Business inefficiencies that cost your
employees time cost your business money - and lots of it. One Asana study
showed that 60% of an employee's time is spent on "work
about work," rather than actually moving projects forward.
Fortunately, with today's technology, it's
easy to take advantage of machine learning and data analysis to make your
organization more efficient. This will not only save you money but will also
allow your talented employees to spend their time doing valuable work, which is
more engaging.
How can your organization harness machine
learning and other technology solutions to improve efficiency? Here are some
strategies you can use right away.
Machine Learning and Business
Efficiency
There are many ways to improve efficiency in your organization: you
can set effective goals that are SMART (specific, measurable, achievable,
relevant, and timely), map and improve processes, and provide helpful feedback
to employees.
However, one of the most impactful ways to
improve efficiency is to invest in technology. Machine learning (ML) tools can
learn from the data in your organization, recognize patterns in that data, and
then make predictions when new data is introduced.
For example, if you use machine learning in
your sales process, ML tools can recognize patterns in the data - from your
current leads and customers' information - and predict how likely a lead is to
convert to a customer. The software may also be able to suggest specific sales
strategies that can increase close rates.
This would make the sales process more
efficient and effective, helping sales reps be more successful and increasing
revenue in your organization.
Ways Machine Learning Can Reduce
Inefficient Tasks
Today, many organizations have inefficient
processes simply because "that's the way it's always been done."
Seeing the benefits of automation, machine
learning, and data analysis may change that default, however. Companies that
deploy machine learning see benefits that include increased
profitability and revenue, better differentiation from competitors, and
increased accuracy in business processes.
Here are a few ways machine learning can
reduce repetitive busy work and help improve business operations.
Machine Learning in Accounting
and HR
Using automated machine-learning tools can
help reduce the amount of time that employees spend entering data into multiple
systems, evaluating documents, and submitting reports.
In HR, machine learning can be used to
highlight and surface the best resumes among hundreds that come in for a
position. You may even decide to have machine learning tools score candidates
after interviews and move the best applicants forward to the next stage.
In accounting, machine learning can detect
errors by noticing when typical patterns are interrupted. For example, a large
increase in an electricity bill can be brought to the attention of business
leaders without the need for a human combing through each expenditure. Machine
learning can also help pick up anomalies that indicate errors or potential
fraud, alert leaders when financial results fall outside required compliance
rules, and more.
Finally, in all areas of business, the right
AI and machine-learning tools can automatically move data from one system to
another, eliminating the need for repeat data entry and reducing the
possibility of human error.
Improving Manufacturing and
Logistics
Another application of machine learning is found in
manufacturing and logistics. The right ML solution can help your company
understand the entire manufacturing process, including the supply chain, and
highlight bottlenecks and inefficiencies.
Other tools analyze data from manufacturing
equipment, helping to predict when repairs are needed so that you can schedule
downtime efficiently rather than being surprised by it. ML software can also
help streamline quality control, shipping routes, warehouse organization and
storage, and more.
The improvements in manufacturing, shipping,
and quality control can help your organization see significantly higher profits
and lower rates of human error.
Making Sales More Efficient
The sales process is more than just finding
ideal leads and talking to them. It's important to understand the entire
customer journey, know how many leads it takes to hit required sales results,
and create and deploy sales assets at the right time to move leads forward in
the sales process.
All of this can be made far more effective
using machine learning. A single individual can't have a complete understanding
of the entire sales process, all of the data involved, and use that data to
improve the sales operation. However, a machine learning tool can do that
quickly and easily.
As a result, machine learning allows your
organization to more accurately forecast sales results and review results
regularly to ensure the sales department is on target to meet company goals.
Machine learning can also help predict customer behavior, point reps to the
best sales assets for each lead, and uncover other actionable information.
When sales are streamlined and effective, your
organization will be able to grow quickly and your sales reps will be more
engaged and successful.
Use Machine Learning to Drive
Better Outcomes
Being able to harness and understand the data
within your organization is essential to having an efficient operation. Some
research shows that data intelligence can make your company's business outcomes
three times better.
The way to see these results in your
organization is to let go of how things have always been done in the past and
invest in technology solutions and automation tools that move the company
forward, from machine learning to other forms of AI.
When you see your financial reports a few
years from now, you'll be glad you made the change!
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ABOUT THE AUTHOR
Beau
Peters is a creative professional with a background in service and
management. He is also an avid researcher and a writer of “all the
things.” He has a passion for purpose-driven content and bettering the
human experience. In his free time, he enjoys having a good cup of
coffee and seeing the world.