Can We Finally Pierce the Productivity Paradox?
This Week in Enterprise Tech, Episode 22, explores the balance between innovation and efficiency, and how AI may finally break the old habit of evaluating technology solely based on productivity.
👋 Hi and welcome to The DX Report — the research hub of The DX Institute all about Digital Transformation, the Digital Experience, and the Digital Enterprise. I’m industry analyst, author, and speaker Charles Araujo, and I’m all about providing insights and analysis for enterprise IT leaders as you make the big bets about your organization’s future!
I've spent a lot of time recently (and used plenty of digital ink) discussing the challenge that CIOs and other IT leaders face: balancing the demands of driving innovation with operational optimization (or efficiency, if you prefer).
I've talked about how Generative AI has been the thing that has finally made non-IT executives and boards realize the strategic relevance of IT.
Implicit in this shift is that IT has always overwhelmingly focused on improving efficiency and optimization. There's nothing inherently wrong with seeking to boost efficiency — except when it becomes the sole measure of impact.
The conundrum that CIOs face in balancing this mandate for innovation against the need to drive optimization is clear in how economists look at technology.
The Productivity Paradox
One of the articles we discussed in this week's episode of This Week in Enterprise Tech is about Daron Acemoglu, an MIT labor economist, questioning whether we'll see a significant enough productivity boost from AI to finally resolve the so-called productivity paradox. He's skeptical.
The productivity paradox is the observation that despite decades of employing technology, organizations haven't seen significant productivity gains—unlike the massive gains during the industrial revolution.
There are many theories as to why this productivity paradox exists, but few answers.
Acemoglu argues that he is skeptical AI will resolve this paradox, and thus its hype is unwarranted.
His position created a paradox of its own — at least for me. As I've made clear repeatedly, I believe that AI (Generative AI, specifically) is over-hyped, to say the least. Still, I don't agree with Acemoglu's position mostly because I think productivity is the wrong way to assess the impact of AI — and almost every other enterprise technology — in the first place.
Piercing the Productivity Paradox by Focusing on Competitive Value
Another article we discussed from The Wall Street Journal highlights how many CIOs are changing how they evaluate AI investments. They are no longer primarily looking at AI from a productivity perspective, finding it difficult to justify on that basis.
Instead, they are looking at AI and evaluating its revenue contribution potential. And the primary way that AI is providing revenue opportunities is by creating an experiential differentiation that is elevating the organization's competitive value and positioning in the market.
If that sounds familiar, it should.
I've been extolling for years that enterprise value now hinges more on the digital experience than on efficiency and optimization. This shift is because the experience has become the main driver of competitive differentiation.
We've been in this value-creation transition for over a decade. AI is just the latest tool for creating experience-driven, value-generating differentiation. If it’s what finally awakens enterprise executives, then I celebrate it.
However, the fact is that old habits die hard. I expect most members of the executive team — and even many CIOs — will continue to look at technology investments primarily though the lens of efficiency and optimization. That will be a mistake that ultimately holds them back from making the critical investments that have the potential to truly transform their organizations and competitive positioning. But those investments — and the transformation that will result from them — can only happen if we pierce the productivity paradox once and for all.
Happy listening!
🗓️ This Week in Enterprise Tech, Episode 22
In this episode, we delve into the rapidly evolving landscape of enterprise technology, with a spotlight on innovations and disruptions brought by AI and automation. Key discussions include the transformative potential of AWS App Studio in democratizing app development and the strategic push by AMD through its acquisition of Silo AI to challenge NVIDIA in the AI ecosystem. We also address the contentious debates around AI’s role in productivity, employee replacement, and revenue generation, underscored by emerging thought leadership and industry reactions.
Segment descriptions and links to all the articles we discuss are in the Show Notes, below.
Watch the full episode here:
Or listen to the episode here: https://www.buzzsprout.com/2319034/15420786
📔 Show Notes
[01:05] Will AWS App Studio Disrupt Enterprise App Development?
Amazon Web Services has just launched their App Studio with the goal of democratizing enterprise app development. Charles Araujo and Hyoun Park take a step back and ask "just what IS an enterprise app?" They contextualize where the App Studio fits into the current world of low-code and no-code app development.
TechCrunch: https://techcrunch.com/2024/07/10/aws-app-studio-promises-to-generate-enterprise-apps-from-a-written-prompt/
Tags: Amazon Web Services, App Studio, Low-code app development, no-code app development, Generative AI
[08:49] Celonis + Emporix = End-to-End Process Automation?
Celonis and Emporix announced the launch of an end-to-end process orchestration offering combining Celonis' process mining and task automation capabilities with Emporix' Orchestration Engine to sequence tasks into end-to-end processes. Hyoun Park and Charles Araujo study this offering in context of the process and workflow automation markets as well as prior enterprise application process augmentation promises to assess this launch.
Diginomica: https://diginomica.com/celonis-and-emporix-aim-end-end-process-automation-launch-new-orchestration-engine
Tags: Celonis, Emporix, process orchestration, orchestration engine, process mining, process automation
[16:43] AMD Acquires Silo AI to Pressure NVIDIA
AMD acquires the largest AI lab in Europe, Silo AI, with over 600 employees and over 200 AI deployments under its belt. This acquisition helps AMD to build custom models for enterprise clients and to be a one-stop shop across chip, software tools for AI management, and the professional services needed to deploy models. In trying to compete with NVIDIA, AMD is racing to have the most complete AI ecosystem under its corporate umbrella. Hyoun Park and Charles Araujo look at this announcement from the CIO's perspective of supporting AI in production.
ComputerWorld: https://www.computerworld.com/article/2517498/ai-chip-battleground-shifts-as-software-takes-centerstage.html
Tags: AMD, AI Lab, Silo AI, NVIDIA, Lisa Su, Jensen Huang, LLM
[22:35] Why CEOs Fail to Replace Employees with AI
A recent editorial in VentureBeat points out how CEOs are getting excited about replacing employees with AI, only to see poor results and dissatisfied customers. Charles Araujo and Hyoun Park discuss what AI should really be used for.
Cangrade CEO Gershon Goren for VentureBeat: https://venturebeat.com/ai/can-we-please-stop-talking-about-replacing-employees-with-ai/
Tags: employee replacement, AI for business, customer experience
[29:06] What if AI Doesn't Improve Productivity?
MIT labor economist Daron Acemoglu posits that AI will only provide incremental gains to productivity, which flies in the face of the hyped gains pushed by Goldman Sachs, McKinsey, and other think tanks. What happens if Acemoglu is right?
MIT labor economist Daron Acemoglu for the New York Times: https://www.nytimes.com/2024/07/13/business/dealbook/ai-productivity.html
Tags: Daron Acemoglu, MIT, labor economist, Goldman Sachs, McKinsey, value mapping, pricing
[33:50] AI Should Be All About The Money
This Wall Street Journal article written by Isabelle Bousquette points out that AI needs to be about improving top-line revenue. Charles Araujo and Hyoun Park discuss some of the metrics associated with AI, such as the potential for increasing revenue and profit up to 5% with AI, the Sequoia Capital estimate of AI becoming a $600 billion business, and the real-life assumptions that CIOs need to make if these are the metrics that define AI investments in the enterprise.
Isabelle Bousquette for the Wall Street Journal: https://www.wsj.com/articles/its-time-for-ai-to-start-making-money-for-businesses-can-it-b476c754?mod=djemCIO
Tags: Isabelle Bousquette, top one revenue, AI, Sequoia Capital
[37:12] OpenAI Debuts A New Maturity Scale for Gen AI
OpenAI recently discussed a new 5 Level Scale for AI and claims everyone is working at Level 1. But Hyoun and Charles agree that this scale looks odd when it comes to describing the current state of AI.
Tags: OpenAI, PhD level AI, Autonomous AI, AI Hype
[41:30] Generative AI Can't See, Needs Bullshit Detector
Based on several articles, Charles and Hyoun quickly look at the latest science stating that generative AI struggles to "see" images because of a lack of training data. And then they also see how people are fighting back against generative AI bots and malware both through dedicated solutions such as Patronus AI as well as simply counterprompting with the simple phrase “ignore all previous instructions.”
VentureBeat: Meet Patronus: https://venturebeat.com/ai/meet-patronus-ais-lynx-the-open-source-bullshit-detector-outsmarting-gpt-4/
NBC News: Hunting for AI Bots? https://www.nbcnews.com/news/amp/rcna161318