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Using AI to Extract Insights from Data: A Conversation with Snowflake

Using AI to Extract Insights from Data: A Conversation with Snowflake

Published

February 6, 2025

During Snowflake’s World Tour stop in Chicago, Data Cloud Now anchor Ryan Green sat down with leaders from OneSix. During the conversation, Co-founder and Managing Director Mike Galvin and Senior Manager Ryan Lewis note how Snowflake’s technology has changed the game, allowing them and its customers to focus less on how to build data infrastructure and more on how to extract insights from data, be it via the use of AI or reporting or dashboarding.

Get More from Your Data with Snowflake

As a Premier Snowflake Services Partner, we drive practical business outcomes by harnessing the power of Snowflake AI Data Cloud. Whether you’re starting with Snowflake, migrating from a legacy platform, or looking to leverage AI and ML capabilities, we’re ready to support your journey.

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Data and AI Horizon 2025: Key Trends and Tips

Data and AI Horizon 2025: Key Trends and Tips

Written by

Jacob Zweig, Managing Director

Published

December 6, 2024

Data & AI Strategy
Manufacturing
Retail & Consumer Goods
Healthcare & Life Sciences
Financial Services
Snowflake

As we enter 2025, organizations face an unprecedented convergence of technological advancements in AI, computing, and human-centered innovation. This year marks a pivotal shift from experimentation to operationalization, with a focus on measurable ROI, ethical governance, and sustainable practices. Industries from manufacturing to healthcare are leveraging these trends to drive efficiency, collaboration, and customer-centric solutions.

By embracing cutting-edge tools such as autonomous AI systems, hybrid computing architectures, and AI-driven personalization, businesses can transform operations, unlock new opportunities, and thrive in a rapidly evolving digital landscape.

Drawing on insights from Snowflake’s AI + Data Predictions 2025, Coalesce’s Top Data Trends for 2025, PwC’s 2024 Cloud and AI Business Survey, and Gartner’s Top 10 Strategic Technology Trends for 2025, this guide explores key trends, industry-specific impacts, and strategic recommendations to help leaders navigate and harness the transformative potential of 2025.

Table of Contents

Top 3 Trends

TREND 1

Practical, Value-Focused AI

AI remains a cornerstone of innovation, but 2025 marks a decisive shift from exploratory projects to operationalized solutions that deliver measurable ROI.

Aligning AI with Business Goals

Over the past two years, businesses faced immense pressure to rapidly adopt AI technologies, driven by demands from investors, boards, and executives. This rush often resulted in disjointed experiments with tools like ChatGPT, revealing both the potential and the challenges of unstructured adoption. Now, companies are recalibrating their focus, aligning AI initiatives with broader data strategies for strategic, well-defined outcomes.

“People are coming to the realization that building an AI solution is very easy, but building an AI solution that actually adds value is much more difficult.”

Satish Jayanthi, CTO and Co-Founder at Coalesce
Governance as a Foundation

Ethical AI governance is no longer optional. Transparent guardrails and accountability are critical to mitigate risks like bias and data poisoning while fostering stakeholder trust.

“You can have a use case with AI, but if you have not put the right guardrails around that and understood governance and responsible AI, then obviously you leave yourself exposed as an organization. It’s really all about governance and transparency.”

Rinesh Patel, Global Head of Financial Services at Snowflake
Generative AI and Automation

Generative AI and autonomous agents are transforming productivity by automating workflows, streamlining repetitive tasks, and introducing novel use cases. Tools like Retrieval-Augmented Generation (RAG) enhance reliability by grounding outputs in verifiable data, addressing the challenge of hallucinations.

67%

of top-performing companies are already realizing value in using GenAI for products and services innovation.

PwC’s 2024 Cloud and AI Business Survey
Related Insights
Making AI More Human: The Power of Agentic Systems
Read Blog
TREND 2

Seamless Data Architectures

Effective AI relies on data architectures that are robust, scalable, and interoperable. These architectures ensure seamless data integration and processing, enabling AI to deliver reliable and impactful outcomes.

Unified Storage for Seamless Processing

Organizations are adopting unified storage solutions that integrate with multiple compute engines, enabling consistent, efficient data processing across diverse systems.

“AI models require large amounts of clean, high-quality data to function effectively and produce accurate results. Enterprises will increasingly leverage user-friendly data integration tools to centralize data from various operational data stores to create a corpus for AI training.”

Taylor Brown, COO and Co-Founder of Fivetran
The Rise of Open Table Formats

Open-source table formats like Apache Iceberg are the future of data architecture because they provide for enhanced governance and interoperability across various data platforms. Data platform leaders like Snowflake are rapidly adding features to leverage the power of Iceberg.

“Iceberg will go mainstream and finally combine operational and analytical data.”

Amanda Kelly, Co-Founder of Streamlit
Related Insights
Iceberg Tables: The Future of Data Architecture
Read Blog
TREND 3

Human-Centered AI Innovation

In 2025, technology will go beyond operational efficiency to reshape how humans work, collaborate, and engage with technology. Human-centered innovation empowers individuals through intuitive systems, driving unprecedented productivity and creativity.

Intelligent Workforce Automation

In 2025, technology will go beyond operational efficiency to reshape how humans work, collaborate, and engage with technology. Human-centered innovation empowers individuals through intuitive systems, driving unprecedented productivity and creativity.

“If you talk to developers about the software development lifecycle, across the design, development and testing phases, you’ll learn that pretty much no one likes QA. Good QA is very cumbersome and time consuming. If we can offload 40% or more of the testing process to an AI-powered assistant — with human supervision and assurance — we move faster, and developers spend more time doing what they love to do.”

Sunny Bedi, CIO and CDO at Snowflake
Enhanced Team Collaboration

AI and data platforms are fostering a new wave of collaboration. Fusion teams, which combine technical and domain expertise, are driving efficient AI applications and bridging departmental gaps. Real-time data sharing enables informed decision-making and cultivates a culture of innovation.

Personalized Experiences at Scale

AI is tailoring experiences to individual needs, from customized training programs to hyper-personalized customer engagement. These advancements elevate user satisfaction, accelerate skill acquisition, and create impactful business outcomes.

“AI will transform how brands personalize and automate every step of the customer journey. Marketers will move past manual A/B testing and static targeting, embracing ML-driven experiences that continuously learn and adapt for each user.

Tejas Manohar, Co-CEO and Co-Founder Hightouch
Related Work
Maximizing casino player profitability with a one-to-one marketing engine
Read Case Study

Industry Impacts

Manufacturing

Manufacturing will experience significant advancements with the adoption of large vision models—AI systems capable of interpreting visual inputs. These technologies will:

  • Revolutionize Quality Control: Identify defects with unmatched precision, ensuring higher-quality outputs.
  • Enhance Product Design: Simulate product improvements and explore trade-offs, leading to more innovative designs.
  • Boost Productivity: Accelerate inspection processes and increase throughput, reducing production bottlenecks. Additionally, AI tools will enable manufacturers to achieve environmental sustainability goals by optimizing resource use and predicting supply chain downtime.
Related Work
Automating food inspection using deep learning for Sunsweet Growers
Read Case Study

Financial Services

Financial services will continue to lead in AI adoption but with a focus on balancing innovation and fiscal responsibility:

  • ROI-Driven Applications: AI investments will prioritize demonstrable outcomes, such as improving operational efficiency and reducing fraud.
  • Governance and Transparency: Strong regulatory frameworks will mitigate risks like biased decision-making and ensure ethical AI usage.
  • Risk Management and Compliance: Organizations will use AI to safeguard sensitive data, monitor compliance, and anticipate potential regulatory changes.

Healthcare and Life Sciences

Healthcare and life sciences will adopt AI cautiously, focusing on measured applications to ensure safety and compliance:

  • Pharma Leads Innovation: AI will accelerate drug discovery and development, creating new revenue opportunities for pharmaceutical companies.
  • Enhanced Diagnostics and Prevention: Prevention: AI-driven tools will improve diagnostic accuracy and enable predictive healthcare. Patient care, however, will progress at a slower pace due to the high stakes involved in clinical decision-making.
Related Work
Leveraging AI to increase provider engagement for a top pharmaceutical company
Read Case Study

Retail and Consumer Goods

The retail industry will focus on incremental successes with AI to address challenges and enhance customer experiences:

  • Supply Chain Optimization: AI will improve inventory management, ensuring the right products are available at the right time.
  • In-Store Experiences: Retailers will leverage AI for real-time insights to personalize shopping experiences and optimize store layouts.
  • Marketing Transformation: Privacy-preserving AI technologies will enable better targeting and engagement without compromising consumer trust. As data foundations improve, retailers will see a clearer ROI from their AI investments, paving the way for broader adoption.

Strategic Recommendations

Focus on Practical Applications

Learn from early adopters and prioritize use cases with measurable outcomes.

Invest in Governance

Implement frameworks that ensure ethical AI usage and compliance with regional regulations.

Embrace Open Source

Adopt open standards like Iceberg to enhance collaboration, interoperability, and vendor independence.

Upskill the Workforce

Equip teams with the skills to leverage AI and advanced computing for strategic problem-solving.

Adapt Business Models

Align organizational strategies with emerging technologies to stay competitive.

From Insight to Impact

As we navigate the horizon of 2025, the convergence of data, AI, and innovation presents organizations with immense opportunities to transform their operations and unlock new avenues of growth. By embracing these shifts, leaders can position their organizations to thrive in a rapidly evolving digital landscape. Connect with our experts today to start building a future-ready data and AI strategy that combines innovation and practicality.

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Culture Matters: Building a Data-Driven, AI-Powered Mindset in Private Equity

Culture Matters: Building a Data-Driven, AI-Powered Mindset in Private Equity

Written by

Mike Galvin, Managing Director

Published

October 9, 2024

Data & AI Strategy
Portfolio AI Strategy
Financial Services

Private equity (PE) firms must leverage every available advantage to stay competitive and deliver superior returns. One of the most powerful tools at their disposal is the strategic use of data and artificial intelligence (AI). However, the true power of these tools is unlocked only when they are deeply embedded in the firm’s culture. Here’s what success looks like in building a data and AI culture at a private equity firm.

Trust your gut data.

At the heart of a data and AI culture is the firm’s commitment to making decisions based on data and AI insights. This means that at the executive level, decisions are informed by robust data analysis and predictive models. This approach ensures that strategic decisions are grounded in empirical evidence rather than intuition alone. A successful data and AI culture is characterized by:

Data-Driven Strategy

Investment strategies are crafted based on detailed data analysis, identifying trends and opportunities that might be invisible to the naked eye.

AI-Enhanced Due Diligence

AI tools are used to perform due diligence more efficiently and effectively, uncovering insights that might otherwise be missed.

Performance Monitoring

Continuous monitoring of portfolio performance using AI-driven analytics to identify areas of improvement and potential risks.

Empower teams.

For data and AI initiatives to be successful, it’s crucial that all employees, from analysts to partners, understand the tools at their disposal. This involves comprehensive education on the AI models in place, their capabilities, and their limitations. Key elements include:

Training Programs

Regular training sessions to keep employees updated on the latest data analytics and AI technologies.

Knowledge Sharing

Encouraging the sharing of best practices and insights across the firm to ensure everyone is aligned and knowledgeable.

Transparency

Clear communication about how AI models make decisions, fostering trust and understanding among employees.

Always be innovating.

Pilot Projects

Regularly initiate pilot projects to test new AI tools and data analytics methods.

Innovation Labs

Create dedicated spaces where employees can experiment with new technologies without the fear of failure.

Feedback Loops

Establish mechanisms for feedback and iteration, allowing successful experiments to be scaled quickly and unsuccessful ones to provide learning opportunities.

Engage tech communities.

Engagement with the broader data science and AI community can significantly enhance a firm’s capabilities. This involves:

Open-Source Contributions

Encouraging employees to contribute to open-source AI projects, fostering innovation and collaboration.

Academic Partnerships

Collaborating with academic institutions to stay at the forefront of AI research and development.

Industry Conferences

Active participation in industry conferences and workshops to stay updated on the latest trends and technologies.

Invest in the future.

Investing in research and development (R&D) for advanced analytics and AI is a hallmark of a forward-thinking private equity firm. This commitment manifests as:

R&D Budget

Allocating a specific budget for exploring new data and AI technologies, ensuring continuous innovation.

Long-Term Vision

Developing a long-term vision for data and AI integration, with clear milestones and goals.

Resource Allocation

Ensuring that data scientists, engineers, and other key personnel have the resources they need to succeed.

Assess Your Firm’s
Data and AI Maturity

If you’re unsure where to begin, understanding your business’ current data and AI maturity level is a solid first step. Take our 5-minute assessment, and based on your results, you’ll get custom recommendations and next steps.

Take the Assessment

AI Activation in Private Equity: What Success Looks Like

AI Activation in Private Equity: What Success Looks Like

Written by

Jacob Zweig, Managing Director

Published

September 12, 2024

Data & AI Strategy
Portfolio AI Strategy
Financial Services

For private equity firms, artificial intelligence (AI) stands out as a game-changer, offering unprecedented opportunities for enhancing decision-making, optimizing operations, and driving value creation. AI success in a private equity firm involves a strategic approach that encompasses advanced analytics, robust AI operations, and impactful AI applications. Here’s a detailed look at what AI success looks like in this context.

Advanced Analytics and AI Fundamentals

Advanced analytics and AI have the potential to transform operations and drive significant value in portfolio companies.
Clearly Defined AI Strategy

Successful firms identify and define AI use cases that align with their business objectives and have the potential for significant impact. These use cases guide AI investments and development efforts.

Implementation of Predictive Models and ML Algorithms

AI success begins with the effective implementation of predictive models and machine learning (ML) algorithms in production environments. These tools can analyze vast amounts of data to predict future trends, identify investment opportunities, and mitigate risks.

In-House Data Science Expertise

Building a strong in-house team of data scientists, statisticians, and ML engineers is crucial. These experts drive AI initiatives, develop sophisticated models, and ensure the effective use of AI across the firm.

Experimentation with Advanced ML Techniques

Experimenting with and implementing advanced machine learning techniques, such as deep learning, enables the firm to tackle complex problems and discover new insights.

Maintaining a Feature Store

A feature store, which centralizes and standardizes features used in various models, is essential for efficient and consistent AI model development.

AI Operations and Infrastructure

To fully realize the benefits of AI, it’s essential to have robust operations and infrastructure in place.

Formal MLOps Framework

Adopting a formal MLOps framework is critical for managing the lifecycle of AI models, from development to deployment and monitoring. This framework ensures reliability, scalability, and continuous improvement of AI systems.

Monitoring Dashboards

Reliable monitoring dashboards are essential for tracking the health and performance of AI models in production. These tools provide real-time insights and enable proactive maintenance.

Integration with Operational Systems

Successful AI implementation extends beyond research projects, integrating AI models with operational systems, workflows, or core products to drive tangible business value.

Access to Data and Compute Resources

Providing data scientists with full access to necessary data sources and compute resources is crucial for maximizing AI model performance and innovation.

Versioning and Tracking

A robust process for versioning and tracking changes in AI models and datasets ensures transparency, reproducibility, and continuous improvement.

AI Applications and Use Cases

AI applications can provide substantial improvements in various business processes. Focus on practical use cases that drive operational efficiency and enhance customer experiences.

Computer Vision Applications

Implementing computer vision technologies, such as image recognition, object detection, and object tracking, can significantly enhance business processes and operational efficiency.

Natural Language Processing (NLP)

Utilizing NLP technologies for tasks like text classification, sentiment analysis, and chatbots enhances communication, customer service, and data analysis capabilities.

Large Language Models (LLMs)

Exploring and implementing large language models (LLMs) for various business applications can unlock new opportunities for automation and intelligence.

Supply Chain Optimization

AI-driven demand forecasting, inventory optimization, and other supply chain-related tasks enhance efficiency, reduce costs, and improve service levels.

AI-Driven Recommendation Systems

Implementing AI-driven recommendation systems enhances personalization and customer experience, driving higher engagement and conversion rates.

Assess Your Firm’s
Data and AI Maturity

If you’re unsure where to begin, understanding your business’ current data and AI maturity level is a solid first step. Take our 5-minute assessment, and based on your results, you’ll get custom recommendations and next steps.

Take the Assessment

Data Modernization in Private Equity: What Success Looks Like

Data Modernization in Private Equity: What Success Looks Like

Written by

Mike Galvin, Managing Director

Published

August 28, 2024

Data & AI Strategy
Portfolio AI Strategy
Financial Services

For private equity firms, leveraging data effectively can unlock new opportunities, enhance decision-making, and reduce investment risk. But data success is not just about having the latest tools and technologies; it’s about creating a holistic strategy that aligns with business objectives, managing infrastructure efficiently, and ensuring data accessibility and usability across the organization. In this blog, we dive into what data success looks like for private equity firms and their portfolio companies.

Data Strategy and Alignment

Having a clear and actionable data strategy is critical for aligning the interests of the PE firm and its portfolio companies.

1. Clearly Defined Data Strategy

Success starts with a well-defined data strategy that aligns with the firm’s overarching business objectives. This strategy should outline how data will be used to drive investment decisions, optimize portfolio performance, and create value.

2. Prioritized Roadmap for Data Initiatives

Having a prioritized roadmap for data initiatives is crucial. This roadmap should be regularly reviewed and adjusted to reflect changing business priorities and emerging opportunities.

3. Collaboration Between Business Units and IT

Strong collaboration between business units and IT is essential for driving the data strategy. This collaboration ensures that data initiatives are not only technically sound but also aligned with business needs.

4. Quantifiable Measures of Success

It’s important to have quantifiable measures of success for data initiatives. These metrics allow the firm to track progress, measure impact, and make informed decisions about future investments.

5. Understanding Critical Data

Not all data is created equal. Successful firms understand which data is critical for their competitive advantage and which is ancillary.

Data Infrastructure and Management

A solid data infrastructure is the backbone of any successful data strategy.

1. Centralized Data Location

Centralizing all data from various source systems into a single location, such as a data lake, is a key aspect of data success. This centralization simplifies data management and improves access.

2. Automated Data Cleaning and Standardization

Automated processes for data cleaning and standardization are essential for maintaining high-quality data. These processes reduce errors and ensure consistency.

3. Scalable Data Ingestion

Data ingestion processes must be scalable to handle changes in operational systems without impacting analytics. This scalability ensures that the firm can adapt to growth and new data sources seamlessly.

4. Leveraging Cloud Platforms

Utilizing cloud platforms like AWS, Azure, or Google Cloud for data infrastructure offers flexibility, scalability, and robust security.

5. High-Availability and Disaster Recovery

Ensuring high-availability and disaster recovery solutions for the data platform is crucial for maintaining business continuity and protecting valuable data assets.

Data Accessibility and Usability

Making data easily accessible and usable is crucial for enabling data-driven decision-making across portfolio companies.

1. Easy Access to Relevant Data

Business users across the organization should have easy access to relevant data in a central location. This accessibility empowers them to make data-driven decisions quickly and effectively.

2. Enterprise-Wide Reporting Tool

Using an enterprise-wide reporting tool to share data insights internally and externally enhances transparency and collaboration.

3. Consistent Data Security Measures

Consistently applying data security measures ensures appropriate levels of data visibility while protecting sensitive information.

4. Accessible Metrics for Decision-Making

Employees should have easy access to metrics that support data-driven decision-making in their daily work. This accessibility enhances operational efficiency and strategic planning.

5. Centralized Business Metrics Calculation

A single, centralized calculation for all business metrics ensures consistency and reliability in reporting and analysis.

Assess Your Firm’s
Data and AI Maturity

If you’re unsure where to begin, understanding your business’ current data and AI maturity level is a solid first step. Take our 5-minute assessment, and based on your results, you’ll get custom recommendations and next steps.

Take the Assessment

Transforming Private Equity: AI’s Role in Deal Sourcing, Value Creation, and Exits

Transforming Private Equity: AI’s Role in Deal Sourcing, Value Creation, and Exits

Written by

Jacob Zweig, Managing Director

Published

August 14, 2024

Data & AI Strategy
Portfolio AI Strategy
Financial Services

For private equity firms, the rapid rise in interest rates and heightened competition in 2023 has resulted in sharp declines in deal-making, exits, and fund-raising. According to Bain & Company’s 2024 Global Private Equity Report, these market conditions have led to a significant amount of capital sitting on the sidewalks, a high number of unexited assets, increased pressure to generate EBITDA, and investors are seeking “safer” investments.

To successfully navigate the current landscape of the PE industry, firms must excel in three critical areas: Deal Sourcing, Value Creation, and Exit Strategy. Leveraging Artificial Intelligence (AI) can help PE firms increase speed and efficiency across these three areas.

1. Deal Sourcing

Effective deal sourcing is the cornerstone of successful private equity operations. Traditionally, this process involves building robust networks with industry experts, brokers, and advisors to access lucrative deals at the earliest stages. However, with the advent of AI, this landscape is changing dramatically.

AI Use Cases for Deal Sourcing:

Unified Portfolio Analytics & Market Analysis

AI can scan vast amounts of data to identify potential opportunities, perform detailed due diligence, and develop accurate valuation strategies.

Targeted Outreach

AI-driven tools can generate personalized email drafts, ensuring that firms connect with the right contacts at the right time.

Deal Prioritization
  • AI helps prioritize deals based on strategic fit and potential ROI, reducing the risk of missing out on high-value opportunities.
Efficiency & Accuracy

The use of AI accelerates the deal sourcing process and increases the likelihood of identifying high-potential investments.

2. Value Creation

In private equity, rapidly improving EBITDA is crucial for driving growth and maximizing returns. AI plays a pivotal role in enhancing portfolio value creation by optimizing various operational aspects.

AI Use Cases for Value Creation:

Demand Forecasting

AI can predict future demand trends, enabling firms to make informed decisions on inventory, production, and resource allocation, ultimately driving revenue growth.

Customer Segmentation

By analyzing customer data, AI can identify distinct segments, allowing firms to tailor their strategies and offerings to meet the specific needs of different groups, enhancing customer satisfaction and loyalty.

Cost Reduction & Pricing Strategies

Advanced analytics powered by AI drive cost reduction initiatives and improve pricing strategies.

Uncovering Hidden Opportunities

AI analyzes unstructured data to provide insights into customer behavior, market dynamics, and operational performance, leading to innovative growth strategies.

3. Exit Strategy

A well-planned exit strategy is essential for realizing the full value of an investment. AI-driven insights elevate this process to new heights.

AI Use Cases for Exit Strategy:

Market Trends & Buyer Identification

AI analyzes market trends and identifies potential buyers, ensuring firms are well-prepared for exits.

Valuation Model Refinement

AI refines valuation models, providing more accurate and data-backed assessments.

Stakeholder Alignment & Smooth Transitions

NLP tools analyze communication patterns and stakeholder sentiment, aiding effective engagement and negotiation strategies.

Timing Optimization

Predictive analytics forecast industry trends and buyer behavior, helping firms time their exits for maximum return.

Streamlined Due Diligence

AI streamlines the due diligence process for potential buyers, making the firm’s exit offering more attractive.

Maximizing Portfolio Value
with Data & AI

The intersection of data and AI is continuing to shape the business landscape. For private equity firms, leveraging data and AI is a necessity for driving value creation and operational efficiency in portfolio companies. Check out our playbook that guides private equity firms and their portfolio companies through seven essential components of a robust data and AI strategy, helping achieve and maintain data maturity.

Get the Playbook

Top 10 Data & AI Trends Private Equity Firms Should Watch in 2024

Top 10 Data & AI Trends Private Equity Firms Should Watch in 2024

Written by

Mike Galvin, Managing Director

Published

August 1, 2024

Data & AI Strategy
Portfolio AI Strategy
Financial Services

As we navigate through 2024, the landscape of data and artificial intelligence (AI) continues to evolve at a rapid pace. For private equity firms, staying ahead of these trends is crucial for making informed investment decisions, optimizing portfolio company performance, and driving value creation. Here are the top data and AI trends that private equity firms should keep an eye on this year.

1. Generative AI and Large Language Models (LLMs)

Generative AI and large language models, such as OpenAI’s GPT-4, are revolutionizing the way businesses operate. These models are capable of generating human-like text, which can be used in a variety of applications from content creation to customer service automation. According to a report by Gartner, the use of AI in content creation is expected to grow by 30% by 2024. Private equity firms can leverage these technologies to enhance marketing efforts, streamline communication processes, and improve decision-making through advanced data analysis.

Source: Gartner “Predictive Analytics Market Research Report”

2. AI-Driven Predictive Analytics

Predictive analytics has become a cornerstone for data-driven decision-making. With advancements in AI, predictive models are now more accurate and capable of processing vast amounts of data in real-time. According to McKinsey, companies that use predictive analytics report a 20-25% increase in their overall performance. Private equity firms can use AI-driven predictive analytics to forecast market trends, identify potential investment opportunities, and mitigate risks by anticipating financial downturns or market shifts.

Source: McKinsey & Company “The Value of Predictive Analytics”

3. Edge AI and IoT Integration

The integration of AI with Internet of Things (IoT) devices is creating new opportunities for data collection and analysis at the edge of networks. Edge AI allows for real-time processing of data on devices such as sensors and cameras, reducing latency and improving efficiency. IDC predicts that by 2025, 75% of enterprise-generated data will be created and processed at the edge. Private equity firms investing in industries like manufacturing, healthcare, and logistics should watch for advancements in edge AI to capitalize on improved operational efficiencies and innovative business models.

Source: IDC “Edge Computing and IoT Market Forecast”

4. AI Ethics and Governance

As AI becomes more pervasive, the need for robust ethics and governance frameworks is increasingly important. Regulatory bodies around the world are beginning to implement stricter guidelines to ensure AI is used responsibly and ethically. A PwC survey found that 85% of CEOs believe AI ethics and governance will be critical to gaining public trust. Private equity firms must prioritize investments in companies that adhere to ethical AI practices and have strong governance structures in place to avoid regulatory pitfalls and maintain public trust.

Source: PwC “AI Ethics and Governance Survey”

5. AI in Cybersecurity

Cybersecurity remains a top concern for businesses across all sectors. AI is playing a crucial role in enhancing cybersecurity measures by detecting threats in real-time, automating response actions, and predicting potential vulnerabilities. According to a report by Capgemini, 69% of organizations believe AI will be necessary to respond to cyber threats. Private equity firms should consider the cybersecurity capabilities of their portfolio companies and look for investment opportunities in AI-driven cybersecurity solutions to safeguard assets and data integrity.

Source: Capgemini “AI in Cybersecurity Report”

6. Sustainable AI and Green Technology

Sustainability is becoming a key focus area for investors and businesses alike. AI is being used to drive sustainable practices across various industries, from optimizing energy consumption to improving supply chain efficiencies. A study by Accenture found that AI has the potential to boost renewable energy efficiency by up to 20%. Private equity firms should monitor advancements in sustainable AI and green technologies, as these can offer both financial returns and positive environmental impact, aligning with the growing demand for ESG (Environmental, Social, and Governance) investments.

Source: Accenture (2023) “AI and Sustainability Report”

7. AI-Powered Automation and Workforce Transformation

Automation driven by AI is transforming the workforce landscape. From robotic process automation (RPA) to AI-driven customer service bots, businesses are increasingly adopting automation to enhance productivity and reduce costs. According to Deloitte, 58% of business leaders have implemented some form of AI automation, and this number is expected to grow. Private equity firms need to understand the implications of AI-powered automation on workforce dynamics and consider investing in companies that are leading the way in innovative workforce transformation.

Source: Deloitte “AI-Powered Automation Adoption Survey”

8. AI in Healthcare and Life Sciences

The healthcare and life sciences sectors are experiencing significant advancements due to AI. From drug discovery to personalized medicine, AI is enabling breakthroughs that were previously unimaginable. The AI in healthcare market is projected to reach $45.2 billion by 2026, growing at a CAGR of 44.9% from 2021. Private equity firms should keep a close watch on AI developments in these sectors, as they present lucrative investment opportunities with the potential to drive significant societal impact.

Source: MarketsandMarkets “AI in Healthcare Market Forecast”

9. Quantum Computing and AI

Quantum computing is still in its early stages, but its potential to revolutionize AI and data analytics is immense. Quantum computers can process complex calculations at unprecedented speeds, which could lead to breakthroughs in areas such as cryptography, material science, and large-scale data analysis. According to a report by Boston Consulting Group, the quantum computing market is expected to reach $850 billion by 2040. Private equity firms should stay informed about advancements in quantum computing and consider the long-term implications for AI and data strategies.

Source: Boston Consulting Group “Quantum Computing Market Analysis”

10. AI-Enhanced Customer Experience

Enhancing customer experience through AI is becoming a competitive differentiator for businesses. AI-powered chatbots, personalized recommendations, and predictive customer analytics are just a few examples of how AI is being used to improve customer satisfaction and loyalty. According to Salesforce, 57% of customers are willing to share personal data in exchange for personalized offers or discounts. Private equity firms should look for investment opportunities in companies that are leveraging AI to deliver superior customer experiences, as this can drive growth and profitability.

Source: Salesforce”State of the Connected Customer Report”

Maximizing Portfolio Value
with Data & AI

The intersection of data and AI is continuing to shape the business landscape. For private equity firms, leveraging data and AI is a necessity for driving value creation and operational efficiency in portfolio companies. Check out our playbook that guides private equity firms and their portfolio companies through seven essential components of a robust data and AI strategy, helping achieve and maintain data maturity.

Get the Playbook

Shifting Perspective on AI: From Cost to Strategic Investment

Shifting Perspective on AI: From Cost to Strategic Investment

Written by

Jacob Zweig, Managing Director

Published

July 23, 2024

Data & AI Strategy

Despite the transformative potential of Artificial Intelligence (AI), many organizations still view AI as a significant expense rather than a strategic investment. The perception of AI as a cost stems from various factors:

Initial Investment

The upfront costs of AI projects, including software, hardware, and skilled personnel, can be substantial. This often leads to sticker shock and a focus on short-term financial impacts.

Complexity and Uncertainty

AI initiatives can be complex and uncertain. The fear of failure and the unknowns associated with AI can make it challenging for leaders to justify the investment.

Lack of Immediate ROI

AI projects may not deliver immediate returns. The benefits of AI often manifest over time through increased efficiency, improved decision-making, and innovation, making it difficult for leaders to see the value upfront.

This misconception can hinder the adoption and successful implementation of AI initiatives, ultimately limiting the potential for long-term growth and competitive advantage. In fact, a recent survey by IDC revealed that businesses are experiencing an average 3.5x return on their AI investments, equating to a 250% ROI, highlighting the substantial value derived from AI initiatives (VentureBeat).

The Solution: Shifting Perspective from Cost to Investment

To fully harness the transformative power of AI, organizations must shift their perspective from viewing AI as a cost to recognizing it as a strategic investment. Here’s how they can achieve this shift:

1. Understand the Long-Term Benefits: AI can drive long-term growth by enabling better decision-making, enhancing customer experiences, and uncovering new business opportunities. By focusing on these long-term benefits, organizations can justify the initial investment.

2. Highlight Efficiency Gains: AI can automate routine tasks, reduce operational costs, and increase productivity. These efficiency gains can provide a significant return on investment over time.

3. Leverage Competitive Advantage: AI can provide a competitive edge by enabling organizations to innovate faster, personalize offerings, and respond more quickly to market changes. Viewing AI as a strategic tool can help organizations stay ahead of the competition.

4. Develop a Clear ROI Framework: Establishing a clear framework to measure the return on investment for AI projects can help leaders see the tangible benefits. This includes setting specific goals, tracking key performance indicators (KPIs), and regularly reviewing progress.

5. Invest in Talent and Skills: Building a skilled workforce is crucial for the successful implementation of AI. Investing in training and development can ensure that the organization has the necessary expertise to leverage AI effectively.

6. Adopt a Phased Approach: Rather than embarking on large-scale AI projects, organizations can start with smaller, pilot projects that demonstrate quick wins. This can help build confidence and support for larger investments.

Real-World Impact: How AI is Transforming Three Sectors

Artificial Intelligence (AI) is revolutionizing various industries by enhancing efficiency, driving innovation, and creating new business opportunities. Here are some key statistics and benefits across Healthcare, Finance, and Manufacturing.

Healthcare

AI in healthcare is improving patient outcomes and operational efficiency:

Diagnosis and Treatment

AI-powered tools can analyze medical images and patient data to assist in diagnosis and treatment planning. For instance, AI systems can detect early signs of diseases such as cancer with an accuracy rate of up to 95% (Journal of the American Medical Association).

Operational Efficiency

AI can streamline administrative tasks, reducing costs. McKinsey estimates that AI applications could save up to $150 billion annually in the U.S. healthcare system by 2026 (McKinsey & Company).

Finance

The financial industry leverages AI for fraud detection, customer service, and investment strategies:

Fraud Detection

AI algorithms can detect fraudulent activities by analyzing transaction patterns. The use of AI in fraud detection can reduce false positives by up to 50% and improve detection rates by up to 90% (The Financial Times).

Customer Service

Chatbots and AI-driven customer service platforms handle millions of inquiries efficiently. Juniper Research predicts that chatbots will save banks $7.3 billion globally by 2023 (Juniper Research).

Investment Strategies

AI-driven algorithms optimize investment portfolios, with robo-advisors managing assets expected to grow to $1.2 trillion by 2024 (Business Insider).

Manufacturing

AI in manufacturing is boosting productivity and reducing downtime:

Predictive Maintenance

AI-driven predictive maintenance can reduce equipment downtime by up to 50% and extend the life of machines by 20-40% (PwC).

Quality Control

AI systems can detect defects in products with up to 99% accuracy, significantly improving quality control processes (Deloitte).

Embracing AI as a Strategic Investment

Viewing AI as a strategic investment rather than a cost is crucial for organizations aiming to thrive in the digital age. By understanding the long-term benefits, highlighting efficiency gains, leveraging competitive advantage, and adopting a phased approach, organizations can harness the transformative power of AI. Investing in AI today can drive sustainable growth, innovation, and competitive advantage for years to come. The future belongs to those who see beyond the initial costs and recognize the strategic value that AI can bring to their organizations.

 

The impact outweighs the investment.

Embrace AI now and position your organization as a leader in the digital age. Get in touch with us to learn how we can help you embark on your AI journey and capture transformative business value.

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Laying the Groundwork for AI: Establishing a Data-Driven Culture

Laying the Groundwork for AI: Establishing a Data-Driven Culture

Written by

Mike Galvin, Managing Director

Published

July 11, 2024

Data & AI Strategy

A recent Deloitte survey revealed that 51% of CEOs consider data challenges the primary obstacle to generating business value with Artificial Intelligence (AI), highlighting the critical importance of data readiness in AI implementation.

AI’s power lies in its ability to analyze vast amounts of data and derive actionable insights. However, this process hinges on the quality and integration of the data fed into AI systems. Poor data quality, fragmented data sources, and a lack of integration between systems can significantly hamper the effectiveness of AI initiatives. Let’s delve into these challenges and explore how building a data-driven culture can serve as a solution.

1. Poor Data Quality: AI systems rely on high-quality data to function effectively. Data quality issues, such as inaccuracies, inconsistencies, and missing values, can lead to erroneous insights and decisions. Poor data quality can stem from various sources, including human error, outdated information, and inadequate data governance practices.

2. Fragmented Data Sources: In many organizations, data is scattered across multiple systems, departments, and formats. This fragmentation creates silos that hinder the comprehensive analysis needed for AI to be effective. Fragmented data sources result in a lack of a unified view, making it difficult to draw meaningful conclusions.

3. Lack of Integration Between Systems: Integration issues arise when disparate systems and technologies cannot seamlessly communicate and share data. This lack of integration prevents the smooth flow of information, leading to inefficiencies and missed opportunities. Incompatible data formats, legacy systems, and insufficient interoperability are common culprits.

In a recent Deep Dish Data panel, best-selling author Joe Reis shares:

How often do you find that data is ready for any particular use case or consumption? Most datasets are really gnarly. We need to focus on establishing the foundation and getting the fundamentals in place.

Joe Reis

Laying the Foundation for a Data-Driven Culture

Creating a data-driven culture involves more than just deploying advanced technologies; it requires a holistic approach to managing data effectively. By focusing on the key areas below, organizations can transform their data into a valuable asset that drives informed decision-making and competitive advantage.

1. Establishing Data Governance

Effective data governance is the cornerstone of high-quality data. Implementing robust data governance policies ensures data accuracy, consistency, and reliability. This involves defining data standards, establishing data ownership, and implementing processes for data validation and cleansing.

2. Centralizing Data Management

To combat fragmented data sources, organizations should centralize data management. This can be achieved through data warehouses, data lakes, or cloud-based platforms that consolidate data from various sources. Centralized data management provides a unified view, enabling comprehensive analysis and facilitating better decision-making.

3. Enhancing Data Integration

Integrating data across systems requires the adoption of modern data integration tools and technologies. Application Programming Interfaces (APIs), middleware, and data integration platforms can facilitate seamless data exchange between systems. Emphasizing interoperability and adopting industry standards can further streamline integration efforts.

4. Promoting Data Literacy

Building a data-driven culture goes beyond technology and processes—it involves people. Promoting data literacy across the organization empowers employees to understand, interpret, and leverage data effectively. Training programs, workshops, and fostering a culture of continuous learning are key to enhancing data literacy.

5. Leveraging AI for Data Quality

AI itself can be harnessed to improve data quality. Joe Reis points out: “If the approaches to data management and governance that we’ve taken for the last several decades are having mixed results, will AI be a hail mary to make it work? I’m willing to take the bet that maybe this is worth considering.” Machine learning algorithms can detect anomalies, automate data cleansing, and validate data entries. By leveraging AI for data quality management, organizations can ensure that their data is accurate, consistent, and reliable.

Breaking Down Data Silos: A Success Story

How a Private Equity firm unified data for enhanced portfolio insights and reduced risk​

Our client, a private equity firm managing a diverse portfolio of investments, faced challenges in leveraging data for strategic decision-making. The firm needed a modern data infrastructure to consolidate disparate datasets, enhance analytical capabilities, and gain deeper insights into overall portfolio performance.

The implementation of a comprehensive data infrastructure provided the private equity firm with a holistic view of portfolio performance while ensuring data security and integrity. This empowered the firm to make more informed decisions, enhance operational efficiency, and gain deeper insights into their investments, ultimately mitigating risks and fostering long-term profitability.

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Pro Tip: Start Small with Rule-Based Systems

As Winston Churchill famously said, “perfection is the enemy of progress.” While it’s important to focus on the above holistic approach to AI-ready data, you don’t necessarily need to wait until you reach the finish line before you can get started with AI. There’s a common misconception that you need enormous volumes of data to get started. But it’s not true. The reality is that you can start small and still make significant strides. 

One approach is to begin with rule-based systems. These systems use predefined rules to process data and make decisions, providing immediate value without the need for massive datasets. Think of it as laying a solid foundation upon which you can build more sophisticated machine learning models over time. Starting with rule-based systems allows you to:

1. Gain Initial Insights: Even with limited data, rule-based systems can uncover valuable patterns and trends.

2. Improve Gradually: As you collect more data, you can refine and enhance your models, gradually transitioning to more advanced machine learning solutions.

3. Mitigate Risks: By starting small, you can test and validate your AI strategies without the risk of over-investing in unproven approaches.

The key to successful AI and ML outcomes isn’t the volume of your data—it’s how effectively you use the data you have.

Transforming Data into a Valuable Asset

Overcoming data quality and integration challenges is not merely a technical endeavor—it is a strategic initiative that transforms data into a valuable asset. By building a data-driven culture, organizations can unlock the full potential of their AI investments. High-quality, integrated data serves as the foundation for AI to deliver accurate insights, drive innovation, and create a competitive advantage.

By establishing robust data governance, centralizing data management, enhancing data integration, promoting data literacy, and  leveraging AI for data quality, organizations can overcome these challenges. Embracing a data-driven culture is not just a solution—it is the key to transforming data into a powerful asset that propels AI effectiveness and drives business outcomes.

It starts with a solid data foundation.

Embrace AI now and position your organization as a leader in the digital age. Get in touch with us to learn how we can help you embark on your AI journey and capture transformative business value.

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From AI Confusion to Clarity: Overcoming the Uncertainty of Where to Start

From AI Confusion to Clarity: Overcoming the Uncertainty of Where to Start

Written by

Jacob Zweig, Managing Director

Published

July 3, 2024

Data & AI Strategy

Artificial Intelligence (AI) holds incredible promise for revolutionizing industries and unlocking new business value. However, many companies find themselves at a crossroads when it comes to initiating their AI journey. The one of the biggest pain points? Confusion on where to start. According to a recent Deloitte survey, 55% of CEOs state the identification of the right use cases as the primary obstacle to realizing AI’s business value.

Beyond Algorithms: Identifying
Transformative AI Use Cases

If your business doesn’t have at least three AI use cases delivering significant value by the end of 2024, you’re falling behind. Most companies initially perceive AI implementation as a technology challenge. They think the key lies in acquiring the latest algorithms, software, or hardware. However, the real challenge is not in the technology itself but in identifying compelling AI use cases that can genuinely transform their business.

To leverage AI effectively, companies must shift their mindset. The most impactful AI use cases are not just incremental improvements to existing processes. They often require a fundamental rethinking of how the business operates, including redesigning business processes, understanding the human experience, and effectively managing change to ensure adoption. Addressing ethical and privacy concerns is also essential for responsible AI implementation. Without this capability, even the most advanced AI technologies can fail to deliver substantial value.

5 Steps to Get You from Zero to AI

By following a structured approach, your organization can navigate the complexities of AI implementation and unlock its transformative potential. Here’s a step-by-step guide to help your organization get from zero to AI, ensuring you address critical cultural and organizational challenges, identify high-impact use cases, and scale AI solutions effectively.

Step 1: Start Now, Don’t Wait

The journey to AI success begins with a single step: start now. Waiting for the perfect moment or the ideal technology setup can lead to missed opportunities. Begin by fostering a culture of innovation and openness to AI within your organization. Addressing cultural and organizational challenges is crucial. In a recent LinkedIn post, Ryan Litvak, Director of Data and Analytics at Jabil, explains: 

Businesses often face cultural and organizational challenges. AI needs a lot of good data, and if a company still relies on manual processes or just wants to use AI to automate those processes without rethinking their business model, success will be hard to come by. Plus, if systems aren’t connected and can’t share data easily, AI projects won’t get off the ground. It’s crucial to build a data-driven culture and ensure your systems work well together to unlock AI’s full potential.

Ryan Litvak, Director of Data and Analytics at Jabil

Step 2: Identify High-Impact Use Cases

Look for areas where AI can make a significant difference. These use cases should have the potential to transform your operations, improve efficiency, enhance customer experience, or open new revenue streams. The key is to think big and envision how AI can fundamentally change the way you do business. Making ideas happen with incisive strategy and a bias toward action is crucial. In a recent LinkedIn post, Sean Eidson, CEO of Merit Golf, shares a helpful tip:

Give yourself a constraint framework. Ask, “How might we?”

“How might we create 10x the versions of the email with the same creative budget?”

“How might we review 10x the applications (loan applications, job applications, content submissions) with the same budget?”

Sean Eidson, CEO of Merit Golf

Step 3: Pilot Projects

Implementing pilot projects is a practical way to start. Select a few high-priority use cases and develop pilot projects around them. These pilots will help you test the feasibility and impact of AI solutions on a smaller scale before scaling them up. Ensure that these projects address your specific needs and deliver measurable outcomes.

Step 4: Learn and Iterate

AI implementation is an iterative process. Learn from your pilot projects, gather feedback, and refine your approach. This iterative cycle will help you build the expertise and confidence needed to scale AI initiatives across your organization.

Step 5: Scale and Integrate

Once you have successfully implemented and refined your pilot projects, it’s time to scale them up. Integrate AI solutions into your broader business processes and continuously monitor their performance. The insights gained from initial projects will guide you in expanding AI’s impact throughout your organization.

Embark on Your AI Journey with Confidence

Embarking on an AI journey can be daunting, but you don’t have to do it alone. OneSix’s expertise in identifying and implementing high-impact AI use cases can help you navigate this complex landscape. We design, build, and deploy custom AI solutions that address your specific needs, ensuring you see tangible results and business value from your AI initiatives. Let’s take the first step together, transforming confusion into clarity and making AI a driving force for your business success.

The best thing you can do is start now.

Embrace AI now and position your organization as a leader in the digital age. Get in touch with us to learn how we can help you embark on your AI journey and capture transformative business value.

Contact Us