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If you want your business to succeed in a world of AI, you’ll need to operate in a boundless manner. Boundless companies transcend the limits of traditional organizations and they are designed to achieve shared success, generating value for their customers, business partners, and communities, as well as for themselves and their employees.
This success is realized by resources that are individually empowered to be autonomous, connected, and mobile, and that are collectively organized to be integrated, distributed, and continuous. A boundless company is also a company that exploits artifical intelligence (AI) — because the future business is AI-driven.
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The latest State of IT 2023 Report by Salesforce, which is a survey of 4,300 IT decision makers and leaders, found that nine out of 10 CIOs believe generative AI has gone mainstream. As much as 86% percent of IT leaders believe generative AI will have a prominent role in their organizations in the near future.
Technology analysts also have an optimistic view of generative AI and its impact on the future of the enterprise. Researcher IDC suggests global AI spending increased by 26.9% in 2023. And a recent survey of customer service professionals found adoption of AI had risen by 88% between 2020 and 2022.
Analyst Gartner predicts that 40% of enterprise applications will have embedded conversational AI by 2024, up from less than 5% in 2020. And by 2025, 30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.
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In short, AI is the electricity of the twenty-first century. Ignore it and your business will be left in the dark. After all, we already know the many ways that generative AI will shape how we work.
The accelerated adoption of AI in businesses today is largely driven by the promise of greater productivity. For example, generative AI adoption in marketing reveals promising productivity dividends ahead. Marketers estimate generative AI can save them the equivalent of more than a month per year, making room for more meaningful work. Forrester notes that AI will spur the age of creativity. Enterprise AI initiatives will boost productivity and creative problem-solving activities by 50%. Current AI projects already cite improvements of up to 40% in productivity for software development tasks.
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Research based on insights from more than 10,000 analytics, IT, and business leaders reveals the need for a strong data foundation to fuel AI adoption and benefits. The key lesson is this: every AI project begins as a data project. The value of AI in your organization will depend on data — historical data for initial learning, and live data for refinement and for relevance and responsiveness. AI businesses will have a different operation model that is needed to improve their sense-and-response capabilities. An AI-driven boundless business operating model is one that can sense, understand, decide and act (SUDA).
The first important step toward this approach is to connect, organize, and harmonize your company data, so you can understand and meet the needs of your customers with AI-powered solutions. Nearly all analytics and IT decision makers surveyed (92%) say trustworthy data is needed more than ever before, according to Salesforce’s State of Data and Analytics report. Salesforce surveyed 5,540 analytics and IT decision makers and 5,540 line-of-business leaders worldwide. The report’s executive summary includes the following key points:
A strong data foundation fuels AI: Advances in AI are fast moving, which puts pressure on data management teams to supply algorithms with high-quality data. As much as 87% of analytics and IT leaders say advances in AI make data management a high priority.Data’s full potential remains elusive: Analytics, IT, and business leaders all cite security threats as the top barrier to successful data management. However, misalignment between data strategy and business goals complicates efforts. Meanwhile, the amount of data that companies generate is expected to increase 22% on average during the next 12 months.The road to data and AI success is winding: To secure and scale data and analytics capabilities, analytics and IT leaders use a combination of strategies, such as reimagining data governance, strengthening internal data culture, and deploying cloud technologies. Simplifying IT management is the biggest driver for moving apps and analytics to the cloud.
Data quality is the most important success factor for AI-driven businesses. A recent report found that 23% of customers do not trust AI and 56% are neutral — this deficit in trust can swing in either direction based on how companies use and deliver AI-powered services. Trust in data is key for success in AI-driven outcomes.
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The report found that 92% of analytics and IT leaders agree the need for trustworthy data is higher than ever. However, only 6% of these leaders describe their data maturity as below industry standard or nonexistent, representing — at best — the difficulty of benchmarking maturity against peers, or — at worst — overconfidence in data strategy and capabilities.
Eighty-six percent of analytics and IT leaders agree that AI’s outputs are only as good as its data inputs. A real example of quality data fueling the adoption of AI is e-commerce — almost 20% of digital sales during the 2023 holidays were impacted by AI. Generative AI is intensifying these demands, and analytics and IT leaders are racing to fortify their data foundations. The report suggests the top priorities for analytics and IT leaders are:
Improve data qualityStrengthen security and complianceBuild AI capabilitiesImprove company-wide data literacyModernize tools and technologies
The report also found that business leaders are not satisfied with the value they currently derive from their data. As many as 94% of business leaders feel their organization should be getting more value from its data.
So, how does an organization ensure data quality? The quality of data in your business relies on: integrated systems; access to all data (structured and unstructured); end-to-end process flow (scaling how you action the data); and ‘datafying’ all stakeholder interactions (employees, customers, partners, and the communities you serve). Here’s how to apply the boundless design principles and concepts to improve AI success in your business:
Design your company as process, not structure, to maximize data flow.Design your business process to be end to end, to maximize data completeness, accuracy, and integration.Implement systems and platforms that support these end-to-end business processes, not point solutions.Standardize and simplify everything — focus on user experience and the jobs to be done. The goal is to deliver value at the speed of need.Change incentive programs to reward flow rates and quality outcomes, instead of quantities of assets/resources. Leaders should be assessed on the performance of those “next” to them in core business processes and on overall performance, not on their own individual metrics.Infuse SUDA thinking and practices at every level (SUDA is “fractal”, meaning it is equally relevant at operational, tactical, and strategic levels, at individual, team, department, and enterprise levels, and at activity, project, and program levels).Pumps, not Stage Gates — make flow, movement, progress the default, and make stopping this process the exception (allowable to correct errors, but not the norm). This approach is about creating a culture of movement, where flow is optimized based on speed and access to the right data at the right time and for the right reasons — delivering value and improving the quality based on real-time sense and response.
Businesses today are competing in an experience-led economy that is based on trust, personalization, speed, and intelligence. Most people are concerned about the implications of generative AI on data security, ethics, and bias. In fact, 81% of customers want a human to be in the loop, reviewing and validating generative AI outputs. Businesses that focus on using trusted data are well positioned to compete in a hyper-connected, hyper-personalized, mobile-first, and a more decentralized knowledge-sharing economy.