AI Solutions will see greater Success


Autor: Andreas Seufert

Unleash the power of AI – Findings and Implications: Part 1

The Article is a summary of some key findings conducted by our research [Set 22] on the AI ecosystem and its implications for companies. (provided with kind permission of the original source: „Unleash the power of AI – Findings and Implications. In: Seufert (Hrsg.) df&c – Magazin für #Digital #Finance #Controlling, Schwerpunkt Digitale Transformation. Heft 1-2022, Steinbeis Edition, Stuttgart 2022, (Seufert, A./ Nelson, M./ Setlur, V./ Turner-Williams, W./ Wright, K./ Myrick, N.“)

Mark Nelson is President and CEO at Tableau. He sets the vision and direction for Tableau, and oversees company strategy, business activities, and operations. Prior to becoming President and CEO, Mark was the Executive Vice President of Product Development for Tableau, helping broaden and deepen the company’s industry-leading analytics platform to support customers globally.

Vidya Setlur is the Tableau Research Director, leading a team of research scientists in areas including data visualization, multimodal interaction, statistics, applied ML, and NLP. She earned her doctorate in Computer Graphics in 2005 at Northwestern University. Vidya previously worked as a principal research scientist at the Nokia Research Center. Her research combines concepts from information retrieval, human perception, and cognitive science to help users effectively interact with systems in their environment.

Wendy Turner-Williams manages Tableau’s Enterprise Data Strategy, Data Platforms and Services, Data Governance and Management Maturity, Data Risk, and Data Literacy. She and her team are fuelling data-driven business innovation, transformation, and operational excellence at Tableau. Wendy has 20+ years of management experience across sectors, most recently leading the Information Management & Strategy Enterprise program at Salesforce.

Kate Wright is an analytics leader with 17+ years of development, product management, and leadership experience. She’s responsible for Analytics Engineering, Product Management, and overall User Experience for Tableau and Tableau CRM. Neal Myrick is VP of Social Impact for Tableau and the Global Head of the Tableau Foundation. He leads the company’s philanthropic investments to advance the use of data for a more just and equitable world. Neal is an active angel investor and sits on several global health and development advisory boards.

Andreas Seufert is professor at the University of Ludwigshafen and director of the Business Innovation Labs. Andreas leads the expert group controlling & analytics of International Association of Controllers.



During the last two years many organizations had to adjust strategies and adapt to a new world. Changes to the way we live, connect, communicate, and work has forced every person and organization to become even more digital and data-driven than ever before.

When many organizations transitioned operations online, it came with a huge influx of information because every digital interaction generates valuable data that can provide insights and support faster decision-making in this digital-first world.

To get deeper insights, we conducted research and spoke with experts, customers, and other thought leaders to learn what emerging forces continue to evolve how we work, the role data and analytics play, and what this means to the future of companies.

Following we briefly discuss some of our key findings:

  • AI solutions will see greater success by reducing friction and helping to solve defined business problems.
  • Competitive organizations expand their definition of data literacy, invest in their people, and double-down on Data Culture.
  • There is growing recognition of data’s strategic value drives flexible, federated data governance techniques that empower everyone across the organization.
  • Responsible organizations will proactively create ethical use policies, review panels, and more to improve experiences and business outcomes.

Findings Part 1: AI solutions will see greater success by reducing friction and helping to solve defined business problems

We’re experiencing a golden age of data and technology—and there is no sign of it slowing. Artificial intelligence (AI) technology continues to improve.

Machine learning (ML) models are processing trillions of lines of data, natural language processing (NLP) advancements are moving towards understanding human intent, and algorithms are getting faster.

We’re seeing more simple, repetitive tasks be automated, giving rise to new opportunities to enable humans to do what they do best: reasoning critically and understanding data in context.

As Innovation accelerates, so do AI Investments and Adoption, with 99% of Fortune 1000 Companies planning to invest in Data and AI in the next 5 years [NVP 21].

Business and IT leaders believe it’s critical to the future survival of their business. But many considerations factor into the long-term success and sustainability of AI solutions: increasing amounts of data, costs of maintaining this technology, difficulty in staffing highly specialized roles, and scaling AI pilots to widespread adoption.

84% of C-suite executives believe they must leverage artificial intelligence to achieve their growth objectives, yet 76% report they struggle with how to scale

[Rei 19]

Businesses recognize that they need to do more to innovate and better serve their customers.

While AI opens up opportunities, most investments have yet to deliver on their potential value.

In 2022, AI technologies will reach new levels of success through human augmentation: assisting and enhancing people to think critically and make data-driven decisions. Think of analytics and AI as being supporting members of the team.

Data Culture and data literacy—the ability to explore, understand, and communicate with data—also help organizations figure out their AI and ML strategy and perspective [Gop 21].

These change management and workforce development efforts affect how they’ll stay competitive and manage the spectrum of human augmentation, beginning with questions like:

  • What tasks will be completely automated with AI technology? Examples of automation that free up people to focus on more sophisticated tasks: Basic language translation and image editing. Rather than spending hours manually editing a photo to change the background, editing can be done with default image editing technology that incorporates AI to handle lighting and blending techniques. These automated tools facilitate new levels of creativity.
  • Which tasks will be semi-automated and require human involvement and interpretation? Examples of AI that distils useful patterns and insights to empower people to make data-driven decisions in context: -> To more accurately weight climate and pandemic models, ML techniques are applied to help researchers understand trends, impacts, and patterns to help with policy decisions. -> Machines can inspect unlabelled voice data (e.g. customer calls) using NLP and ML algorithms to better understand user intent, adding relevant categories and labels. These signifiers and semantics inform people of what action to take next.

Having common behaviours, beliefs, and data skills also facilitate the ability to scale AI solutions, supporting sustainable implementation and innovation.

In a recent report, Gartner found that the

lack of skills was cited as the No. 1 challenge to the adoption of artificial intelligence and machine learning.

[Pid 21]

Because investing in the development of your people and AI techniques is an ongoing process, constantly evolving alongside the technology.

Having your entire workforce in agreement and appropriately skilled may mean the difference between seeing AI proofs of concept become scalable, practical applications or fail entirely.

Organizations that invest in change management were 60% more likely to report that AI initiatives exceeded expectations and 40% more likely to achieve outcomes than those that don’t. [Ama 21]

In collaboration with IT leadership, business leaders have an opportunity to drive data and AI strategies grounded in business context.

For AI technology to be relevant, maintainable, and explainable, it needs to empower people and be tied to business strategy and goals.

We’ll see AI solutions move from a proof-of-concept model to widespread implementation for business- and industry-specific use cases.

Companies in our study that are strategically scaling AI, report nearly 3x the return from AI investments compared to companies pursuing siloed proof of concepts

[Pid 21]

Various industries are developing and using AI in innovative ways.

A recent study by KPMG examined AI deployment across five industries (retail, transportation, healthcare, finance, and technology), finding that for 91% of healthcare industry respondents, AI is increasing access to care for patients [KPM 20].

And although most businesses manage their supply chains manually, those that adopt AI in the coming months and years will achieve significant competitive differentiation [McK 21].

Thanks to cloud computing, AI has become more affordable and accessible, leading to greater innovation across experiences and industries.

And with an additional focus on business success, we’ll see solutions which combine different AI techniques to achieve better results (also known as composite AI) added to support people, specifically optimizing this intelligence to specific workflows [Goa 21].

Companies must deliver creative new uses of technology to enable the organization to scale digitalization rapidly.

Collaboration between business and other IT is key to create teams that fuse business and IT skills from various disciplines [Gro 21].


Treat AI as a team sport.

Identify what tasks and functions would best support human augmentation by saving people time or elevating their skills or expertise.

Begin by looking at your customers’ needs and pain points to understand where your AI solution can add value for them.

Ask yourself these questions to see if a proof of concept or pilot is worth developing:

  • How many customers have similar needs or experience these same issues?
  • How often are these issues happening?
  • Are these issues solvable with AI technology?

Focus on business use cases and success factors to leave the AI proof-of-concept stage and successfully scale.

  • Drive intentional and contextual AI by connecting solutions to real business problems with defined goals to realize their value.
  • Identify where AI can enable and reduce friction. Avoid trying to enable AI in all aspects of your product suite—you’ll struggle to scale by spreading your resources too thin.
  • Be wary of “shiny,” pipe-dream projects. While attractive, they rarely move beyond the proof-of-concept stage. And tune out the noise by setting realistic time and scope expectations for AI projects, balancing all resources like budget, time, highly technical staff, and infrastructure

Invest in data literacy to upskill and develop your workforce

  • Poor data quality results in inaccurate and ineffective AI solutions. And a data-literate workforce can improve issues with data quality, building and/or training AI, ML, NLP, etc. algorithms and models with accurate, timely, and relevant data.
  • Even a basic, Data training, whether developed internally or offered through a third-party, can give business users what they need to answer their questions. This will reduce the number of simple or lower-stakes analytics requests that go to advanced analytics and data science teams—freeing them up to work on high-value, large-scale projects.


[Ama 21] Ammanath et al.: Becoming an AI-fueled organization Deloitte’s State of AI in the Enterprise, 4th Edition, 2021,… (access 16.03.2022)

[Goa 21] Goasduff: The 4 Trends That Prevail on the Gartner Hype Cycle for AI, 2021,…, September 22, 2021, (access 16.03.2022)

[Gop 21] Gopa: How Data Culture Fuels Business Value in Data-Driven Organizations, May 2021

[Gro 21]: Groombridge et. al: Top Strategic Technology Trends for 2022,…, 18 October 2021, (access 16.03.2022)

[KPM 20] KPMG: Living in an AI World. Achievements and challenges in artificial intelligence across five industries, 2020,… (access 16.03.2022)

[McK 21] McKendrick: AI Adoption Skyrocketed Over the Last 18 Months,, September 27, 2021 (access 16.03.2022)

[NVP 21] NVP – New Ventage Partners: Big Data and AI Executive Survey 2021, Executive Summary of Findings,… (access 16.03.2022)

[Pid 21] Pidsley/ Idoine: Maximize the Value of Your Data Science Efforts by Empowering Citizen Data Scientists, December 2021

[Rei 2019] Reilly et. al: Scaling to new heights of competitiveness, Research Report Accenture, (access 16.03.2022)

[Set 2022] Setlur et. al: Data Trends 2022,, February 2022, (access 18.03.2022)