Table of Content
- 1 1. Rapid shift from experimental to applied technology
- 2 2. Automation, AI, analytics, and low-code platforms are converging
- 3 3. Enterprise demand is growing
- 4 4. New organizational capabilities are critical
- 5 5. Internal governance emerging as a key area
- 6 6. The need to control AI
- 7 7. Rise of AI-as-a-Service
- 8 8. AI could shift the competitive landscape
- 9 Also see
Artificial intelligence can determine the success of an organization. Here’s how advanced businesses are using this tech.
Not only are investments in artificial intelligence (AI) growing, but so are AI staffing numbers, according to KPMG.
Organizations with more mature AI capabilities spend an average of $75 million on AI talent and have an average of 375 full-time employees working with AI. AI staffing numbers are projected to grow by nearly 200 in the next three years, according to KPMG’s ‘s AI Transforming the Enterprise report.
The report, released Tuesday, interviewed senior leaders from the 30 of the Global 500 companies and analyzed job postings and media coverage from 200 of these large organizations. By identifying eight key AI adoption trends, the report aims to guide organizations lagging in AI deployment toward successful AI integrations.
SEE: Artificial intelligence: A business leader’s guide (free PDF) (TechRepublic)
Digital transformation initiatives are now critical for business survival, which is why enterprise organizations plan to double AI projects by 2020, a recent Gartner report found. Nearly all executives interviewed in KPMG’s report confirmed that AI plays a major role in determining if a company is a winner or loser.
Here are the eight trends, outlined in the report, for businesses looking to have a winning AI strategy:
1. Rapid shift from experimental to applied technology
In the past three years, AI has shifted from being a technology on the horizon, to being a necessary technology for adoption. The change was spearheaded by fast digitization, movement in machine learning technology, and the growth of data. In the next three years, organizations are making the combination of horizontal and vertical AI applications a priority.
While 26% of companies have deployed robotic process automation at scale today, 83% said they plan to in the next three years. Similarly, only 17% of companies today have integrated AI or machine learning at scale, but half said they plan to in the next three years.
2. Automation, AI, analytics, and low-code platforms are converging
Executives emphasized the benefits of deploying automation, AI, analytics, and low-code capabilities together. The combination allows for more data-intensive feedback that can provide deeper insights for organizations, as opposed to traditional analytics on its own.
3. Enterprise demand is growing
A consistent response in all of the report’s interviews revealed a heavy investment in AI by large companies. Across all 30 organizations, the majority reported AI investments to increase by 50% to 100% in the next three years.
The most high-priority areas for future AI initiatives included customer and market insights, back office and shared services automation, streamlined finance and accounting, and unstructured voice and text data analysis.
4. New organizational capabilities are critical
AI success is heavily dependent on the right organizational capabilities and leadership oversight. Half of the companies interviewed said the CIO holds a leading role in their AI strategy, and 40% said a senior line of business leader occupies major roles.
If organizations want to see AI success, they must have the support and leadership of upper management.
5. Internal governance emerging as a key area
Governance and scale work together. This means organizations should make governance a company-wide priority. Strong governance of AI deployment standards, responsibility designations, and team training help to drive better AI outcomes.
Organization failing to build an enterprise-wide AI governance are not seeing the full success of AI. Companies should begin by assessing its current governance framework and identifying the gaps in opportunities and delegations.
6. The need to control AI
While investments in AI are crucial, investments in control frameworks are just as important. Without controlling the evolution of AI, businesses could be at risk of creating unintended or biased results from these systems, resulting in major revenue loss and ethical concerns.
Investments in digital tooling allow companies to create transparency and visibility with its AI systems, which is critical for sustainable AI success.
7. Rise of AI-as-a-Service
The “as-a-service” market for AI is growing, providing organizations with more options for deploying AI. However, these services should not replace an enterprise-wide AI strategy, rather, it should work in tandem with these strategies.
8. AI could shift the competitive landscape
Since AI determines enterprise winners and losers, companies must invest and deploy AI across multiple functions to stay ahead. Companies that are investing in AI saw an average 15% improvement in productivity.
However, some early stage companies are seeing less success, because they fail to integrate AI into the overall business plan. The competitive differentiator is whether the company has full-scale AI deployment from back office productivity, to front office product innovations and customer engagement.
For more, check out 5 ways to improve AI/ML deployment on TechRepublic. e230