Artificial intelligence has a long history, having evolved from ideas rooted in antiquity. The ancient philosophers toyed with the ideas that human thoughts could be mechanized. Those early thought processes continued throughout the 1700s and beyond. In 1940, they eventually led to the invention of a programmable digital computer, the Atanasoff Berry Computer.
The field reached a key turning point when British mathematician Alan Turing created a test to measure a machine’s ability to replicate human actions to a level indistinguishable from humans themselves. Then, in the mid-1950s, a seminal conference at Dartmouth College produced the term ‘artificial intelligence’. From that point on, the field of AI exploded. Over subsequent decades, a host of scientists, programmers, and researchers catapulted the field from mere fantasy to every-day reality.
Many AI use cases are emerging into our lives, for example, self-driving cars. Once part of science fiction, these vehicles use deep learning to understand and navigate the space around them. Another common AI use case is automated machine translation in web browsers. Facebook broke new ground in 2020 with its enhanced AI tool, translating directly from one language to another, (such as from Chinese to French) with no need to involve English.
In 2021, AI is fast becoming a key driver for growth across multiple industries. Business leaders have become confident that AI and machine learning can transform their businesses across the board by streamlining operations, reducing costs, and boosting growth.
AI adoption grew massively between 2015 and 2019, with the number of enterprises using AI technologies increasing by 270%. According to a recent Forbes survey, customer service, sales, and marketing are three areas most likely to use AI—underscoring its potential to skyrocket business growth.
Think about your AI project from all sides
In the following sections, we recommend considerations for how you can fast-track AI projects, staff your AI team, develop an AI roadmap, improve AI security, and successfully scale your project.
Maximize project impact
First, consider where AI can provide a quick win. This is especially important if your organization is new to AI. You should aim to create maximum business impact with your project and increase the potential for senior management to buy into future projects. One way to do this is by closely aligning the project with your organization’s existing business processes. Ideally, your project will showcase how AI can improve business growth.
Decide where to create value
Have a laser-focus on where your project will create value. Examine your organization for inefficient processes, excessive costs, or places where better decision-making is needed. Can AI improve any of these areas? In addition, it’s a good idea to target your AI project at improving small repetitive tasks, instead of implementing large transformations. For example, an organization might use AI to mine social media data in order to gain insights about its reputation among potential customers, or to optimize logistics for managing corporate assets.
Get familiar with the data
Everyone who works in AI understands the importance of data. After all, garbage in, garbage out, right? Before starting your AI project, take a close look at the available data. Figure out what its limitations are and how they might affect your project. Is the data messy? How much project time will you need to spend cleaning it?
In general, most AI projects involve three key stages: scoping, building, and deployment. Let’s examine them in more detail.
Understand 3 key stages of an AI project
First, select the project’s AI use case. Then you’ll need to carefully define strategic business objectives and outcomes, manage stakeholder expectations, plan which resources are needed and define what success looks like.
This stage is an iterative process including the following actions: acquiring data, exploring it, preparing and cleaning it, engineering the model’s core features, testing the model, then running the model on the data. Model validation is also critical; how will you determine how well the model has achieved its objective?
This final stage is where your AI project gets deployed and starts to contribute value to your organization. After all that work, it shouldn’t just sit on the shelf. Ideally, the deployed model should bring significant value when used in production.
Staff your AI team appropriately
AI leader Andrew Ng recommends that an effective AI team be kept small and agile and include these roles:
- A leader (e.g., VP of AI), who understands the technology well, and can work cross-functionally
- Engineering roles: Machine learning engineers, data scientists, and data engineers
- A product manager (depending on the nature of the project)
For faster decision-making, the AI team should ideally have its own budget. It’s critical for team leadership to be able to think cross-functionally and work closely with business leaders.
What else makes an AI team high-performing?
- The AI team leader needs sufficient knowledge of the technology to understand what can and can’t be achieved.
- The technical team needs deep understanding of modeling and machine learning capabilities.
- A diverse team should be a top priority. Inclusive hiring helps lessen the potential of bias in your AI models. In turn, varied backgrounds lead to more creative thinking, which can further improve project success.
Use an AI roadmap
It’s easy to get carried away in experimenting with various AI projects. But that approach is risky, potentially wasting time and resources. To maximize your organization’s transformation to AI success, define a strategic AI roadmap early on.
The roadmap enables you to carefully plan and select the best opportunities for your organization.
Creating an AI roadmap involves evaluating a range of possible AI opportunities to identify those best suited to drive business value. This AI roadmap should then act as an overarching strategic guide to help your team prioritize projects down the line.
Basic AI roadmap phases:
- Explore a range of potential AI use cases
- Evaluate each use case in terms of risk, value potential, and implementation challenges
- Prioritize AI use cases according to strategic business objectives
Boost AI security
As AI has advanced, bad actors have developed new ways to leverage the systems to compromise organizations’ inner workings.
When organizations depend on AI systems to handle critical business functions, it further incentivizes hackers and cyber criminals. That’s why AI security should be a key concern for any new project.
AI security issues can take many different forms. Watch out for these common scenarios:
- Hackers and cybercrime. Bad actors are always searching for new ways to exploit organizations. Automation gives them a prime opportunity to do so. For example, machine learning can be used to create attacking scripts that can quickly take over a system before humans can identify them.
- Bots. Mostly discussed in the context of fake news, bots can also be leveraged as an AI security threat. For example, to gather critical user data, AI chatbots can form part of social engineering and phishing campaigns.
- Spear-phishing. Another AI security risk occurs when machine learning is used to create persuasive fake messages that imitate real humans. Skilled hackers can easily leverage this ability to run rampant through an organization.
Please don’t ignore AI security threats. Done right, machine learning can help you detect threats in real time. You’ll be able to understand your organization’s infrastructure and potential attack vectors, as well as know exactly when to bring in human and machine resources to mitigate threats.
Prepare to scale AI projects quickly
It’s a common problem, especially in large organizations, for new AI projects to stay stuck at the proof-of-concept stage. To experience real business transformation, it’s vital to learn how to scale AI. In fact, recent research from Accenture concluded that the most successful organizations abandon proof of concept altogether and take their AI projects directly to scale.
One key concern when preparing to scale AI projects is getting data into a suitable condition for effective modeling. Having pre-collected, annotated and validated training data in your project speeds up the process and enables you to get your product to market faster.
Measure AI performance with multiple metrics
Defining metrics to measure your AI project performance is an essential step, especially when scaling.
Effective measurement of AI project performance depends on several key metrics. Start with a set of suitable business KPIs, such as monthly recurring revenue or operating cash flow. These measure how your AI project will influence your organization’s overall success from a business standpoint.
Next, it’s important to evaluate how your AI project influences operational KPIs, such as cost per acquisition, retention rate, conversion rate, or number of trial signups. Ideally, the stronger the AI capabilities of your project, the more it will affect operational KPIs.
For example, let’s look at the Google Assistant. It contains strong AI capabilities. We could safely hypothesize that rising sales figures is an operational KPI that correlates with the performance of the Google Assistant AI model.
Finally, add a set of AI proxy metrics that quantify the accuracy of your AI model. These may not directly affect high-level financial KPIs but should positively correlate with them. Some popular proxy metrics include:
- Objective function
- Classification accuracy
- Mean absolute error (MAE)
When planning your project, evaluate these metrics before deployment, as a baseline, then again after project completion (to evaluate how the project has performed in relation to that baseline). Taken together, business KPIs, operational KPIs, and AI proxy metrics will provide a comprehensive picture of how your AI project performs.
In a nutshell
AI adoption can bring massive tangible benefits to your organization. When projects are managed intentionally and responsibly, you save time, money, improve customer retention, and level up your company’s competitive advantage. It’s even possible that you can produce unexpected insights that lead to entirely new revenue streams.
Build our recommendations into your overall AI plan, include key performance metrics and name who is responsible for gathering, analyzing and reporting them. Finally, have a mitigation strategy. Be able to identify actions your team can take should events which could negatively impact your project occur within your organization or AI team unexpectantly.
Let us help you fast-track your AI project. Visit our training data catalog today.