Intelligent Automation Trends
This interactive report examines the current RPA/IA landscape in 2019 and what to look forward to in 2020. Some of the key questions answered include:
- How much are organizations spending on RPA?
- Which end-to-end processes do the bots sit in?
- How much efficiency savings, cost savings and increased accuracy are gained from automation?
- What are the key challenges in implementing and scaling automation?
- What are the top priorities for 2020?
- How data-ready are organizations for new cognitive/AI tools?
View this article to uncover how you can position your automation programs for greater value and how you can uncover new opportunities, as well as:
- How you can introduce design thinking into your digital transformation initiatives
- Techniques to move from small-scale success to large-scale transformation
- And more!
Many organizations that have already implemented automation at scale are in the same boat— they have realized significant returns, but are left wanting to achieve greater value, ROI, and impact. How can new opportunities be uncovered for larger-scale transformation? And how can automation programs be positioned for greater value– right from the very start?
Design thinking, a human-centered design, is an immersive process that helps organizations build leading products and services by looking beyond individual tasks (such as requesting
an Uber on your phone) and to the larger intended outcome (such as arriving at your destination).
When design thinking is applied to automation, the focus shifts from individual business actions, which accomplish specific tasks, to broader strategies that accomplish entire outcomes, resulting in a more comprehensive transformation and higher returns. Executives who promote design thinking can accelerate the adoption of various IA technologies, achieve organizational alignment, and drive commitment to goals— while reducing resistance to organizational change at the same time. Design thinking practices makes it possible for companies to pivot quickly when market conditions change, since the organization is already aligned around a core set of agile decision-making principles that are embedded in the company culture.
In the vast majority of studies related to change management and business transformation, we have read that many change efforts end up failing. It is common to find citations such as "40 percent of automation efforts fail," "Seven from ten lean initiatives are not successful," or "70 percent of digital business change actions do not bring value." These statistics prove that the ratio of successful transformations, or change initiatives, (even supported by external consultants) fall miserably low.
With the above in mind, check out the top five reasons why change and transformation actions fail.
There is a big difference between the way humans communicate with one another, and the way we “talk” with computers. When writing programs, we have to use very careful syntax and structure, but when talking with other people, we take a lot of liberties. We make short sentences. We make longer sentences, we layer in extra meaning, we use puns and sarcasms. We find multiple ways to say the same thing.
Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech. NLP is a sub-field of Artificial Intelligence (AI) that is focused on enabling computers to understand and process human languages, and to get computers closer to a human-level understanding of language.
The AI 2020 Update is a refreshed look at our acclaimed AI2020 Report that the AI & Intelligent Automation Network produced last year. The AI & Intelligent Automation Advisory Board reviewed the questions asked in 2018 and delved deeper into the mindset of the corporate enterprise AI practitioner as we made our way into calendar 2019.
As you’ll see in the demographics – our audience is pretty evenly segmented around the globe and per industry. The AI & Intelligent Automation Network however is not evenly distributed in job function. We have an extremely senior-level group. Nearly a quarter (23.3%) of the AIIA Network is C-Level. 69.9% of AIIA is Director-level or above. There are some clear themes of the results:
1. Culture is the biggest challenge and opportunity
2. Predictive Analytics is a clear focus for global corporate enterprise
3. Linking initiatives to ROI is a focus but it’s still difficult to clarify
Global corporate enterprise AI practitioners are clearly still dealing with infrastructure issues related to talent and technology. End-to-end processes remain stubbornly carbon-based. Rule-based automation is truly not yet globally scaled across the majority of organizations. And yet, the AI & Intelligent Automation Network members went from 21%, having deployed Intelligent Enterprise solutions to over 44% in just one year’s time.
The stated goal for deployment is just under 83% by the end of 2020. Considering the fact that they’ve essentially got two years, and those ranks have doubled in one year – doubling again in two years is achievable. Incidentally, that same number was only 67% a year ago.
Global corporate enterprise is in fact slowly but surely transforming into the intelligent enterprise of tomorrow. Having said that, it will be more difficult for the roughly 50% of institutions who would like to be established, globally scaling or refining their scale program in AI by the end of 2020, to actually achieve that status.
It's time to take a step towards smarter operations. It is high time to begin your automation journey and the first step is RPA. Learn more about the steps to take along the way, how other companies are implementing it themselves, and key enablers that will guide you towards success. To receive a copy, please email firstname.lastname@example.org
Two forces of technological change, automation and artificial intelligence, are collaborating to revolutionize how organizations operate, create value, earn customer loyalty and stay relevant.When businesses are able to combine the strengths of AI, RPA, robots, low-code application development platforms, and other resources (including humans), they can achieve an entirely new level of process optimization.
View this eBook to uncover actionable takeaways in use case examples:
- Customer service and support
- Fraud detection
- Legal research
- Financial advice
How prevalent is your transition from intelligent automation to artificial intelligence? Explore this report with research, state of the market, and state of investment data that will guide you along the path of an effective transition from IA to AI in efforts to keep your enterprise at the top of the industry. To receive a copy in your inbox, please e-mail: email@example.com
To thrive in the near future means adopting automation as soon as possible. Those who invest, tinker, pilot, scale, and mature before the competition will have the advantage of greatly increased operational speed and agility, as well as a leg up above the coming waves of market disruption. To receive a copy in your inbox, please e-mail: firstname.lastname@example.org
The debate over Artificial Intelligence’s value in business is quickly moving into a new phase. Questions around why AI investments should be a strategic corporate priority are quickly fading, especially when firms such as PwC are estimating that AI could contribute $15.7 trillion to the global economy by 2030; no business wants to miss out on that kind of opportunity. As a result, discussions on when, how and in what form an AI implementation should take are accelerating in enterprises across industries.
With market hype around AI at an all-time high, it’s important to be clear about what AI encapsulates. In this report, learn more about:
- RPA, Machine Learning
- Logic-based/Statistical Data Science
- Cognitive AI, and much more.
For a copy in your inbox, e-mail email@example.com