Build your AI muscle at scale in 9 steps
The AI frenzy is here and shows no signs of slowing down – the global generative AI market will reach $51.8 billion by 2028. Generative AI plays a big role in data processing, creative outlining, and countless other data-driven capabilities, and more than half of US employees are already using generative AI in some form. Data teams and companies that rely on long data processes are finding massive reductions in labor hours with the help of AI. When deployed thoughtfully, AI can be a transformative force multiplier across all organizational functions.
But with so much noise about generative AI, some business leaders are falling into the fear trap: fear of missing out, being wrong, and compromising their proprietary data. Organizations that ignore AI or choose an AI tool quickly because they don’t want to miss out on the next big thing will likely not see transformative results. They may even find their progress hindered or worse.
How is AI Being Used
AI-powered software and applications.
This is AI embedded in software and applications, such as email spam filters, search engines, and customer relationship management systems. Organizations use AI to automate tasks, improve accuracy, and produce insights. AI can also support employees as a “helpdesk” agent, using documentation and manuals as a backbone.
Generative AI for personal productivity.
AI tools and applications such as ChatGPT, Jasper, and Copy AI are designed to help individuals be more productive. They can streamline scheduling, manage to-do lists, brainstorm ideas, improve writing, and summarize articles. They can even serve as co-pilots with technical skills like automated testing and code writing.
AI as a core capability to drive better outcomes.
AI can also transform businesses and industries. For example, AI is used in healthcare to develop new drugs and treatments, in manufacturing to optimize production processes, and in retail to personalize the shopping experience.
So, how do you take advantage of this transformational technology without risking customer data breaches? How can generative AI fuel growth within your business?
AI Uses in Marketing by State from Smart Insights
9 steps to build a successful AI strategy
Deploying AI thoughtfully and effectively requires an operational, systematic approach, with specific tools, guardrails, and policies to guide its use. While trained professionals have successfully used traditional AI like Machine Learning (ML) and Natural Language Processing (NLP) in controlled environments, Generative AI presents a broader access and usage challenge. This calls for a more operational approach, enabling your team to explore Generative AI’s potential while mitigating risks.
Here are 9 steps to consider as you begin operationalizing AI:
1. Start with a plan
Determine the problem(s) you’re trying to solve with AI and the business goals you’re trying to reach. It’s critical to have a clear vision of where you’re going so you can make informed decisions about selecting the right tools. It’s also essential to establish policies for continuous monitoring and system improvement to uphold legal, ethical, and social standards.
One example of a clear goal is: We aim to increase the quantity and, more importantly, the quality of content on our website. Not sure where to start? Tools like our AI Accelerator can help you workshop where Generative AI can add value to your processes and business functions.
2. Create a sandbox
Data security for your company, shareholders, and customers is the most important aspect of planning. Create a testing “sandbox” where your team can experiment with AI tools and build this muscle safely without introducing your proprietary data to the public – either by scrubbing the data or building a walled ecosystem within your tech environment.
A financial services company recently sought our guidance on this step of the process. Our team created a secure working environment with layers of protection for customer data, which allowed us to ask analytical questions using a form of generative AI called a Large Language Model.
3. Up your data game
Data fuels AI, meaning the “garbage in – garbage out” concept should be top of mind. If you haven’t already, put someone in charge of data strategy, which includes creating processes and policies to acquire, clean, protect, and operationalize data. This is especially important with AI – having clear data processes is key to reducing bias in AI decisions and output.
4. Keep the customer front and center
Consider how AI implementation will benefit your customers and end-users. Will it make their lives easier, or will the new tool inadvertently add extra steps or confusion? For example, many of our customers are improving first-level help response with more sophisticated chatbots that pull from a broader knowledge base. For end-users, this means being able to access help much quicker without frustrating transfers.
5. Prioritize ethics to build helpful, honest, and harmless AI
AI is ultimately based on human language and behavior, and all humans have inherent and often unconscious biases. Those biases can have dire consequences. Rather than hoping for the best, take proactive steps to prevent these unconscious biases from bleeding into your AI capability. That means being aware of bias from the earliest phases of the project, building continuous testing and validation processes, and maintaining thorough and transparent documentation.
6. Amplify security and data governance
Beware of security concerns and concerns related to data governance. AI, by design, seeks to connect and share data, including sensitive information like personally identifiable information (PII), protected health information (PHI), and confidential data. It’s essential to establish clear policies and procedures for controlling data exposure both externally (to public-facing AI tools) and internally (between departments). AI will attempt to share in all cases, so policies and procedures around data security and data governance are essential.
7. Remember change management
Implementing AI isn’t a simple technical update – it’s a significant shift that’ll change how your team works. Maintain clear and candid communication and take steps to help the team adjust and see the benefits.
Despite the challenges ahead, growth for your retail business doesn’t have to be put on hold. Your customers are more than just hoping; they expect to buy from brands that modernize to meet their needs as we enter the new year.
About 70% of significant technology implementations fail to meet expectations due to poor change management.
8. Embrace continuous learning
Watch out for teams or individuals becoming siloed in their work with AI. Establish a feedback loop between departments and stakeholders for continuous fine-tuning and improvement around AI use and policies. The most successful companies, like Amazon, Google, and Toyota, are built to experiment and learn continuously.
9. Check your progress and celebrate successes
Establish systems to monitor progress and track the AI success metrics you established in the first step. Create a communications plan to share how people use the tools and what they learn. Sharing examples of attempts, learnings, failures, and successes can inspire others.
As we’re already witnessing, AI can be a game-changer when done right. During the big data boom, post-internet companies like Amazon, SoFi, and Redfin unleashed data across their teams. They empowered their employees to use it for decision-making, optimization, insights, and improvement. It’s essential to take a similar approach to AI integration.
Generative AI will become a core capability you’ll need to develop to compete in the next economy. Start taking the time to invest thoughtfully and build your team’s AI muscle today – or risk being left behind.
Not sure where to start? Want someone to bounce ideas off of? Contact me and let’s talk!
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