TEC's AI predictions 2020: Innovating toward true AI

TEC's AI predictions 2020: Innovating toward true AI

AI Business

January 8, 2020

7 Min Read

Predrag Jakovljevic, TEC 8 January 2020

The desire for innovation and improved
outcomes is the most significant force that pushes artificial intelligence (AI)
closer to the emulation of human behavior, sometimes referred to as “true AI.”

Plenty of business and consumer products—both software and hardware—already
feature some type of AI or machine learning (ML) augmentation. In 2020, the use
of AI technology will continue to grow, even if a major new platform or revolutionary
breakthrough appears unlikely.

Companies that begin the decade by
seamlessly integrating AI and ML with existing, best-fit business processes
will enjoy the largest gains from these types of software tools.

Gradual advance toward true AI for business

The landscape for artificial intelligence
in 2020 will continue to evolve toward human-like thought, even as the current
set of tools perform specific feats of cognition. True AI, advanced analytics,
and traditional business intelligence (BI) and reporting are like professional
football, college football, and indoor arena football in that entirely
different levels of results and insight are delivered on similar playing fields.

Functionalities such as visual recognition,
voice recognition, text processing, and analogous AI algorithms tend to be
considered close to true AI. Predictive and prescriptive analytics occupy a
lesser tier of sophistication, using ML algorithms that can be seen as
augmented intelligence. BI visualizations with ML or AI support, such as dashboarding
and reporting, fall into a lower tier than predictive and prescriptive

The likelihood of a brand new ML or AI platform appears relatively low, even from companies such as Salesforce and SAP. Instead, their focus on AI platforms will be for mostly analytical workflow and model management with a plethora of open source AI/ML solutions inside. Open source AI or ML software solutions are easy to fit into any platform, which serves as a presentation layer for the end user.

Salesforce recently acquired Tableau, the
most popular presentation layer at the time of acquisition. Salesforce Einstein
will remain the platform for a number of AI and ML solutions, many of which are
based on open source tools, and will work in conjunction with Tableau’s
visualization features. Along similar lines will be Infor’s Coleman AI and
Birst BI or SAP’s Leonardo AI and SAP Analytics Cloud. We should expect similar
mergers of traditional BI vendors with vendors with new AI portfolios.

2020 AI predictions

Here are a few predictions in the area of
artificial intelligence for the upcoming year (some
of which were adapted from a recent IDC webinar).

  • In 2020 and beyond, AI or ML
    tools will support nearly all aspects of business to facilitate innovation and
    improved business outcomes.

  • A minimum of a third of new
    enterprise software releases will include some built-in AI or ML features that
    support key business processes covered by the software. However, few, if any
    features, will be game-changing true AI (emulating human reasoning) or create a
    disruption within their industries or vertical segments.

  • The distinction between AI and
    intelligent or cognitive robotic process automation (RPA) will continue to
    persist, as RPA tools remain unable to learn like a true AI tool can.

  • AI or ML tools will
    increasingly redefine the enterprise software user experience (UX) and user interface
    (UI). About 20% of software user interactions will be augmented by computer
    vision, speech recognition, natural language processing (NLP), gesturing, and
    augmented reality (AR), virtual reality (VR), and mixed reality.

  • Toward the end of 2020, 10% of
    customer experience (CX) and talent management software will feature deep
    personalization, such as product or service recommendations and call to action
    (CTA) suggestions.

  • Throughout 2020, the majority
    of Fortune 2000 companies will work on formal programs to encourage public
    trust in their digital brand, in terms of not only customer data privacy but
    also AI ethics and explainability (to avoid any undesirable biases). Full
    adoption of these programs will take place sometime in 2021.

RPA vs. DPA (Digital Process Automation)

RPA vendors may incorporate AI-like
mechanisms on a minor scale, but to deliver a game-changing digital
transformation, enterprise data from all sources must be integrated into AI
solutions. As RPA mimics human action instead of simulating intelligence, RPA
isn’t capable of learning like AI can. As such, RPA doesn’t contain the same
potential for transformation.

Digital process automation (DPA), or
intelligent automation, does provide a foundation for digital transformation
while extending business processes to customer-facing functions. DPA uses
digital technology to perform processes that enable a workflow or
functionality, whereas RPA accomplishes a specific task. This allows DPA
activities to connect disparate sources of data to string processes together,
while RPA is relegated as a stopgap that improves efficiency for easily
repeatable tasks.

As RPA is relatively easier to implement,
more companies will be experimenting with achieving quick wins through RPA,
which automates simple and mundane tasks well. However, as intelligent
automation can improve the processes themselves, DPA will be more appropriate
for companies seeking transformation through AI. One of the drawbacks of RPA is
the inability to improve suboptimal processes, even if the RPA actually reduces
inefficiency by performing repeatable tasks quickly.

Potential AI ramifications in 2020

  • Information technology (IT)
    departments will need to exercise caution to avoid being overwhelmed by a
    growing “digital workforce” of AI bots, especially RPA-based solutions.

  • Businesses will need to avoid the
    shadow IT that can proliferate with the availability of low-code or no-code AI
    or ML tools. All IT solutions, including AI- or ML-based software, should be
    brought within some type of central IT governance.

  • As transparency is key,
    companies will need avoid the black-box effect of AI. They need to divulge the
    reasoning behind their AI decision-making to their employees, including ethical
    issues such as potential prejudice unintentionally baked into AI or ML

  • IT departments will need to
    support the incorporation of AI tools into existing business processes in nearly
    all lines of business.

  • More companies will need to
    invest in employee retraining and development to acquire the new skills required
    to properly adopt the AI or ML software tools throughout the business.

AI opportunities in 2020

IT departments will need to learn how to
support the incorporation of AI tools into existing processes across all lines
of business. Companies and individuals that are capable of successfully
integrating AI insight and recommendations will have a significant advantage
over competitors, especially those who believe that RPA provides the same
benefits and efficiencies as AI-based tools such as DPA solutions.

Customers constantly seek a greater level
of convenience and customization of products and services, along with the
option of exercising more control over their shopping experiences. AI provides
an avenue for businesses to fulfill this consumer desire while supporting their
need to achieve their own digital transformation.

Predrag Jakovljevic is principal analyst at Technology Evaluation Centers (TEC). TEC is an impartial advisory firm, helping its customers find the best software solutions for their needs.

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