AI in Tech: Balancing Cost Optimization With Employee Engagement

Interest in artificial intelligence (AI) has soared in recent years, with tech companies at the forefront of AI adoption. However, adoption hasn’t come without its challenges, as some tech companies struggle to push past roadblocks to integration.

Tech company leaders may be tempted to try to introduce AI into the organization’s overall strategy immediately. However, these companies are likely to face adoption challenges if their leaders do not lay the groundwork for adoption by fostering a culture that’s open to AI.

To create this culture, tech company leaders need to deploy AI for specific use cases that demonstrate its value to the entire organization. From there, tech company leaders can explore more ambitious AI initiatives, eventually integrating AI into their broader business strategies.

Many tech company leaders might ask: What’s the best use case for introducing AI into my organization? Given the need for tech companies to preserve cash amid ongoing economic instability, cost optimization represents an excellent first use case for AI adoption. Here, we’ve identified some of the most common cost optimization use cases for tech companies and a roadmap for adoption.

Common Roadblocks to AI Adoption

Before tech company leaders introduce AI into their organization, they need to proactively address two common roadblocks to AI adoption: Lack of buy-in and organizational structure.

Organizational Structure

Large companies that have clear and fixed processes and systems may have cultures that prefer stability and resist disruptive technologies like AI. Smaller and/or new companies, however, may not have strong data governance programs, which are essential to make sure data is reliable and valuable for technologies like AI.

But organizational structure can also offer advantages. For instance, large companies may compensate for their lower agility with stronger data governance programs. They may have better data foundations to adopt AI than small companies, who may have the flexibility to embrace disruptive technologies faster than large ones.

Ultimately, each organizational structure comes with its own unique challenges and opportunities. Tech company leaders should proactively identify these challenges and opportunities, so they can tailor their approach to AI to play to their organization’s strengths.

Lack of Buy-in

Many employees, across title levels, may view AI with some concerns about job security. Other employees may find learning to use AI too daunting and feel resistant to AI adoption. It’s important to acknowledge and address employee concerns. To do this, tech company leaders should clearly communicate the value AI offers — not just to the company, but to individual employees. For instance, AI can streamline various processes and free up employees from tedious and manual tasks. This allows teams to concentrate on more meaningful work that motivates them.

Tech’s Top Cost Optimization Use Cases

High inflation and interest rates have made it difficult for tech companies to raise capital and secure funding. At the same time, many tech organizations have already explored more obvious cost cutting measures to preserve liquidity and help them stay afloat until economic conditions stabilize.

Fortunately, AI represents a new avenue to further optimize costs. While there are endless possibilities for cost optimization use cases, there are several common examples that can provide substantial benefits.

  • Product Development: AI can help tech companies that make software for clients by automating tasks like product design and coding. Automation can improve efficiency, reduce problems like coding errors or bugs, and allow employees to focus on creating new products and improving existing ones, which can be more satisfying and interesting than finding bugs in code.
  • Product Design: Generative AI can assist in simplifying new product design testing by producing a variety of prototypes that match the demand of a virtual customer, or digital twin, so the designer can more easily select the best prototype for their final user. It can also combine and understand market research and examine product reviews to generate suggestions for new products and improvements, reducing time and resources in the ideation stage. This way, designers can deliver better products to market quicker and more effectively.
  • Finance: AI can automate functions in areas like sales, collections, and accounting. For example, AI can be used to automate data entry, invoicing, and financial reporting. This allows companies to allocate resources to more innovative and strategic initiatives, reduce human errors, and collect and process payments more efficiently.

Cost optimization is an excellent gateway for AI into tech organizations. However, it’s crucial to understand that any AI initiative requires significant upfront investment from the company, which may initially offset expected gains from cost optimization use cases. Tech company leaders must consider how they’ll fund these investments, whether they need to secure outside funding or recoup costs by passing them along to end-users.

Once tech companies have identified their AI investment strategy, they can move forward exploring adoption. However, deploying AI isn’t as simple as pressing a button. Instead, tech company leaders need to take a step-by-step approach to ensure that they have the right foundation in place to support AI. At BDO, we recommend a five-step approach to AI deployment.

BDO’s Five Steps to AI

1. Educate

Start by educating your employees on how AI can support them. Focus on your initial goal — optimizing costs — and explain how AI can help the company accomplish that goal while also benefiting employees. It’s crucial to help employees understand the value that AI can bring them and explain that AI is not a replacement, but an enhancement to their everyday work.

2. Define Your AI Strategy & Prioritize Your Use Case

When selecting your cost optimization use case, consider what will provide immediate, measurable value to the company for the least amount of risk. For example, if your finance team is short-staffed, it may make the most sense to deploy AI to support the finance function.

3. Establish the Foundation

After you select your cost optimization use case, make sure you have the right foundation in place to move forward. That means evaluating your data governance and ensuring you have the right controls in place to provide high-quality data for your AI.

4. Prepare Your People

Once you’ve established a solid foundation for your project, you can move forward with AI adoption. Make sure that the teams directly impacted by the project understand how it will impact their workflow. You should also take time to assess how initial deployment could disrupt their workday and proactively communicate with your team to reduce potential frustration.

5. Go & Grow

Assess the performance of your use case and communicate your successes across the company. This step is crucial to securing buy-in for future AI projects. Empower your teams to think creatively about how AI could help them in their day-to-day work. Start proposing more ambitious use cases to encourage greater AI use throughout the organization. From here, you can start bringing the AI conversation into your broader business strategy.

Beyond Cost Optimization

A good way for tech companies to start using AI is to choose and implement a use case that reduces costs. However, tech company leaders should not forget that their main objective is to guide the overall business strategy. AI can play a crucial role in supporting business strategy, but if leaders hesitate, they might lose their advantage, especially as their more daring competitors explore the range of opportunities that AI provides.

Ready to explore your AI opportunities?

Read our insight, The Importance of Data Governance, first to make sure you have the right foundation for AI success.