Boosting Enterprise Productivity With AI: The Data-Driven Advantage

Part of our Insights series: How to Tackle Business Transformation in the Age of AI 

For my entire career as a technology and consulting professional, I have tried to help organizations use their data and adopt analytics and AI, to unlock business value. But it hasn’t proven easy for organizations to corral growing volumes of data, ensure easy and secure access to it and deliver business insights that employees know how to extract and are willing to use.

But with increased movement of data to the cloud — and the most recent catalyst for data and AI technology adoption, generative AI — we’ve seen more concerted efforts to capture business value that are yielding real, measurable results. As AI technologies evolve and mature, the pressure to adopt them is only increasing. And this pressure is not just about keeping pace with competitors alone, but also to innovate while sustaining profitable growth. However, this next phase of data and AI/GenAI integration is still likely to be difficult as organizations try to balance legacy and modern technology in fragmented environments and drive change adoption.

Why productivity remains the cornerstone of enterprise success, and how AI tech is enabling it

Workplace productivity has always been a cornerstone of organizational success. It is directly linked to profitability, revenue growth and employee retention, even though in the recent past, many organizations have prioritized market expansion and M&A strategies over workforce upskilling, process optimization or productivity as growth strategies. However, with the rapid developments and adoption of AI solutions the productivity conversation is back in the center, especially amidst challenges of talent shortage and the recalibration of global powers. Respondents in our global GenAI survey reflected this sentiment on productivity too and are confident --- with 96% agreeing, and 48% strongly --- that GenAI will have a material impact on improving productivity.

Productivity has also been a critical part of the conversations I have with my AI clients as we help them overcome their challenges with data and AI-enabled technologies. We recently built a GenAI assistant for an insurance company, and it has improved their adjustors’ productivity by 70%. Now, instead of spending hours looking through an ocean of data, adjustors can ask the AI tool direct questions about medical records – even complicated ones – and get instant, accurate answers, with links to source documentation. The solution has accelerated the company’s claims review process, while ensuring quality and accuracy.

In manufacturing, autonomous mobile robots (AMRs) are being used to reduce dependence on labor, while creating safer work environments. Life sciences companies are investing in data and AI solutions for increased productivity and faster drug discovery. In our own company we’re rolling out Microsoft Copilot, which works with Office 365 applications, automates tasks, creates content and improves team collaboration and productivity. We’re also helping global clients bring GenAI solutions faster to market and with less risk by leveraging a global repository that has pre-built assets and accelerators and an AI-assisted workbench for training, connecting and enabling development teams.

Based on our work with clients – as well as our own internal initiatives – here are some best practices for using AI to increase productivity:

• Start with a business case: Get a clear understanding of the productivity gains you’re seeking and how, and which technologies can help achieve them; aligning technology with business strategy is critical.
• Make it a team effort: Involve all stakeholders early in the decision-making process to ensure buy-in and smooth implementation.
• Put employees front and center: Include employees in the entire solutioning process and provide comprehensive training and support to help employees adapt to new technologies. Check out my colleague, Kim Curley’s blog on change adoption to successfully navigate this transition.

The next wave of AI-driven productivity: Agents

You can’t talk about AI-related productivity without talking about the newest entrant on the block – agentic AI. This game-changing technology has higher levels of autonomy than traditional AI systems, is more adaptable and can make decisions independently or with minimal human intervention.

What’s most exciting about the developments in agentic AI is their ability to provide accurate, data-driven insights that guide business decision-making. For example, we have a large CPG client who brought all of their data together in a unified platform to become truly data-driven. However, end users still struggled with the complexity of pulling the right data and conducting the appropriate analysis required to make decisions. We applied an agentic AI solution that guides non-technical users through the analytics gathering process and serves up custom visualizations and dashboards based on user queries. Everyday business users can now get answers to questions such as “Which outlets are underperforming?” or "Where is our new product exceeding expectations?” This GenAI-powered approach is leading to faster, data-driven decision-making and revenue-generating opportunities to the tune of $10 million annually.

Apart from helping decision-makers, AI agents can also act on their behalf. For example, a retail company can use AI agents to analyze customer purchase patterns and inventory levels, identify trends, and independently make the required stock adjustments. This can truly open super-cycles in productivity and enterprise efficiency.

Salesforce, a leader in CRM solutions -- and both a client and partner of NTT DATA’s -- has integrated AI agents into its platform to help organizations make more informed decisions. This AI-powered tool can predict customer behavior, optimize sales strategies and automate routine tasks, allowing sales teams to focus on building relationships and closing deals. This not only improves sales productivity and efficiency but also improves customer experience.

AI solutions can help organizations do things smarter, better and faster, but it’s the getting-things-done element of agents that will enable organizations and their workers to double down on productivity and move up the value-creation ladder.

But what outcomes are organizations really trying to solve? This is the most critical question. And enterprises must answer this clearly. Starting with the business outcome in mind is essential.

Starting with the business need

Often, AI is seen as a golden hammer. But from this perspective, every problem then begins to look like a nail. Not every problem needs AI. That’s why we encourage clients to consider the full Enterprise AI Continuum when trying to solve a business problem with data and AI. For example, simple rule-based systems or traditional machine learning techniques can often be more efficient, cost-effective and easier to implement than AI-driven approaches, especially for smaller and medium-sized enterprises.

In manufacturing, many assembly line tasks can be handled efficiently using programmable logic controllers and robotic arms that follow pre-set instructions. Introducing AI for such repetitive tasks can add unnecessary complexity and cost without significant benefits. In retail, inventory tracking can often be managed deterministically using barcode scanning or RFID tags rather than probabilistic demand forecasting. As mentioned previously, while AI can improve inventory predictions, many small retailers can maintain efficient stock levels using simpler, cost-effective methods.

While working with a leading U.S. healthcare equipment and service provider who wanted timely and accurate interactions with their customers, we recommended robotic process automation (RPA) and robotic desktop automation (RDA), which relies on software bots — robotic assistants — to emulate human activities. The repeatable nature of the business problem made automation a good choice for increasing productivity and efficiency for employees and providing an overall better experience for patients.

AI is most certainly becoming the tool for increasing productivity, however applying the right set of AI tools is an important part of realizing true value. That's why it's critical to lead with the business outcome rather than the technology. Here’s how:

1. Clearly define your business goals and the metrics you will use to measure success
2. Conduct a thorough analysis of your current processes to identify areas where technology can make the most impact
3. Identify the most suitable technologies for your business need
4. Pilot and deploy with an eye toward scaling to avoid dead-end investments
5. Continuously monitor and evaluate the effectiveness, adjusting as needed

With their innate abilities to process, analyze and extract insights from data, AI-enabled technologies are driving productivity and delivering real-world results across all industries. As these technologies continue to mature, organizations will unlock even greater value, including revenue generation and innovation, enhanced, intelligent experiences that will yield true competitive advantage.

How are organizations mastering their AI? Get diverse perspectives, including strategy and transformation, innovation and technology, people, culture and ethics, and sustainability. Click here to view our GenAI Report infographic.