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.
Financial Services: Preparing for the Looming Credit Crisis
The COVID-19 pandemic has significantly affected financial institutions – slowing the growth of loan originations, increasing credit costs, contracting economic activity, and causing record levels... + 2020-05-15
Can retailers find strategies that balance short-term recovery with long-term sustainability to lead them out of the COVID-19 crisis?
Retail and fashion business leaders are currently focusing on business continuity (“keep the lights on”) and crisis management (“sell the right stuff”), but soon they... + 2020-05-05
Transforming Business Decisioning in the Pandemic era is more crucial than ever.
Our human tendency toward incremental thinking limited us from foreseeing how “a few cases of the flu” would balloon into the impact we see today.... + 2020-05-01
Surviving COVID-19: How can Retailers free up working capital tied-up in inventory?
Among the many sectors affected by COVID-19, retail will be one of the hardest hit. The sudden decline is attributed to country-wide store shut-downs and... + 2020-04-23
Don’t let your shiny AI models lose their luster!
Businesses have made significant investments in building AI/ML models in recent times. While models are increasingly driving significant operational efficiencies and differentiation for businesses, they... + 2020-04-22
AI Modeling in the time of Covid-19
Companies of all sizes are facing unprecedented uncertainty and challenges due to the global impacts of COVID-19. It has created a major systemic shock to... + 2020-04-14
How to derive value from your AI investments with Decision Intelligence
Ultimately, a company’s value is measured by the sum of its decisions. In order to succeed, the organization must make and execute decisions—across all levels... + 2020-01-24
Why isn’t my enterprise getting value from AI at scale?
Why do most AI initiatives for business fizzle out? Why do so many teams’ best efforts to develop or deploy new algorithms or predictive models... + 2019-11-21
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... + 2019-08-01
Data In Wisdom Out
I am often asked by the curious, what do we actually mean by “wisdom out”. Is it just a marketing ploy or is there something more to it?... + 2018-10-31
Is Data a source of value?
We have always been exposed to natural and man-made events and have wondered and been impacted by their outcomes... + 2018-10-16
The Future of Decision Making: Human-AI Symbiosis
When we have to make an important decision, we face numerous challenges: uncertainty, complex data that’s difficult to interpret, competing priorities... + 2018-09-20
Digital Transformation- a $900B failure this year alone
Digital transformation has become a major priority for most organizations in some form or another, but for many, it’s proving to be quite the challenge... + 2018-09-20
So What’s AI’s Dirty Little Secret?
One could read the massively hyped claims of the trillions in productivity gains and then how far AI still has to go for real-world application... + 2018-09-20
How AI can rescue your BI “situation”
Even with a few credible upstarts in the past couple years, Self-Service BI still seems to be dominated by some large players that also require some very large and "very ongoing" commitments... + 2018-03-20
Business First! – how diwo aspires to flatten the knowledge pyramid
We can all agree on the fact that we are sitting on an unprecedented volume of data, and it is continuing to accumulate exponentially... + 2017-09-18
The Growing Market Impact Of AI
The future is always uncharted territory, and in the hype that currently sorrounds AI, with its ambiguous... + 2017-09-18
AI: How It Will Redefine My Job?
In the past few years, there has been an explosion in innovation and interest around Artificial Intelligence... + 2017-09-18
Wait Less, Sense More, Act Fast!
“If I had more legs, I could walk faster. If I had more hands, I could handle more, and if I had extra senses, I could respond wiser” – did you ever wish that?... + 2017-09-11
Unlocking the holy grail of UX design: bias-free user feedback in real time!
The potential of measuring emotional feedback from users is an exciting proposition in many fields, not least because it offers the possibility of unlocking... + 2017-07-25
What’s the Deal With AI Connectors
Artificial Intelligence (AI) is changing our lives, and these days feels more omnipresent than ever before. From Siri to autonomous cars... + 2017-07-25
Business Analytics-Why Search is Not Enough
Due to the popularity of internet searches, many businesses have attempted to adapt the search paradigm to tame their own unruly document clutter... + 2016-09-15
Drowning in data? Still no excuse for inaction!
Wouldn’t it be fantastic if we had a total understanding of the laws governing the reality around us? Imagine for a... + 2016-09-15