The 6 most common data technologies

In the 1990s, Business Process Reengineering (BPR) was the Big Bet. Companies launched tightly controlled pilot programs with hand-picked teams, custom software, and executive backing. The results dazzled on paper.

But when it came time to scale? Reality hit. People weren’t ready. Systems didn’t connect. Budgets dried up. The pilot became a cautionary tale, not a blueprint.

We’ve seen this before with Lean, Agile, even digital transformations. Now it’s happening again with AI, only this time, the stakes are different. Because we’re not just implementing a new solution, we’re building into a future that’s unfolding. Technology is evolving faster than most organizations can learn, govern, or adapt right now. That uncertainty doesn’t make transformation impossible, but it does make it easier to get wrong.

And the dysfunction is already showing up, just in two very different forms.

Two roads to the same cliff

Today, we see organizations falling into two extremes. Most companies are either overdoing the control or letting AI run wild.

Road 1: The free-for-all

Everyone’s experimenting. Product teams are building bots, prompting, using copilots. Finance is trying automated reporting. HR has a feedback chatbot in the works. Some experiments are exciting. Most are disconnected. There's no shared vision, no scaling pathway, and no learning across the enterprise. It’s innovation by coincidence.

Road 2: The forced march

Leadership declares an AI strategy. Use cases are approved centrally. Governance is tight. Risk is managed. But the result? An impressive PowerPoint, a sanctioned use case, and very little broad adoption. Innovation is constrained before it ever reaches the front lines.

Two very different environments. Same outcome: localized wins, system-wide inertia.

The real problem: Building for optics, not for scale

Whether you’re over-governing or under-coordinating, the root issue is the same: designing efforts that look good but aren’t built to scale.

Here’s the common pattern:

• A team builds something clever.
• It works in their context.
• Others try to adopt it.
• It doesn’t stick.
• Momentum dies. Energy scatters. Or worse, compliance says no.

Sound familiar?

It’s not that the ideas are flawed. It’s that they’re built in isolation with no plan for others to adopt, adapt, or scale them. There’s no mechanism for transfer, no feedback loops for iteration, and no connection to how people actually work across the organization.

So, what starts as a promising AI breakthrough (a smart bot, a helpful copilot, a detailed series of prompts, a slick automation) quietly runs out of road. It works for one team or solves one problem, but without a handoff or playbook, there’s no way for others to plug in. The system stays the same, and the promise of momentum fades, lost in the gap between what’s possible and what’s repeatable.

We’ve seen this before

These aren’t new problems. From BPR to Agile, we’ve learned (and re-learned) that:

• Experiments are not strategies. Experiments show potential, not readiness for adoption. Without a plan to scale, they become isolated wins; interesting, but not transformative.
• Culture is the operating system. If the beliefs, behaviors, and incentives underneath aren’t aligned, the system breaks, no matter how advanced the tools.
• Managers matter. Without their ownership and support, change stalls.
• Behavior beats code. Tools don’t transform companies. People do.

Design thinking promised to bridge this gap with user-driven iteration and empathy. But in practice? Most efforts skip the hard parts. We tinker, test, and move on, without ever building the conditions for adoption.

AI and the new architecture of work

Many organizations treat AI like an add-on—as if it’s something to bolt onto existing systems to boost efficiency. But AI isn’t just a project or a tool; it changes the rules of how decisions are made, how value is created, and what roles even exist. It’s an inflection point that forces companies to rethink how work gets done.

Companies making real progress aren’t just chasing use cases. They’re rethinking how their organizations operate, end to end. They’re asking:

• Have we prepared people to reimagine how they work with AI, not just how to use it?
• Are we redesigning workflows, decision rights, and interactions—not just layering new tech onto old routines?
• Do we know what success looks like when it’s scaled and sustained, not just when it dazzles?

If the answer is no, whether you’re too loose or too locked down, you’re not ready.

The mindset shift AI demands

AI isn’t just a tech rollout. It’s a mindset shift that asks leaders to reimagine how value gets created, how teams operate, and how people grow. But that reimagination isn’t about the tools. The tools will change—rapidly. It starts with new assumptions, new stances, and a new internal leader compass.

Here are three essential mindset shifts every leader must make, not just to keep up with AI but to stay relevant in a world being reshaped by it:

1. From automation to amplification

Old mindset: AI automates tasks and cuts costs.

New mindset: AI expands and amplifies human potential, enhancing our ability to think strategically, learn rapidly, and act boldly. The question isn’t what AI can do instead of us, but what it can do through us—helping people make better decisions, move faster, and focus on higher-value work.

2. From efficiency to reimagination

Old mindset: How can we use AI to make current processes more efficient?

New mindset: What would this process look like if we started from zero with AI as our co-creator, not a bolt-on?

3. From implementation to opportunity building

Old mindset: Roll out the tool. Train everybody. Check the box.

New mindset: AI fluency is a core human capability that creates new realms of curiosity, sophistication in judgment, and opportunity thinking. Soon, AI won’t be a one-time training. It will be part of how we define leadership, collaboration, and value creation.

From sparkles to scale

In most organizations, the spark isn’t the problem. Good ideas are everywhere. What’s missing is the ability to translate those isolated wins into something durable, repeatable, and enterprise-wide.

Too many pilots are built to impress, not to endure. They dazzle in one corner of the business but aren’t designed for others to adopt, adapt, or sustain. The result? Innovation that stays stuck in the lab—or dies.

Designing for scale means thinking beyond the “what” to the “how”:

• How will this spread?
• What behaviors and systems need to change?
• Can this live in our whole world, not just my sandbox?

It’s not about chasing the next use case. It’s about setting up the conditions that allow innovation to take root, grow, and multiply, without starting from scratch every time.

Here’s how to make that shift:

1. Test in the wild, not just in the lab

Skip the polished demo. Put your solution in the hands of real users, in real conditions, with all the friction that comes with it. Use messy data. Invite resistance. That’s where the insights live, and where scale begins. If it only works in ideal settings, it doesn’t work.

2. Mobilize managers

Executives sponsor. Front lines experiment. But it’s team leaders who connect and spread. Equip them as translators and expediters, not blockers. Every leader is a change leader.

3. Hardwire behaviors, not just tools

The biggest unlock in AI is not the model—it’s the muscle. Invest in shared language, habits, and peer learning that support new ways of working. Focus on developing behaviors that scale, such as:

• Change readiness: the ability to spot opportunity, turn obstacles into possibilities, and help teams pivot.
• Coaching: getting the best out of your AI “co-workers” just like human ones.
• Critical thinking: applying human judgment where it matters most—context, nuance, and ethics.

4. Align to a future-state vision

To scale beyond one-off wins, people need a shared sense of where they’re headed. A clear future-state vision acts as an enduring focus, allowing everyone to innovate in concert. That alignment doesn’t stifle innovation. It multiplies it, turning a thousand disconnected pilots into a coherent transformation.

5. Track adoption, not just “wins”

Don’t mistake a shiny, clever prompt for progress. A great experiment means nothing if it can’t be repeated by many people. From day one, design with scale in mind: Can this be adopted elsewhere? What would need to change for it to work across teams, roles, or regions? Build for transfer, not just applause.

The real opportunity

AI will not fail because the tech wasn’t good enough. It will fail because we mistook experiments for solutions, or because we governed innovation into paralysis.

You don’t need more control. You don’t need more chaos. You need design for scale, not just scale in hindsight.

Let’s stop chasing sparkles. Let’s build systems that spread.

E-learning designers are still catching up to what brand differentiation experts have known for a long time. Experience matters.

Consider Bubly, a maker of sparkling water, recently purchased by PepsiCo. Bubly doesn’t try to differentiate at the product level: in a blind taste test between Bubly and LaCroix, participants were unable to tell one from the other. Instead, Bubly focuses on the consumer’s experience of the product.

To begin, there’s the enthusiastic welcome: each can features a pull-tab greeting that mimics text messages – “hey u,” “hiii,” or “yo,” – simulating the kind of playful rapport you might have with friends and family. Next, the product’s peach, pineapple, and grapefruit-toned cans and smiling logo work together to convey positivity, creating a look and feel that aligns seamlessly with its slogan: “no calories. no sweeteners. all smiles.” Finally, Bubly gamifies buying. As writer Elizabeth Demolat points out, no store stocks all twelve flavors at any one time, leading to online and in-person buzz about where to find specific flavors. This strategy, along with the release of a variety of limited-edition flavors, has essentially turned “the act of purchasing a product into a treasure hunt.”

Bubly’s brand differentiation leverages enthusiasm, emotion, and excitement—experiential elements that echo the design pillars of best-in-class e-learning. Here’s how to incorporate each.

1. Enthusiasm

Find new ways to breath energy into the experience. Take, for example, a short, animated video that uses action film motifs to explore emotional awareness in the workplace.

The sequence begins with an establishing shot of a manager providing constructive feedback to an employee. The action moves quickly into the employee’s brain, which is set up as a command center. A group of intelligence agents, straight out of Mission Impossible, look on with alarm. One more word from the manager on “areas for improvement,” and the emotion-regulating amygdala will be triggered, hijacking the employee’s normal reasoning processes. The intelligence agents strategize, introducing different tools and techniques that can be used to regain perspective, and the learning journey begins to take shape.

Greeted with a fresh, playful take on a critical workplace competency, learners are primed to go deeper.

2. Emotion

How do you get beyond the rational regions of your brain – the ones that “control language, but not decision-making” – to tap into feelings and emotions? One particularly creative course on human anatomy leverages powerful visuals to reach learners on that deeper level.

Participants begin by learning that there are more nerve cells in the brain than stars in the Milky Way, observing a close-up of the brain’s circuitry dissolving into tiny specks lighting up the night sky. Because the underlying anatomy remains hidden, medical-aesthetics practitioners learn that they will essentially be working in the dark. The stars fade out slowly, one by one, until there’s nothing left on screen but total darkness—a strange, slightly unnerving experience that drives home the importance of understanding anatomical structures on a visceral level.

3. Excitement

Give people something they’ve never experienced before by challenging the norms of typical training.

Data-protection policies, for instance, are critical safeguards wherever they’re in place, but existing e-learning on the subject is almost always designed as a passive, one-way transmission of information. One exceptional data-protection course takes a different approach, using live-action video and a dramatic soundtrack to depict a privacy breach occurring in real time.

While this can get gimmicky, immersing learners in a volatile environment with uncertain outcomes builds tension, a key lever for creating buy-in.

‍So, how can we help clients build better learning experiences?

‍Many clients see digital learning as a product, one that looks a lot like what’s already out there: didactic, uninspired, dull. By nudging clients toward digital learning courses that mirror what they already know about branding, we might just be able to help them build experiences that stand out in a crowd.

Transformation in troubled times

In a world already defined by constant change, the pandemic acted as an accelerant for the adaptation of technology. In a matter of months, companies advanced their digitization by the equivalent of several years, according to previous plans. Technological advancements aside, people were also forced to work in new ways, incorporating a greater focus on agility, remote work, and the need to find and adopt new business models. Regardless of size and industry, almost all businesses faced these common challenges – but unfortunately, none were easy to overcome.

A leaders’ approach to managing change must evolve to fit the company’s pace and needs. Knowing that the traditional separation between managing day-to-day activity and managing change is non-existent, leaders must learn at forced speeds, and acknowledge that no normal activity is immune to change.

As a result, it’s critical for leaders to rethink how they drive and manage change in organizations. The idea of change as a period of transition amid stability clashes with reality on a daily basis, as traditional investment-based transformation schemes and long-term planning are overcome by the uncertainty and complexity of the current environment. The world is, and will remain, in a state of constant change and adaption.

Rather than managing organizational change, shouldn’t we change organizational management to enable this rapid and continuous adaptation? It’s time to move on to organizational transformation.

Basic rules for a new approach to transformation

When it comes to making the best transformation cocktail, it depends on the specific tastes of each company. Knowing that there’s no single magic recipe, there are some basic rules that can help companies determine the best approach:

1. Replace long-term plans with vision
   ‍What’s the point of drawing up long-term plans when you know that you can’t follow through? Instead, start by agreeing on a vision or image of where you would like to be in a few months, a year, or two years. Use this vision as the compass that points all of your daily efforts towards true North. Reinforce this vision among all people, customers, and suppliers, so they feel like they are a part of it too.
2. Provide certainty in the process
   ‍This communication rule can help provide structure in any uncertain context. When an outcome is uncertain, create a clear structure around the responsibilities and stages of the journey. “Work out loud”: let everyone know what is being worked on, by whom and around which dates. The basic techniques for achieving this are:
   • Timeboxing: specific and short periods of time, during which a task needs to be completed
   • Prioritization: only doing tasks that will make it possible to make effective decisions and learn
3. Include everyone
   ‍There is no single change or transformation; there are as many changes as there are people enduring them.
   
   Each person lives their own version of change, as levels of focus, interest, and motivation vary by each level of the organization. Therefore, a change, which from the point of view of someone at a strategic level is urgent, may be viewed by someone at the team-level as a loss of quality. Much inevitable resistance will arise from these differences in perception.
   
   Therefore, listen. Play an active role for all people in change, ensuring that problems receive solutions. Combine everyone’s view into the best solution, without losing sight of your true North – the vision that you want to achieve – and collaborate so that the solution is adapted to all levels of need. Seek ways to create an environment in which such collaboration and diversity of though is possible; generate a continuous conversation around the vision to keep everyone on the same page.
4. Go from time to market to time to learn
   ‍Time is the most precious resource in periods of accelerated change, and how you spend what little you have on will determine your ability to adapt. Invest in your efforts to maximize learning, and in the process, apply the law of minimum effort to building the best solution, step-by-step.
   ‍
   Experiment with the hypotheses you are proposing, and look for ways to confirm or reject them with minimal impact to the organization.

Finally, dare to change! Organizational change and transformation, for each and every member of the team, begins with you.

Each business revolution has reshaped not only how businesses operate, but how they organize themselves and empower their people. From the industrial age to the information era, and now into the age of artificial intelligence, technology has always brought with it a reconfiguration of authority, capability, and judgment.

In the 19th century, industrialization centralized work and knowledge. The factory system required hierarchical structures where strategy, information, and decision-making were concentrated at the top. Managers at the apex made tradeoffs for the greater good of the enterprise because they were the only ones with access to the full picture.

Then came the information economy. With it came the distribution of information and a need for more agile, team-based structures. Cross-functional collaboration and customer proximity became competitive necessities. Organizations flattened, experimented with matrix models, and pushed decision-making closer to where problems were being solved. What had once been the purview of a select few, judgment, strategic tradeoffs, and insight became expected competencies for managers and team leads across the enterprise.

Now, AI is changing the game again. But this time, it’s not just about access to data. It’s about access to intelligence.

Generative AI democratizes access not only to information, but to intelligent output. That shifts the burden for humans from producing insights to evaluating them. Judgment, which was long the domain of a few executives, must now become a baseline competency for the many across the organization.

But here’s the paradox: while AI extends our capacity for intelligence, discernment, the human ability to weigh context, values, and consequence, is still best left in the hands of human leaders. As organizations begin to automate early-career work, they may inadvertently erase the very pathways and opportunities by which judgment was built.

Why judgment matters more than ever

Deloitte’s 2023 Human Capital Trends survey found that 85% of leaders believe independent decision-making is more important than ever, but only 26% say they’re ready to support it. That shortfall threatens to neutralize the very productivity gains AI promises.

If employees can’t question, challenge, or contextualize AI’s output, then intelligent tools become dangerous shortcuts. The organization stalls, not from a lack of answers, but from a lack of sense-making.

What organizations must do

To stay competitive, organizations must shift from simply adopting AI to designing AI-aware ways of working:

• Build new learning paths for judgment development. As AI replaces easily systematized tasks, companies must replace lost learning experiences with mentorship, simulations, and intentional development planning.

• Design workflows that require human input. Treat AI as a co-pilot, not an autopilot. Embed review checkpoints and tradeoff discussions. Just as innovation processes have stage gates, so should AI analyses.

• Make judgment measurable. Assess and develop decision-making under ambiguity from entry-level roles onward. Research shows the best learning strategy for this is high-fidelity simulations.

• Start earlier. Leadership development must begin far earlier in career paths, because judgment, not just knowledge, is the new differentiator.

What’s emerging is not just a flatter hierarchy, but a more distributed sense of judgment responsibility. To thrive, organizations must prepare their people not to outthink AI, but to out-judge it.

P R E S S R E L E A S E
Stockholm, May 5, 2025

STOCKHOLM, SWEDEN – BTS Group AB (publ), a leading global consultancy specializing in strategy execution, change, and people development, has agreed to acquire Nexo Pesquisa e Consultoria Ltda., Nexo, a boutique consulting firm headquartered in São Paulo, Brazil.

Nexo has been growing continuously since it was founded in 2017. With revenues of approximately 12 million Brazilian Reales (approx. 2.1 million USD) in 2024, and a highly capable team of 21 members, Nexo has built a strong reputation for delivering transformative projects in strategy, innovation, leadership, and culture.

Nexo collaborates with a great portfolio of clients across sectors such as financial services, consumer goods, and technology, assisting both local and global companies in navigating uncertainty, unlocking creativity, and activating strategy through people. Their work encompasses culture transformation, leadership development, employer value proposition, innovation culture, and vision alignment – supported by proprietary methodologies and frameworks.

BTS currently operates in Brazil servicing both local and multinational clients with a team of 13 employees. By acquiring Nexo, BTS not only increases the Group’s footprint in Brazil but also adds significant capabilities in culture and transformation services. Nexo’s client base has limited overlap with BTS, creating strong growth potential and synergy opportunities.

“Nexo is known for helping leaders and organizations tackle some of the most complex, human-centered challenges with creativity, empathy, and strategic clarity and the Nexo team is loved by their clients,” says Philios Andreou, Deputy CEO of BTS Group and President of the Other Markets Unit. “Their products and services complement and elevate our existing offerings, especially in culture transformation, and we are thrilled to welcome the Nexo team to BTS.”

“We’re excited to join BTS. We’ve long admired BTS’s approach and unique portfolio to support large organizations and leaders in connecting strategy with culture across the organization,” says Andreas Auerbach, co founder of Nexo. “Becoming part of BTS, allows us to scale our impact and bring more value to our clients while staying true to our values and culture,” adds Mariana Lage Andrade, co-founder of Nexo.

Upon completion of the transaction, Nexo’s business and organization will merge with BTS Brazil. Nexo’s founders will assume senior management roles in the joint operation.

The acquisition includes a limited initial cash consideration. Additional purchase price considerations will be paid between 2026 and 2028, provided Nexo meets specific performance targets. A limited portion of any such additional purchase price considerations will be paid in newly issued BTS shares. The transaction is effective immediately.

BTS’s acquisition strategy continues to focus on broadening our service portfolio, expanding our geographic reach, and enhancing our capabilities to support future organic growth in a fragmented market.

For more information, please contact:
Philios Andreou
Deputy CEO
BTS Group AB
philios.andreou@bts.com

Michael Wallin
Head of investor relations
BTS Group AB
michael.wallin@bts.com
+46-8-587 070 02
+46-708-78 80 19

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Work today is too complex for individuals to succeed in isolation. Almost every critical decision, innovation, or transformation depends on teams working effectively together. Leaders rely on their teams to deliver results. Teams, in turn, rely on their leaders to create the conditions where performance is possible. This exchange, what leaders need from their teams, and what teams need from their leaders, sits at the heart of what we call teaming.

When teaming is strong, leaders get what they need from their teams [creativity, resilience, execution] and teams get what they need from leaders [direction, support, and the conditions to thrive]. It’s how strategy becomes action, how uncertainty becomes opportunity, and how businesses stay competitive in a fast-changing world.