Looking to implement a shiny new solution powered by artificial intelligence? It all starts with a well-crafted AI strategy. We take you through the steps to defining one.
With all of the excitement around artificial intelligence, it’s only human to wonder if you’re falling behind in the race. In fact, research we conducted recently showed that 78% of businesses have plans to invest in AI in the next 12 months, but 73% are unprepared to integrate AI into their operations.
Clearly, there’s a yawning gap between what businesses want to do and what they are ready to do at this moment. That can be a major concern in many types of business transformation activities – for example, investing in a new cloud platform or implementing a trendy marketing technique. But in the case of implementing AI, the stakes are high because the power inherent in the technology can have significant technical, legal, financial, and ethical implications for an organization.
Being circumspect is a strategic imperative, but when it comes to AI there’s even more reason to avoid coming out of the gate pointed in the wrong direction. The wind is gusting with great strength, and it won’t be easy to stop or turn around once you’re underway. It’s critical to start the journey toward your company’s AI-enabled future with the best possible strategy in place.
Find gain in the pain
According to our Strategy Director Chris Bradshaw, any strategic exploration of AI needs to start with an exploration of pain points and business objectives.
This is not just automation or speed. It’s about having the right insights into what your business needs, learning what your customers need, and accurately uncovering the opportunities for your unique situation
The allure of the technology can be quite magnetic, so it’s critical to stay grounded and not start throwing implementations against the proverbial wall. “You’ll want to do opportunity mapping,” Bradshaw says, “and determine why, where, when, or if you should deploy AI.”
For example, in our project for Philips’ popular NutriU app, the client sought deeper consumer engagement, so we leveraged machine learning models to recommend recipes and articles that would be highly relevant to individual users. We also supported the team that was creating a model for predicting food type and amount by leveraging image recognition.
Envision obstacles and opportunities
Once the opportunity has been identified, it’s important to examine the potential implications. AI can significantly impact your people and business processes, so you need to uncover what those factors will look like in practice. Here are some of the steps and questions you will want to begin asking yourself before you jump into product development.
Evaluate your data
Determine if you have high-quality data ready for processing and capable of generating valuable insights. It’s possible that it will be too unstructured or inaccessible to yield the desired results. If your data is unstructured, confirm that you currently have the teams and plan in place to do that work without outside help. (If you don’t, we can jump in.)
Thinking outside the box can help. When we worked with Mount Sinai Hospital on their prosthetic implant application AIdentify, there was a lack of reference image data, so we came up with a plan to crowdsource them from other medical professionals.
Examine your existing tech stack
Evaluate your current data platform’s ability to handle large datasets required for AI processing. Does your workflow system integrate seamlessly with generative AI tools? Are your dashboards equipped to display AI-driven analytics? Think about how much value is reasonable to expect from your tech stack. If a broad integration is a challenge for your organization, consider a smaller proof-of-concept play to avoid system gaps or unintended knock-on effects, and then expand in stages.
Consider how generative AI will impact your team
It’s important to recognize how AI can free up resources by shifting the nature of work from repetitive tasks to more strategic, creative, or analytical tasks. For instance, marketing teams might spend less time on data analysis and more on creative strategy, while finance experts could shift from number-crunching to financial forecasting and strategy.
Assess the regulatory and compliance issues
AI systems often process large amounts of data, some of which may be sensitive. Ensure compliance with data protection laws, and consider the implications of AI-driven decisions on consumer rights and non-discrimination laws.
Consider sustainability in your AI strategy
AI is a power-hungry technology. Considering its implications on energy usage, any climate pledges you’ve made, and the potential for offsets now or in the future.
Before pressing the go button and starting the development process, it’s vital to game out these scenarios (and more) to the best of your ability with the right team members on board and involved in the conversations.
Grab the right yardstick
Once you’ve identified the opportunity and plotted probable and potential impacts for your business model, you should also formulate a measurement plan for your AI strategy. If your initiative is about enhancing customer experiences, for example, you might measure success through metrics like customer engagement levels, personalization accuracy, or response time reductions.
It’s important to know what these metrics are telling you, so it’s a good idea to identify industry benchmarks for similar AI initiatives to set realistic and competitive targets for your project.
At the end of the day, verifiable and demonstrable results are key to unlocking the next steps in your AI journey and understanding your success.
Map your path
With the destination set and measurement modes established, your map can be created. Naturally, imbuing this map with value takes time and involves a lot of detail, but like any project, it’s critical to delve into roles, responsibilities, tools and tech, budget, and timing. You’ll want to outline the following, to name a few:
Establish who will own the project’s success. It could be a single stakeholder, a board, or a committee, but you will need to know (and agree on) who’s your ultimate responsible party. AI moves fast, and you need a strong hand on the rudder.
Know which departments will be charged with which tasks. As noted above, it’s possible that many of the capabilities you’ll need for your AI project exist in your current organization. Carefully outline the specific role for each: strategy, product, UX, design, research, engineering, IT, legal, compliance. By mapping these roles in detail, you’ll find the gaps that AI creates. Knowing the gaps, you can bring in help as needed.
Establish the milestones by which you’ll gauge progress along the way. Any technology project risks feature creep and scope growth. AI can multiply this phenomenon by opening up more capabilities and generating novel outputs along the way. Be realistic and disciplined when you create these markers.
Determine which tech will be shortlisted or selected. Again, there are many options, so it’s vital to do the required research, survey the competition, and think through all your short-, mid-, and long-term needs.
Agree on the investment that will be needed. Look at the integration and licensing costs, of course, but also the costs related to scaling and the downstream impacts AI will have on staff re-skilling or new hires you may need, as well as new agency partnerships you may want to leverage.
There’s no magic “map it” button to press for your company (at least not yet, but maybe we can build one!), so this is the part of the process where we put pencils to paper with the right level of detail, where teams can rally together, and everyone knows where to go next.
Bake in continuous improvement
Due to the fast-evolving nature of technology, it’s essential to plan for feedback loops and ongoing optimization in your AI strategy. This is not a situation where teams can meet, share ideas, and then disperse to their corners of the organizational universe.
Implement regular feedback sessions, user surveys, system performance analytics, and real-time monitoring dashboards to gather insights on AI performance and customer satisfaction.
Define clear checkpoints in your AI project timeline for assessing progress, such as after the integration of a new data set, a significant update to the AI model, or following user experience enhancements.
It’s always a good idea to keep a record of lessons learned and best practices from each improvement cycle. This knowledge base becomes invaluable for future AI projects and initiatives.
Set sail for success with your AI strategy
Once your strategic foundation is formed, you’ll be in position to begin the implementation process that will help you meet your business goals. “AI helps you leverage the superpowers of your teams,” says Bradshaw.
That’s the real promise of artificial intelligence. When conceptualized and calibrated well, AI can free up teams to do what they do best: to think harder about the next problem, to brainstorm better about the next opportunity, to anticipate and respond to unexpected changes along the way; to work smarter and not harder. And what business doesn’t want that?