Creating an enterprise AI strategy can be an overwhelming prospect. Many companies struggle to succeed, which means wasted investments, misallocated time, and a failure to deliver on commitments that are made to shareholders.
But that doesn’t mean you should put off your AI initiatives—in fact, the companies who create winning strategies see massive success. According to arecent Forrester Studycommissioned by our team at RapidMiner, early adopters of AI expect to see a9.3x return on their investmentin the near future.
So, what’s preventing more companies from seeing this type of success? Based on what I’ve seen working with enterprises, most failures come from not having three critical things in place. If you want to develop a comprehensive enterprise-level AI strategy, you need:
- Strong, clear leadership to coordinate efforts from the top.
- Tooling that supports and empowers your employees to be change-makers.
- A change in company culture that promotes and prioritizes data-driven decision-making.
I think of these three things as three legs of a stool—they’re all equally important to the strategy, and if any one of them is lacking, you’re unlikely to succeed in your efforts.
If these three aspects aren’t in place, companies will often shift their focus to tactical AI projects designed to solve only one specific problem, like identifying churn or optimizing production. Reverting to this approach is one of the most common ways companies hamstring their own efforts to produce true digital transformations. Here’s why.
Embedded Data-Driven Decision-Making is Key for Enterprises
虽然小,但战术博士项目当然可以ive business value, at the enterprise level, it’s necessary to think strategically about whatAIandMLcan do for you. That’s because the real advantage of AI for enterprises lies inembedding data-driven decision-making throughout the organizationto attack inefficiencies, optimize processes, and get ahead of the competition.
These are important themes for many of the companies that we work with. Organizations that operate in commodities, for example, already have narrow margins and extensive optimizations in place—competitive advantage can be hard to come by. Firms that compete against foreign players are usually at a disadvantage when it comes to costs, labor, and even environmental and safety regulations.
Regardless of the situation, the only way to drive the next level of competitive advantage is to strategically identify multiple areas of your business where rapid high-volume decision-making would benefit from better in-moment insight.
A few fitting examples of use cases for embedded decision-support include product pricing in dynamic markets, ecommerce cross-sell offers, and fraudulent transaction identification. The more feasible use cases you identify, the sooner you can get started. The sooner you get started, the better.
Remember the “early adopters” from the Forrester study I mentioned before? They’re defined as companies that have 11 or more models in production—and again, this group expects their ROI to be9.3x in 2-3 years, compared to 5.5x公司有10或更少的生产模式. These figures highlight the importance of getting more models into production sooner to fully reap the benefits of data science.
In this blog post, we’ll explore the three aspects of a comprehensive AI strategy that are the most critical to your enterprise AI success and will enable you to get started down the right path, quickly.
The 3 Key Aspects of Creating an Enterprise AI Strategy
Knowing that the path forward for enterprises is a complete transformation and not just one-off projects, let’s take a deeper dive into each of the three key aspects of an effective enterprise AI strategy.
1. Leaders must lead
The first leg of the stool for any enterprise AI strategy is a leadership team that’s deeply committed to leading the initiative. This doesn’t mean they’ve simply signed off on a plan from someone on their team; they must be personally invested in the cause and willing to dedicate time and effort to drive change across the organization. A comprehensive enterprise AI strategy requires commitment from the top-down.
At RapidMiner, we’re fond of saying that data science is a team sport—and every team needs a coach who can make a plan and help each contributor reach their goals. Without a clear play-by-play strategy planned out with know-how from the top, you’re going to be stuck in the bush leagues, while your competitors are competing for championships.
Why leadership so important
Leadership buy-in is critical because leading a digital transformation and creating an enterprise AI strategy is going to touch every aspect of your business. You need top-down leadership to plan initiatives, make resources available, and create vision. There are simply too many moving parts in an enterprise to succeed without top-down orchestration of your efforts.
If you’re only looking at one tactical AI project—say,preventing churn—you can probably get away with nothing more than sign-off from the exec that owns the data and the resources. But if you’re looking to transform the way your organization does business, you need a top-down initiative to provide direction, create enthusiasm, and plan logistics like team structure and budget for each project.
Consequences of not having leadership
I can’t tell you the number of times that I’ve personally witnessed a push for an enterprise-wide AI strategy become listless and fail when a key C-suite advocate leaves a company. Without clear alignment and consistent direction from the entire leadership team, it’s just not possible to get everything in place to truly transform an organization with the power of AI and ML.
Want to learn more about the impact committed leadership can have on your organization? Check out our ebookA Leader’s Guide to Successful DXfor some real-world examples and tips to come out on top.
2. Tooling transformation
Although strong leadership commitment is the first leg of our AI stool, it can’t support enterprise-wide initiatives on its own.
Transforming the way that your company does business with AI and ML is only possible if you give employees the tools and resources that they need to be able to execute on projects in support of your high-level initiative. That means tools that are easy to understand, use, and integrate with the systems that you already have in place—a comprehensive enterprise data science platform.
Why tool transformation is so important
You can’t simply decide you’re going todigitally transformyour organization and make it happen without setting your employees up for success. Imagine running an initiative to adopt a flexible work policy for the first time without bothering to get licenses for something like Zoom.
That might seem like a silly example, but that’s only because hindsight is 20/20. I regularly see organizations trying to accelerate their digital transformation and AI strategy initiatives without seriously rethinking their tool stack and how everything fits together. And that’s no different than trying to support hybrid work without virtual meeting solutions and modern collaboration platforms.
Consequences of not having the right tooling
As with strong leadership, you might be able to get away without new tooling if you’re only trying to address a single challenge—and you happen to have an extremely knowledgeable domain expert who can also write code. But that simply will not work at scale.
我雇佣一个领域专家有很强的编码能力s a stretch—hiring a full team of them is a pipe dream. You need tooling that lets people with diverse backgrounds and skills build change cooperatively, playing to their strengths and encouraging collaboration rather than silos.
3. Upskilling and empowering your employees
And that brings us to the third leg of our enterprise AI strategy stool. Tools aren’t much good if you don’t have people who know how to use them, and who have bought into the vision of transforming your organization with the power of AI.
Providing tooling alone isn’t enough to succeed, though—you need to change your company culture and evolve the way your employees view their workflows and decision-making. Then, make sure they can productively use the tools that you’ve implemented. This involves hands-on tasks and training that let your employees improve their data skills and learn how they can meaningfully contribute to analytics projects.
We call this upskilling, and it’s an absolutely critical part of any enterprise AI strategy.
What upskilling provides
A far too common scenario that I see with enterprises just beginning to think about transformation is the idea of building a data science center—essentially just hiring a bunch of data scientists and setting them free.
We’ve previously addressed the problems that thedata science silocan create, but it’s worth restating here. The business world isn’t the place for experimentation, unless that experimentation is clearly creating ROI for the organization.
If you’re really going to succeed, it’s better to think about your data science efforts as a kind of factory rather than a research center. You need people throughout your organization who are looking at data, building models, and creating change to drive ROI for your enterprise—and you get there with upskilling.
Consequences of not upskilling
We recently published aguest blog postby Andreas Engler, who worked as the Director of Engineering at Nexible, a German insurance start-up that’s using AI to disrupt the industry. One of the biggest roadblocks that he discusses is the resistance that the entire industry has to true digital transformation. The mindset is simple: “This is how we’ve always done things, so that’s how we do them.”
I want to be very clear when I say this—the world is changing, and if you don’t change with it, you will fall behind your competitors.At best, you’ll have some catching up to do. At worst, you’ll end up so far behind that you have no chance of catching up.
Don’t believe me? Let’s look at the manufacturing sector. According toAccelerate Your Data-Driven Transformation, only 2% of manufacturers indicated that machine learning, artificial intelligence, and advanced analytics was their most important investment area 2-3 years ago. But in the next 2-3 years,56% of the same manufacturers anticipate it becoming the most important investment area.
You simply don’t have the luxury of not getting started with your AI transformation today, if you want to stay ahead of the competition.
Wrapping Up: Why You Should Get Started Today
By now, I hope you’re convinced that creating a comprehensive AI strategy is a critical first step for your business, that the three aspects above are essential, and that the sooner you get started, the better off you’ll be.
But I’m guessing that some of you still think you aren’t quite ready yet. Maybe you need more data. Maybe you’ve already planned out your upcoming quarter or fiscal year. Don’t fall into this trap.
The best time to get started with machine learning projects was yesterday. The second best time is today. The sooner you start your data science transformation, the sooner you’re going to start seeing ROI, and the more ROI you’re going to see in the coming years.
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