To successfully scale AI, get your ducks in a row early. That means
1) knowing what market value means to you,
2) turning that concept into a business strategy,
3) concentrating on AI strategies that deliver on the most important components of that strategy directly. In your own specific context, if you have already established value, you can use AI to multiply the value, not just increase it slightly, plotting a path that truly aligns with the strategic goals of your company and produces unparalleled returns. Strategic Scalers accept this necessity, with over 70 percent directly relating their AI goals to their overall business strategy.
Each day, it seems that more and more AI applications are emerging. But how do you decide which apps, whatever that means for your particular sense, are going to provide value? Finding true value begins with identifying what really matters to an organization and aligning the AI agenda with the strategic plans of the highest level. Ask yourself: What are the short- and long-term goals for the boardroom? How can AI help accomplish the C-suite such as organic growth, expansion into a new market, or new product development?
Although you need to look at the short-term return AI can produce for your business, through a broader lens, you also need to look at value and therefore your high-level objectives. In the 'human-plus-machine' age, where is your organization headed? What is the industry's future? Would this shift the way you perceive worth in the next three to five years?
Beyond individual firms, AI has the ability to disrupt well. It is already blurring conventional market boundaries, threatening legacy businesses, and creating the ability for agile new entrants to make an impact quickly. Make sure that you pay attention to what is already affecting your business, how society and the world as a whole are shifting, and change your plan, behave bravely, and invest in order to buy your way into action. When you put the macro into play, you will find yourself making numerous choices.
Think of your AI ventures as a portfolio of things you're trying to do in order to be good on your AI journey. This implies holistic thinking about where you're going and handling the iterative nature of AI programs while staying consistent with strategy and value. Scaling value relies on a formally defined AI roadmap that can help you produce with more rigor more quickly and get to production faster.
The first step of the life cycle is to build an "idea pipeline" and populate it with possible ideas of AI that are yet to be evaluated for your company's viability and value. Iteratively, but rapidly, form, create and examine certain ideas before a "go/no go" decision. In terms of their potential to succeed, the ideas you produce will differ, so it will be important to have a holistic view of the collective success of your AI projects.
Assimilating AI into your organization, therefore, brings a new form of risk of project execution with just a portion of your concepts and experiments that are supposed to go to production. But the good news is that, like the one here, following an AI roadmap helps to easily and efficiently qualify ideas, so ideas that fail, fail quickly, and can be shelved before moving on to something else with minimal investment.
Every journey of AI transformation starts with data. Research shows that almost 75 percent of AI Strategic Scalers agree that a significant success factor for scaling AI is a core database. More precisely, they acknowledge the significance of having a data strategy, a design and intent that underpins what information is collected, in what manner, and for what reason. As much as AI does, the data strategy drives value.
And it is not always better with more info. It can be enticing to collect more and more in a world where data is proliferating, and data produces more data. Having a powerful data strategy means that you curate the correct information to produce the desired result and then collect your insights to fuel an AI strategy that delivers the result at speed and scale.
Data can be mined to produce insights that help optimize both the strategy of the company and the AI systems themselves once the data strategy is set. You will need to specifically incorporate "feedback loops" into business decisions in an organized way to really get the best out of this endless stream of data-driven insights, such as fine-tuning your business plan and/or making appropriate changes to your AI initiatives at the same time. This involves a new way of working: an agile, iterative approach to decision making with data at the heart, as well as AI development.