Using AI to improve, rather than replace, your workforce

Intelligent enterprises are the future of the retail and consumer products industries

Concerns abound over artificial intelligence (AI) and other digital tools making jobs obsolete. While these fears may be grounded in some reality, we consider them largely overblown. However, the fact remains that digital workforce tools are moving into every retail and consumer products function. We believe companies should embrace the organizational effectiveness opportunities this technology provides by transferring manual, repetitive, or complex tasks to machines. 

That said, displacing old ways of working is a potentially risky journey for many employees, filled with unnecessary costs and lost productivity from either shifting too fast or too slow. To help organizations evolve into “intelligent enterprises” and pragmatically identify how they can increase productivity from new digital tools, AlixPartners is launching a series of articles over the coming months to provide our lessons learned and share our future outlook to best harness these new capabilities as they take flight.

In the aftermath of the pandemic, lingering supply chain disruptions and geo-political conflicts lead us to believe that more disruption is right around the corner. In the 5^th annual AlixPartners Disruption Index, we heard:

• 50% of retail executives expect significant change to their business models over the next 12 months, the highest of any industry surveyed
• 49% of retail executives say their company cannot keep up with the rate of technological change
• 38% of retail executives worry about losing their jobs due to industry disruption

These sentiments underpin the need to equip every functional team with tools and approaches that leverage what are now proven use cases of machine learning while building the skills to fully harness the power of AI. However, before moving forward it is critical to clearly understand the complementary branches of AI to determine where these technologies can be applied for the most effect (Figure 1):

Building

Given that these capabilities are designed to work together, one form of generative AI that uses natural language processing infused with machine-driven self-learning automation can train the automation of previous rules-based approaches for continual improvement. For retail and consumer products companies, this means any function that has previously relied on rules-based tools—including software, apps, or customized Excel—is ripe for higher-quality, lower, cost, faster work (Figure 2).

Given the proliferation of use cases, it’s difficult for companies to know when to be a fast follower and when to take the lead on a new opportunity. At the moment, three areas where retail and consumer product companies should already be utilizing AI and ML to scale their teams include: 

Customer service: Organizations can use thousands of transcripts from positive contact center interactions to train ChatGPT to provide customer service representatives with recommended responses. For one client, this strategy saved one minute per call on average and over $500,000 in labor in its initial use. As the models learn with more contact center data, companies can ultimately enable a fully automated chatbot.

Labor forecasting and productivity: Developing legally compliant, cost-optimized, employee-satisfied, customer-demand-centric schedules based on historical data has always been a challenge. AI is now supplementing and optimizing this process through sophisticated machine learning that incorporates external influences to predict foot traffic patterns and customer service needs with more precision.

Pricing: Retailers can use AI to dynamically adjust prices in real time—based on consumer demand, competitor actions, and inventory levels—to increase margin and profit and make changes at scale.

Meanwhile, retail and consumer product companies should keep the following three emerging AI and ML use cases on their radar:

Inventory management: AI and ML applications will increasingly help to improve inventory productivity, by optimizing ‘right product, right place, right time’ allocation and replenishment decisions by serving as a wrapper on software solutions. 

Recruiting: Hiring managers use AI-based recruiting software (e.g. Sapia.ai) to interview, screen, and rank applications to reduce the time spent figuring out who to approach for video or final interviews.  AI for recruiting is not without risk—AI can create or perpetuate biases—but when applied carefully, AI can reduce bias and advance DE&I goals.

Customer segmentation: Gen AI can boost marketing effectiveness by evolving a singular email blast to support the rapid development of emails with personalized subject headers and content to different customer microsegments. Companies can test tailored messaging, offers, and calls to action to discern what resonates best. 

As AI and ML technology continue to mature, more use cases will become table stakes for retail and consumer product companies to implement into workflows. Many will need assistance in effectively implementing these tools, which is where we hope our “intelligent enterprise” article series can help.

In the months to come, we will dive into topics including how AI is unlocking new change management approaches, how to empower agile cultures to make data-driven decisions, how to prepare your workforce for the AI age, and more. Stay tuned.

And read our “Practical AI playbook” for more on AlixPartners’ approach to AI.