The genie is out of the bottle, with AI poised to grant the
wishes of every competitive enterprise with efficiency, profit, and
growth. However, relying on the promises of nascent technology
heedless of its potential pitfalls can undermine its eagerly
anticipated benefits. Companies risk mismanaging massive amounts of
data without defining and aligning tangible business goals with
clear objectives. Rich Fennessy, CEO
of Trace3, encourages businesses to develop robust data management
strategies that support the successful integration of AI and other
emerging technologies to avoid stagnation, increased cybersecurity
threats, and missed financial opportunities.
IRVINE,
Calif., July 8, 2024 /PRNewswire-PRWeb/ -- It's
becoming less a matter of "if" or "when" companies will incorporate
AI tools into their business model, but rather "how" they will
integrate its advantages into their daily operations. According to
research conducted by Exploding Topics, 77% of companies are
currently using or exploring the applications of AI in their
businesses, with 83% reporting that AI is a top priority in their
future plans. Nine out of ten organizations believe AI offers a
competitive advantage. (1) However, despite all the optimism
surrounding its limitless possibilities, 70% to 80% of all AI
projects fail. (2) Rich Fennessy,
CEO of Trace3, advises, "To mitigate the financial risks associated
with the high failure rate of AI projects, companies should adopt a
practical and strategic approach. Focus on selecting the right use
cases that align closely with business objectives and offer clear,
measurable outcomes. Prioritize high-impact, feasible projects that
can demonstrate quick wins, thereby building confidence and
securing further investment."
"Maintaining a competitive edge in today's
AI-driven landscape requires a purposeful approach," emphasized
Rich Fennessy, CEO of Trace3. "It's
about aligning technology with business strategy and ensuring AI
readiness for success."
Emerging technologies arrive in tandem with associated risks
that are often overlooked. Misalignment with strategic goals can
lead to poor ROI and low buy-in and shortcuts in technology
infrastructure impede progress and increase costs. The demand for
experts and workforce retraining slows adoption, while a lack of
expertise among executives can impact decision-making and risk
assessment. Treating emerging technology as incremental improvement
rather than transformative can limit success. The lightning-fast
pace of innovation can outpace risk assessment and regulation,
leading to safety and security concerns. Large volumes of data
create vulnerabilities that require data governance, secure
infrastructure, and compliance measures. (3)
Essential Strategies for Successful Technology Integration
Fennessy offers the following solutions to successfully overcome
the inherent risks when implementing emerging technology like
AI:
1. Develop Robust Data Management Strategies for AI Integration:
Data preparation is essential, as many enterprises struggle with
data quality and availability issues. Establish strong data
governance with clear policies for quality, security, and privacy.
Use data profiling to identify inconsistencies and anomalies.
Implement scalable architectures like data lakes or warehouses to
store, manage, and organize diverse data types efficiently.
2. Align AI Projects With Business Objectives: Engage both business
and technical stakeholders to identify specific AI use cases that
align with business objectives and deliver measurable outcomes,
using techniques like "art of the possible" workshops to uncover
potential ideas. Conduct pilot projects to test and refine these
use cases, focusing efforts on high-impact areas and being prepared
to pivot if necessary.
3. Mitigate Financial Risks of AI Project Failures: Poor data
quality and management are common reasons for AI project failures.
Invest in reliable data assessment, cleansing, and validation.
Establish strong data governance frameworks to maintain data
integrity, security, and compliance. Choose the right platforms for
AI deployment, such as cloud solutions offering advanced AI and
machine learning tools, pre-built models, and infrastructure,
significantly reducing initial setup costs and time.
4. Address Ethical Concerns in AI and Emerging Technologies:
Communicate clearly on data usage and AI decision-making processes.
Implement oversight mechanisms to prevent misuse and ensure
compliance. Adhere to data protection regulations, employ
anonymization techniques, and audit AI systems to identify and
mitigate bias.
5. Balance Operational Disruptions with Innovation: AI should be
implemented as an integral business component, requiring visionary
leadership and a culture of testing assumptions to foster technical
trust. While explainability is crucial in critical use cases,
preparing for future adoption remains essential for evolving
technologies.
6. Cybersecurity Measures for Emerging Technologies: Organizations
must ensure transparency in data handling, aligning with the
General Data Protection Regulation (GDPR), California Consumer
Privacy Act (CCPA), and other privacy standards while enhancing
anonymization and cybersecurity. Mitigating bias involves using
diverse datasets, regular audits, and adopting bias detection tools
for AI. Establishing ethical AI frameworks and ongoing employee
education promotes responsible deployment aligned with
organizational values.
7. Ensure Regulatory Compliance in Technology Implementations:
Regular risk assessments identify tech vulnerabilities and monitor
threats. Solid security systems like the NIST Cybersecurity
Framework, ISO/IEC 27001, and CIS Controls ensure comprehensive
protection with advanced endpoint security, network segmentation,
encryption, and strong access controls. Implementing vulnerability
management, incident response plans, and ongoing training to defend
against cybersecurity threats are crucial for organizations using
third-party services.
Trace3 transforms enterprises through collaborative, strategic
consulting, adaptive technology, and convergent solutions that
deliver visible and measurable results. Fennessy concludes,
"Maintaining a competitive edge in today's AI-driven competitive
landscape requires a purposeful approach. It's not simply about
getting on board with the latest technology, it's about bending it
to align with your strategy and desired business outcomes, while
ensuring your AI data readiness supports a roadmap that leads
straight to success."
About Trace3:
Today there is a great deal of noise in the technology industry
around AI, but not much practical intelligence is offered. Trace3,
based in Irvine, California,
delivers over 20 years of expertise in delivering innovation in the
form of emerging technology, providing unique technology solutions
and consulting services to change this – and drive its
implementation across enterprises. Their elite engineering and
dynamic innovation provide convergent solutions that embrace
emerging technology and drive measurable value. Trace3 embodies the
spirit of a startup with the advantage of a scalable business.
Trace3 believes that ALL Possibilities Live in AI. For more
information, visit http://www.trace3.com.
References:
1. Tprestianni. "131 AI Statistics and Trends for 2024."
National University, 1 Mar. 2024,
nu.edu/blog/ai-statistics-trends/#:~:text=According%20to%20research%20completed%20by,priority%20in%20their%20business%20plans.
2. Rschmelzer. "Top Reasons Why AI Projects Fail." Cognilytica,
26 Dec. 2023,
cognilytica.com/top-10-reasons-why-ai-projects-fail/#:~:text=The%20Shocking%20Truth%3A%2070%2D80%25%20of%20AI%20Projects%20Fail!,-Despite%20the%20buzz&text=Not%20surprisingly%2C%20there%20are%20a,ways%20to%20navigate%20these%20challenges.
3. "Eight Overlooked Emerging Tech Risks and How to Mitigate Them."
ISACA,
isaca.org/resources/news-and-trends/newsletters/atisaca/2024/volume-9/eight-overlooked-emerging-tech-risks-and-how-to-mitigate-them.
Accessed 1 July 2024.
Media Contact
Karla Jo Helms, JOTO PR™,
727-777-4629, khelms@jotopr.com, jotopr.com
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