No longer hype, AI technology has become a concrete reality that is revolutionizing business. We look back to when AI started intertwining with business and track how far it’s come.
Artificial intelligence has rapidly evolved in just the past five years to become an indispensable asset that businesses can use in diverse ways to amplify operational efficiency and productivity.
Consider AI’s journey: Five years ago, AI was limited to basic applications like chatbots and recommendation engines. Now, advanced AI like ChatGPT can engage in sophisticated natural conversations and generate nuanced content. Similarly, while data scientists previously used AI for narrow predictive analytics, it can now automate entire workflows and processes like scheduling and task management.
From revolutionizing project management to optimizing team collaboration, AI is proving its worth as a transformative technology.
We will explore AI’s expanding capabilities and how it drives innovation across multiple facets of business. We examine the ROI of AI investments, the integration of AI in workflows to boost productivity, and the latest improvements in using AI for project management.
Our goal is to paint a picture of AI technology’s immense promise and how you can harness its potential to gain a competitive edge.
AI has cemented its status as a game-changer. Now, let’s see how you can use it to change your game as well.
AI Technology Improves Projects, Not Just Products
In those earlier days, it was considered important to adopt a product- more than a project-oriented mindset for AI because products have a longer lifecycle than projects and are more amenable to input from teams. AI, supposedly, was better positioned to deliver outcomes, as opposed to project process outputs, that would improve the experience of all relevant stakeholders – the customers, employees and business partners.
As such, research and development (R&D) teams at companies like PepsiCo, Nestle and General Mills were deploying algorithms that could learn what foods were popular and why people liked them. It’s how PepsiCo got an insight that led to it brainstorming the development of a savory flavored snack with seaweed called Off the Eaten Path.
That’s certainly not the case now because AI, powered by big data, machine learning (ML), and natural language processing, is making it possible to simulate project solutions as well as draw up and automatically update project plans. As a result of these and other technological innovations, AI will run 80 percent of all project management functions by 2030, according to research-based forecasting from Gartner.
Integrating ChatGPT, one of the highest-profile AI features, with a cloud-based solution through a simple interface can also make project portfolio management (PPM) more effective by improving project planning, execution and tracking. Furthermore, it can help project managers better predict outcomes, make communications clearer and more fruitful, and deploy resources in the cloud’s immense on-demand computing power.
Such refinements in AI technology have yielded richer, more targeted, and more efficient project management experiences.
Project Managers Can Streamline with AI
The following proposals, taken from use cases developed by Peter Kestenholz, the founder and PPM expert at Projectum and a member of the Forbes Technology Council, illustrate how AI makes real-life project management improvements.
Instead of relying upon fixed, predetermined schedules that specify the project’s critical tasks, stages, and landmark goals, AI tech can now analyze the available data and add more detailed context to produce more complex and accurate schedules that better estimate task durations and, hence, help project directors save a lot of time.
Since project managers can’t always apply their own experience to create logs that identify potential risks, they can integrate the cloud-based PPM tool with generative AI to create a risk log that classifies and ranks the severity of risks simply by explaining the project context and asking for a set number of risks.
Finally, integrating a financial grid within the tool to ChatGPT automates the whole project cost estimation process, eliminating the time-consuming manual inputting of expected costs throughout the entire grid and giving managers the flexibility to estimate costs for a variety of time frames, from months to years.
As for teams, AI-based collaboration tools are essential to improved communications and more harmonic working conditions that help automate basic project components such as workflows, meetings, and responsibilities. There is a myriad of these high-quality solutions available for project management, such as Taskade, Trello, Todoist, ClickUp, Dewo, BIT.AI, ProofHub, and Airtable.
Returns on Big Data Investments Aren’t Limited to Money
Given the limitless supply of big data from sources such as websites, mobile apps, social media, back-office systems, and third-party vendors, and AI’s limitless appetite for raw data to inform predictive behavioral models, it’s no surprise businesses have been making heavy investments in big data for at least a decade.
The idea was that AI and big data could eventually tap into human-centered design thinking to elevate end-user experiences and, consequently, make operations more efficient and boost profitability – the most basic AI measurement. This is what happens when businesses develop people to be fully steeped in the user experience and able to discern the meanings beneath the stories end users tell them.
Matching the deeper understanding of user needs that results from this training endeavor with AI’s processing of vast amounts of data and its capacity to spot patterns and discover insights helped businesses align product and service offerings with their customers’ wants and needs.
Human-centered design could go beyond unintuitive data to account for unusual or enigmatic situations that require common sense or contextual understanding. For instance, instead of a cab driver getting lost in driving to a destination because of muddled directions on a smartphone app, he could have gotten to the right place if he had paid more attention to his instinctive knowledge and common sense than the algorithmic suggestions.
What hasn’t been clear before now is how those investments are delivering such returns and what kind of returns – monetary or otherwise – businesses can expect.
Because AI’s advanced algorithms can swallow and assess so much data, they can unearth patterns and trends that might elude the attention and understanding of human analysts. That yields precise and exhaustive real-time information about what customers prefer and what markets present opportunities – which in turn helps companies stay abreast of and competitive in a changing business environment.
AI’s predictive analytics power also pays dividends through heightened business agility, proactivity and efficiency. Its analysis of historical and real-time data alerts businesses to future market demands and customer behaviors. It enables them to optimize inventory stocks, apportion resources, and take other actions to lessen risks and exploit opportunities.
An understanding also has emerged that ROI is more than quantitative growth – in revenues, cost savings, productivity, and even time savings. It also includes a higher valuation for businesses and the benefits of greater employee satisfaction and retention, newly acquired skills, and a stronger brand.
These so-called “soft” returns involve data investments, too. The amount, quality, permissibility, and accessibility of available labeled data are vital contributors to creating powerful machine-learning models. The computing investment is no less important to a company’s transition from simple to complex deep-learning models that process all the data.
A corollary to all that data investment is investments in training and coaching the data science personnel who must make sense of it.
Another emerging realization has been that it’s challenging to quantify ROI from big data investments, partly because it’s challenging for AI technology investments in general. It’s not just that the metrics for determining ROI can be more complex and involved than hard-and-fast items such as revenue. It’s also hard to calculate how much AI directly contributes to a business outcome.
While McKinsey reported that AI investments doubled between 2017 and 2022, only 10 percent of organizations announced a significant increase in ROI. The first substantive research to quantify tangible monetary returns, which came out late in 2023, is a Microsoft-commissioned global study by IDC that surveyed more than 2,000 C-suite officials involved in actualizing AI at their companies.
It found that for every $1 companies invest in AI, they’re getting back $3.5, with five percent of the respondents disclosing an average ROI of $8.
For example, consider Nuance’s deployment of an automated clinical documentation tool where the company developed and integrated generative AI to help Atrium Health’s primary care physicians rapidly produce clinical summaries. The ROI here is daily time savings of almost 40 minutes, with 84 percent of physicians saying this improved their documentation experience and 68 percent saying it improved providing care. Data is only one piece of the AI investment pie here, albeit an important one.
Integrating AI into Team Workflows Creates Powerful, Productive Collaborations
Businesses realized early on that implementing AI to make truly transformative operational improvements had to be an enterprise-wide collaboration. As such, they began developing a basis for best practices, where marketing, IT, operations, and product teams engaged in cooperative work to build and execute AI adoption plans.
Businesses recognized, over time, that making optimal use of the digital ecosystem needed for a successful AI execution required the right teams of people to be in place – tech teams to code the chosen platform, software vendors who could meet evolving process and technological needs, and consultants who could mentor the workers involved in every stage of the AI lifecycle – from concept to creation to ongoing performance.
Today, applying artificial intelligence technology to make teams and collaborative activity more effective is in full swing. Businesses are plumbing the value of AI in team collaboration to such an extent that 95 percent of managers recently surveyed by beautiful.ai are using AI instruments to boost team productivity levels.
Integrating AI into team workflows has had major impacts that haven’t been readily apparent before now.
AI’s predictive analytics and pattern recognition capacities have helped teams generate data-based ideas, which leads to brainstorming innovative solutions and devising strategies to pre-empt developing problems that could sabotage project plans.
Since predictive algorithms can continuously monitor equipment data to identify trouble-in-the-making anomalies, maintenance professionals have figured out how to keep potential breakdowns from becoming system failures. Automating data analysis and various often-tedious tasks frees small teams to concentrate on growth strategies and distinctive selling points that give them an edge against business rivals.
Wasted time is wasted productivity: AI’s power to create templates can avoid procedural dead-ends so that teams can move right into valuable, necessary work, and because it can streamline processes and automate notifications, AI can eliminate some of the distractions that sap team motivation and take team members away from doing that work.
Possibly the biggest and most recent influence AI technology has had on business collaboration has come through ChatGPT, which became available a little more than a year ago (and whose contributions to project management have already been reviewed here).
With attributes such as app integration and real-time language translation, ChatGPT can eliminate language barriers so that remote team members worldwide can easily work together. Its natural language processing powers also enable it to comprehend customer inquiries and respond to them with correct and pertinent information – the upshot being faster and more effective customer service and greater customer loyalty.
Conclusion
No longer an aspirational capability whose applications are simple and limited in scope, AI technology has grown in recent years, which has seen it become more sophisticated, versatile and powerful. Yet, with the prospect of a truly transformational generative AI still a fledgling idea, AI has just scratched the surface of what it can do.
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