Many AI programs do not generate business gains. For successful
deployment, make sure there’s scientific, stakeholder, and
Companies around the world areexpected
to spend $97.9 billion on artificial intelligence by the end of
2023, but many AI initiatives fail or donât turn a
profit. A 2019
study found that 40% of organizations that make significant
investments in AI do not report business gains.
Successful AI programs require an approach called AI alignment,
according to a new
research briefing from the MIT
Center for Information Systems Research. Since 2019, CISR has
investigated 52 AI solutions, which they define as applied
analytics models that have some level of autonomy. Out of those, 31
have been deployed at a large scale.
CISR principal research scientist Barbara Wixom, University of
Queensland lecturer Ida Someh, and University of Virginia professor
Robert Gregory found that the successful AI programs achieve three
interdependent states of consistency: scientific consistency,
application consistency, and stakeholder consistency.
Scientific consistency between reality and the
AI model. AI programs have to be trained to represent reality, and
successful models have to be accurate. To create scientific
consistency, teams used comparing activities, that
is, comparing the output of AI models with empirical evidence. If
they discovered inconsistencies, AI teams corrected course by
adjusting data, features, the algorithm, or domain knowledge.
For example, General Electricâs environment, health and safety
team created an AI-enabled assessment that vets contractors hired
by the company. The team went through the time-consuming process of
building a training dataset, and reviewers looked over the
machine-learning assessments. This led to model adjustments and
retraining, which made the machine more accurate.
Application consistency between the AI model
and the solution. An AI model doesnât just need to be accurate,
it also needs to achieve goals and avoid unintended consequences.
AI deployment requires scoping activities, which
look at impacts and consequences and adjust the modelâs
restrictions, boundaries, automation, and oversight if needed.
The Australian Taxation Office, a large government department,
launched a new AI program that prompted taxpayers filing online to
review an item. The program was intended to improve noncompliance
by taxpayers, but it also had to pass scrutiny from regulators and
meet the best interests of taxpayers and the government.
The department made sure the program reflected its principles,
and designed the program so it did not engage in policing efforts.
The behavioral analytics team developed gentle, respectful
techniques to encourage productive behaviors when residents were
filing a claim, according to the research brief.
And when using a neural network algorithm, the organization
decided not to enable continuous learning for the neural network
during a tax cycle; that way, results would stay consistent no
matter when a tax claim was filed, and results could be
Stakeholder consistency between the solution
and stakeholder needs. The program should generate benefits across
a network of stakeholders like managers, frontline workers,
investors, customers, citizens, and regulators. Consistency
happens when an AI program creates value that stakeholders
understand, support, and benefit from. A company should engage
in value-creating activities to look at costs,
benefits, and risks, and to fix any problems.
Satellogic, based in Buenos Aires, Argentina, combines
proprietary satellite data and advanced analytics techniques to
solve problems such as how to increase food production or
efficiently generate energy. The company worked with a client, the
Chilean holding company HoldCo, on an innovative way to predict
crop location based on satellite-imagery technology.
When HoldCoâs internal agricultural advisors doubted the new
approach was viable, Satellogic asked them for help with the model
training process. The HoldCo specialists guided the Satellogic team
in labelling satellite images, training the model, and validating
Also key was a Satellogic domain expert who educated the client
about the technical mechanics of the analytics and managed the
âclient last mile,â the gap between outcomes from the AI and
Manage the forces that shape your AI program
Achieving alignment in all three areas is difficult because
factors can change, the researchers write. For example, changing a
setting can change an algorithm, the COVID-19 pandemic or other
disasters can change stakeholder needs, and a change in one area
can affect the other two.
Fundamentally, achieving AI alignment requires that leaders
manage the forces that shape and are shaped by an AI model core
which constantly evolves.
âSuccessful leaders will embrace the dynamism of and
incorporate new activities that sustain AI solutions and create
virtuous cycles of learning and adaptation,â the researchers
Originally published by
Sara Brown, News Writer | January 6, 2021
MIT Management Sloan