Enterprise executives must undertake operational refinements in 2020 to uncover analytics opportunities to drive innovation in their organizations. They need to infuse analytics¹ into critical business processes so everyone in the organization, at all levels, has access to recommendations.

Firms using analytics must address the challenges that arise including questions about the data themselves, difficulties processing the data, and concerns about data management. Because decision-making is increasingly data-driven², companies must obtain valuable information in an efficient way from a rapidly changing data environment.

Data analytics has revolutionized marketing analytics and marketing in general. It has provided new concepts and new ways of doing things to generate a competitive advantage.

Analytics enables service innovation that creates strategic value³ for companies.

Here are some main barriers facing implementation of data analytics

1. Enterprise Restructuring Constraints

Enterprises that have built their business processes, technological systems, and the overall enterprise architecture⁴ over multiple decades, will find that AI may not be as easy to incorporate with these systems. AI applications require newer hardware and supporting platforms which in turn makes legacy systems less effective.

Using AI may mean completely overhauling their existing systems, procedures, and even strategies and policies. This may come with many associated costs in terms of both money and time, which can potentially cause them to lose their footing in the market and fall behind their competitors. Thus, #artificialintelligence, although necessary for their sustainability, may seem like too big a step for these enterprises.

Such organizations must adopt a multi-phased approach toward adopting AI. They must start small on projects that are highly likely to succeed. They must incrementally scale up their AI implementation⁵ by capitalizing on the small wins and later perform exploratory research to eventually achieve disruptive transformation.

2. Stakeholder Support Problem

While the buzz surrounding the technology in the mainstream and the numerous cases of successful AI implementation may influence most investors and leaders to commit to AI, the same may not necessarily work for everyone at the organization. Even then many among the leadership and management may see AI as an extravagance.

Most employees may feel threatened by the idea of incorporating AI into different business processes. They may feel control slipping out of their hands and may fear becoming redundant due to AI

Technology leaders⁶ must recognize these resistances as natural human responses and therefore, commit to educating their employees and the C-suite leaders about the need for AI. #Csuite executives must focus on making their employees aware of the fact that AI will only make life easier for them by assisting them instead of replacing them altogether.

They must also implement appropriate change management strategies, as people’s inherent resistance to change may also make them averse to any transformative efforts, especially something as disruptive⁷ as AI.

3. Talent/Skill Challenges

In all but the largest or most #data-focused companies, picking projects to work on across the enterprise is often hampered by a shortage of AI brainpower and talent. In PwC’s Digital IQ survey, only 20% of executives said their organizations had the skills necessary to succeed with AI.

Companies with many analytics teams spread across different business units and functions often see these teams vary greatly in size, capability and skill level. Some are eager to tackle AI while others are not. Most of the machine learning talent lives in a few teams, but the demand for #machinelearning⁸ skills is growing fast across the organization.

In an environment where AI talent is scarce and in very high demand, many companies are scouting innovation from third party sources, such as university labs, the open source community and hackathons, as well as incubators and accelerators.

4. Lack of Accurate Scoping of Problems

Clarifying who owns AI is a start, but as soon as that person sits down at their desk, they will be faced with dozens of potential AI pilots and applications.

Our AI study shows that 67% of business executives see the potential of AI to automate processes and increase efficiency. Moreover, 70% agree that AI has the potential to enable humans to concentrate on meaningful work.

However, what that often means is an analytics team is working on lots of small projects on the fringes of the core business but not on fundamental work that will move the needle. These pilot projects may even push the envelope in machine learning science, but they often fail to get the attention of senior leaders.

Rather than picking projects based on who in the organization asked for help with AI, or based on what #algorithms the team knows or what clean, labeled data we have, enterprises should factor in questions about business priorities.

Exploring these, types of questions, coupled with the data driven approach, will yield a more engaged business and maybe more impactful applications of AI.

5. Bureaucracy from the C-Suite

To create a clear AI implementation plan, businesses must draw up all the possible use cases of AI that can benefit their organization. Then they can assess the feasibility of individual cases and their expected impact on the business’s mission and long-term strategy.

They can prioritize the different use cases by starting with simpler applications and pilot projects and incrementally transitioning to larger, overarching ones. Doing so will ensure that businesses can accelerate AI adoption and minimize the time to value.

A clear AI strategy can enable businesses to avoid the “analysis paralysis” trap, where, due to the numerous benefits and newly emerging applications of AI, businesses struggle to prioritize what they want to achieve with AI. They then end up investing in initiatives that may not necessarily align with their long-term objectives.

While this may not necessarily be disadvantageous, but can lead to hefty opportunity costs, which may, at least temporarily, derail them from their desired direction.

Smaller organizations that are constrained for resources may feel the impact of such initiatives in the worst possible way, leaving them with no fallback options and resources.

Call to Action to the C-Suite

There are numerous other challenges that must be addressed before embarking on the ambitious AI projects envisioned for the future. These barriers include those involving the quality of data used with AI, safety concerns, regulatory uncertainty, ethical implications, and a host of other related issues.

Overcoming these challenges will require the collective effort of the global tech and AI community, regulators, and the #enterprises adopting the technology, which can be motivated by the potentially groundbreaking applications and benefits of AI.

Adopting this technology, regardless of how beneficial they might have been, have also raised a few challenges. These barriers to AI adoption, mostly stemming from the rapid and exponential growth of AI capabilities combined with the lack of preparedness of businesses as well as governments, must be addressed before we transition to an AI-driven future.

Works Cited

¹Analytics, ²Data-Driven, ³Strategic Value, ⁴Enterprise Architecture, ⁵AI implementation, ⁶Technology leaders, ⁷Disruptive, ⁸Machine Learning