Why Agile Teams Are Winning the Race to Create AI-Ready Cultures
The following article is from Inc.com, one of the most prestigious SMB magazines in the United States covering HR, marketing, leadership, IT, and more. In this article, they interviewed Andrea Fryrear, a member of the Entrepreneurs Organization (EO) in Colorado, author, and co-founder of Agile Sherpas, which helps leaders modernize their marketing teams to work smarter, move faster, and achieve better results with agile frameworks.
The missing ingredient in your AI implementation strategy could be a mindset shift.
We asked Fryrear how business leaders can create AI-ready cultures.
For business leaders, AI can have a huge impact on how their organization operates.
In most cases, the challenge they face is creating AI-ready cultures.
Getting your staff to adopt a technology as fast-moving and revolutionary as AI isn’t easy.
Conservative cultures resist change, people may feel AI threatens their roles, and even when people are convinced to try the technology, they may relapse.
However, a recent report analyzing 430 marketing professionals revealed that a particular group of professionals was adopting AI much faster than their peers.
It also shows clear steps business leaders can take to get more departments to adopt AI and reap its benefits.
Who’s really adopting AI?
In our survey of 430 marketing professionals to find out who had fully implemented AI, a clear pattern emerged.
Fully agile marketers (those who embrace the agile mindset and use frameworks like Scrum or Kanban) were three times more likely to have fully integrated AI into their work, compared to somewhat agile marketers (36% vs. 11%).
What about marketers who don’t follow an agile methodology?
None had fully integrated AI.
The data clearly shows that agile working methodologies dramatically increase the likelihood that marketers will fully integrate AI and be successful overall.
So, what aspects of agile methodology drive these stark differences?
Understanding Agility
Agile methodology can best be understood as a reaction to old ways of working.
Instead of spending weeks formulating detailed plans that the team will follow for months (only to achieve a result that is no longer relevant to the client), agile methodology is based on breaking work down into smaller chunks.
It allows teams to test, iterate, and gather feedback.
They can also constantly evolve their work as it is done to ensure goals are achieved.
In practice, this means working flexibly, eliminating silos for more open collaboration, rigorously prioritizing work, and limiting work in progress.
It also generally means delivering work faster so you can get feedback and integrate that knowledge into the next iteration.
There are many ways to be agile.
However, what connects them is an agile mindset based on the principles of the original agile manifesto.
Stepping back a bit, consider what this data means.
It indicates some key, data-driven ways business leaders can build AI-ready cultures:
- Support Instead of Mandates
A common mistake leaders make when encouraging AI adoption is simply telling teams to start using it.
While permission is the first step, a mandate isn’t enough.
In fact, 73% of fully agile teams reported that their leaders consider agility essential for success.
As a firm with expertise in agile marketing, we consistently see that teams need strong support from leaders to fully transition to agile methods.
Servant leadership is crucial in this case.
You must actively support your team in their transition to AI.
This means providing training and coaching, and helping with key hurdles like regulatory compliance.
Communicate clearly what long-term AI integration will entail.
It’s not enough to dismiss your team members’ fears. They need reassurance about what their work will be like in an AI-driven future.
- Autonomy and Experimentation
What applies to virtually all business tools also applies to AI.
You won’t gain a competitive advantage by using the same tools and techniques as everyone else.
For AI to reach its full potential, your team needs time, space, and resources to experiment.
Your team’s autonomy and ability to focus on a small number of high-value activities are directly related to AI success.
With the growing number of AI tools, your team needs the freedom to experiment and discover what works. Imposing a specific AI tool or method deprives them of this essential process.
Experimentation Will Be Continuous
With the breakneck pace of technological advancements, to create AI-ready cultures, your team will need to experiment to stay competitive.
This requires building a culture of experimentation and continuous improvement. It goes beyond simply asking your team to try a new tool.
- Focus
On average, more than six new AI tools are released per day in the marketing sector alone.
Your team could easily become overwhelmed by decision fatigue.
Developing the habit of rigorously prioritizing work so your team can focus on the tasks that truly matter can be a huge help.
Instead of postponing AI testing because of the sheer number of available options, decisions are made, tests are conducted, and the results are used to improve team performance.
- Mindset Over Practices
While it’s tempting to simply adopt agile practices, such as dividing work into sprints, the true value of agility lies in the agile mindset.
When teams without an agile mindset try to be agile, they tend to fall back into old habits, which undermines the entire endeavor and leads to a lot of frustration.
Implementing AI, like almost everything in business, depends on hundreds of small decisions that people make daily.
If your team members don’t have an agile mindset, those decisions will distance the team from agile ways of working and inhibit AI-ready cultures.
So, what is an agile mindset?
It’s an approach to work that encompasses flexibility, continuous improvement, experimentation, customer focus, and prioritization.
The bottom line isn’t to think, «To successfully implement AI, we need to work in sprints and use a Kanban board.»
Instead, you need to invest in training and capacity building to change the way your team members think about their work. The practices may come later, but the foundation must be there from the start.
The Agility Factor
What unites all of these factors is the agile mindset and ways of working.
Agile teams are built on prioritization, experimentation, and autonomy.
It’s no surprise that these cultures excel at implementing AI.
They’re ready to dive right in and start using tools, learn valuable lessons, and iterate until they succeed.
Whether you want to focus on building an agile culture or simply implement a few of its core components, the data shows that agility is vital for leaders looking to create the right conditions for AI implementation.
AI Culture: The Missing Ingredient in Your AI Business Strategy
The following contribution is from the Medium portal and the author is Nicky Verd, a digital futurist who connects people with technology.
Companies invest millions in AI tools, but they forget the one thing that really makes it all work: people.
You can’t build an AI-driven business on a fear-based, rigid, or outdated culture.
Most companies don’t fail at AI or digital transformation because of bad technology.
They fail because of a mindset stuck in the past.
They buy the tools, hire the consultants, and implement the dashboards, but beneath the surface, the real problem remains: a culture that isn’t ready to think, lead, and operate in an AI-driven world.
We love to talk about AI strategy.
But strategy without culture is just a wish list. AI business transformation is a mirror that reflects and amplifies the culture that already exists.
While executives obsess over the latest AI trends, they often ignore the underlying problem: fear of change, outdated mindsets, rigid hierarchies, and a workforce too intimidated or uninformed to experiment with AI.
This is like trying to drive a 21st-century revolution with 20th-century thinking.
Technological Upgrade vs. Mindset Shift
A business AI strategy shouldn’t just be about technological upgrades, but also about a mindset shift.
AI transformation requires curiosity, cross-functional collaboration, and leadership that knows how to manage disruption and uncertainty.
If your workplace is built on fear, control, or bureaucracy, AI will multiply the dysfunction.
But if your culture is built on curiosity, adaptability, and collaboration, AI will become rocket fuel.
In this article, I want to explain why AI culture is the missing ingredient in most business strategies and what it takes to create a culture where humans and machines not only coexist, but co-create.
It’s not about algorithms. It’s about humans.
It’s about the lack of human context in the AI debate and why the world’s most powerful technology can’t save a company whose culture is stuck in the past.
What is AI culture, really?
AI culture isn’t a technological initiative.
It’s a mindset shift that redefines how people think, work, and lead in the age of intelligent machines.
It’s not about implementing new tools, but about redefining old habits.
It’s easy to invest in technology, but it’s much harder to unlearn outdated mindsets.
Without that mental transformation, even the best AI tools won’t have a lasting impact.
AI culture is the shared values, beliefs, and behaviors that shape how people in your organization interact with AI and each other in an AI-enhanced world.
While digital culture focused on adopting tools and processes for the internet age, AI culture focuses on coexistence, collaboration, and co-creation with intelligent machines.
Culture is your organization’s operating system
AI is just an application. If the operating system is outdated, even the best applications will fail.
It’s less about installing the right software and more about installing a new way of thinking, one that:
– Prioritizes experimentation over perfection,
– Prioritizes learning over knowledge,
– Encourages ethical reflection over mechanical efficiency.
AI culture involves creating a space where humans not only use it, but partner with it.
In this type of culture, failure is feedback, learning is constant, and adaptability is rewarded.
That is the true foundation of AI success: not the code, but the courage. Not the infrastructure, but the intention.
Why most AI strategies fail without a culture of adaptation
You can’t outsource culture. Many organizations invest millions in AI tools and platforms, but forget to ask themselves: Does our culture support this transformation?
Common signs of cultural unpreparedness:
– Employees afraid to experiment or express themselves
– Leaders unsure of how to lead with AI
– Siloed departments hoarding information
– Lack of trust in data or leadership decisions
– Disengaged talent or anxious about being replaced
In these environments, AI adoption not only stalls, it backfires. People resist. Misinformation spreads.
Innovation quietly fades into the background, and ROI is a very expensive metric that no one uses.
AI requires an adaptive, not a rigid, culture.
The cultural ingredients that underpin AI success
For AI to work, organizations must foster a culture that acts as fertile ground for AI transformation.
This means embracing:
Curiosity over certainty
People must feel safe to ask, «What if…» rather than clinging to, «This is how we’ve always done it.»
Continuous Learning
In an AI world, learning isn’t a one-time training. It’s a lifestyle. Curiosity must be ingrained in the DNA of your culture.
Cross-Functional Collaboration
AI affects every function: marketing, HR, operations, finance, etc. Siloed thinking slows down AI transformation. Collaborative thinking accelerates it.
Psychological Safety
If people feel punished for experimenting, they’ll avoid AI altogether. Culture must make failure safe and learning visible.
Agile Leadership
Leaders can’t operate from a position of control. They must lead with questions, not just answers. They must exemplify what it means to be adaptable, vulnerable, and visionary.
Human-Centered Values
Efficiency must never trump ethics. AI culture must keep humanity at the center of decision-making.
AI will amplify any culture you already have.
This is the part most organizations overlook: AI won’t fix a broken culture. Scale.
If your company’s culture is toxic, AI will make it faster, colder, and more chaotic.
Think of AI as a cultural amplifier
It doesn’t just follow instructions, but patterns.
If the dominant pattern in your organization is silos, fear of failure, or resistance to change, AI will simply help you move faster… in the wrong direction.
On the other hand, if your culture empowers people to think critically, collaborate freely, and adapt with agility, AI will fuel that momentum.
Therefore, before investing in more AI tools, invest in a culture that knows what to do with them.
AI will amplify any culture you already have, as it reflects the values, behaviors, and mindsets of those who use it.
Technology doesn’t change culture; it exposes and accelerates it.
From Cultural Lag to Cultural Fit
Many companies are experiencing what sociologists call «cultural lag,» where technology evolves faster than the people, mindsets, leadership, and systems designed to support it.
Organizations adopt powerful AI tools, but they still operate with outdated leadership styles, rigid processes, and fear-based thinking.
The result is often friction, confusion, and resistance.
Moving from culture mismatch to culture fit involves aligning internal culture with the pace and demands of AI.
It’s about creating a workplace where innovation is fostered, learning is continuous, and people feel empowered, not threatened by intelligent machines.
Advanced Tools with an Outdated Mindset
Culture mismatch refers to adopting advanced tools with outdated attitudes and building digital infrastructure with analog mindsets.
It’s time to change the script. Culture mismatch creates friction. Culture fit creates fluidity.
Culture fit in the age of AI isn’t about hiring the right people, but about cultivating the right conditions.
It takes more than just AI talent. You need AI-savvy teams within cultures that are emotionally and strategically prepared to evolve again and again.
Before you invest in more AI, invest in culture.
So here’s the real question for every business leader reading this: Is your company’s culture ready for the age of intelligent machines?
If your people aren’t ready, neither is your AI strategy. Not just being ready to use AI, but also to think with it, learn from it, question it, and evolve with it.
This is the aspect many leaders overlook: AI adoption is not just a technical implementation, but a cultural transformation.
You can’t expect exponential results with a linear mindset. You can’t expect innovation in a culture that clings to tradition.
And, of course, you can’t expect teams to embrace AI if they’re still operating in survival mode… afraid of making mistakes, losing relevance, or being replaced.
Investing in culture means:
– Equipping teams with AI skills, not just tools.
– Encourage questions, not just compliance.
– Empower leaders to model adaptability, not just demand it.
Because AI business transformation is only as powerful as the culture in which it’s implemented.
AI will either accelerate the transformation or expose the dysfunctions.
So, if you’re serious about AI, start where it matters, because technology tools alone don’t create digital transformation. People do.
The companies that will succeed in this AI era aren’t just those with the best data scientists or the most innovative tools.
They’re those with an AI culture—those that understand that AI isn’t just a technical challenge. It’s a cultural challenge.
And if you want to build an AI-driven business, the first thing you need to rewire… is your culture.
Unlocking AI Success: How Agile Teams Create Winning Cultures
The following contribution is from the Find a Business Pro portal, which defines itself as follows:
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Authored by the team
Why Agile Cultures Are Essential for AI Integration
In today’s ever-evolving business landscape, incorporating artificial intelligence (AI) isn’t just a competitive advantage; it’s a necessity. However, the speed with which organizations adapt to emerging technologies often depends on the culture they foster.
A recent survey highlighted that marketers who adopt an agile mindset
are significantly ahead of their less agile colleagues in integrating AI into their workflows.
This survey of 430 marketers revealed that fully agile teams were three times more likely to have successfully implemented AI compared to their less agile counterparts. What drives this stark contrast?
Understanding the Agile Mindset
Agility isn’t simply a methodology; it represents a cultural shift in how teams operate.
Traditional business structures are often based on exhaustive planning followed by long timelines.
In contrast, agile frameworks like Scrum and Kanban encourage iterative work from teams.
This allows them to quickly test ideas, gather feedback, and adapt their strategies in real time, ensuring that the results remain relevant and effective.
The Power of Supportive Leadership
One of the survey’s most significant findings indicates that successful agile teams perceive their leaders as visionaries and advocates, rather than simply policy enforcers.
Approximately 73% of fully agile teams indicated that their leaders consider agile practices crucial to overall success.
This supportive approach encourages teams to innovate and adapt rather than be pressured by demands, fostering an environment where AI tools can be effectively explored and utilized.
Building AI-Ready Cultures: Key Strategies
So, how can business leaders cultivate an environment that embraces AI?
Here are the key strategies to consider:
Embrace Gradual Change
Instead of overwhelming teams with AI demands, introduce technologies gradually.
Start small by integrating AI tools into existing processes and allowing teams to become familiar with their capabilities.
Encourage Collaboration
Breaking down silos is essential to the success of the agile methodology.
Create cross-functional teams that encourage diverse viewpoints and collaborative problem-solving, which can improve the AI integration process.
Prioritize Learning
Foster a culture of ongoing training around AI.
Workshops and training sessions can allay fears related to AI and equip employees with the skills needed to leverage these technologies effectively.
Counterarguments
The Risks of Ignoring Agility
While some organizations may strive to implement AI technologies without initially adopting an agile framework, this approach can be dangerous.
Resistance to change can lead to significant losses in productivity and morale. Companies that delay adopting agile methods may find themselves at a disadvantage, unable to keep pace with competitors who have seamlessly integrated AI into their operations.
The Way Forward
Future Trends in Agile Methodologies and AI
As AI continues to advance, the companies that will thrive will be those that remain flexible and responsive.
The future lies in a hybrid approach that combines traditional strategies with agile methodologies.
This adaptability will allow companies to continually innovate in a landscape characterized by rapid technological evolution.
From adopting a supportive leadership style to fostering collaborative environments,
the path to an AI-ready culture is critical for organizations aspiring to success in this data-driven era.
By prioritizing agility, companies can unleash the enormous potential of AI tools and technologies.
It’s time to act! Future-proof your organization by gaining knowledge on how to effectively implement agile frameworks. Request help selecting your preferred provider today.
In the Age of AI: Speed matters more, scale matters less, innovation is everything.
The following contribution is from the PriceWaterhouseCoopers portal and was written by the team.
The year 2025 is already marking a turning point for artificial intelligence (AI).
In just the first few weeks, AI dominated conversations at the Consumer Electronics Show in Las Vegas, took center stage at the World Economic Forum in Davos, and became a key focus for the new US administration.
These milestones highlight the extraordinary pace at which AI is advancing and its growing influence on all aspects of business and society.
Changing the Rules of Competition
As PwC’s AI Predictions 2025 highlighted, the rules of competition are changing rapidly, and the traditional workforce is about to be transformed. Even those of us who lead the AI industry—transforming our organizations and guiding our clients through their own AI journeys—can’t fully anticipate what lies ahead.
The magnitude of the disruption makes it impossible to have all the answers.
What we do know is that success in this transformative era depends on how leaders approach strategy, adapt to the changing nature of work, and prioritize trust.
The tactics that win today won’t necessarily be the ones that win tomorrow. Crucial shifts in strategy will prioritize speed, scalability, and, above all, innovation.
The Need for Speed
The rate at which competitive capabilities change is accelerating exponentially, and these next few years of disruption will likely produce winners that can persist for decades.
Periods of discontinuity are rapid, and their results have a lasting effect.
This has been the case for most major technology-driven industries, such as the energy, automotive, and technology sectors. Given that many businesses today are technology-based, this accelerated innovation cycle is notable.
Accelerate the Business Cycle
AI can accelerate the business cycle, including information velocity, decision-making, capability development, and organizational change.
Building trust in AI and incentivizing organizations ready for change should be important success factors. Agile and fast-moving companies gain the advantage.
How will this translate to your business and the economy?
Analyzing past innovation cycles can provide valuable insights.
Driven by next-generation technologies like the internet, average year-over-year labor productivity in the U.S. doubled to 2.8% during the decade ending in 2005, compared to the previous two decades.
This represented trillions of dollars in real GDP.
Labor productivity experienced a significant decline after 2010, with year-over-year averages falling as much as 1.2% through 2022.
Today, AI shows similar, if not greater, potential to generate the next technology-driven GDP boom.
Strategy in the Age of AI
Regarding the human factor in defining strategy in the age of AI, consider an analogy with Formula 1 racing.
The F1 car is a marvel of engineering, a complex system comprised of many subsystems. But it is the driver who is at the center, commanding performance, supported by a large team performing various tasks behind the scenes.
What creates a competitive racing team?
An operating model that brings a good strategy to life: strategic alliances, a well-organized team, high-performance processes, plenty of talent, and, of course, technology, including AI. Lots of AI.
During an F1 race, vast amounts of data about the competitors, car performance, and track conditions are transmitted in real time.
It’s the driver and team who make AI an intrinsic part of the race, a natural part of every corner and tactic.
Between races and seasons, the data is continuously modeled, studied, and leveraged to make larger strategy adjustments that help teams go faster and win more.
The Executive Cockpit for Your Business
Like a racing team, your business is a complex system.
To succeed, you must combine capabilities in the right way, and just like in racing, your leadership teams must be prepared to react to rapid changes.
Those who use data and AI to detect and understand market events, industry dynamics, and their own company’s performance can generate insights that help drive competitive advantage.
To navigate disruption, management teams need better instrumentation and more intelligence, and AI is poised to provide just that.
AI capabilities enable an executive cockpit with near-real-time data on market activity, industry benchmarks, tax and regulatory considerations, and corporate metrics.
It detects early patterns, opportunities for improvement, and threats that require strategic countermeasures.
Using AI to dynamically inform strategy can help companies reap rewards far greater than current baseline results.
We’re already seeing management teams regularly achieve productivity improvements of 30% thanks to AI solutions, which are being used to generate cost savings, improve margins, or capture greater market share.
In PwC’s 28th Annual Global CEO Survey, more than half of CEOs (56%)
who responded say GenAI has improved the efficiency of their employees’ time use, while a third reported increased revenue (32%) and profitability (34%).
CEOs also say they plan to continue systematically integrating AI into their businesses, including technology platforms (47%), processes and workflows (41%), workforce and skills (31%), and core business strategy (24%).
A third (30%) are integrating AI into their products and services. GenAI has enabled them to go beyond simply predicting customer preferences to personalizing those preferences and differentiating service experiences. Hyper-personalized products and services promise to generate new incremental revenue.
The future of work means that scale matters less.
Scale has long been a valuable component of business strategy, serving as a moat and generator of crucial capabilities.
Deep specialization, large technology budgets, and greater pricing power are privileges of scale.
Interestingly, AI can have a mitigating effect on scale as a differentiating strategy. New disruptors can use AI to mimic the scaling capabilities characteristic of established companies.
Software developers, for example, are often attracted to large companies with large technology budgets. Now, specialized developers aren’t the only ones capable of programming.
Large language models (LLMs) have made it possible to create code generators that non-technical professionals can use to create quality code.
This is just one example illustrating how AI, and AI agents in particular, challenge established operating models to scale. Today, AI-native companies or smaller organizations are adopting an agency approach to reach a level similar to their established competitors.
Consider a financial services startup that uses AI to assess creditworthiness by analyzing hundreds of variables, thereby surpassing traditional methods.
This approach allows the company to approve a significantly larger number of borrowers while automating most lending processes.
This has generated double-digit revenue growth, achieving scale and efficiency previously reserved for large, established companies.
Reimagining Operating Models and Value Chains
For established companies, AI agents offer a unique opportunity to reimagine operating models and value chains.
It also gives them access to an unlimited and flexible workforce that can deliver augmented intelligence to their human workforce.
Every employee could practically gain a significant multiplier effect with large teams of AI agents specialized in various disciplines.
Access to a wide range of specialties was virtually out of reach for most small businesses, but an agent workforce is changing this.
Large companies that dominate the business landscape and rely on their scale as a competitive advantage could see the value of scale diminished.
Leadership skills, workforce strategies, business processes, sourcing strategies, and cost profiles will change as the future of work becomes agentic.
The agentic organization will be defined by high-performance interactions between humans and AI.
The more intrinsic AI agents become to the way work is done, the greater the value that can be generated.
Our analysis, based on our work with clients and our own AI adoption, shows that this human-AI collaboration could increase productivity and speed by 50%.
The result will be a transformation from the traditional work pyramid to a diamond-shaped pyramid.
As in the Formula 1 team, humans will remain in charge. People will direct AI agents on what to do, correct their mistakes (with the help, of course, of other AI agents), and use AI to turn ideas into new products and better ways of working.
But this requires more than new tools and skills. Your team must also know that your goal is for AI to increase their value.
Only then can they feel confident enough to embrace AI and innovate with it to reinvent their roles.
Why Innovation Is Critical
During this period of disruption, technological innovation will reign supreme.
Management teams capable of creatively transforming themselves with smart, highly effective offerings are positioned to succeed in increasingly competitive markets.
Entire business categories will be created. Consider that just two years ago, very few people knew about LLM providers.
Now, as their capabilities continue to advance, their platforms are critical to modern business, and as a result, LLM providers have achieved extraordinary market valuations.
Many companies we work with are beginning to embrace innovation and rethink their business and operating models.
It’s not uncommon for a company to have hundreds of AI-based use cases that generate new savings, insights, and differentiators. From innovative regional banks to tech industry giants, we work with many clients to achieve just that.
They are taking a ground-up approach and evaluating dozens of use cases across the enterprise, prioritizing those that can deliver the greatest initial and long-term value.
To keep pace, companies must take advantage of a wide range of opportunities.
In some cases, this might take the form of small improvements.
But incremental gains add up.
Resolve to make AI an intrinsic part of your business, a natural part of everything you create and do.
One of the most valuable forms of innovation comes from using AI to continually improve ways of working.
Teams that are comfortable using AI in their daily work are more likely to innovate with it.
The biggest innovations can come from many sources, but there are some steps you can take. First, foster a culture that empowers teams to imagine and think differently.
Find, incentivize, and reward AI enthusiasts.
They are the ones who think big about AI and, ultimately, can be the ones who achieve great things with it.
Don’t wait for customers to tell you what they want.
Scan the market, see where there is white space, and get creative to drive more significant changes.
Second, create an investment fund to finance experimentation and a technology foundation that includes R&D capacity—a place where teams can experiment and, most importantly, fail.
This is a portfolio approach that will generate successes and failures.
Companies that successfully innovate know the importance of investing in multiple options.
Third, manage AI-based innovation programmatically.
Innovation is a discipline, underpinned by leading practices that teams can adopt and achieve results with.
You need to cultivate great ideas, manage investments, reward winners, scale value, and optimize the full spectrum of innovation.
Tip: Don’t let bureaucrats pick winners.
Let market-leading entrepreneurs take responsibility for AI investments and value creation.
A media company we work with rethought how to deliver custom content for a major global event. Using AI, it created a platform to create personalized playlists, generate text-to-speech content, and automate production and governance.
This approach enabled it to deliver more than 5 million variations of high-quality content to its audience.
The Enablers: Technology and Engineering
Build modern foundations with next-generation engineering and transform IT.
The data is available, and the evidence is overwhelming.
PwC’s 2024 Cloud and AI Business Survey revealed that companies that effectively utilize next-generation cloud architectures and the latest AI capabilities are more likely than their competitors to improve profitability, productivity, time to market, and more.
But acquiring the necessary technology at a reasonable price isn’t easy.
The growing demand for processing power and capacity is straining supply.
Your technology team must plan how to meet those needs and how to manage the environmental impact.
How your technology team uses AI is also critical.
It must reinvent itself by transforming software development, improving cybersecurity, and accelerating data modernization.
The benefits can be especially significant for data modernization, an imperative for AI and other digital initiatives.
GenAI can interpret unstructured data, reducing cost and streamlining modernization, but it requires the right engineering talent to achieve this.
Trust is earned with responsible AI.
Realizing value from AI can advance as quickly as trust is earned in it.
This is achieved through the programmatic use of responsible AI disciplines.
You, your board, and other senior leaders must trust that AI can deliver on its promises by understanding, reporting, and managing its key risks.
Your staff must trust that AI will make them more valuable, not displace them.
Your customers must trust that you use data responsibly and that the AI you use, or with which they interact in their relationship with you, is trustworthy.
An effective approach to responsible AI must span the entire organization, involve all senior management, and encompass risk management, audit and controls, security, data governance, privacy, bias, and model performance.
Furthermore, responsible AI can no longer be a theoretical exercise: it must be implemented and digitally enabled.
PwC’s 2024 US Responsible AI Survey found that only 11% of executives have fully implemented the core capabilities of responsible AI.
When implemented correctly, responsible AI empowers the business. It creates defined barriers and processes within which your organization can innovate at speed.
By increasing stakeholder trust, responsible AI can also increase investment flows and encourage workplace adoption.
Racing to Win in the Age of AI
Creative destruction is done proactively. While some companies continue to experiment with AI, leading enterprises are integrating it into everything they do.
New customer expectations, cost profiles, and clock speeds are redefining the foundations of competition.
Strategic advantage is being built or rebuilt right now with AI-driven capability systems.
As management teams work to define their AI agenda and confirm their business strategy, a new way of thinking should guide those efforts.
If speed is paramount, teams must prepare for change.
They must be incentivized to keep pace with or outpace market innovation cycles.
Technology architectures should be freed from technical debt and reinvented in the cloud with a solid AI technology foundation to build upon.
If scale matters less, small and midsize businesses should look for ways to achieve the benefits of scale with AI that were traditionally only available through sourcing, procurement, and contracting.
Large competing companies should rethink their competitive advantages.
Scale is no longer a sufficient differentiator.
Creating an executive suite equipped with market, industry, and corporate performance analytics that can serve as a strategic AI thinking partner will become a crucial leadership capability.
If innovation is paramount, big ideas should have a space to be tested and realized.
Leaders should incentivize risk-taking and reward those who manage them well by activating cutting-edge capabilities.
While innovating on the next big project, leaders should also harness the power of small strategies. Continuous improvement—those daily incremental gains that occur across the organization—have the potential to become something big.
And most importantly, adopt a growth mindset. AI promises to be disruptive, but it also promises significant rewards for companies that can build their future with it.
5 Best Practices for Implementing AI in Agile Organizations
The following contribution is from the ICAgile portal, an accreditation and certification body for agile training.
ICAgile offers formal, independent quality assurance for agile courses and their instructors. Courses and instructors that meet our standards have demonstrated their commitment to being among the best. We also award globally recognized certifications to those who complete accredited courses and demonstrate competency throughout our learning paths.
The author is Emily May, part of the team.
Agile organizations understand the importance of implementing AI in their teams to boost productivity and improve results, which poses a complex and urgent question for many leaders: where do we start?
The recent and exponentially growing popularity of generative AI and its limitless applications generates excitement, fear, confusion, and a race to optimization.
But don’t worry: we’ve consulted experts, dedicated countless hours to research, and even designed a basic AI course, so ICAgile has your back in the AI department.
We sat down with our CEO and Chief Learning Officer to explore some of the fundamental practices agile organizations can use to develop an effective, customized, adaptable, and compliant AI strategy.
Why AI and Agile?
Artificial intelligence isn’t going away.
At both the individual and organizational levels, growth and success will depend on who can effectively approach and manage AI strategies.
«It’s not that AI is going to take anyone’s job, but the workers of the future who know how to leverage machines to accelerate tasks will,» says Christina Hartikainen, Chief Learning Officer at ICAgile.
Companies stuck in traditional, non-adaptive ways of working are being left behind.
In today’s market, artificial intelligence isn’t an option, but a necessity to keep up with the competition and accelerate value delivery.
Organizations that practice an agile mindset are in a prime position for AI adoption.
From iterative work for continuous improvement and collaboration in cross-functional teams to fostering a culture of learning and leadership, agile teams have the experience of approaching new challenges with curiosity and reasoning.
5 Best Practices for AI Implementation in Agile Organizations
Embrace AI Strategically, Not Reactively
Shannon Ewan, CEO of ICAgile, shares the importance of adopting an agile culture as the first step in developing an AI strategy.
«Leaders who have invested in the organizational, structural, and cultural changes necessary for agility will be much better positioned to adopt AI strategically, rather than reactively.»
Ewan emphasizes the importance of decentralized decision-making, noting that autonomous teams are empowered to solve the problems most relevant to their area of expertise.
Conversely, managers in an autocratic work environment may never gather input or feedback from employees on tools and processes, resulting in a poorly customized AI strategy that doesn’t adapt to the team’s unique functioning.
When developing a plan to implement AI in your team, remember that the goal is to generate positive outcomes for all stakeholders and optimize organizational workflow, allowing your team to focus on the tasks that generate the greatest impact.
There is no single set of tools that will drive the greatest success, as every business has uniquely different needs.
Hartikainen adds: “If you work with AI for the sake of it, you could get stuck in a constant cycle of searching for the next innovation without making any impact or progress on what you’re trying to achieve.”
Continuously Adapt Your AI Approach
Continuous improvement is one of the hallmark practices of an agile mindset, and when it comes to technology tools, there are endless opportunities to learn and adapt. The speed at which AI is changing is unprecedented, and when you decide to adopt AI technologies in your company, the journey is just beginning.
New AI tools are coming to market daily, and new regulations surrounding this technology are on the horizon.
Teams must avoid becoming too attached to specific tools and be open to change and experimentation.
Hartikainen points out how many AI tools and their capabilities are «here today, gone tomorrow,» with an unprecedented rate of change.
Agile teams must be prepared to frequently inspect, adapt, and update their approach to AI.
Support Machines as Part of Your Team
Artificial intelligence offers far more capabilities than just being considered a toolset; agile organizations must embrace machines as a valued part of their team.
AI has dramatically increased human potential to innovate in all markets.
«Machines are producing outputs that actively influence human and collective intelligence, so it’s about thinking about how we can use AI to bring out the best in each other,» Ewan describes.
The symbiotic relationship allows AI and its users to continue learning from each other.
However, humans must continue to critically evaluate AI outputs, in the same way we conduct peer reviews of each other’s work. After all, artificial intelligence has limitations.
«Understanding how these systems are trained and how they learn from biases» is critical to understanding the data generated by AI, Hartikainen explains.
Consider Ethics
Ethics is a major topic of debate surrounding artificial intelligence, and rightly so.
Agile organizations must be cautious when making AI-related decisions that may affect areas such as bias, job loss, privacy, and regulatory compliance.
Ewan highlighted the prevailing concerns around job loss, explaining that each international market is structured differently, and we must consider how AI affects the global workforce.
«We have a responsibility to reflect on these impacts and the standards we’re setting to figure out how humans and machines can work together to accomplish things neither could do alone.»
He suggests that agile organizations should incorporate AI to enhance existing talent on their teams rather than seeking to replace human workers.
Other ethical factors, such as privacy, regulatory compliance, and the use of intellectual property, also influence organizational decisions related to AI. For example, Hartikainen points out that some AI-powered models have been trained on copyrighted material, contributing to a significant problem that lawmakers are working to address.
Understanding the ethical nuances of AI tools is vital, leading some teams to invest significant energy in this effort, such as appointing AI ethics officers or leading dedicated AI ethics initiatives.
Establishing a Shared Language Around AI
By establishing a shared language around AI, teams are equipped with the preliminary knowledge and context needed for strategic planning, adaptation, and ongoing productive conversation.
“Education and learning are absolutely crucial,” says Ewan.
He describes how agile organizations should adopt a learning culture to reduce the fear surrounding AI in the business world.
Teams can research, read, take a course, develop policies and procedures, and practice transparency around AI knowledge and implementations in the workplace.
ICAgile recently developed the first AI foundation course for agile organizations.
Rather than recommending a specific set of tools that will be available today and gone tomorrow, thought leaders have created a comprehensive course on how global organizations can develop a tailored AI strategy that considers historical knowledge, regulatory compliance, and continuous adaptability in an ever-evolving industry.
With the incredible amount of information available on AI, we recommend starting with the basics. By creating a solid foundation to build upon, teams will be well positioned to launch new technologies and tools.
Conclusion
By following these five best practices, your team will be ready to turn the unknown into knowledge and implement AI with an established action plan.
Agile Methodologies for AI Project Success: Best Practices and Strategies
The following contribution is from RTS Labs, which defines itself as follows: We are the ideal AI team for you: we drive growth, transform your visions into reality, and turn your digital aspirations into lasting successes. We offer tailored AI solutions and expert advice to transform challenges into a measurable return on investment (ROI).
Agile Methodologies for AI Project Success
The adoption of Agile methodologies has become increasingly indispensable for effective project management.
Originally designed for software development, Agile practices are particularly well-suited to the dynamic and sometimes unpredictable nature of AI projects.
By prioritizing flexibility, continuous improvement, and collaboration, Agile methodologies help teams adapt to changing requirements, optimize processes, and increase productivity.
Recent statistics underscore the growing importance of Agile methodologies in AI project management.
In PMI’s 2023 Annual Global Project Management Survey, 21% of respondents report using AI always or frequently in project management;
82% of senior leaders anticipate AI will impact project management within five years;
and a PMI Customer Experience survey reveals that 91% believe AI will have a moderate impact on the profession,
and 58% anticipate a significant or transformative effect.
These findings highlight the transformative impact of Agile methodologies on AI project management, enabling teams to address the complexities of AI development more effectively and achieve results aligned with business objectives.
As AI continues to permeate diverse industries, integrating Agile practices is not only beneficial but essential to maintaining competitiveness and responsiveness in a technology-driven marketplace.
Agile Methodology Fundamentals
Agile methodology is based on a set of principles that prioritize flexibility, iterative progress, collaboration, and customer feedback.
The core principles, as outlined in the Agile Manifesto, include:
Individuals and interactions over processes and tools
Valuing human communication and collaboration as key to successful outcomes
Working software over comprehensive documentation
Prioritizing prototypes and working products over detailed documentation
Customer collaboration over contract negotiation
Continuously interacting with customers to adapt to changes and ensure the product meets their needs
Responding to change over following a plan
Being receptive and responsive to changes, even in the later stages of development, to best achieve project objectives. Agile Differences in the Context of AI Projects
Applying Agile to AI projects presents unique challenges and modifications to these principles:
Experimentation and Learning
AI projects typically involve a high degree of experimentation and learning to refine algorithms and models.
The iterative nature of Agile allows for continuous refinement and adaptation, which is important for managing the uncertainties inherent in AI development.
Data-Centric Processes
Unlike traditional software development, AI projects rely heavily on data quality, availability, and alignment with specific use cases.
Agile methodologies in AI emphasize the importance of iterative data collection and validation as a core component of development cycles.
Interdisciplinary Collaboration
AI projects require close collaboration between data scientists, AI engineers, software developers, and subject matter experts.
Agile facilitates this interdisciplinary interaction, ensuring that all perspectives are integrated into the development process.
Planning Flexibility
Due to the exploratory nature of AI, plans may require more frequent adjustments compared to typical software projects.
The flexibility of Agile allows teams to adapt quickly based on findings from continuous testing and feedback loops.
Planning Your AI Project with Agile Methodologies
When planning an AI project with agile methodologies, you should set realistic milestones that reflect the iterative nature of AI development.
Unlike traditional projects, where deliverables can be defined from the outset, AI projects benefit from establishing milestones that allow for discovery and refinement. Here are some tips for establishing these milestones:
Start with a prototype
At the beginning of the project, focus on developing a minimum viable product (MVP) or prototype that incorporates core AI functionalities.
This approach allows you to test assumptions and gather feedback quickly.
Iterative Improvements
Set milestones for iterative improvements based on feedback from the initial testing phases.
This includes improving algorithms, expanding data sets, or refining user interfaces.
Performance Benchmarks
Include specific performance benchmarks as milestones, such as accuracy rates for machine learning models or response times for AI applications.
These benchmarks help measure progress and guide future development efforts.
Scalability Assessments
Plan milestones that assess the scalability of the AI solution, ensuring it can handle increased workloads or expanded use cases as it progresses.
Importance of Flexibility in AI Project Planning
Flexibility is fundamental to the Agile methodology and is especially important in AI project planning due to the inherent uncertainties and rapid advances in technology.
Why flexibility is vital:
– Adapting to New Insights: As AI projects progress, new insights derived from data or technological changes can significantly alter the project’s direction. Agile planning must adapt to these changes to take advantage of new opportunities or mitigate emerging risks.
– Evolving Requirements: User requirements can evolve as stakeholders interact with AI capabilities and better understand their potential impacts. Agile enables continuous refinement of requirements based on real-world usage and feedback. – Technological Changes: AI technology is subject to rapid change, including new frameworks, tools, or algorithms. Flexible planning ensures that projects can incorporate these advancements without extensive modifications. By integrating flexibility into the planning process, AI projects managed with agile methodologies can maintain their adaptability and responsiveness, ensuring user needs are met and staying at the forefront of technological innovation. This approach not only improves the success rate of AI projects but also fosters a culture of continuous learning and improvement.
Sprints and Iterations in AI Development
Sprints, a key component of the agile methodology, are short, time-bound periods during which specific work must be completed and prepared for review. Adapting sprints to AI development involves several strategic modifications to accommodate the unique challenges and requirements of AI projects:
Variable Sprint Lengths
While traditional agile sprints may have a fixed length (e.g., two weeks), AI projects can benefit from variable durations depending on the complexity of tasks, such as data preprocessing, feature engineering, or model training.
Specific Objectives
Each sprint in an AI project should have a clear and specific objective that aligns with the overall project objectives, whether it is to improve model accuracy, integrate new data sources, or implement a new algorithm.
Review and Retrospective
At the end of each sprint, review the completed work.
This should include evaluating the AI model’s performance against predefined parameters and analyzing possible improvements in the next iteration.
Case Studies of Successful Sprint Implementations
Several organizations have successfully implemented agile sprints in their AI development projects, demonstrating significant benefits:
Mitsubishi
This multinational company, active in sectors such as aerospace and power generation, implemented agile methodologies through comprehensive workshops that introduced agile practices to various departments.
The initiative began at headquarters in Japan and gradually expanded to other factories, fostering a culture that values continuous improvement and rapid response to change (GitScrum Skyrocket Productivity).
Accenture
In collaboration with a leading UK bank, Accenture applied agile methodologies to scale data innovation, identifying more than 60 high-value analytics initiatives. This approach involved repeatable use case models and a «Value-Discover-Experiment-Demonstrate-Scale» methodology, emphasizing the agile mindset’s ability to quickly adapt and innovate in response to changing business needs (Accenture | Let There Be Change).
IBM
IBM emphasizes the importance of identifying clear business objectives from the outset of an AI project and advocates an iterative approach to project development. This includes evaluating available data, defining the scope of project requirements, and developing a proof of concept before full-scale deployment. This method ensures that AI projects remain aligned with business values and can flexibly adapt to changing circumstances (IBM – United States).
Agile Tools and Techniques for AI
The complexity of AI projects requires robust tools that can handle the complexities of data management, code, and collaboration.
Below are some highly recommended tools for AI project tracking and management:
Jira
Widely used for agile project management, Jira offers powerful features for software planning, tracking, and releases. Its customization makes it ideal for managing AI project workflows, issue tracking, and sprints.
Trello
For teams looking for a more visual tool, Trello uses boards, lists, and cards to organize tasks and track progress. It’s excellent for managing smaller AI projects or specific aspects of a larger project, such as data collection or model testing.
GitHub
Essential for version control, GitHub allows AI development teams to collaborate on code, manage revisions, and track changes. It also integrates with project management tools like Jira, improving workflow synchronization.
Asana
Asana is a project management tool that excels at task assignment and schedule tracking. Its simple interface is excellent for managing multifaceted AI projects and ensuring all team members are aligned on goals and deadlines.
MLflow
Designed specifically for machine learning projects, MLflow helps manage the machine learning lifecycle, including experimentation, reproducibility, and deployment. It is especially useful for tracking different experiments, model versions, and parameters.
Techniques for Maintaining Momentum and Focus on AI Projects
Maintaining momentum and focus on AI projects can be challenging due to their complexity and the iterative nature of model development.
Below are effective techniques for keeping AI projects on track:
– Daily Stand-Ups: Implementing daily stand-up meetings helps keep the team aligned on their daily tasks, immediately address any obstacles, and quickly adapt to changes. This constant communication is vital for synchronizing the team’s efforts.
– Iteration Reviews: At the end of each sprint, conduct iteration reviews to assess achievements against the sprint goals. This not only highlights progress but also motivates the team by showing tangible results.
– Retrospectives: Regular retrospectives allow the team to reflect on what went well and what didn’t during a sprint. This is essential for continuous improvement, especially in AI projects, where learning from each development phase is key.
– Clear documentation: Given the technical complexity of AI, it is necessary to maintain clear and comprehensive documentation. This ensures that all team members, from data scientists to product managers, understand the technical details and progress of the project.
– Buffer time: Building buffer time into sprints to address unexpected challenges or additional research can help maintain momentum. AI projects often encounter unforeseen issues, and having a scheduled buffer prevents these from impacting the project schedule.
By leveraging these tools and techniques, teams can improve their efficiency and effectiveness in managing AI projects, ensuring they stay on track and meet their goals, adapting to the iterative and exploratory nature of AI development.
Overcoming Challenges in Agile AI Projects
Agile AI projects, while flexible and dynamic, are susceptible to certain obstacles that can slow progress if not managed carefully. Below are some common challenges and strategies to avoid them:
– Underestimating data preparation
AI projects rely heavily on data quality and preparation, which can be time-consuming and complex. To avoid this pitfall, allocate sufficient time and resources for data collection, cleaning, and preprocessing from the planning phase.
– Model overfitting
There is a tendency to continually fine-tune AI models to perform well on training data, which can lead to overfitting. Implement cross-validation techniques and continually test the model with new, unpublished data to ensure proper generalization.
– Lack of clear objectives
Without clear and measurable objectives, AI projects can lose their way. Set specific and achievable goals for each sprint and ensure they align with overall project goals.
– Silos between teams
AI projects often require collaboration between data scientists, engineers, and business stakeholders. Avoid silos by encouraging regular communication and integrating tools that enhance collaboration.
Strategies for Managing Uncertainty and Complexity in AI
AI projects inherently involve uncertainty and complexity, especially around data variability and algorithm behavior.
Below are strategies for managing these challenges effectively:
– Incremental Development
Break the project into smaller, manageable chunks and focus on generating incremental value through sprints. This approach helps manage complexity by addressing one aspect of the project at a time and adjusting plans as new information becomes available.
– Robust Testing Frameworks
Establish robust testing frameworks that simulate real-world conditions as faithfully as possible. This involves testing not only the accuracy of AI models but also their performance in different scenarios, which helps understand and mitigate risks from the outset.
– Flexible Planning
Maintain flexibility in project plans to adapt to new insights and changes. Use tools like Scrum or Kanban boards to visualize progress and make adjustments in real time.
– Expert Consultations
For highly complex or uncertain aspects, consider consulting with external AI experts or conducting joint development sessions with other AI teams. External input can provide fresh perspectives and innovative solutions to complex problems.
– Risk Management
Develop a risk management plan that identifies potential issues and outlines strategies to address them. Regularly update the risk management plan as the project progresses and new risks emerge.
By recognizing and preparing for these challenges, teams can leverage agile methodologies not only to address them but also to excel in developing AI projects.
These strategies ensure that projects remain adaptable and resilient, ultimately leading to successful outcomes despite the complexities and uncertainties inherent in AI technology.
Future-Proofing AI Success: The Crucial Role of Agile Methodologies in AI Development
Agile approaches are essential for AI projects because they offer several advantages, including the ability to continually improve AI models in response to new data and technological advancements.
This flexibility, coupled with the emphasis on user input, ensures that projects meet market and user needs while keeping pace with the rapid evolution of the artificial intelligence field.
Future developments will make Agile’s adaptability and responsiveness increasingly essential as AI technologies proliferate across numerous industries.
Agile’s iterative, feedback-driven methodology promotes efficiency and innovation, increasing the likelihood that it will continue to develop and remain a key factor in the success and technical growth of businesses.
Your AI Strategy Will Fail Without a Culture to Support It
The following contribution is from the Gallup Institute and the authors are Vibhas Ratanjee and Ken Royal.
Corporate investment in AI technologies is booming.
Global spending on AI is projected to reach approximately $632 billion by 2028.
In addition to AI, companies are investing heavily in new digital technologies.
According to the Bureau of Economic Analysis, the new digital economy accounts for 9% of US GDP.²
Workplaces Are Not Ready for AI
The rapid adoption of AI tools for business and other digital technologies is having a profound impact on organizations and their employees, as non-digital processes and ways of working are radically transformed.
This workplace revolution has created a gap between the ready availability of AI tools and the degree to which employees use them.
Employees are broadly optimistic about the potential of AI:
Two-thirds believe AI will have a somewhat positive (52%) or extremely positive (14%) impact on their work.
However, nearly seven in ten employees never use AI, and only one in ten uses AI applications at least once a week.
One reason for this usage gap is that employees don’t feel well informed about their organization’s plans to implement AI or feel they haven’t received sufficient AI training.
Only 15% of US employees strongly agree that their organization has communicated a clear AI strategy.
Relatively few employees (11%) feel «very prepared» to work with AI and related digital technologies in their role, a six-percentage-point decrease from Gallup’s 2023 finding (17%).
Among US employees who say their organization has communicated a clear strategy for integrating AI into their business, 87% indicate they believe AI will have an extremely positive impact on their productivity and efficiency.
Is your culture ready for AI?
How can leaders bridge the gap in AI use and ensure the success of their organization’s AI-driven digital transformation?
A recent study revealed that only one in five digital transformation initiatives achieves their growth or efficiency goals.
A common factor in many of these failures is organizational culture.
Removing uncertainty and helping employees embrace AI and its enormous potential requires organizations to prioritize the cultural component of AI adoption and digital transformation.
Establishing a culture that fosters new ways of working and encourages the adoption of new technologies that add value to the organization, and ensuring employees feel a strong connection to that culture, is essential.
Leaders must consider and address three key dimensions of organizational readiness, essential for building a culture that prepares employees to take full advantage of AI and other digital technologies.
Strategy:
– Is there a clear vision of how AI will help the organization achieve its goals?
– Is your team optimistic about the impact of AI on individual, team, and organizational performance?
– Do you have the organizational agility to adapt your vision as the organization increases its implementation of AI tools and applications? Leaders need to develop and communicate a well-articulated AI strategy with specific objectives and provide clear direction and alignment on the optimal use of resources to efficiently implement the strategy.
Skills:
– Are your employees familiar with AI and its tools?
– Have you implemented a robust learning strategy to ensure the organization continually tests, adapts, and evolves its vision for AI technologies and their implementation?
– Have you created an effective feedback loop to test and learn as AI adoption grows across the organization?
Nearly half (47%) of employees who use AI say their organization has not provided them with training on how to use it in their work.
Leaders must overcome the AI skills gap to alleviate employee frustration and resistance to AI adoption.
Security:
– Do all employees understand your organization’s AI policies and guidelines?
– Have you anticipated and planned for potential limits and barriers to AI adoption?
– On what assumptions have your organization’s security measures been based?
Are you simply trying to control AI or unleash its full potential for the organization?
Addressing the above will strengthen employee confidence in the organization’s implementation of AI technology, provide clear guidance on how to use AI, and substantially reduce the risks associated with privacy and data protection.
A Human-Centered Approach to AI
To help leaders address key dimensions of organizational readiness, Gallup has developed a human-centered framework for AI adoption and digital transformation with four essential elements:
Diagnose Culture
Leaders should approach the integration of AI technologies by first assessing their organization’s cultural readiness.
An effective qualitative and quantitative cultural assessment will provide crucial insights to leaders and inform the organization’s AI vision and strategic roadmap.
Align Investment with Purpose
Investments in new technologies should be aligned with the organization’s purpose, or the reason behind the investment.
For example, a company with a culture that prioritizes agile and collaborative decision-making, extreme innovation, or exceptional customer service should invest in AI technologies and applications that enable it to perform these tasks more effectively and strengthen its competitive differentiation. It is crucial to align decisions about AI implementation and investment across functions and departments with the aspects that generate the most value for customers or fulfill the organization’s purpose.
Communicating a Clear AI Narrative
For employees, adopting new ways of working requires a compelling rational and emotional justification that helps them understand what will change and what will stay the same.
AI is more than just a rational business investment in the organization’s future; it must also inspire people.
Leaders must create a credible and compelling narrative for AI implementation that addresses employee concerns and fosters buy-in, energy, and engagement.
When employees strongly agree that leaders have communicated a clear plan for AI implementation, they are 2.9 times more likely to feel highly prepared to work with it and 4.7 times more likely to feel comfortable using it in their role.
The plan should include clear guidelines defining how and where AI tools will be applied and empowering employees to experiment with them and perform their jobs differently and better.
It should also address the need for job-specific training so employees can take full advantage of the potential of the AI tools at their disposal.
Maintaining Adoption
Adoption of new technologies will fail if leaders don’t encourage and replicate the right cultural behaviors.
Initial enthusiasm for AI must become habits. Barriers to new ways of working will inevitably emerge if leaders and employees don’t identify, communicate, and overcome them.
Leaders must continually celebrate and reinforce success stories and best practices to sustain AI’s potential to transform the workplace.
Prepare Your Culture for the AI Revolution
AI has the potential to improve employee productivity, drive innovation, and increase efficiency.
When implemented within an organizational culture that encourages the adoption of innovative technologies, AI can help teams work more efficiently, boost growth, and reduce costs.
Ultimately, AI can free people from burdensome tasks so they can spend more of their valuable time on the high-value work that humans do best.
There is no sign of a slowdown in corporate investment in AI and other digital technologies. The AI revolution is here, and it’s up to leaders to ensure their organizations and employees are prepared to take full advantage of it.
Create a culture that supports your employees’ transition to AI.
Learn more about using culture as a competitive advantage.
Develop a robust and unique AI strategy with Gallup’s AI consulting expertise. Author(s)
Vibhas Ratanjee is a Senior Leadership and Organizational Development Practice Expert at Gallup.