Manny Bernabe • 2023-03-30
I'm glad you're taking the time to explore this guide.
This guide is a culmination of the knowledge and insights I've gained throughout my 10+ years working with large corporations and startups in launching AI, machine learning, and data-driven products across various industries. I'm excited to share this with you!
Incorporating artificial intelligence (AI) and machine learning (ML) into traditional organizations can be a daunting task. However, the steps in this guide will significantly enhance your chances of success.
Here's a quick overview of the initial steps you should take:
I've designed this systematic approach to filter your AI ideas, evolve them to a strong business use case, and adapt them to changing market conditions.
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Now, let's dive deeper into each step for a more in-depth understanding.
“And, one of the things I've always found is that you've got to start with the customer experience and work backwards for the technology. You can't start with the technology and try to figure out where you're going to try to sell it.” — Steve Jobs
First, consider the needs of the business. I suggest approaching this in two stages: bottom-up from the business units, and top-down from the board.
Start with a bottom-up approach, where you examine each business unit's needs, initiatives, pain points, and obstacles. These units could be factories, regions, or functions like HR, marketing, sales, or operations. By understanding their specific goals and key metrics, you can present a compelling AI use case that addresses their concerns and helps achieve their objectives.
Figure 1: Linda Avery (Analytics at Verizon) on Mapping Business Needs Before AI
Next, consider the broader industry landscape and corporate initiatives identified by executives and the board. Stay informed about trends in your industry and align your AI projects with these initiatives to secure funding and executive support more easily. This research will help ensure executive backing for AI projects.
In the beginning, much of your AI work will involve piloting, proof of concepts (POCs), fact-finding, and managing cross-department dependencies. Executive support can provide the political capital needed to gain backing from other departments, such as sales, operations, finance, or IT. Aligning your AI projects with the company's overall strategy and industry trends will make it easier to secure this backing.
Figure 2: Digital Transformation Roadmap
Example Digital Transformation Road Map for Financial Services Company Source: “Digital Transformation” by Tom Seibel
Before diving into the implementation of AI technologies, it is crucial to first understand the business needs and identify the pain points that can be addressed with AI. Leading with technology without considering these factors may result in projects that, while technically interesting, fail to gain support from the business unit or executive team. Ultimately, such projects tend to end up being a waste of time and resources.
Now that you’ve familiarized yourself with business, let's get to matching those needs with AI technology.
“Start With the Customer and Work Backward”
— Jeff Bezos
Now that you've gained an understanding of your organization's business needs, it's time to begin brainstorming potential AI use cases. Embrace a creative mindset and aim to generate a diverse range of ideas at this stage. In the following steps, we'll curate the most promising use cases to ensure alignment with your objectives. So, without further ado, let's dive into the process of discovering impactful AI solutions for your business.
Figure 3: AI Use Case Mapping Example for Manufacturing
1. Establish a Clear Objective
Prior to delving into specific AI technologies (such as vision, text, or time series), it's crucial to determine your primary goal. What do you intend to accomplish with AI and analytics? Are you aiming to boost operational efficiency, improve customer experience, or create new revenue streams? Establishing your objective will streamline the process of identifying suitable use cases in the next step.
2. Choose a Use Case
It's crucial to convey these use cases in a manner that business unit managers and project managers can easily comprehend. For instance, rather than referring to "deep learning neural nets," mention that you're implementing "fleet analytics" to optimize inventory levels. Clearly defining the use cases will facilitate stakeholder buy-in.
Figure 4: Mapping Out the Industry Value Chain (Manufacturing)
List of use cases across the manufacturing value chain.
If the objective is to enhance operational efficiency, you might consider predictive maintenance, yield analysis, demand forecasting, etc. It's essential that these use cases address genuine pain points or functions within your organization. A marketing manager is concerned with "Sales Forecasting" rather than deep learning neural net models. Speak to the user's pains. This is very important.
3. Determine Success Metrics
Upon defining the use cases, establish the metrics to gauge the success of each AI implementation. This is critical for assessing the business value and ensuring the AI project's productivity and worth.
Decide how you'll assess the success of your chosen use case. In the case of predictive maintenance, success might be measured through downtime, response time, efficiency, or other pertinent metrics. This will largely depend on your "buyer" or "user." By outlining your metrics in advance, in close collaboration with your end user, you'll obtain a clear understanding of what "success" entails.
4. Evaluate AI and Analytics Technologies
Finally, with a solid grasp of business needs and objectives, begin investigating the diverse AI technologies that can tackle these challenges. Consider the relevant data types (e.g., time series, images, video, documents) and algorithms for the specific use cases. Additionally, explore related AI technologies and functions, such as simulation and synthetic data.
Figure 5: List of AI Algorithms
Note: This table provides an overview of machine learning algorithms, with each category encompassing a range of techniques. Remember that these categories can overlap, and some algorithms may be applicable to multiple types.
It's only after defining your objective, use case, and metrics that you should consider which AI and analytics technologies to employ. Numerous options exist, including machine learning, computer vision, natural language processing, time series analysis, etc. The aim is to narrow down the choices by using user and business needs a filtering mechanisms to select the most impactful technologies.
Figure 6: Example of AI Use Case for Manufacturer
Using this framework, brainstorm potential use cases for various business units, striving to generate a list of 10 to 20 promising ideas that address the identified pain points and objectives.
The key to successfully integrating AI solutions into your business lies in first understanding the organization's needs and objectives, and then identifying and prioritizing use cases that cater to those needs. By following this approach, your AI projects will prove valuable and garner the necessary support from the business unit and executive team.
"I think it's very important to have a feedback loop, where you're constantly thinking about what you've done and how you could be doing it better.”
— Elon Musk
By now, you have identified various needs and cross-referenced them with diverse AI technologies, enabling you to brainstorm a range of potential AI projects. Here are some examples to consider.
Ranking Your Best Use Cases
The next step will be to rate and sort out the best option for your team to pilot. Here there are several factors to consider.
Figure 7: Ranked AI Use Cases
It’s best to consult different perspectives across the organization on these factors. I recommend gathering this information in a collaborative way. For example, you might hold a workshop with business unit and corporate leaders as subject matter experts from IT and marketing to help access the impact of particular use cases. Additionally, you’ll need to consult IT about effort and data availability.
This will be one of the more challenging parts of the process, however, at the end of it you should start to see clear winners of a use case for you to consider. Use the top use cases as a jumping put to gather additional information on potential tools, vendors, and industry practices.
After you feel comfortable with a particular use case, it will be time to launch a low-cost, lean pilot to test your key assumptions, surface key risks, and validate user and business value.
I’ll be writing more about that in my next guide: “Lean AI — Testing your AI Concept in 4 Weeks”. If you want to be the first to update, sign-up below.
Integrating AI and machine learning into traditional organizations can be challenging, but by following the systematic approach presented in this guide, you'll be well-equipped to uncover business needs, generate AI use cases, and select the best ones to gain corporate support and delight your users. As you embark on your AI journey, remember that understanding your organization's objectives and aligning AI projects with those goals is crucial for success.
Don't miss out on our upcoming guide on testing AI concepts in 4 weeks or less, which will be available soon. Be sure to sign up for updates and follow me on social media to stay informed about the latest insights and resources. Together, we can revolutionize the way businesses leverage AI and machine learning to drive a success! Are you in?!
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