- 8th May 2019
- Posted by: Manolis
- Category: algorithms, Artificial intelligence, machine learning, robotics
In these transformational times, it’s hard to ignore the headlines heralding the power of big data and artificial intelligence (AI). For the most part, these discussions center around dramatic breakthroughs and how these technologies are remaking entire organizations and industries. Will AI really threaten millions of jobs or even human survival?
A more constructive approach might be to think of AI more in terms of value creation, competitive advantage, and future growth for your company. For example:
- Do you know the threats and opportunities AI creates for your organization?
- How can you leverage AI to create value for current and future customers?
- What human and data resources are required to build a solid AI capability?
Here are eight steps to take that can help you move forward and accelerate your progress—including some missteps to avoid at all costs and two essential keys to building a competitive advantage.
1. Find AI partners (do this first!)
The commercial use of AI is relatively young, but it is evolving rapidly. Human practitioners with intelligence and experience are invaluable. When building your team, look for people who have proven experience in implementing AIs to guide you. The AI space is noisy and full of folks who may understand data and concepts, but not how to implement these technologies in the real world.
With experienced team members, you can avoid false starts and project delays. Beyond their advisory roles, the right partners can help you begin to chalk up early wins and build momentum, rather than simply making presentations about what good AI practices look like.
Pro tip: find these partners as soon as you can, because as more companies realize the strategic importance of AI, talent becomes increasingly more expensive and hard to find.
2. Hire key builders
Key builders are the most senior members of your team, and they truly are the key to your AI capability—the visionaries, architects, and leaders who can help your enterprise achieve 10x future outcomes. Key builders know how to navigate the nooks and crannies of implementing AI within your enterprise, and how to deal with industry and company-specific challenges.
You need team members embedded in your environment to unlock these challenges that may block your progress. Key builders will often add value by telling you what not to do, and by translating your team’s learnings into a specific vision and architecture for your company’s AI future. Again, find your key builders early and connect them with your AI partners and your company’s senior leadership to launch your AI capability.
Your partners and key builders can help you create awareness in your organization about what AI is, why it is important, and why rapid implementation is critical. This awareness will help you identify and prioritize opportunities to use AI in your organization. Broad awareness also enables you to identify talent and resources within the organization who can help you drive progress. When scouting for potential AI team members, choose existing employees who have the domain, data, and business expertise to be successful and quickly add value.
3. Create awareness and build trust
Perhaps most importantly, early awareness and involvement can help you build trust for the use of AI in your organization. There is nothing more frustrating than building a great AI solution only to discover that people don’t trust it and won’t use it. You can prevent this early on by building awareness and credibility in small ways before asking your organization to trust AI capabilities for live production and heavy lifting.
4. Identify opportunities for competitive advantage
It may seem odd to say that you can build a competitive advantage even before starting to build your AI capability. How can this be? To a key AI builder, the important question is, “What do I build, and why?” By combining AI experience with customer knowledge, industry trends, and competitive intelligence, you can discern which AI capabilities will benefit your organization.
This simple tactic is a powerful advantage. Identify, understand, and prioritize opportunities now to provide a roadmap for current and future AI capabilities. Use your partners and key builders to lead the way. Like it or not, you are racing against industry and non-industry competitors that may already have AI capabilities and are creating value from these resources.
5. Identify data for competitive advantage
Data is the fuel that enables your AI capability to learn and grow to the point where it’s a powerful competitive advantage for your organization. So, another power move you can make before you begin to build your AI capability is to identify appropriate data resources. Once you have figured out the most important questions to ask, you can identify the most important data set to create or obtain.
Remarkable value can be created by a simple AI that’s connected to just the right data set. You can pave the way for success by obtaining the essential data required to tap into your key opportunities. Now is the time to negotiate data partnerships—or better still, to obtain permanent, exclusive rights to key data. If the data you need doesn’t exist, find a way to create an exclusive data set that will afford your team a competitive advantage. It is difficult to take exclusive ownership of an AI opportunity. It is much easier to take exclusive ownership of the data needed to operationalize that opportunity.
6. Leverage free and open-source tools
Nearly all the best AI tools are open-source, and so everyone has free access to them. This trend is picking up steam as cloud providers have begun sharing cutting-edge AI tools. For example, Google makes TensorFlow, an open-source machine learning framework, freely available to attract users to their cloud offerings.
Open source tools work best with open-source tools—when you add proprietary tools, you add complexity that your AI builders will have to overcome. AI tools are, in general, a commodity. After all, if a tool is being offered to everyone on the open market, could it truly be a differentiator? To be clear, some paid AI tools can add valuable functionality. But it’s good practice not to add proprietary tools to the mix until your AI capability is operational, and you have determined proprietary tools are worth the costs.
7. Build on a foundation of talent
Although there is a race for top AI talent going on, it’s important to build your team in phases. The lessons you learn early on will enable you to define your needs more clearly than trying to fully staff your AI capability up front. For example, AI tools are abstracting more and more every day—meaning they are becoming easier to use and require less training.
More students and career-switchers are seeking opportunities to learn AI because of the promising career options. AI principles are being taught more widely, at younger ages, around the world. All of this means that the AI talent pool is growing rapidly.
As noted, your first year or so of building an AI capability should be focused on the higher-level needs, including:
- Bringing in partners and key builders
- Creating broad awareness across the company
- Surfacing opportunities
- Creating or acquiring data
- Prioritizing a road map
Bring these components together before you fully staff your team. When you are ready, the tools may be easier to use, the talent pool richer, and your vision clearer—and you will be able to demonstrate the kind of momentum that attracts top AI talent to your team.
8. Set appropriate expectations
As you gain confidence in your AI capability’s potential to drive future growth and competitive advantage, it can be tempting to paint a glowing picture of the future for stakeholders. You may have some management pressure to do so. But do not set early expectations about what AI will do for your organization in the future. If you set expectations incorrectly, you could be setting up your AI capability for failure.
Building AI is nearly always a discovery process—using what you know to determine what you don’t know, learning, and improving—and repeating the entire cycle until you converge on something exciting and valuable. It’s challenging to estimate progress in a series of scientific experiments because each one builds on the last. By definition, for any discovery process, you cannot know what to expect.
Get your AI capability up and running, and be clear on what is feasible now, in the short term, and in the long term. When you and your AI team are confident you have created a powerful foundation to support predictable, measurable progress, then you can start setting expectations.
Take your AI capabilities beyond the buzz
It’s true that AI is one of today’s most-hyped technologies, and more and more companies claim to have expertise in creating AI-powered solutions. But beyond the buzz, it is a fact that having a strong data strategy and AI capability is critical to every organization’s success and survival.
Mastering AI and data is one of the most important things today’s organizations can do to transform themselves into exponential enterprises, achieve exponential growth, and avoid disruption. The companies leading the way in AI also lead the way in breakthrough results in this time of disruption and accelerating change. By taking a focused approach to building a world-class AI capability, your organization can join the ranks of the exponential leaders as well.