Two in three business leaders now say that making more productive use of artificial intelligence and machine learning is critical to building and maintaining competitive advantage, according to Experian Research.
With global spending on AI tech set to reach $632 billion by 2028, it’s becoming increasingly clear that organizations wnat to harness new technologies to shape their future. But with nearly three in four companies currently struggling to recognize, let alone scale value from these investments, understanding how to effectively make the leap from a traditional to an intelligent, AI- and cloud-powered enterprise is crucial.
“At this point, it’s practically a foregone conclusion that tomorrow’s organization will be more data-driven, self-aware, and connected at every touchpoint,” said Chris Zimmerman, senior analyst for market researcher FutureProof Strategies.
“New technologies like artificial intelligence and automation are only expected to have more of a transformative impact on the shape of business in the years ahead. Given these advancements, executives in every field are actively pushing to fundamentally redesign their companies to capitalize on real-time insights, virtualized applications and environments, and automated workflows at scale.
As an IT leader, it’s no longer a question of if but when you plan on making the shift… and, in such a fast-changing market, how effectively you can design your processes and systems to keep up.”
Among the most immediate and pressing concerns for management teams today is determining how to build an IT infrastructure that most effectively supports new AI-powered operating models and the teams that underpin them. Those that have already started, tend to build or buy AI applications that boost productivity of particular business functions such as customer support, sales prospecting and engineering services. IT teams are adding their own code to modify tools such as NetBox or readily available open source software.
Identifying High-Impact Opportunities
The Wall Street Journal pointed out that even major technology industry players struggle to get their heads around effective corporate cloud and online infrastructure needs and requirements. Making the upgrade to smarter, more automated systems may seem straightforward in theory, but can be far harder to implement, according to Sean Donohoe, senior solutions manager at Nutanix. He told The Forecast that instituting AI solutions across businesses is no longer optional. It’s knowing how and where to begin deploying such offerings requires deep analysis.
Zimmerman said the first step to successful AI implementation is typically identifying the right place to leverage new smart technology offerings. He said it’s best to start by focusing on a number of priority areas for implementation like:
Repetitive, rule-based tasks: Everyday demands like data entry, document processing, and routine marketing or communications functions are prime candidates for automation. Technology offers the potential to free employees from tedious tasks to perform higher value, more creative work.
Data-intensive decision-making: Business needs that require analysis of large datasets (e.g. inventory management, financial forecasting, or customer segmentation) can also benefit tremendously from the use of AI-powered insights.
Customer-facing processes: While it’s still important to provide clients with a human touch during more complex or nuanced exchanges, don’t forget. Intelligent interactions powered by chatbot software personalities, recommendation engines, and personalization tools can also enhance many everyday customer experiences or automate time-consuming requests while reducing operational costs.
Knowledge management: When it comes to surfacing actionable information, AI tools have the ability to help organize, retrieve, and generate insights from an organization's collective knowledge more readily, making information more accessible.
All of which is to say that the most successful artificial intelligence implementations tend to start with solving specific business problems and defining measurable milestones that teams can track.
Boosting Productivity Through Intelligent Automation
In the Harvard Business Review, Christoph Ridel, professor at Northeastern University’s school of business, suggested that all rollouts of AI capabilities should be designed with a bigger-picture overarching goal in mind.
“Using AI to increase the collective intelligence of the entire organization,” Ridel said.
Identify actionable business outcomes, Steve McDowell, chief analyst at NAND Research, told The Forecast. He said finding practical ways to drive value by strategically instituting artificial intelligence solutions is among the most pressing challenges that today’s IT leaders cannot afford to ignore.
“Let's start talking about, ‘what are we going to do with [AI] and how is it going to change my business?’,” McDowell asked. “Once you start talking about what the business use is, and the value you're trying to extract from any technology, that then is going to drive the [subsequent] technology decisions.”
Areas where AI may improve productivity:
Workflow Automation
Modern automation platforms have the potential to connect disparate systems to handle complex tasks, effectively creating seamless workflows across departments. For example, when a sales team closes a deal, automated tools can be keyed to trigger automatically-generated contracts, provision software access, update CRM records, and notify relevant teams — all without manual intervention.
AI-Enhanced Decision Support
Machine learning models can also be utilized to analyze historical data to provide recommendations that improve decision quality and speed. For instance, AI has the capacity to help procurement teams quickly identify optimal suppliers based on reliability, cost, and quality metrics, or assist supply chain leaders in choosing vendors that meet certain sustainability requirements or HR and operating criteria.
Intelligent Document Processing
Using natural language processing and computer vision technologies, it’s now possible to extract information from unstructured documents (emails, PDFs, images) and route it to appropriate systems as well. Doing so eliminates the need for manual data entry and lowers overhead costs while improving accuracy and compliance.
Cognitive Assistants
Custom-trained virtual assistants also have the capacity to help employees accomplish tasks more efficiently by providing relevant information, automating routine actions, and offering contextual suggestions. Examples include AI-powered writing assistants that help marketing teams draft promotional content faster or coding assistants that accelerate software development. To maximize productivity gains, experts say not just to roll out AI implementations in service of well-defined tasks and purposes. To achieve peak performance, it’s also important to focus on augmenting human capabilities with the help of new technologies rather than simply seeking to replace actual workers with AI stand-ins. The big picture goal should be creating human-machine partnerships that leverage the strengths of both to achieve more creative and forward-thinking solutions, Zimmerman explains.
Rethinking Operating Models
AI and automation also enable fundamentally new ways of working as well, like the Brookings Institution points out. According to technology leaders, in addition to topics like and application development and IT oversight, that requires executives to also think about subjects like how such advancements have the potential to fundamentally transform any given enterprise’s leadership and operating models.
“In theory, what artificial intelligence and machine learning do is automate repetitive, time-consuming tasks and free up more time on staffers’ schedules to focus on higher-value and more creative work,” Zimmerman noted. “They also democratize access to information and enable staffers at every level to enjoy the ability to create new solutions and innovate on the fly. A lot of organizational knowledge and even IT or coding tasks will be offloaded to smart or virtual helpers. From an everyday standpoint, it means that human workers will largely focus on finding more inventive ways to prompt and put these tools to work for them in practice.”
Noting this, executives are advised to consider adapting management strategies accordingly. For instance, one of the biggest changes an intelligent enterprise must make, researchers suggest, is making the shift from experience-based, leader-driven decision making to data-driven decision making that’s enacted by workers operating at the front line. With tomorrow’s workers expected to enjoy greater access to actionable insights and more contextualized views at every turn, hints and tips that Zimmerman offers for better empowering staff are as follows.
Introduce More Data-Driven Operations
By embedding AI throughout operations, organizations have the chance to create self-optimizing processes that continually improve based on performance data. For example, an AI-powered supply chain can be set to automatically adjust inventory levels, logistics routes, and supplier selections based on real-time demand signals. Introducing such solutions allows workers to spend less time researching and contemplating where to focus, and more making smarter, more informed and contextualized decisions capable of producing more rapid and tangible impacts on the business.
Implement Hybrid Work Models
Artificial intelligence and automation make distributed work more effective by facilitating collaboration, managing workflows, and maintaining worker productivity regardless of location. Embracing virtualization of desktops, apps and working processes enables organizations to tap into global talent pools while providing flexibility that employees increasingly demand. Likewise, it empowers staffers in all departments and at every level to more effectively connect, collaborate and surface actionable insights.
Move from Reactive to Predictive Operations
By leveraging predictive analytics, organizations can shift from reacting to problems toward anticipating and preventing them. Consider, for instance, how AI-powered predictive maintenance can identify equipment likely to fail before it causes downtime, while customer churn prediction models can flag at-risk accounts for proactive sales team intervention. Introducing more proactivity into operations enables employees to concentrate on innovative and creative solutions to bigger-picture challenges rather than focusing on the day-to-day minutiae of running a business.
Note that when reimagining a firm’s operating model, IT advisors say to start by questioning fundamental assumptions. For instance: Do processes need to be sequential? Could decisions be distributed? Are traditional department boundaries still relevant? Technology pros will find that AI capabilities often enable entirely new answers to such questions.
Empowering Workforce Innovation at Scale
As leading authorities also remind, technology alone doesn't drive successful transformation — people do. Per researchers at Deloitte, it’s therefore important to consider the human component of change as part of any enterprise-wide upgrade as well. To empower teams and workforces to better innovate with AI and automation, pros say to:
Democratize Access to AI Tools
Low-code/no-code AI platforms enable employees without technical backgrounds to build automated workflows, create prediction models, or develop virtual assistants. Empowering staffers at every level allows innovation to come from anywhere in the organization, not just IT or data science teams.
Foster Continuous Learning
As AI increasingly begins to shoulder and handle routine tasks, employees need to develop higher-order skills like critical thinking, creativity, and emotional intelligence in turn. That being said, as part of designing a more intelligent enterprise, it’s also important to implement continuous learning programs that help employees grow alongside technology, including:
Personalized learning paths based on individual roles and aspirations
AI skills training for all employees, not just technical teams
Retraining and skill refreshes on a routine basis, e.g. every 6-12 months
Communities of practice where employees can share knowledge
Hands-on scenario-based role-playing exercises
Provide employees at all levels with a detailed understanding of data concepts, including:
Training leaders to make more data-informed decisions and ask AI systems more pointed questions through better prompt engineering.
Helping employees understand how data tangibly supports their work and job role – and who else in the organization may benefit from such insights.
Building awareness of ethical considerations in data use.
Creating a common vocabulary around data and analytics.
Implementing data management tools to better standardize, organize, share and utilize information across the business.
Providing visualization solutions that make it easier for employees to digest and absorb information at a glance.
“It’s about making innovation tools more accessible to workers at every level and championing smarter use of technology in everyone's job throughout the business,” says Zimmerman.
“That means not just having to get employees up to speed when it comes to data literacy. It also means helping workers understand the practical benefits of and reasons for using smart technology so as to better promote the active use of high-tech solutions across the organization and ensure more collective buy-in from staffers as well.”
Promoting Cross-Functional Collaboration
Note that successful AI-based initiatives typically operate outside of traditional organizational department confines as well, like the Agile Business Consortium reminds. To overcome potential silos that may arise as you contemplate where to institute smart technologies in innovative across an enterprise, try to:
Create cross-functional teams with representation from IT, operations, and business units.
Establish shared objectives that incentivize collaborative problem-solving.
Understand where data tends to pool and reside across the business, who can benefit from the info, and how to tap into it.
Implement common platforms and tools that facilitate access to technology solutions and information sharing.
Provide more user-friendly apps, interfaces and ways of accessing data that employees at all levels can understand.
Implementing Change Successfully
Even with the right technology, experts observe that organizational transformation typically requires careful change management practices to be utilized. Zimmerman offers tips:
Start with Pilot Projects – Begin with small-scale implementations that demonstrate value quickly. Success in AI and automation pilots builds momentum and provides learnings that inform broader deployment.
Communicate Purpose, Not Just Process – Help employees understand how AI and automation support the organization's mission, job roles and personal growth, not just how solutions transform the shape of day-to-day tasks.
Address Concerns Proactively – Be transparent about how AI will impact staffers’ roles and responsibilities. Emphasize that the goal is augmentation, not replacement, while acknowledging that the demands of any given will evolve.
Measure and Celebrate Success – Establish clear metrics for AI initiatives, look for ways to utilize learnings or gains on different fronts, and be sure to celebrate achievements. It builds confidence and sustains momentum through what can be a multi-year transformation journey.
Advanced Implementation Strategies – Also note that once organizations have gotten their heads around basic AI implementation, firms generally choose to explore more sophisticated approaches. For instance:
Building AI Centers of Excellence – To scale AI capabilities effectively, companies establish dedicated teams and leaders that develop reusable AI components, models, and best practices; Provide expertise and support to business units; Standardize tooling and methodologies across projects; and Coordinate training and knowledge sharing initiatives. Such centers of excellence typically combine technical experts with business translators who help connect AI capabilities to specific business challenges.
Creating AI Platforms and MLOps Capabilities – As AI use grows, organizations also need robust platforms and practices for managing models throughout their lifecycle. For instance: Implementing standardized development environments and toolchains; Establishing automated testing and deployment pipelines; Creating monitoring systems that detect model drift and performance issues; Building model registries that track versions and dependencies. In effect, investing in MLOps (machine learning operations) capabilities can help transform AI-based efforts from individual, one-off projects into sustainable, enterprise-grade systems.
Developing Custom AI Solutions – Commercial AI tools offer quick wins, making it easy to add intelligent capabilities to existing enterprise IT infrastructures. At the same time, organizations with more mature capabilities often look to leverage these solutions in even more advanced ways to sustain competitive advantage: Creating custom AI models that leverage unique company data; Building industry-specific solutions that build upon generalized baseline offerings; Developing integrated AI systems that span multiple business processes; and Embedding AI capabilities into existing products and services.
Looking Ahead: Building an Adaptive Organization
Zimmerman said that the most successful organizations today tend to view AI implementation not as a one-off effort but as an ongoing enterprise capability that requires continuous updating and maintenance.
“The companies who most effectively transition into intelligent and innovative enterprises don’t just create feedback loops where insights and learnings gained from initial deployments inform and steer future initiatives,” he said. “They also continuously reassess which people, programs and processes could benefit from the use of smart and emerging technologies – and how to best leverage these solutions.”
By thoughtfully implementing AI and automation tools though, any organization has the potential to achieve significant productivity gains while empowering its workforce to innovate in ways that were previously unimaginable.
“The end result isn't just a more efficient version of your current organization,” said Zimmerman. “Rather, it’s a fundamentally smarter and more adaptive enterprise that’s capable of thriving amid constant change and disruption.”
Scott Steinberg is a business strategist, award-winning professional speaker, trend expert and futurist. He’s the bestselling author of Think Like a Futurist; Make Change Work for You: 10 Ways to Future-Proof Yourself, Fearlessly Innovate, and Succeed Despite Uncertainty; and Fast >> Forward: How to Turbo-Charge Business, Sales, and Career Growth. He’s the president and CEO of BIZDEV: The International Association for Business Development and Strategic Partnerships™. Learn more at www.FuturistsSpeakers.com and LinkedIn.
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