McKinsey reports in recent research that 56 percent of respondents have adopted AI
in at least one function, but there are also examples showing how hard it is
to deliver business impact from AI. For example, Gartner reported
that 85 percent of ML fail to deliver, and other reports share why
deploying ML to production is so hard.
Is failing to bring ML to production a failure, or is it part of the
experimentation and learning required for organizations?
I think the latter, that AI is learning and experimental, and here’s
why:
For many organizations, it will require integrating multiple technologies to
deliver long-lasting business outcomes.
Emerging Tech Requires Integrated Platforms
We only have to look back over the last two decades of web technologies for
examples of why business outcomes require the integration of multiple
technologies before emerging ones can deliver business impacts – especially
for SMBs and many enterprises. Examples:
-
Developing websites started in the late 1990s, but it wasn’t until retail
businesses leveraged e-commerce, content management systems, and digital
marketing practices that online retailing became a growing business
channel. -
Enterprises have been storing unused dark data in their data centers for
decades, but when analytics (including ML, data visualization, and other
data science programs), DataOps (and specifically, the ability to automate
third-party data integration), cloud databases (including data lakes and
NoSQL stores), and APIs became mainstream, it enabled more businesses to
launch and grow commercial data services. -
While most businesses created mobile-friendly websites and deployed mobile
apps, it required a combination of social sign-on capabilities, easy
payment integrations, location services, and other phone + cloud services
to enable rapid/viral user adoption.
You can see that it’s not just one technology that drove widescale business
adoption and successful business outcomes – especially for mainstream SMBs
and for less technical enterprises.
Business Outcomes: AI, IoT, AR/VR, Hyperautomation
So, the success criteria for ML may not be its deployment into production.
Success might be better defined as showing today’s ML as a step in the
journey of using a business’s proprietary data, analytics, ML, or AI in an
integrated, customer-enabling set of products and services.
This will be true for other emerging technologies. AR/VR needs businesses to
develop content, evolve experiences, and create unique value propositions
for the emerging tech to evolve to mainstream business capability.
Large-scale IoT needed in smart cities and smart buildings need more than
just sensors and require architectures that likely include edge computing,
5G, data streaming, and real-time ML.
In my recent episode of 5 minutes with @NYIke, I share three emerging tech integrations that are transitioning out of
the emerging categories. SMBs and enterprises should experiment and look for
opportunities because, in a few years, laggards may face an uphill battle to
catch up.
