Building the algorithmic business: data driven operational excellence and cost management
Written by Peter Wahlgren, Jens Eriksvik & Frida Holzhausen
To achieve sustainable cost savings, businesses must first gain a deep understanding of their cost landscape—the key cost buckets and areas of expenditure that impact overall financial performance. By mapping these costs, companies can identify where inefficiencies lie, making it easier to target specific areas for savings while improving operational performance. This approach ensures that cost-cutting efforts are strategic, sustainable, and aligned with long-term business goals.
AI and advanced analytics can play a critical role in each area of the cost landscape, enabling smarter decision-making, automation, and optimization throughout the organization. Our use-case focused approach to successful AI projects is a useful tool to leverage to drive AI-driven cost management and efficiency improvements.
"An AI-driven, use-case-based approach to cost management offers a significant opportunity for businesses to unlock untapped efficiencies and drive smarter decision-making. By aligning AI with key cost areas, companies can not only achieve sustainable savings but also position themselves for long-term operational excellence and agility in a dynamic market."
– Jens Eriksvik, CEO of Algorithma
Understanding the cost landscape and cost buckets
Before launching any cost-saving initiatives, businesses need an understanding of their cost landscape. This includes identifying the main cost buckets such as supply chain expenses, technology infrastructure, employee costs, and operational inefficiencies. The BCG model outlines both function-specific and cross-functional cost management. For example:
Function-specific cost management targets areas like production, support functions, sales, marketing, and product development.
Cross-functional cost management includes procurement, global business services, and technology-driven initiatives such as AI and digital transformation.
By understanding the specific functions within these cost buckets, businesses can design tailored strategies that address inefficiencies at both the function-specific and cross-functional levels. AI and advanced analytics can further enhance these strategies by providing predictive insights, automating tasks, and enabling data-driven decision-making.
"I’ve seen how traditional approaches often leave significant value on the table. By leveraging AI and data-driven insights, businesses can target inefficiencies more precisely and unlock a new level of cost optimization. This is about moving from reactive measures to strategic, long-term improvements."
– Peter Wahlgren, Managing partner at Algorithma
Types of cost savings strategies enhanced by AI and advanced analytics
Once the cost landscape is understood, businesses can follow cost buckets to implement targeted savings strategies across different areas, with AI and advanced analytics playing a crucial role in each.
By leveraging AI, businesses can implement cost-saving strategies effectively, driving efficiency and aligning resources with demand while optimizing operations across all areas.
"By integrating AI into the cost management process, businesses can turn complex data into actionable insights, helping them identify new cost-saving opportunities across their operations. This approach empowers organizations to be more proactive, ensuring that cost efficiency is continuously optimized in ways that were previously unattainable."
– Frida Holzhausen, Management consultant at Algorithma
AI-driven approach to cost savings: a use-case based framework
To maximize AI's potential in delivering cost savings and operational excellence, businesses should adopt a use-case-driven approach that integrates AI and advanced analytics into each stage of their cost landscape. Following a structured process like our use-case framework ensures that AI-driven initiatives are tailored to business needs, offering transparency, accountability, and tangible results.
Step 1: Establish an AI use-case catalog for cost efficiency
Begin by conducting a detailed inventory of all potential AI-driven cost-saving use-cases within the business, or by working through your use-case catalog to identify cost-saving use-cases already in the pipeline. This catalog should include:
Purpose: Define how each use-case will contribute to cost reduction and efficiency.
Data sources: Identify key data points and sources that will fuel each AI initiative.
AI algorithms: Determine which AI or machine learning models (e.g., predictive analytics, process automation) will be applied to solve specific problems.
Cost buckets addressed: Clearly define which cost areas (e.g., supply chain, technology, labor) will be optimized through AI.
Step 2: Assess use-case-specific risks in the cost landscape
For each use-case, assess potential risks related to data quality, model accuracy, and operational disruptions:
Supply chain AI: Risks include inaccurate demand forecasting due to flawed data or delays caused by supply chain disruptions.
AI-driven automation: Potential errors or system failures may impact labor costs and operational efficiency.
Technology alignment: AI might incorrectly allocate resources, leading to unnecessary expenses if systems aren't monitored.
By identifying risks specific to each cost bucket (e.g., product manufacturing, service operations, support functions), businesses can proactively address challenges and mitigate disruptions.
Step 3: Tailor AI-driven cost-saving strategies to each use-case
AI models should be developed with a focus on the specific cost bucket they aim to optimize. Following the cost landscape approach, companies can apply tailored AI strategies such as:
Predictive analytics for demand management and inventory optimization, driving efficiency across the supply chain.
Automation and AI-powered workflows to reduce labor costs in repetitive tasks, such as service operations and general administration.
AI-enhanced operational efficiency: Use real-time AI insights to continuously monitor KPIs and identify inefficiencies in processes, such as technology utilization and labor management.
When implementing AI-driven strategies, businesses should evaluate whether to build custom AI solutions internally or purchase off-the-shelf AI tools. The "make" option allows for customized solutions tailored precisely to the company's specific cost buckets, but requires significant internal resources, time, and expertise. The "buy" option enables quicker deployment through pre-built platforms with established features, but may offer less flexibility in addressing unique operational needs. Carefully weigh factors like implementation speed, cost, customization needs, and long-term scalability to ensure alignment with cost-saving goals.
Selecting the right AI tools, including platforms that offer real-time monitoring and data-driven forecasting, ensures that AI initiatives are strategically aligned with the organization’s cost-cutting goals.
Step 4: Conduct regular "quick audits" to assess AI use-case impact and identify new opportunities
Implement regular quick audits across cost buckets to measure the efficiency and impact of AI-driven use-cases. These audits should focus on evaluating how well AI initiatives are performing in terms of cost reduction and operational optimization, while also identifying new opportunities within the broader cost landscape. Drawing from frameworks like BCG’s, the key objectives include:
Assessing efficiency: Regular audits help gauge whether AI use-cases are meeting their intended objectives, such as cost savings, improved productivity, or enhanced resource allocation.
Measuring impact: Evaluate how each AI use-case influences overall performance across different cost buckets (e.g., procurement, production, labor), ensuring alignment with broader business goals.
Opportunity scouting: Use audits to discover untapped areas or emerging opportunities for further AI application, driving additional value beyond the initial scope.
The “quick audit” (or review) ensures that the approach remains dynamic and adaptive to evolving cost-saving priorities while scouting for new efficiencies within the cost landscape.
Step 5: Ensure transparent AI-driven decision-making
As AI becomes central to cost-saving efforts, it’s critical to ensure transparency in AI-driven decisions:
Dashboards: Implement dashboards that provide clear insights into AI decisions across all cost areas. Stakeholders can use these to understand the reasons behind AI-optimized inventory levels, resource allocations, or technology investments.
Generative AI for data accessibility: Leverage Generative AI to transform complex datasets into easily understandable summaries, enabling decision-makers to access and interpret insights more effectively. This increases engagement and empowers stakeholders to make informed decisions without needing deep technical expertise.
Transparency in cost reduction: Make AI decision-making processes accessible to internal stakeholders, especially in critical areas like procurement and global services, to build trust and align with overall business objectives.
This combination ensures that AI outputs are not only transparent but also accessible to decision-makers at every level, fostering a more data-driven approach to cost reduction.
Step 6: Integrate human oversight in key use-cases
In alignment and integrated with the quick audits/reviews, ensure that you for critical cost-saving areas such as high-value procurement, supply chain, and workforce productivity, integrate human-in-the-loop processes to ensure oversight:
Monitor AI decisions: Human oversight helps ensure that AI models are optimizing costs without introducing unintended biases or inefficiencies.
Critical adjustments: In key decision points, such as workforce reduction or contract negotiation, human intervention is needed to add a human layer to AI-driven recommendations.
It is imperative that AI outputs are not only transparent and responsible/ethical but also accessible to decision-makers at every level, fostering a more data-driven approach to cost reduction.
Step 7: Iterate and improve AI-driven cost-saving models
AI models require continuous monitoring and iteration, especially in complex cost structures. As highlighted in Algorithma's article on Navigating Data Drift to Future-Proof Your ML Models, data drift can significantly impact the long-term performance of AI models. To ensure sustainability, organizations should:
Ongoing model refinement: Use AI platforms that support continuous data integration and offer insights into cost trends across multiple buckets. Regularly monitor for data drift, as shifting data patterns can reduce model accuracy over time.
Adaptation to changing environments: As market conditions and organizational needs evolve, refine AI models to ensure they remain aligned with long-term cost-reduction goals. Addressing data drift proactively ensures that models stay relevant and effective in delivering cost-saving results.
Step 8: Conduct quarterly AI-driven reviews
At a strategic level, conduct quarterly reviews of AI performance across all cost buckets where you take a strategic view, and aggregate all quick audits during the period:
Evaluate impact: Assess the effectiveness of AI-driven initiatives across product development, sales, and administration. Review metrics like cost savings achieved, operational efficiency improvements, and resource optimization.
Update guidelines: Adjust data governance and AI model deployment strategies based on the review’s findings, ensuring continuous alignment with cost-cutting goals and regulatory compliance (e.g., EU AI Act).
The use-case focused approach to becoming an algorithmic business offers a transformative path to sustainable cost savings and operational excellence. By automating tasks, optimizing processes, and enhancing decision-making, businesses can improve efficiency across all cost buckets and gain the agility to respond to market shifts with real-time insights. However, success in this approach hinges on overcoming challenges such as ensuring data quality, safeguarding privacy, and fostering a culture that embraces AI-driven solutions. By following Algorithma’s use-case-driven framework, organizations can strategically unlock the full potential of AI, positioning themselves for long-term competitive advantage.
