5 trends that will impact AI, analytics and data governance in 2020
Heading into the New Year, many enterprises are looking at how AI and data analytics can drive their digital transformation journeys.
Artificial intelligence at the enterprise level has truly reached critical mass. It’s no longer an “if” question, but more of an inquiry into “how” and “when” AI will be a major component of every strategic initiative.
So what started out as a way to perform simple tasks and data mining has become a crucial part of many organizations’ strategic planning and competitive decision-making.
Heading into 2020, many enterprises are entering a new phase, moving from experimentation and pilots to implementation across the organization, and looking at how AI and data analytics can drive the digital transformation journey.
In particular, there are five trends that will be key to enterprises’ competitive abilities.
The move to “transformation-as-a-service”
Enterprise executives realize they need to fundamentally transform major operations and services. More than that—they need to prepare to continuously respond to the latest technology changes that can have an impact on operations. The largest enterprises are embedding AI and machine learning tools throughout their organizations to help analyze data, increase efficiencies, predict consumer behaviors and gain competitive insights.
For many others, however, the time and resources it takes to get AI bots and programs in place can be daunting. The transformation-as-a-service model enables these organizations to gain access to AI technologies with basic tasks and knowledge already “baked in,” as well as other data, cloud and mobility technologies. What’s more, it helps these organizations to more quickly shift their technology mix as customer needs and demands change.
Customer experience is the main battleground
While efficiency will likely continue to be a major goal for enterprises, digital transformation is increasingly about reimaging customer experience and personalization. Rather than simply responding to customer demands, the most competitive organizations must predict what those needs will be and offer services and products that address those predictions. This means unlocking insights from alternative data sources, generating real-time competitive insights, and building rapid-response decision-making into experience programs.
High End Data Cables Feed Into Servers
Redefining experience can help to improve the bottom line for organizations by reducing errors and therefore costs but can also carry through to creating differentiated personalized experiences for end users.
The growing value of the “human-in-the-loop”
AI and analytics improvements have provided access to more data than ever before. In fact, by 2025 it’s estimated that 463 exabytes of data will be created each day. However, data on its own cannot drive business actions. The last mile of decision-making still belongs to humans.
Equally important is that as the cost for data is decreasing, the value of human judgment is increasing. Given this shift, and the resulting evolution of job functions and requirements, there is a vital need to reskill the “humans-in-the-loop.” In 2020, executives must close the gap on reskilling and upskilling programs to better meet worker and client expectations.
While improvements have been made, our research shows a disconnect between how employers and employees perceive reskilling—only 35 percent of workers say their companies have reskilling options, compared with 53 percent of senior executives who say they offer such training. Successful organizations will move reskilling out of traditional classroom settings and focus on programs that can better harness the collective intelligence of experts throughout their business.
The ethical governance of data, AI and digital
As the volume of data increases, so do questions about its use. From the algorithms that drive credit limits to the use of facial recognition software, AI-driven technologies are under scrutiny from consumers and governments alike.
As a result, many organizations are expected to add Digital Ethics Officers in the coming year. These officers will be responsible for implementing ethical frameworks to make appropriate decisions about new technologies, addressing considerations such as data security and bias. In addition to soothing immediate consumer concerns in these areas, these officers will also be looking ahead to the technology challenges that are still to come, building in new standards of governance for the intended use of technologies, as well as new check-and-balance systems that can ensure these preventative measures remain effective.
Increased modularity in the form of accelerators
By 2025, organizations that are AI leaders will be 10 times more efficient and hold twice the market share of those who fail to embrace the technology. Breaking down time and resource barriers to accelerate AI adoption is becoming a matter of survival for organizations—a matter that most executives recognize.
Introducing modularity, in the form of pretrained AI accelerators, is the first step to breaking down those barriers and democratizing technology. Already trained with the necessary domain expertise, this augmentation technology will be key to enterprises looking to leap ahead in the next couple of years.
In 2020, AI and data analytics technologies will become even more ubiquitous across industries. How enterprises understand and apply these technologies in the next year will play a pivotal role in how they can accelerate efficiencies both in the immediate future and over the next decade.
So what started out as a way to perform simple tasks and data mining has become a crucial part of many organizations’ strategic planning and competitive decision-making.
Heading into 2020, many enterprises are entering a new phase, moving from experimentation and pilots to implementation across the organization, and looking at how AI and data analytics can drive the digital transformation journey.
In particular, there are five trends that will be key to enterprises’ competitive abilities.
The move to “transformation-as-a-service”
Enterprise executives realize they need to fundamentally transform major operations and services. More than that—they need to prepare to continuously respond to the latest technology changes that can have an impact on operations. The largest enterprises are embedding AI and machine learning tools throughout their organizations to help analyze data, increase efficiencies, predict consumer behaviors and gain competitive insights.
For many others, however, the time and resources it takes to get AI bots and programs in place can be daunting. The transformation-as-a-service model enables these organizations to gain access to AI technologies with basic tasks and knowledge already “baked in,” as well as other data, cloud and mobility technologies. What’s more, it helps these organizations to more quickly shift their technology mix as customer needs and demands change.
Customer experience is the main battleground
While efficiency will likely continue to be a major goal for enterprises, digital transformation is increasingly about reimaging customer experience and personalization. Rather than simply responding to customer demands, the most competitive organizations must predict what those needs will be and offer services and products that address those predictions. This means unlocking insights from alternative data sources, generating real-time competitive insights, and building rapid-response decision-making into experience programs.
Redefining experience can help to improve the bottom line for organizations by reducing errors and therefore costs but can also carry through to creating differentiated personalized experiences for end users.
The growing value of the “human-in-the-loop”
AI and analytics improvements have provided access to more data than ever before. In fact, by 2025 it’s estimated that 463 exabytes of data will be created each day. However, data on its own cannot drive business actions. The last mile of decision-making still belongs to humans.
Equally important is that as the cost for data is decreasing, the value of human judgment is increasing. Given this shift, and the resulting evolution of job functions and requirements, there is a vital need to reskill the “humans-in-the-loop.” In 2020, executives must close the gap on reskilling and upskilling programs to better meet worker and client expectations.
While improvements have been made, our research shows a disconnect between how employers and employees perceive reskilling—only 35 percent of workers say their companies have reskilling options, compared with 53 percent of senior executives who say they offer such training. Successful organizations will move reskilling out of traditional classroom settings and focus on programs that can better harness the collective intelligence of experts throughout their business.
The ethical governance of data, AI and digital
As the volume of data increases, so do questions about its use. From the algorithms that drive credit limits to the use of facial recognition software, AI-driven technologies are under scrutiny from consumers and governments alike.
As a result, many organizations are expected to add Digital Ethics Officers in the coming year. These officers will be responsible for implementing ethical frameworks to make appropriate decisions about new technologies, addressing considerations such as data security and bias. In addition to soothing immediate consumer concerns in these areas, these officers will also be looking ahead to the technology challenges that are still to come, building in new standards of governance for the intended use of technologies, as well as new check-and-balance systems that can ensure these preventative measures remain effective.
Increased modularity in the form of accelerators
By 2025, organizations that are AI leaders will be 10 times more efficient and hold twice the market share of those who fail to embrace the technology. Breaking down time and resource barriers to accelerate AI adoption is becoming a matter of survival for organizations—a matter that most executives recognize.
Introducing modularity, in the form of pretrained AI accelerators, is the first step to breaking down those barriers and democratizing technology. Already trained with the necessary domain expertise, this augmentation technology will be key to enterprises looking to leap ahead in the next couple of years.
In 2020, AI and data analytics technologies will become even more ubiquitous across industries. How enterprises understand and apply these technologies in the next year will play a pivotal role in how they can accelerate efficiencies both in the immediate future and over the next decade.
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