From Hype to Meaningful ROI
The age of artificial intelligence has arrived but leveraging it to drive measurable value to the bottom line is not straightforward.
Organizations need a new focus to achieve the impact they seek. Today, business leaders review a parade of AI applications, each promising transformative benefits. Meanwhile, tech leaders face mounting pressure to implement solutions swiftly and demonstrate progress. This environment can lead to overemphasis on technology deployment rather than maintaining focus on solving business problems. Our experience has shown that to achieve tangible benefits, the emphasis should be reversed, starting with business solutions and supporting them where needed with AI.
By adhering to three fundamental principles, organizations can pave a clearer path to achieving meaningful impact through AI:
The Right Use Case
Uniting for Implementation
Realizing the Value
The AI Conundrum
AI is increasingly a chief concern in the C-Suite. AI was cited by management on 199 earnings calls in less than one quarter, a record high and more than twice the previous five years’ average. The most stated goal by companies in leveraging this rapidly evolving set of tools is achieving cost savings and efficiencies. Given the level of focus on AI and the objective to drive profit, why are the results so often a perpetual cycle of pilots and experiments rather than concrete value driven changes in work?
Almost all companies in the F500 are using AI
Most see AI as a lever for cost savings and efficiencies
Yet a minority of operators are creating significant value leveraging AI
Business-Driven Use Cases Should Be the Foundation for AI Efforts
Rapid or widespread AI adoption is not sufficient to achieve the financial outcomes that companies seek. In our experience, unfortunately many companies reverse the goal setting and technology deployment process by focusing on rapid deployment before creating the necessary environment to drive operational and financial impact. This can compound when the technology organization spearheads deployment of AI. The result is often an environment where use cases are inefficiently pushed by tech or vendors to businesses, becoming “solutions in search of a problem” that fail to deliver tangible value for the business.
The following is what we recommend so that our clients can get it right:
Get Everything on the Table
Cast a wide net to identify opportunities for quantifiable efficiency gains or revenue capture. These should be informed by both front-line experience and technology possibilities. Though the latter is obvious, businesses often miss the crucial necessity of gaining early investment in work changes from those who actually do the work. Failure in this step creates both misdirected effort and adoption leakage after solutions are created.
Find the Right Use Cases
It is important to consider if AI is even the correct lever to pull while evaluating opportunities. Consideration of alternative solutions like process optimization or leveraging existing technologies may offer more cost-effective or rapid results.
Justify Your Investments
Use wholistic valuation to balance the expected benefits of AI projects against the associated costs. The benefits of the valuation equation should be driven by the business who will realize the incremental efficiencies or revenue. Equally important, the development costs must be provided by technology partners.
Utility Client Case Study
At an electric utility client, a team, assembled as part of an EHS Partners program, focused on finding efficiencies in the distribution organization. They identified an opportunity to improve the workflow for billing of pole attachments (e.g., antennas or relays mounted on utility poles by cellular and other companies).
The team developed a business case that projected both cost savings and revenue capture through more efficient and comprehensive billing practices. By refining the billing workflow, the client could bill for a greater number of attachments, thereby capturing more revenue. However, in order to fully realize the benefits of the new workflow, the team needed a process for rapidly identifying existing attachments across hundreds of thousands of poles.
The team hypothesized that AI could automate this step, improving not only the speed for identifying attachments but also reducing the amount of resources required to do the work. In concert with their tech organization, the team created an implementation plan for an AI capability that enabled rapid image assessments, allowing timely billing despite the high volume. With the investment requirements and expected returns clearly defined, leadership quickly approved the investment and set the project in motion.
The operational need and financial commitment drove the AI implementation, rather than the reverse. As a result, the client realized millions of dollars in both ongoing cost savings and revenue that may not have been realized had the AI solution been imposed rather than pulled in to meet a commitment by the end user.
Efficient Technology Partnership is Essential in Selecting and Implementing Use Cases
Enabling the Art of the Possible
To stimulate robust ideation, companies must educate business operators in the fundamentals of AI. Technology partners are instrumental in ensuring that opportunity sets stretch the imagination while remaining realistic. The “art of the possible” must be injected into ideation by providing a lens that incorporates technical feasibility and AI governance guard rails. The business defines the “what” and tech offers options on the “how”.
Assessing Investment Feasibility
To accurately evaluate investment requirements and timelines against the potential benefits of AI applications, stakeholders need input from technology teams. Establish a process where use cases are swiftly assessed for timing and costs at a high-level, “tee shirt size” scale by the technology teams responsible for execution. Technology “owns” the cost. This level of detail enables stakeholders to quickly determine if a project aligns with their investment criteria and how it should be prioritized.
Partnering for Bottom-line Impact
The joint goal shared by the business and technology organizations should be value creation rather than successful technology deployment. Realizing value requires continuous collaboration between business and technology teams to ensure that assumptions remain current and relevant, especially regarding implementation timing and costs. This partnership thrives on multiple rapid test-and-feedback loops and collaboration on adjustments to meet the joint goal.
Disciplined Commitment to Value Realization and Tracking
Once the execution phase begins, the challenge shifts to ensuring that the solution solves the original business problem and delivers anticipated efficiencies and revenue gains. Detailed tracking mechanisms act as guardrails, ensuring that the intended financial benefits materialize.
Monitor Multiple Outcomes
Tracking should encompass financial benefits, personnel impacts, and operational execution. Effective tracking requires a team with stakeholders from Finance, Tech and the impacted functional area(s) monitoring operational and financial milestones at set intervals.
Embrace Accountability
Leadership must maintain focus on successful change well into the execution phase and hold managers accountable for bottom line impact, rather than simply process execution.
Stay True to the Change
Maintaining fidelity to the original problem-solving intent is critical. Businesses gain sustainable value by changing the way they work. Leaders should prioritize accountability for the work change and its impacts, not simply the financial outcomes. Even if the technical approach must be altered, the problem being solved should not.
EHS Partners is an independently owned boutique consultancy that specializes solely in leading broad operational improvement efforts for leading F500 companies. CEOs choose EHS Partners for their unique model that pairs experienced senior professionals with proprietary processes and tools, delivering unparalleled financial results quickly and with certainty.
Sources: 1. EHS Partners Analysis 2. IBM Global AI Adoption Index 2022; 3. Artificial Intelligence Index Stanford Institute for Human-Centered Artificial Intelligence; 4. John Butters, FactSet: Highest Number of S&P 500 Companies citing “AI” on Earnings Calls over Past 10 Years
