Gartner Eyes Future of Autonomous Finance
finance function is changing quickly – with the support of new technologies,
particularly artificial intelligence (AI) and machine learning – into an
environment where people and machines collaborate to transform finance and
business capabilities, drive process efficiency, and automate data pipelines,
according to Gartner.
“A future autonomous finance organization will require people and machines to
work together in a learning loop,” said Mark D. McDonald, senior director,
research in the Gartner Finance practice. “People will exercise judgment,
perform controls, validate output and ensure financial integrity while machines
will focus on the analytical work and repetitive processes that, comparatively,
humans struggle with.”
This Hype Cycle introduces finance leaders to the technology themes underpinning
the concept of autonomous finance, that will drive the cycle of technology
evolution in finance over the next decade (see Figure 1).
“There are three broad themes to the technologies on
this hype cycle,” said McDonald. “Firstly, there are technologies, such as
decision intelligence, that drive effective and efficient organizations.
Secondly, there are a group of transformational technologies, such as composable
applications, that can drive new digital business capabilities. Thirdly, there
are technologies, such as augmented data quality, that automate the collection,
storage and retrieval of data and increases accuracy.”
Given that only evolving and future-looking
technologies are included in the Hype Cycle for Emerging Technologies in
Finance, 2023, Gartner experts recommend that finance leaders are selective:
picking out the trends that align best to their organizational needs, develop
short- and long-term roadmaps to align finance to developing trends, and allow
their organizations to evolve gradually.
“Begin with small steps and lower-risk iterations not
only to avoid big mistakes but to give the finance organization time for such
gradual evolution,” said McDonald. “Over time, iterative cycles of improvement
will cover a broader range of processes and responsibility.”
In this year’s Hype Cycle for Emerging Technologies in Finance, three technology
innovations stand out as being on a path to mainstream adoption within five
years and having transformational potential for the finance organization.
In a departure from the monolithic and inflexible
technology applications commonly associated with enterprise technology,
composable applications have arisen in response to greater demand for business
adaptability in more volatile times.
Composable applications, which are at the Peak of Inflated Expectations, are
modular in nature and are built to support fast, safe, and efficient application
changes in the face of frequent disruption and new opportunities. The improved
agility of business technology drives resilience and adaptability throughout the
Composable applications are built as flexible compositions of well-packaged
modules of business application capabilities. The “composers” tend to be a
business-IT fusion team while the creators of the modules may be application
vendors or central IT software engineering teams.
Decision intelligence (DI) is at the Innovation
Trigger of the Hype Cycle. DI is a practical discipline used to improve decision
making by explicitly understanding and engineering how decisions are made, and
how outcomes are evaluated, managed and improved via feedback. The current hype
around automated decision making and augmented intelligence, fueled by AI
techniques in decision making has revealed the brittleness of legacy business
processes in this new environment.
An increasingly complex business environment, with an increasingly uncertain
pace of business, and ever more decisions taken by machines have created a sense
of unease from the human and also regulatory perspective. There is a need to
transparently represent how decisions are being made.
From a pure business perspective, it makes sense to curtail unstructured ad-hoc
decisions that are siloed and disjointed, and properly harmonize collective
decision outcomes across an entire organization. Software tools are now emerging
that will enable organizations to practically implement DI projects and
rollouts of the last decades focused on collecting transactional data. Now,
finance organizations are burdened by the quantity of information collected and
don’t know how to analyze or use it.
A new breed of software vendors is introducing intelligent applications (IAs),
which are entering at the Peak of Inflated Expectations. These applications are
augmented with AI and connected data, from transaction and external sources, to
generate a system that provides contextualized features, experiences, and
processes, and can continually learn, improve and adapt.
The promise of such platforms is that finance can spend more time on business
support and use limited in-house AI resources to build business-specific