Like a lot of business leaders, you’re no doubt aware of the potential benefits to be realized through effective use of artificial intelligence (AI) in your digital transformation efforts. But you’re probably also aware of how difficult scaling AI effectively to realize its full potential can be—particularly for organizations who haven’t yet invested in the necessary technological and cultural changes.
Fortunately, while scaling AI can be challenging, it doesn’t have to be painful. By implementing the right tools and technologies, combined with proactive and positive leadership, you can achieve greater success in deploying AI across your organization and begin capturing business value from strategic insights and optimized business processes.
Challenges in Scaling AI
In order to achieve an optimal business-wide AI implementation, it’s important to understand the roadblocks that commonly create difficulty for business leaders and their organizations.
- Timeframes are shrinking; expectations are growing. The digital age has accelerated nearly every area of economic activity and commercial enterprise. Acting strategically, swiftly, and proactively is essential to securing competitive advantage and protecting business continuity in a global economy ruled by Big Data. Adding pressure is the need for effective and timely recovery strategies in the wake of the COVID-19 coronavirus pandemic.
As a result, implementations that might have been scheduled for five to seven years in previous decades now have much briefer timelines, but are accompanied by expectations for even greater results. In order to hit growth targets, improve customer experiences, and solve business problems, companies absolutely need to find ways to put AI tools to use at the enterprise level—or risk falling behind those who do.
This pressure is a major strategic and operational concern for the c-suite; a 2020 survey conducted by Appen found nearly 75% of organizations said AI is mission-critical for their businesses, but less than half said they’re behind in implementing and scaling AI effectively. And while the vast majority of respondents said they needed high-quality training data to implement AI effectively, 40% said either the lack of available data or poor data management was crippling their efforts to utilize AI effectively at the enterprise level.
- Scaling AI is a marathon, not a sprint. A 2021 survey conducted by McKinsey & Co. found that while business leaders fully appreciate the benefits that come with leveraging artificial intelligence across their organizations, few take the long-haul approach necessary to achieve optimal results. Across 800 respondents, just 10% had invested in the new technologies and practices required to navigate the small hiccoughs and improve AI performance over time on a long-term path to full implementation (“leaders”). Those significantly behind these leaders (“laggards”) made up 60% of respondents, while those in the middle ground (“aspirants”) filled out the remaining 30%.
Taking the long view has significant value for organizations, as McKinsey’s research found AI implementations performed by those following the “leader” path had an operating income impact 3.4 times greater than those performed by laggards. This, in turn, helps them address the challenge of contracting timelines paired with increased performance and value expectations; the strategic adoption of AI has put these organizations in a much better position to gain incremental, short-term benefits from AI over time while continuing to expand their AI capabilities to the enterprise level.
- Company culture needs to change. Hearing “digital transformation” or “artificial intelligence” or “AI initiatives” brings to mind new technologies, software packages, and upgraded IT systems for many business professionals. But one of the most difficult roadblocks to successfully scaling AI for your enterprise might have more to do with human beings than algorithms and machine learning.
In order to execute AI solutions throughout your business, you need to reorganize your workflows and restructure your operating model to prioritize and utilize AI capabilities. You also need to secure buy-in, from the c-suite down, to assure everyone understands what’s changing, why it’s changing, and why it’s important to meet the expectations and obligations placed upon them in the new paradigm.
In order to hit growth targets, improve customer experiences, and solve business problems, companies absolutely need to find ways to put AI tools to use at the enterprise level—or risk falling behind those who do.
Scaling AI Effectively in Your Organization
Whether you’re just starting your digital transformation journey or are ready to break through the roadblocks keeping you from scaling your AI capabilities to the enterprise level, you can address and overcome the most common challenges to effective AI scaling by taking action in a few simple ways.
1. Lead by Example
As AI becomes an increasingly critical part of strategic and operational excellence, chief financial officers (CFOs) are finding their duties blending with those of chief information officers (CIOs). Folks in the c-suite who want their organizations’ AI initiatives to succeed lead by example, practicing what some call “intentional AI,” i.e. playing an active and highly enthusiastic role in championing AI and securing buy-in at all levels.
- Have pinpointed and thoroughly understand the business problems they want to solve with scalable AI.
- Understand that AI integrations are long-haul, collaborative, and iterative—a marathon, not a sprint.
- Also understand that AI pilots require a more agile, test-and-learn approach that improves speed and efficiency while still addressing risk in a realistic way.
- Have invested the time and resources necessary to educate themselves on AI technologies and solutions, rather than simply perusing use cases or relying on data science pros to do the “heavy lifting.”
- Collaborate closely with stakeholders at all levels to choose the software tools and metrics they want to use when scaling their company’s AI capabilities.
- Organize their businesses to support AI implementation and growth by establishing a “hub and spoke” structure. A central hub led by tech leaders in the c-suite handles high-level tasks such as managing the AI systems, establishing and implementing AI-related training and recruiting methodologies, and sets organizational standards for AI implementations and practices. The “spokes” radiating off this main body consist of business units assigned a range of other duties based on their capabilities and expertise; for example, IT might oversee end-user training and education for software.
- Invest in the necessary education and training to provide everyone in the organization a clear understanding of how the company uses AI, why it’s important to reaching company goals, and why everyone needs to do their part in order to realize optimal business value.
2. Choosing the Right Software Tools
From basic automation to natural language processing for customer-facing chatbots to strategic planning driven by analytics-based insights, AI applications work best when they’re supported by clean, clear, and complete data. And to collect, store, manage and analyze that data, you need the best possible AI tools.
Every AI initiative benefits from choosing a comprehensive, cloud-based procure-to-pay (P2P) software solution like Planergy as your central “hub” in managing the data that drives everything from process improvement to strategic sourcing. In addition to built-in robotic process automation (RPA), data management, and analytics capabilities, a best-in-class solution supports AI scaling by:
- Providing a single point of integration and analysis for disparate data sources. Registered users have leveled, role-appropriate access to the information they need in real time, from any device or platform, whether they’re in the office or on the go.
- Automating and optimizing high-volume tasks, eliminating delays, human error, and inefficiencies, making it easier to allocate resources to further optimization efforts. Every optimized process is another building block in your value chain, providing a foundation for improved performance across your organization.
- Providing clean and complete information, organized and optimized for maximum utility. Integrate and analyze diverse data streams, filtering out irrelevant information to reveal actionable insights, exciting opportunities, and areas in need of optimization.
- Supporting communication and collaboration between teams, eliminating data and work silos and making it easy to share and analyze information on demand.
- Supporting purpose-built, modular implementations that match customer budgets, timelines, and ambitions. Scale AI your way, building on your success as you grow.
- Providing detailed, human-focused training and education before, during, and after implementation, ensuring all users understand the software and why complying with internal controls is essential to both AI scaling and meeting organizational goals.
3. Practicing Effective Change Management
Altering company culture to support AI scaling requires some carefully considered change management. To ensure the greatest possible chance of success:
- Educate and train at all levels, with c-suite leadership fully engaged and leading the charge by example. With the CIO and other leaders serving as champions, educating others and demonstrating their own commitment to the new workflows and practices, it’s much easier to secure enthusiastic buy-in and compliance from all team members.
- Create and track performance and compliance metrics. Holding business units, departments, and teams accountable for effectively leveraging AI is easier with clearly defined benchmarks and metrics. Not only will team members be encouraged to put their new knowledge and training to use, but it will be easier to spot and correct potential problems before they can grow into major crises.
- Incentivize excellence. Special awards for the AI program’s biggest cheerleaders or top performers can keep everyone engaged and encourage better compliance and performance.
- Embracing cross-functional collaboration. In order to leverage AI capabilities to address enterprise-level challenges and support organizational goals, professionals from different business units need to move beyond siloed work to close, communication-focused collaboration.
- Moving from leader-driven to data-driven strategic decision making. This is, in effect, another form of collaboration, as your staff uses data and insights provided by AI systems to inform their decision-making for improved results over methodologies that rely strictly on humans or machines alone.
- Prioritizing business agility, flexibility, and resilience. Embracing agility as both a paradigm and an ethos makes it much easier to test, refine, and optimize business processes in a timely and competitively effective fashion.
Scale Your AI Capabilities to Meet Today’s Business Challenges
Finding ways to help your organization take full advantage of the powerful potential offered by modern AI tools can be daunting. But by investing in the proper technologies, prioritizing AI-friendly change management in your company culture, and organizing your business processes and practices to support AI, you can scale AI to the enterprise level and build an agile, data-driven company that meets its goals for growth, innovation, and profitability in today’s competitive economy.