer data, and implementing robust security measures are essential but challenging aspects of AI adoption.
Integrating AI solutions with existing IT infrastructure and systems can be complex and time-consuming. Ensuring seamless integration without disrupting ongoing operations or affecting system performance is a common challenge faced by companies adopting AI.
By addressing these challenges, businesses can successfully integrate AI into their processes. But there’s another key aspect that guides the entire implementation process – AI project management differs greatly from managing traditional projects.
According to surveys, 70% of businesses see no or minimal impact from the introduction of AI systems.
One of the significant insights from successful AI implementations is that AI projects shouldn't be treated like traditional application development or functionality-driven projects. Instead, they should be viewed as data projects or even data products. Unlike traditional software projects that start with a clear idea of the expected functionality, AI projects begin with data and focus on extracting insights or actions from it, shaping the product in the process.
Let’s break down how AI project management differs from managing traditional software development projects:
To successfully manage AI projects, you need a comprehensive approach that addresses both technical and business challenges. Here are the strategies that project managers should consider:
Familiarize yourself with the stages of AI project development, from data collection and preprocessing to model training, validation, deployment, and monitoring. Each stage has its challenges and requirements that need careful planning and execution.
Understand the data's origin and ownership rights to ensure compliance with regulations and ethical considerations. Implement data governance practices to maintain data integrity and security throughout the project lifecycle.
Establish clear communication channels, roles, and responsibilities. Encourage collaboration between data scientists, engineers, business stakeholders, and other team members to ensure everyone is aligned with the project's goals and priorities.
Recognize the potential for growth and evolution in your AI solution. Plan for scalability from the outset by designing flexible architectures and infrastructures that can adapt to increasing data volumes, user loads, or new business requirements.
Shift the focus from purely technical metrics to a broader set of business metrics that reflect the AI project's impact on business goals. Monitor key performance indicators (KPIs) such as revenue growth, cost reduction, customer satisfaction, and ROI to evaluate the project's success and alignment with business objectives.
Adopt a novel approach that integrates Agile principles with AI-specific and data-centric details. This approach emphasizes adaptability to change, collaboration across teams, and a focus on delivering business value. It encourages iterative development, continuous learning, and rapid adaptation to new insights and requirements.
Be willing to adapt and experiment with various AI methodologies and techniques. Traditional project management approaches may not always be suitable for AI projects. Explore alternative methods that emphasize flexibility, experimentation, and iterative development.
Identify the right time and approach to scale your AI solution based on business needs, user feedback, and performance metrics. This may involve adding new features, integrating with other systems, or expanding to new use cases or markets.
These are the essential aspects of AI management to follow in your project.
But as with any other software development project, one of the main factors that contribute to the eventual success is working with a team capable of taking ownership of the project. At Modeso, we provide full-cycle custom software development services and are accountable for successful deployment.
Here are the key takeaways for successful AI adoption.
Impressive vision is great but you need to divide your goals into smaller, achievable steps. Instead of investing tons of resources at the very start, remain flexible and take one step at a time to see what exactly will suit your specific use case and how to achieve it with minimum losses.
Ensure that your AI projects are closely aligned with the broader strategic goals of your company. Consider not only technical metrics such as accuracy but first and foremost KPIs that reflect customer satisfaction, employee productivity, operational efficiency, and other strategic goals that drive tangible business outcomes.
Invest in robust data quality assurance processes so that the data used for training and deploying AI models is accurate, relevant, and reliable. Make sure the data is easily accessible and available for analysis and model training.
Recognize that AI project management is unique and requires continuous adaptation. Establish an effective project management process to accommodate the evolving nature of AI projects, including data updates and model refinements.
By adopting existing AI tools instead of building one from scratch, companies can achieve faster time-to-market and lower project development costs. Modeso can help you build an AI pilot using our Lean software development methodology. Reach out to us to talk about your project.
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