Here’s why you’re probably losing the AI race
As the AI arms race is becoming more heated, more organizations are looking to beef up their competitive advantage, but the current state of AI has yet to live up to most of the hype. Digital transformation is the goal, but with 1.3 trillion spent last year and a 70% failure rate, you might need to take another look at your approach. Here are some of the most significant challenges holding your company back:
Even the best solutions are of little value if they don’t get implemented. Business leaders may lack vision or commitment to AI deployment, or struggle to develop a clear, comprehensive AI strategy for their organization. If change happens too slowly, the business falls out of pace with technological change. Leaders also need to be able to overcome functional silos and deal with potential conflict about the priority of AI within the organization. Even those who are able to develop an effective, holistic strategy will likely face resistance to change in their day-to-day operations.
Limitations in current technology & talent
While AI development has come a long way over the past twenty years, current AI techniques still struggle to adapt past experiences to new circumstances. This is why most of AI’s progress so far has been in solving specific use-cases, rather than generalized learning techniques that can address large-scale problems. While computers excel at analyzing raw information, computers lack an effective cognitive model of the world that enables them to “think” in a top-down approach. As a result, humans still have a clear advantage over machines in transferring their experiences to new scenarios, and it may be decades before AI can close that gap, something that will require the cooperation of academics and corporate resources to tackle large-scale problems.
At the same time, there’s the challenge of talent: there is a global shortage of AI experts—300,000 AI professionals to fill millions of roles, and with fierce competition, it’s difficult to find (or develop) and retain workers with these specialized skills, particularly for smaller organizations.
Lack of quality data
It’s one of the principles of machine learning: algorithms are only as good as the data they’re fed. A major barrier to effective AI is the need to compile large data sets that are comprehensive enough to be used for training, with immense human effort often still needed to label that data before it’s suitable for machine learning.
Risk and mistrust
It’s not enough to know what AI can do; it’s also important to determine what it should do. While AI has many opportunities to benefit society and do good, its implementation may be challenged by a lack of confidence: can its security and privacy be trusted? Is there sufficient regulation and accountability for its potential impacts on human life? The “black box” complexity of deep learning means it can be difficult to explain the factors that led to an AI decision, which can impact trust in applications such as financial lending or criminal justice.
Here’s how diwo is tearing down these roadblocks
As a Cognitive Decision-Making Framework, AI capabilities are leveraged in a complete technology stack to create a human-machine symbiosis that is far more powerful than either a specific AI solution or humans on their own.
diwo’s business-first approach means that not only is it providing value on day one, it also prioritizes the business user’s unique context to focus on optimizing decisions—not just providing more “insights” like the latest advanced analytics tools. As such, it unobtrusively augments the user during their decision-making process.
diwo is designed to be scalable and can be rolled out according to each organization’s needs, and can also integrate existing data and analytics assets, without the need for duplication.
diwo also breaks down barriers to trust and reduces the “black box effect,” as users can interact with the results to see impact in real time if they ‘tweak’ these prepackaged decisions. A conversational persona that explains the reasoning for its suggestions is another groundbreaking interface that provides “guided conversation” where diwo works to understand the user’s intent in the decision-making process, not just “search BI.”
By building on past successes, it removes adoption roadblocks. And as an end-to-end solution, it minimizes the need for specialized talent, and its business-first approach dismantles silos by empowering users in their own unique contexts across the organization. With a completely new approach, diwo (data in, wisdom out) is leapfrogging new technologies to provide the wisdom of decision optimization rather than raw insights. This approach has already started recovering millions in revenue last year, so we’re all excited about the prospects of diwo in 2019.
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